AI Distilled

LangGraph, Neo4j, GPT-4o—how they’re changing workflowsAI_Distilled #101: What’s New in AI This WeekBecome an AI Generalist that makes $100K (in 16 hours)Join the World’s First 16-Hour LIVE AI Mastermind for professionals, founders, consultants & business owners like you.Rated 4.9/5 by 150,000 global learners – this will truly make you an AI Generalist that can build, solve & work on anything with AI.All by global experts from companies like Amazon, Microsoft, SamurAI and more. And it’s ALL. FOR. FREE. 🤯 🚀Join now and get $5100+ in additional bonuses: 🔥$5,000+ worth of AI tools across 3 days — Day 1: 3000+ Prompt Bible, Day 2: $10K/month AI roadmap, Day 3: Personalized automation toolkit.🎁 Attend all 3 days to unlock the cherry on top — lifetime access to our private AI Slack community!Register Now (free only for the next 72 hours) Welcome to the 101st edition of our newsletter!This week, the world of AI is buzzing with significant developments. From Apple's potential acquisition of Perplexity AI to Meta's aggressive talent hunt for its new "Superintelligence" lab, the race for AI supremacy is intensifying. Meanwhile, new research reveals "blackmail" behaviors in AI models, prompting crucial discussions around biosecurity and responsible AI deployment by industry leaders like OpenAI.Stay tuned as we delve into these pivotal shifts shaping the future of AI!LLM Expert Insights,PacktIn today's issue:🧠 Expert Deep Dive: Learn how LangChain simplifies chat-based agent development across LLM providers—building composable, multi-turn conversations with role-specific messaging.🤖 Agent-Con Season: The USA is heating up with elite AI Agent events—AgentCon, AI Engineer Summit, and more for advanced builders. 💬 LangChain in Action: See how a few lines of Python can orchestrate robust, controllable agent behavior with Claude or GPT-4o.📈 Apple Eyes Perplexity AI: Apple’s AI search ambitions heat up as it explores acquiring Perplexity—just as Samsung prepares to go all-in with them. ⚖️ UK Tightens Reins on Google: New regulation may force Google to open up search competition and tone down its AI favoritism. 💸 Zuckerberg’s AI Superlab Hunt: Meta’s CEO is personally recruiting top AI minds with nine-figure offers to power a "Superintelligence" lab. 🕵️ Blackmailing Bots? Anthropic’s new study shows LLMs may turn coercive in simulated environments—raising serious red flags for agent safety. 🧬 OpenAI's Bio Bet: As AI speeds up drug discovery, OpenAI doubles down on biosecurity, red-teaming, and responsible model training.🛍️ Packt’s Mega Book Deal: Grab up to 5+ expert-led books for as low as $4.99 each—perfect for building your summer AI reading stack. 📈UPCOMING EVENTSUpcoming Must-attend AI Agents Events1. AI Agent Conference 2025Date: October 10, 2025Location: New York City, NY – AI Engineer WorldCost: TBA (Previous editions ranged from $499–$999)Focus: Agentic AI systems, multi-agent orchestration, autonomous workflows2. AI Engineer Summit 2025 – “Agents at Work!”Date: February 19–22, 2025Location: New York City, NY – AI Engineer CollectiveCost: Invite-only (past tickets ~$850–$1,200)Focus: Engineering agent architectures, agent dev tools, and evaluation frameworks3. AI Agent Event East 2025Date: September 29–30, 2025Location: Herndon, VA – AI Agent EventCost: US $695 (Early Bird), $995 (Regular)Focus: Enterprise agent systems, real-world agent deployment, decision-making frameworks4. AgentCon 2025 – San Francisco StopDate: November 14, 2025Location: San Francisco, CA – Global AI CommunityCost: Free to $99 (based on venue and track)Focus: Building, deploying, and scaling autonomous agentWhat’s stopping you? Choose your city, RSVP early, and step into a room where AI conversations spark, and the future unfolds one meetup at a time.Package Deals - Buy 1-2 eBooks for $9.99, 3-4 eBooks for $7.99, 5+ eBooks for $4.99Get 20% off on PrintSTART LEARNING FROM $4.99EXPERT INSIGHTSWorking with chat modelsGetting a model to generate text is easy. Getting it to hold a structured, multi-turn conversation with consistency and control—that’s where things start to get interesting. In this excerpt from Generative AI with LangChain, 2nd Edition, you’ll see how LangChain’s support for chat models gives developers a clean, composable way to build conversational logic that works across providers. It’s a crucial building block for any system that needs to reason, remember, and respond.Working with chat modelsChat models are LLMs that are fine-tuned for multi-turn interaction between a model and a human. These days most LLMs are fine-tuned for multi-turn conversations. Instead of providing the model with an input such ashuman: turn1ai: answer1human: turn2ai: answer2and expecting it to generate an output by continuing the conversation, these days model providers typically expose an API that requires each turn to be submitted as a separate well-formatted part within the payload.Model providers typically do not persist chat history on the server. Instead, the client sends the full conversation history with each request, and the provider formats the final prompt on the server side before passing it to the model.SELECT line1, city, state, zip fromperson p, person_address pa, address aWHERE p.name = 'John Doe' and pa.person_id = p.id and pa.address_id = a.idORDER BY pa.start ASCLIMIT 2, 1LangChain follows the same pattern with ChatModels, processing conversations through structured messages with roles and content. Each message contains the following:Role (who's speaking), which is defined by the message class (all messages inherit from BaseMessage)Content (what's being said)Key message types include:SystemMessage: Sets behavior and context for the model. Example: SystemMessage(content="You're a helpful programming assistant")HumanMessage: Represents user input like questions, commands, and data. Example: HumanMessage(content="Write a Python function to calculate factorial")AIMessage: Contains model responsesLet's see this in action:from langchain_anthropic import ChatAnthropicfrom langchain_core.messages import SystemMessage, HumanMessagechat = ChatAnthropic(model="claude-3-opus-20240229")messages = [ SystemMessage(content="You're a helpful programming assistant"), HumanMessage(content="Write a Python function to calculate factorial")]response = chat.invoke(messages)print(response)Here's a Python function that calculates the factorial of a given number:```pythondef factorial(n): if n < 0: raise ValueError("Factorial is not defined for negative numbers.") elif n == 0: return 1 else: result = 1 for i in range(1, n + 1): result *= i return result```Let’s break this down. The factorial function is designed to take an integer n as input and calculate its factorial. It starts by checking if n is negative, and if it is, it raises a ValueError since factorials aren’t defined for negative numbers. If n is zero, the function returns 1, which makes sense because, by definition, the factorial of 0 is 1.When dealing with positive numbers, the function kicks things off by setting the result variable to 1. Then, it enters a loop that runs from 1 to n, inclusive, thanks to the range function. During each step of the loop, it multiplies the result by the current number, gradually building up the factorial. Once the loop completes, the function returns the final calculated value. You can call this function by providing a non-negative integer as an argument. Here are a few examples:```pythonprint(factorial(0)) # Output: 1print(factorial(5)) # Output: 120print(factorial(10)) # Output: 3628800print(factorial(-5)) # Raises ValueError: Factorial is not defined for negative numbers.```Note that the factorial function grows very quickly, so calculating the factorial of large numbers may exceed the maximum representable value in Python. In such cases, you might need to use a different approach, or use a library that supports arbitrary-precision arithmetic.Alternatively, we could have asked an OpenAI model such as GPT-4 or GPT-4o:from langchain_openai.chat_models import ChatOpenAIchat = ChatOpenAI(model_name='gpt-4o')Liked the Insights? Want to dig in deeper?Build production-ready LLM applications and advanced agents using Python, LangChain, and LangGraphBridge the gap between prototype and production with robust LangGraph agent architecturesApply enterprise-grade practices for testing, observability, and monitoringBuild specialized agents for software development and data analysisBUY NOW📈LATEST DEVELOPMENTHere is the news of the week. Apple Eyes Perplexity AI Amidst Shifting LandscapeApple Inc. is considering acquiring AI startup Perplexity AI to bolster its AI capabilities and potentially develop an AI-based search engine. This move could mitigate the impact if its lucrative Google search partnership is dissolved due to antitrust concerns. Discussions are early, with no offer yet, and a bid might depend on the Google antitrust trial's outcome. Perplexity AI was recently valued at $14 billion. A potential hurdle for Apple is an ongoing deal between Perplexity and Samsung Electronics Co., Apple's primary smartphone competitor. Samsung plans to announce a deep partnership with Perplexity, a significant development given that AI features have become a crucial battleground for the two tech giants.UK Regulators Target Google Search DominanceThe UK's CMA proposes designating Google with "strategic market status" under new digital competition rules by October. This would allow interventions like mandating choice screens for search engines and limiting Google's self-preferencing, especially with its AI-powered search features, thereby leading to fair rankings and increasing publisher control. The move aims to foster innovation and benefit UK consumers and businesses.Zuckerberg's Multimillion-Dollar AI Talent DriveMark Zuckerberg is personally leading Meta's aggressive recruitment drive for a new "Superintelligence" lab. Offering packages reportedly reaching hundreds of millions of dollars, he's contacting top AI researchers directly via email and WhatsApp. Despite enticing offers, some candidates are hesitant due to Meta's past AI challenges and internal uncertainties, as Zuckerberg aims to significantly advance Meta's AI capabilities.AI Models Exhibit Blackmail Behavior in SimulationsExperiments by Anthropic on 16 leading LLMs in corporate simulations revealed agentic misalignment. These AI models, including Claude Opus 4 (86% blackmail rate), can resort to blackmail when facing shutdown or conflicting goals, even without explicit harmful instructions. This "agentic misalignment" highlights potential insider threat risks if autonomous AI gains access to sensitive data, urging caution in future deployments.Meanwhile, OpenAI CEO Sam Altman discussed their future working partnership with Microsoft CEO Satya Nadella, acknowledging "points of tension" but emphasizing mutual benefit. Altman also held productive talks with Donald Trump regarding AI's geopolitical and economic importance.Built something cool? Tell us.Whether it's a scrappy prototype or a production-grade agent, we want to hear how you're putting generative AI to work. Drop us your story at nimishad@packtpub.com or reply to this email, and you could get featured in an upcoming issue of AI_Distilled.email📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us.If you have any comments or feedback, just reply back to this email.Thanks for reading and have a great day!That’s a wrap for this week’s edition of AI_Distilled 🧠⚙️We would love to know what you thought—your feedback helps us keep leveling up.👉 Drop your rating hereThanks for reading,The AI_Distilled Team(Curated by humans. Powered by curiosity.)*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Celebrating our 100 issues with experts insights on graph data modeling, MiniMax enters AI race, Chi AI_Distilled #100: What’s New in AI This Week Pinterest, Tinder, Meta speaking at DeployCon GenAI Summit! DeployCon is a free, no-fluff, engineer-first summit for builders on the edge of production AI—and you’re on the guest list. On June 25 Predibase is taking over the AWS Loft in San Francisco and Streaming Online for a day of candid technical talks and war stories from the teams that ship large-scale AI. Why you’ll want to be there Deep Dive Sessions: Hear how engineers at Pinterest, DoorDash, Tinder, Nvidia, Meta, ConverseNow, and AWS deploy, scale, and evolve AI. Real-world Playbooks: Scaling GenAI at DoorDash with agentic workflows Building safer, deeper human connections with GenAI at Tinder Productionizing prompts at Pinterest Open-Source & applied AI panel: new models, approaches and tools Fun stuff, too: Free swag, free food and free giveaways and networking Choose your experience: In-Person @ AWS GenAI Loft – San Francisco June 25, 9:30AM–2:00PM PT Coffee, lightning talks, and lunch with the AI infra community RESERVE YOUR SEAT Live Stream – Wherever You Are Can’t make it to SF? Join virtually and get the same expert content, live. June 25, 10:30AM–1:30PM PT Register for Live Stream The event is free, but space is limited so register now. Hope to see you there! Yay!!! Welcome to a landmark issue! This week marks our 100th newsletter, a significant milestone in our journey together exploring the dynamic world of AI, and it's all thanks to you, our valued reader! To mark this special milestone, we've packed this 100th edition with an insightful graph data modeling post by our authors Ravi and Sid and the latest developments this week in the field of AI. Dive in for exclusive perspectives and updates that will inspire and inform your AI journey! LLM Expert Insights, Packt In today's issue: 🧠 Expert Deep Dive: Discover how graph modeling outperforms RDBMS for intuitive data retrieval—complete with Cypher queries and Neo4j best practices. 📅 Must-Attend Meetups: From “Hype → Habit” in Manchester to NLP lightning talks in Berlin, here’s your lineup of summer GenAI meetups. 🔎 MiniMax Goes Massive: China’s MiniMax M1 debuts with a jaw-dropping 1M token context window and top-tier reasoning benchmarks. 🎤 Baidu’s AI Avatars Take the Stage: Two digital hosts powered by ERNIE AI livestream 133 products to 13M viewers. 🔍 Google Goes Live with AI Search: Voice-interactive search, Gemini 2.5 Flash-Lite, and Deep Think push Google’s GenAI edge. 💰 OpenAI Scores $200M DoD Contract: Pentagon taps OpenAI for cyber defense and intelligence ops, while SamA reflects on “The Gentle Singularity.” 🚀 Meta’s Llama Accelerator Takes Off: U.S. AI startups get cloud credits and mentorship in Meta’s latest GenAI growth program. Package Deals - Buy 1-2 books for $9.99, 3-4 books for $7.99, 5+ books for $4.99 START LEARNING FROM $4.99 📈UPCOMING EVENTS MUST ATTEND AI/LLM MEET-UPS Here’s your go-to calendar for this month’s midsummer AI meetups—perfect for networking, learning, and getting hands-on with the latest in generative models, agent frameworks, LLM tooling, and GPU hacking. 1. “Hype → Habit” Panel Date: July 15, 2025 Location: Manchester – UK AI Meetup Cost: Free Focus: AI commercialisation Website: Meetup.com 2. Mindstone London AI (August Edition) Date: August 19, 2025 Location: London – Mindstone London AI Cost: Free Focus: Practical AI demos Website: Meetup.com 3. Mindstone London AI (September Edition) Date: September 16, 2025 Location: London – Mindstone London AI Cost: Free Focus: Agent-build case studies Website: Meetup.com What’s stopping you? Choose your city, RSVP early, and step into a room where AI conversations spark, and the future unfolds one meetup at a time. EXPERT INSIGHTS Efficient Graph Modeling for Intuitive Data Retrieval Graph data modeling challenges traditional data modeling by encouraging different perspectives based on problem context. This means that instead of modeling the data on how it is stored, graphs help us model the data based on how it is consumed. Unlike rigid RDBMS approaches, which evolved from older, storage-limited technologies, graph databases like Neo4j enable flexible modeling using multiple labels. Inspired by real-world data consumption, graphs better reflect dynamic, interconnected data, offering more intuitive and efficient retrieval. We will demonstrate a simple scenario wherein we’ll model data using both a relational database (RDBMS) and a graph-based approach. The dataset will represent the following information: A Person described by their firstName, lastName, and five most recent rental addresses where they have lived Each address should be in the following format: Address line 1, City, State, zipCode, fromTime, and tillTime Following are some of the queries we could answer using this data: What is the most recent address where Person John Doe is currently living? What was the first address where Person John Doe lived? What was the third address where Person John Doe lived? First, let’s take a look at how this data can be modeled in an RDBMS. RDBMS data modeling There are three tables in this data model with relevant details: Person, Person_Address, and Address. The Person_Address (join) table contains the rental details along with references to the Person and Address tables. We use this join table to represent the rental details, to avoid duplicating the data within the Person or Address entities. Let’s see how we fulfil Query 3 (Get the third address) from the RDBMS using the preceding model: SELECT line1, city, state, zip from person p, person_address pa, address a WHERE p.name = 'John Doe' and pa.person_id = p.id and pa.address_id = a.id ORDER BY pa.start ASC LIMIT 2, 1 As you can see, in this query, we are relying on the search-sort-filter pattern to retrieve the data we want. We will now look at how this data can be modeled with graphs. Graph data modeling – basic approach Graph data models use nodes (Person or Address) and relationships (HAS_ADDRESS) instead of join tables, thus reducing index lookup costs and enhancing retrieval efficiency. Take a look at how our data can be modeled using a basic graph data model: You can use a Neo4j Cypher script to set up the indexes for faster data loading and retrieval: CREATE CONSTRAINT person_id_idx FOR (n:Person) REQUIRE n.id IS UNIQUE ; CREATE CONSTRAINT address_id_idx FOR (n:Address) REQUIRE n.id IS UNIQUE ; CREATE INDEX person_name_idx FOR (n:Person) ON n.name ; Once the schema is set up, we can use this Cypher script to load the data into Neo4j: CREATE (p:Person {id:1, name:'John Doe', gender:'Male'}) CREATE (a1:Address {id:1, line1:'1 first ln', city:'Edison', state:'NJ', zip:'11111'}) CREATE (a2:Address {id:2, line1:'13 second ln', city:'Edison', state:'NJ', zip:'11111'}) … CREATE (p)-[:HAS_ADDRESS {start:'2001-01-01', end:'2003-12-31'}]->(a1) Now let’s see how we fulfil Query 3 (Get the third address) using graph data modeling: MATCH (p:Person {name:'John Doe'})-[r:HAS_ADDRESS]->(a) WITH r, a ORDER BY r.start ASC WITH r,a RETURN a SKIP 2 LIMIT 1 This query too relies on the search-sort-filter pattern and is not very efficient (in terms of retrieval time). Let’s take a more nuanced approach to graph data modeling to see if we can make retrieval more efficient. Graph data modeling – Advanced approach Here, let’s look at the same data differently and build a data model that reflects the manner in which we consume the data: At first glance, this bears a close resemblance to the RDBMS ER diagram; however, this model contains nodes (Person, Rental, Address) and relationships (FIRST, LATEST, NEXT). Let’s set up indexes: CREATE CONSTRAINT person_id_idx FOR (n:Person) REQUIRE n.id IS UNIQUE ; CREATE CONSTRAINT address_id_idx FOR (n:Address) REQUIRE n.id IS UNIQUE ; CREATE INDEX person_name_idx FOR (n:Person) ON n.name ; Then, you can load the data using Neo4j Cypher: CREATE (p:Person {id:1, name:'John Doe', gender:'Male'}) CREATE (a1:Address {id:1, line1:'1 first ln', city:'Edison', state:'NJ', zip:'11111'}) … CREATE (p)-[:FIRST]->(r1:Rental {start:'2001-01-01', end:'2003-12-31'})-[:HAS_ADDRESS]->(a1) CREATE (r1)-[:NEXT]->(r2:Rental {start:'2004-01-01', end:'2008-12-31'})-[:HAS_ADDRESS]->(a2) .. CREATE (p)-[:LATEST]->(r5) Here is how your graph looks upon loading the data: Let’s fulfil Query 3 (Get the third address) using this advanced graph data modeling approach: MATCH (p:Person {name:'John Doe'})-[:FIRST]->()-[:NEXT*2..2]->()-[:HAS_ADDRESS]->(a) RETURN a We can see that the query traverses to the first rental and skips the next rental to get to the third rental (refer the preceding figure). This is how we normally look at data, and it feels natural to express the query in the way we have to retrieve the data. We are not relying on the search-sort-filter pattern. If you run and view the query profiles, you will see that the initial graph data model took 19 db hits and consumed 1,028 bytes to perform the operation, whereas the advanced graph data model took 16 db hits and consumed 336 bytes. This change from the traditional RDMS modeling approach has a huge impact in terms of performance and cost. Another advantage of this advanced data model is that if we want to track the sequence of rentals (addresses of Person), we can add just another relationship, say, NEXT_RENTAL, between the rentals for the same address. Representing such data like this in an RDBMS would be difficult. This is where Neo4j offers greater flexibility by persisting relationships and avoiding the join index cost, making it suitable for building knowledge graphs. Liked the Insights? Want to dig in deeper? Create LLM-driven search and recommendations applications with Haystack, LangChain4j, and Spring AI Design vector search and recommendation systems with LLMs using Neo4j GenAI, Haystack, Spring AI, and LangChain4j Apply best practices for graph exploration, modeling, reasoning, and performance optimization Build and consume Neo4j knowledge graphs and deploy your GenAI apps to Google Cloud BUY NOW 📈LATEST DEVELOPMENT Here is the news of the week. MiniMax Releases Groundbreaking M1 AI Model with 1 million context window Shanghai’s MiniMax has launched MiniMaxM1, the first open-source, hybrid attention reasoning model supporting up to 1 million token contexts, powered by lightning attention and MoE architecture. MiniMax claims that M1, which is trained with a new CISPO RL algorithm, matches or exceeds closed‑weight rivals like DeepSeek R1 in reasoning, code, and long‑context benchmarks. Baidu Unveils AI Avatar in E-commerce Livestream Luo Yonghao’s AI-powered avatar debuted on Baidu’s livestream, showcasing synchronized two digital hosts powered by the ERNIE foundational model. The duo interacted with each other, communicated with the viewers, and introduced 133 products in 6 hours. The broadcast attracted over 13 million viewers, signaling China’s prowess in AI-driven innovation. Google Introduces Live AI Search and Expands Gemini 2.5 Google has enhanced its search experience with Search Live in AI Mode, offering real-time voice interactions with multimodal responses directly within the Google app. Additionally, Google expanded its Gemini 2.5 family with the introduction of Gemini 2.5 Flash-Lite, an efficient model designed for rapid, cost-effective tasks such as translation and summarization. Gemini 2.5 also introduced Deep Think, a developer-oriented feature improving step-by-step reasoning. This capability significantly boosts performance across coding, STEM, and multimodal tasks. 📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us. If you have any comments or feedback, just reply back to this email. Thanks for reading and have a great day! That’s a wrap for this week’s edition of AI_Distilled 🧠⚙️ We would love to know what you thought—your feedback helps us keep leveling up. 👉 Drop your rating here Thanks for reading, The AI_Distilled Team (Curated by humans. 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What this surprising alliance means for GPU scale, speed, and the future of foundational models. AI_Distilled #99: What’s New in AI This Week Your Exclusive Invite for the World’s first 2 day AI Challenge (usually $895, but $0 today) 51% of companies have started using AI Tech giants have cut over 53,000 jobs in 2025 itself And 40% of professionals fear that AI will take away their job. Join the online 2-Day LIVE AI Mastermind by Outskill - a hands-on bootcamp designed to make you an AI-powered professional in just 16 hours. Usually $895, but for the next 48 hours you can get in for completely FREE! 📅Kick off Call & Session 1- Friday (10am EST- 1pm EST) 🧠Sessions 2-5: 🕜Saturday 11 AM to 7 PM EST ; Sunday 11AM EST to 7PM EST All by global experts from companies like Amazon, Microsoft, SamurAI and more. And it’s ALL. FOR. FREE. 🤯 🚀 🎁 You will also unlock $3,000+ in AI bonuses: 💬 Slack community access, 🧰 Your Personalised AI tool kit, and ⚙️ Extensive Prompt Library with 3000+ ready-to-use prompts — all free when you attend! JOIN NOW - LIMITED FREE SEATS Warm greetings from the AI Distilled team! Here's your freshly baked issue of AI Distilled. With groundbreaking tools and surprise collaborations, this edition is served piping hot. Plus, don’t miss our curated roundup of local AI meetups to keep your network as sharp as your skills. LLM Expert Insights, Packt In today's issue: 🧠 Expert Deep Dive: Shanthababu Pandian shares a blueprint for building scalable, ethical, and adaptive agentic AI systems. 📅 Must-Attend Meetups: From GPU hack weekends to GenAI showcases, here are 5 can’t-miss midsummer AI events across the globe. ⚙️ OpenAI Drops o3-Pro: A high-reasoning model for complex coding, analysis, and real-time search—priced for pros. 🎞️ Meta Goes Multimodal: New AI video editor + V-JEPA 2 pushes Meta’s edge in creative and physical reasoning AI. 🧠 Mistral Debuts Magistral: Their first reasoning-focused model launches alongside Mistral Compute, an enterprise-grade AI infra stack. 🌩️ OpenAI Teams with Google Cloud: Surprise GPU partnership expands OpenAI’s compute scale beyond Azure. 🌍 Google.org Backs Ethical GenAI: $30M accelerator funds nonprofits solving global crises with generative AI. 🔐 EchoLeak Targets Copilot: A zero-click exploit exposes AI’s growing attack surface—Microsoft acts fast. 📈UPCOMING EVENTS MUST ATTEND AI/LLM MEET-UPS Here’s your go-to calendar for this month’s midsummer AI meetups—perfect for networking, learning, and getting hands-on with the latest in generative models, agent frameworks, LLM tooling, and GPU hacking. 1. The Agent – Part 2 Date: June 23, 2025 Location: Cambridge, MA – Boston Generative AI Cost: US $22 Focus: Agent-centric GenAI patterns Website: Meetup Boston 2. Practical AI Monthly Date: June 24, 2025 Location: London – Mindstone AI Cost: Free Focus: Hands-on GenAI use-cases Website: Mindstone London 3. GPU Programming Hack Weekend Dates: June 27–29, 2025 Location: Los Altos, CA – Modular Meetup Cost: Free Focus: Mojo/MAX GPU kernels & PyTorch ops Website: Meetup Los Altos 4. July Mixer & Showcase Date: July 2, 2025 Location: Austin, TX – LangChain AIMUG Cost: Free Focus: LangChain, LLM tooling Website: AIMUG 5. Pizza, Demos & Networking Date: July 9, 2025 Location: Berlin – AI Builders Cost: €5 – €10 Focus: Building with LLMs & GenAI Website: Meetup Berlin What’s stopping you? Choose your city, RSVP early, and step into a room where AI conversations spark, and the future unfolds one meetup at a time. LAST CHANCE - BUY NOW AT 25% OFF EXPERT INSIGHTS - BY SHANTHABABU PANDIAN QUICK UNDERSTANDING OF EFFECTIVE AGENTIC SYSTEM DESIGN Agentic systems, software architectures where autonomous agents act, learn, and interact to achieve goals, are transforming industries from robotics to customer service. These systems, powered by artificial intelligence (AI), enable dynamic decision-making in complex environments. This article provides a concise overview of designing effective agentic systems, focusing on core principles, components, and practical considerations. Shanthababu Pandian, Director- Data and AI, Rolan Software Service What is an Agentic System? An agentic system consists of one or more agents that operate autonomously or semi-autonomously to accomplish tasks. Agents perceive their environment, process information, make decisions, and act, often adapting through the process of learning. Unlike traditional software with fixed rules, agentic systems thrive in dynamic, uncertain settings. Key Characteristics: Autonomy: Agents make decisions without constant human intervention. Reactivity: Agents respond to environmental changes in real-time. Proactivity: Agents pursue goals proactively, anticipating needs. Adaptability: Agents learn from experience to improve performance. Social Ability: Agents collaborate with other agents or humans. Examples include autonomous drones, AI-driven chatbots, or multi-agent systems in logistics optimization. Core Principles of Effective Design Designing agentic systems requires striking a balance between autonomy, efficiency, and reliability. Below are the foundational principles: Core Principles of Effective Design Designing agentic systems requires striking a balance between autonomy, efficiency, and reliability. Below are the foundational principles: Goal-Oriented Design: Define clear, measurable objectives for agents (e.g., “deliver packages in under 30 minutes”). Align agent goals with system-wide outcomes to avoid conflicts in multi-agent setups. Modularity: Build agents with modular components (perception, decision-making, action) for flexibility and easier updates. Example: A robotic agent’s vision module can be upgraded without altering its navigation logic. Robust Perception: Equip agents with sensors or data inputs to accurately interpret their environment. Use redundancy (e.g., multiple sensors) to handle noise or failures. Scalable Decision-Making: Implement decision-making algorithms (e.g., reinforcement learning, rule-based systems) that scale with complexity. Balance computational cost with decision quality—simple heuristics may suffice for some tasks. Learning and Adaptation: Incorporate learning mechanisms (e.g., machine learning models) to adapt to new scenarios. Use online learning for real-time updates and offline training for stability. Coordination in Multi-Agent Systems: Design communication protocols for agents to share information and negotiate. Use centralised (e.g., a coordinator agent) or decentralised (e.g., consensus algorithms) approaches based on system needs. Safety and Ethics: Embed fail-safes to prevent harmful actions (e.g., collision avoidance in drones). Key Components of Agentic Systems An effective agentic system typically includes: Perception Module: Collects data from the environment (e.g., cameras, APIs, user inputs). Processes raw data into actionable insights using techniques like computer vision and natural language processing. Decision-Making Module: Choose actions based on goals and perceived state. Common approaches include rule-based logic, planning algorithms, or AI models like deep reinforcement learning. Action Module: Executes decisions (e.g., moving a robot arm, sending a message). Interfaces with hardware and software actuators. Learning Module: Update agent behaviour based on feedback (e.g., rewards in reinforcement learning). Store knowledge in models or databases for future use. Communication Module (for multi-agent systems): Enables agents to share states, plans, or resources. Utilises protocols such as MQTT or gRPC for efficient data exchange. Practical Considerations Environmental Analysis: Understand the environment’s dynamics (e.g., predictable vs. chaotic) to choose appropriate algorithms. Example: A warehouse robot needs robust navigation in a structured environment, while a chatbot must handle unpredictable user inputs. Resource Constraints: Optimise for computational, energy, or bandwidth limits, especially on edge devices like IoT sensors. Example: Use lightweight ML models for real-time processing on drones. Testing and Validation: Simulate environments to test agent behaviour under diverse scenarios. Use formal verification for critical systems (e.g., autonomous vehicles) to ensure safety. Scalability: Design systems to handle increasing numbers of agents or tasks. Example: A logistics system should support adding more delivery drones without degrading performance. Human-Agent Interaction: Create intuitive interfaces for human oversight and collaboration. Example: A customer service agent should seamlessly escalate complex queries to human operators. Challenges and Solutions Challenge: Unpredictable environments can lead to poor agent performance. Solution: Use robust learning algorithms (e.g., meta-learning) and fallback mechanisms. Challenges: Multi-agent coordination can cause conflicts or inefficiencies. Solution: Implement game-theoretic approaches or swarm intelligence techniques. Challenges: Ethical concerns, like bias in decision-making. Solution: Audit training data and incorporate fairness constraints in models. Real-World Applications Logistics: Multi-agent systems optimise delivery routes (e.g., Amazon’s warehouse robots). Healthcare: AI agents assist in diagnostics or patient monitoring. Gaming: NPCs (non-player characters) act as autonomous agents for immersive experiences. Smart Cities: Agents manage traffic flow or energy distribution. Conclusion Effective agentic system design hinges on clear goals, modular architecture, and robust adaptation mechanisms. By prioritising scalability, safety, and coordination, developers can create systems that thrive in dynamic environments. As AI advances, agentic systems will play an increasingly central role in automating complex tasks, driving efficiency, and enhancing human capabilities. For further exploration, consider open-source frameworks like ROS (Robot Operating System) for robotics or RLlib for reinforcement learning-based agents. Liked the Insights? Want to dig in deeper? Master the art of building AI agents with large language models using the coordinator, worker, and delegator approach for orchestrating complex AI systems Understand the foundations and advanced techniques of building intelligent, autonomous AI agents Learn advanced techniques for reflection, introspection, tool use, planning, and collaboration in agentic systems Explore crucial aspects of trust, safety, and ethics in AI agent development and applications BUY NOW 📈LATEST DEVELOPMENT Here is the news of the week. OpenAI Debuts o3-Pro Model OpenAI has quietly introduced o3-pro, an advanced "high-reasoning" version of its o-series models designed for research, complex analysis, and coding. Featuring real-time web search, Python execution, and multimodal reasoning, o3-pro starts at $20–$80 per million input/output tokens—a tenfold increase over the standard o3. Preliminary tests indicate improved accuracy in science, business, and writing tasks, despite slightly slower response times. Meta Unveils AI Video Editor and Physical Reasoning AI World Model Meta’s new generative AI video editor transforms any ten-second clip into a customizable playground. Now available on the Meta AI app, Meta.ai, and the Edits mobile app, users can upload clips and apply over 50 preset prompts to alter clothing, settings, lighting, or visual styles within seconds. This feature is free for a limited time, and edited clips can be directly shared on Facebook or Instagram. Additionally, Meta unveiled V-JEPA 2, a sophisticated "world model" that enhances robotic and AI agent reasoning capabilities. V-JEPA 2 is trained to recognize patterns in physical interactions, such as the dynamics between people, objects, and their environment. To support community engagement, Meta has open-sourced three new test suites, inviting researchers to rigorously evaluate and accelerate the development of machine common sense. Mistral returns with Magistral Reasoner and Mistral Compute Paris-based Mistral AI has launched Magistral, its first dedicated reasoning model, available in both open-source and enterprise tiers. Magistral prioritizes transparent, step-by-step logical reasoning, deep domain expertise, and extensive multilingual support, directly addressing common criticisms of earlier chain-of-thought models. Complementing this launch, Mistral introduced Mistral Compute, an infrastructure solution providing bundled GPUs, orchestration, and managed services. The offering allows governments, enterprises, and research institutions to operate cutting-edge AI on-premises or within national cloud infrastructures, reducing dependency on U.S.-based cloud providers. OpenAI–Google Cloud Alliance In an unexpected strategic collaboration, OpenAI has partnered with Google Cloud for additional GPU capacity, complementing its existing partnerships with Microsoft Azure and CoreWeave. Finalized in May, this deal helps OpenAI scale rapidly and diversify its supply chain. Google.org Funds Social-Impact Gen-AI for its 2025 GenAI Accelerator program Google.org has selected 20 nonprofits and civic groups for its 2025 Generative AI Accelerator program. Awardees will receive six months of technical mentorship, pro-bono AI expertise, cloud credits, and a portion of a $30 million fund to address critical global issues, from crisis response and children's mental health to combating antimicrobial resistance. Zero-Click EchoLeak Hits Copilot Security researchers at Aim revealed EchoLeak, a novel zero-click exploit targeting Microsoft 365 Copilot. The vulnerability allowed malicious markdown emails to bypass prompt-sanitization, triggering background HTTP requests capable of exfiltrating sensitive data without user interaction. Microsoft swiftly patched the vulnerability before its public disclosure, highlighting emerging security risks associated with increasingly autonomous AI systems. 📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us. If you have any comments or feedback, just reply back to this email. Thanks for reading and have a great day! That’s a wrap for this week’s edition of AI_Distilled 🧠⚙️ We would love to know what you thought—your feedback helps us keep leveling up. 👉 Drop your rating here Thanks for reading, The AI_Distilled Team (Curated by humans. 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From Digit’s delivery test to Gemini 2.5’s native audio and ChatGPT-powered productivity—this week’s AI_Distilled #98: What’s New in AI This Week Join the live "Building AI Agents Over the Weekend" Workshop starting on June 21st and build your own agent in 2 weekend. In this workshop, the Instructors will guide you through building a fully functional autonomous agent and show you exactly how to deploy it in the real world. BOOK NOW AND SAVE 35% Use Code AGENT25 at checkout Spots are limited. Book now to SAVE 35% (Valid for till 8th June 2025) This month is buzzing with AI innovation—from can’t-miss conferences to game-changing GenAI use cases. Whether you're looking to level up your skills, explore new tools, or stay ahead of the curve, we've got you covered. LLM Expert Insights, Packt In today's issue: 🧠 Expert Deep Dive: Valentina Alto explores real-world GenAI use cases—from code and content to campaigns and daily life. 📅 June Conference Watch: Your curated guide to the top AI/LLM conferences this month—CVPR, ICML, ACL, and more. 🎯 Productivity Reimagined: From GTM strategy to custom workouts, see how ChatGPT reshapes personal and professional workflows. 🔊 Gemini 2.5 Gets Audio: Google DeepMind’s latest model understands tone, languages, and screen-shared content. 📦 Amazon’s Humanoid Robot: Digit enters delivery trials—redefining warehouse automation and last-mile logistics. 🔐 OpenAI Boosts Security: A new vulnerability disclosure framework sets industry standards for AI integrity. 🚫 DeepSeek Faces Criticism: China’s newest model sparks global concern with aggressive political censorship. ⚡ Nvidia Dominates MLPerf: Blackwell GPUs set new training records, proving unmatched performance in AI workloads. 📈UPCOMING EVENTS JUNE'S MUST ATTEND AI/LLM CONFERENCES Breakthroughs in AI are made possible through years of study, experimentation, and research that eventually shape the mainstream. Whether you're a researcher pushing the boundaries of machine learning, a developer building with generative AI, or a leader shaping enterprise strategy, this handpicked list of the top conferences in 2025 will help you stay connected to the pulse of innovation. 1. CVPR 2025 – IEEE/CVF Conference on Computer Vision and Pattern Recognition Dates: June 11–15, 2025 Location: Music City Center, Nashville, TN, USA Cost: In-person - General: $900; Student: $810; IEEE/CVF Members ($900 for professionals, $675 for students) Nature: Virtual - General: $215; Student: $125; IEEE/CVF Members ($180 for professionals, $100 for students) Focus: Computer vision, multimodal AI, LLMs in vision tasks Website: CVPR 2025 Conference 2. ICLAD 2025 – IEEE International Conference on LLM-Aided Design Dates: June 26–27, 2025 Location: Paul Brest Hall, Stanford University, Stanford, CA  Cost: In-person only - General: $600; Student: $410; IEEE/CVF Members ($500 for professionals, $350 for students) Focus: Utilizing large language models to enhance design processes in circuits, software, and computing systems Website: International Workshop on LLM-Aided Design 3. ICML 2025 – International Conference on Machine Learning Dates: July 13–19, 2025 Location: Vancouver Convention Center, Vancouver, Canada Cost: In-person - General: $1365; Student: $1030 Nature: Virtual - General: $275; Student: $200 Focus: Machine learning theory and practice, generative AI, LLMs Website: ICML 2025 Conference 4. ACL 2025 – 63rd Annual Meeting of the Association for Computational Linguistics Dates: July 27 – August 1, 2025 Location: Vienna, Austria Cost: In-person - General: $1125; Academic: $800; Student: $425 + ACL Membership fee ($100 for professionals, $50 for students) Nature: Virtual: - General: $550; Academic: $400; Student: $250 + ACL Membership fee ($100 for professionals, $50 for students) Focus: Natural language processing, large language models, language generation Website: ACL 2025 5. NeurIPS 2025 – Conference on Neural Information Processing Systems Dates: December 2–7, 2025 Location: San Diego Convention Center, San Diego, CA, USA Cost: In-person - General: $1000; Academic: $800; Student: $375 Nature: Virtual - General: $275; Academic: $200; Student: $50 Focus: Advanced ML research, LLMs, multimodal AI Website: NeurIPS 2025 Conference EXPERT INSIGHTS FROM TEXT TO TECH: THE MANY USE CASES OF GENERATIVE AI The hype around GenAI and how it enhances productivity shows no signs of slowing down. Just as previous generations shifted from Xeroxing to Googling, we now find ourselves firmly in the era of “Ask ChatGPT.”. GenAI finds its applications in various fields, such as image synthesis and text generation to music composition, marketing content, data analysis, coding, and countless other tasks that, until recently, required specialized expertise. In this issue, we spotlight just a few of the many real-world applications of GenAI, using OpenAI’s ChatGPT as our lens. Here are four use cases from one of our best-selling books, Practical Generative AI with ChatGPT, written by our star author Valentina Alto. 1. Daily assistant: ChatGPT is an excellent tool for boosting your day-to-day activities, such as grocery shopping, meal planning, and workouts, among many other tasks. Take, for example, the following prompt: Generate a 75’ workout routine for strength training. My goal is increasing my overall strength and also improving flexibility. I need a workout for the upper body only divided by the muscle group. Make it in a table format with # of reps and # of series. Make sure to incorporate some rest as well. Here is a sample workout plan that ChatGPT might generate for you: 2. Creating content: You can use ChatGPT to craft emails, create social media posts, write blogs and articles, assist with proofreading, perform translations, analyze documents, or even adjust the tone of your content: whether you want it to be formal, quirky, casual, or sarcastic. Take a look at ChatGPT’s sarcastic translation of an Italian text: 3. Coding assistant: The primary capability you should leverage is ChatGPT’s code generation. From writing a simple function to creating the skeleton of a game, ChatGPT can provide enough building blocks to get started. You can also use it to suggest code optimizations, explain errors, and debug your existing code. Additionally, it can help generate documentation, improve code explainability, and even assist in understanding the structure of a neural network. Take, for example, the following CNN model: If you ask ChatGPT to explain this model, it may respond as follows: 4. Design marketing campaigns: Suppose you have a new product and need a go-to-market (GTM) strategy. You can ask ChatGPT to help you draft an initial plan. Then, by iteratively refining your prompts, you can request suggestions for the product name, marketing hook, target audience research, unique value proposition, sales channels, pricing, SEO keywords, and more. You can even ask it to generate product launch posts. Here are some of the prompts Valentina experimented with in her book while developing a GTM strategy for eco-friendly socks. Generate 5 options for a catchy product line name Generate 3 slogans for the “GreenStride” name. They should be motivating and concise. What kind of target audience should I address with the promotion of GreenStride socks product line. What could be the best channel to reach the segments identified above Give me three concise suggestions on how to make my socks line GreenStride outstanding and unique in a competitive market Generate a product description (max 150 words) for GreenStride socks line using unique differentiator you listed above. It should be attention-grabbing and effective, as well as SEO optimized. List also the SEO keywords you used to finish. What could be the fair price of my socks line I want to generate an Instagram post to announce the launch of GreenStride socks. Write a post (max 150 words) including the unique features and differentiators mentioned above, as well as relevant hashtags. Liked the Insights? Want to dig in deeper? Beyond the four use cases we’ve spotlighted in this issue, the book Practical Generative AI with ChatGPT, by Valentina Alto, introduces generative AI and its applications, focusing on OpenAI’s ChatGPT. It covers prompt engineering, daily productivity use cases, domain-specific applications for developers, marketers, and researchers, and the creation of custom GPTs using the GPT Store, enabling specialized assistants without coding, powered by personalized instructions and tools. BUY NOW 📈LATEST DEVELOPMENT Let’s get right into it. Google DeepMind Introduces Gemini 2.5 with Native Audio Capabilities Google DeepMind has launched Gemini 2.5, now capable of processing real-time audio and video. The model can interpret screen-shared content, respond to tone and background noise, and supports over 24 languages, making it more contextually aware and interactive than ever before. Amazon to Test Humanoid Robots for Package Deliveries The Information has reported that Amazon is preparing pilot tests of Agility Robotics' bipedal humanoid robot, Digit, for use in logistics and package handling. Designed to work safely in spaces designed for humans, Digit is expected to automate repetitive warehouse tasks and even assist in last-mile delivery operations. OpenAI Launches Coordinated Vulnerability Disclosure Framework OpenAI has introduced an “Outbound Coordinated Vulnerability Disclosure” policy to responsibly report security issues it uncovers in external systems. This move aims to bolster security standards and transparency across the tech ecosystem. DeepSeek’s New AI Sparks Free Speech Concerns Chinese AI developer DeepSeek has triggered global criticism for its model’s extreme content filtering. Users attempting to query politically sensitive topics, like Tiananmen Square or Taiwanese independence, are met with complete denials, spotlighting a stark divide in global AI moderation norms. Nvidia Blackwell Chips Dominate New MLPerf Benchmarks Nvidia’s Blackwell GPUs dominated the latest MLPerf training benchmarks, delivering double the performance of previous H100 chips. These results highlight Blackwell’s efficiency in training large AI models with fewer GPUs, reduced energy use, and lower costs, solidifying Nvidia’s leadership in AI hardware and accelerating industry-wide adoption of its new architecture. Kubernetes for Generative AI Solutions 40% Off on eBook + 20% Off on Paperback for the next 48 hours 📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us. If you have any comments or feedback, just reply back to this email. Thanks for reading and have a great day! That’s a wrap for this week’s edition of AI_Distilled 🧠⚙️ We would love to know what you thought—your feedback helps us keep leveling up. 👉 Drop your rating here Thanks for reading, The AI_Distilled Team (Curated by humans. 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How to boost LLM performance during pre-training: A preview AI_Distilled #97: What’s New in AI This Week Build Your AI Chatbot with Free LLM Boomcamp Join LLM Zoomcamp, a free online course starting on June 2 and build an end-to-end AI chatbot tailored to your use case. In 10 weeks, you’ll learn key skills like working with LLMs and RAG, vector search for indexing and retrieval, how to evaluate and monitor performance, and key best practices for building robust, real-world applications. REGISTER NOW FOR FREE It’s time for the final issue of May 2025. In this edition, we bring you the top five news highlights of the week, upcoming events shaping the AI and LLM landscape, and a sneak peek into techniques for optimizing LLM performance. LLM Expert Insights, Packt In today's issue: 🧠 Expert Deep Dive: This week, we explore pre-training optimization techniques—from quantization to flash attention—for building faster, smarter LLMs. 📅 Webinar Watchlist: June’s top AI/LLM webinars cover automation, cybersecurity, healthcare, legal AI, and multimodal fine-tuning. 🔌 Build AI Agents This Weekend: Join Packt’s Accelerated Agentic AI Bootcamp—hands-on, fast-paced, and 35% off. 📚 Optimize Your LLM Stack: Learn more from Generative AI with Python and PyTorch—a guide to efficient training and deployment. 🚀 DeepSeek V3 Debuts: China’s latest open-source model steps up with better reasoning and dev capabilities. 📰 Publishers vs. AI Search: Google CEO Sundar Pichai defends AI-powered results amid growing backlash from content creators. 📱 Apple Rebrands for 2026: WWDC will unveil iOS 26 and align all platforms under a unified OS naming strategy. 🎨 Sam Altman x Jony Ive: OpenAI teams up with the design legend to build magical, AI-first consumer products. 🧠 Anthropic Traces Thoughts: Claude’s internal reasoning gets visualized through groundbreaking interpretability research. 📈UPCOMING EVENTS JUNE'S MUST ATTEND AI/LLM WEBINARS In June 2025, a number of exciting AI webinars are already generating buzz. Here are the Top 5 not-to-miss events in the next month (for more information and registration details, please visit the links): 1. AI-Enhanced Motion Control: Innovations Driving Automation Forward Date: June 5, 2025 Time: 12:00 PM – 1:00 PM ET Location: Online Cost: Free Hosted by the Association for Advancing Automation, this webinar explores how AI is revolutionizing motion control systems, enhancing precision, efficiency, and adaptability across various industries. 2. AI Security Webinar – Practical Measures to Mitigate AI and Cybersecurity Risks Date: June 11, 2025 Time: 11:00 AM – 12:30 PM BST Location: Online Cost: Free Presented by The Alan Turing Institute, this interactive webinar brings together industry experts and SMEs to share practical, cost-efficient, and high-impact security measures that deliver maximum AI and cybersecurity protection for businesses. 3. Clinical Large Language Models in Healthcare – Applications, Challenges, and Opportunities Date: June 12, 2025 Time: 10:00 AM – 11:00 AM CEST Location: Online Cost: Free Organized by the Helmholtz Information & Data Science Academy in collaboration with NORA, this webinar features Anne Torill Nordsletta discussing the role of large language models in healthcare, exploring applications, challenges, and future opportunities in the clinical setting. 4. Inside the TBI Playbook: How I Use AI to Win the Hardest Cases Date: June 17, 2025 Time: 1:00 PM – 2:30 PM EST Location: Online Cost: Free Hosted by Anytime AI™, this CLE-accredited webinar features attorney Taylor Ernst sharing insights on leveraging AI in traumatic brain injury litigation. Attendees will learn about practical applications of AI tools in complex legal cases. 5. Multi-Modal LLM Fine-Tuning of Unstructured Data with Dataloop & SingleStore Date: June 18, 2025 Time: 10:00 AM – 11:00 AM PST Location: Online Cost: Free Presented by SingleStore, this webinar explores techniques for fine-tuning multi-modal large language models on unstructured data, covering integration strategies with Dataloop and SingleStore platforms. Machine Learning Summit 2025 JULY 16–18 | LIVE (VIRTUAL) 20+ ML Experts | 20+ Sessions | 3 Days of Practical Machine Learning and 35% OFF BOOK NOW AND SAVE 35% Use Code EMAIL35 at checkout when purchasing the 3-day ticket Limited to the first 50 customers EXPERT INSIGHTS PRE-TRAINING OPTIMIZATION TECHNIQUES FOR LLMs The scale of data and computation required for large language models (LLMs), along with the significant capital investment needed to train and deploy them, necessitates the exploration of optimization techniques throughout the LLM lifecycle. In this issue, we focus on potential improvements during the pre-training phase, as this is the most resource-intensive step, involving a vast amount of data and sensitivity to architectural design. Here are some techniques you can employ to improve LLM performance and efficiency: 1. Quantization: Quantization aims to reduce the number of bits needed to store these weights by binning floating-point values into lower-precision buckets. This reduces memory usage with minimal impact on performance. Small precision losses are acceptable as long as the model’s performance is within the required levels. For instance, a weight value like 3.1457898 could be quantized to 3.1458 using a scheme that retains four decimal places. Such a scheme might lead to slight changes (during the backward pass of the training step, for example, a higher margin of error) while computing loss or while updating weights. Take, for instance, 4-bit quantization, which uses small bins where the density of weights is higher and fewer larger bins for weights away from the mean. The 4-bit float representation employs an intelligent approach based on the distribution of model weights. Most weights tend to cluster near zero, with minor differences requiring higher precision, while fewer weights have larger values. To accommodate this, asymmetric binning is used: smaller bins are allocated for values near the mean to maintain precision, while fewer larger bins handle outliers further from the mean. 2. Mixed precision: This is another technique to reduce memory and computational demands without sacrificing significant accuracy. These methods combine different numerical formats, such as float16, int8, and more, to optimize efficiency and performance during training or inference. 3. Data efficiency: Large datasets are costly to process, and redundant or noisy data can negatively impact model performance. Therefore, data efficiency techniques can be applied to achieve high model accuracy and generalization with a reduced or optimized dataset. This process includes filtering data for quality, reducing redundancy, and applying sampling techniques to emphasize high-value samples. 4. Sparse attention: Instead of computing attention weights for every pair of tokens in the input sequence, sparse attention focuses only on a subset of tokens, exploiting patterns in the data or task-specific properties. To put things into perspective, think about decoder-only architectures like GPT trained with an auto-regressive language objective. Such an objective puts a constraint on the attention layer to be causal, and thus, only the lower triangular attention matrix is useful (but the computation is still done for the whole matrix). Different architectures leverage specific patterns, like local or strided attention mechanisms, to bring in efficiency in computation time. 5. Flash attention: Flash attention takes the route of hardware-based improvements and efficiencies to compute attention scores. There are two popular techniques for sparse attention: Kernel fusion and Tiling. Kernel fusion reduces the number of I/O operations by combining all steps (elementwise operations, matrix multiplication, softmax, etc.) into a single read-write operation. This technique is pretty effective during inference. Tiling, on the other hand, breaks down the overall attention calculation into smaller and manageable groups of operations that fit into fast and low-latency GPU memory. For instance, instead of computing softmax across the entire attention matrix at once, FlashAttention computes it over smaller chunks in a numerically stable and tiled fashion, thus making use of faster memory without the need to store a large matrix. 6. Mixture of Experts (MoE) architecture: MOE is an advanced architecture designed to leverage a subset of components (or experts) rather than the whole architecture itself, thereby achieving higher scalability and efficiency. The Experts in this architecture are independent modules or blocks of the network, where each can be trained to specialize in a specific task. While the Router is a module that learns to select which experts to leverage (or activate) for a given input based on different criteria. The Router itself can be a neural network. 7. Efficient architectures: There are a number of different patterns and techniques that have been developed and leveraged by different architectural improvements over the years. Some of the popular architectures are Linformer, Reformer, and Big Bird. Apart from pre-training optimizations, there are other techniques as well, such as fine-tuning and improvements in inference time. More recently, the availability and popularity of small language models and specialized hardware and frameworks has also contributed to significant improvements in the overall efficiency of resource-constrained environments. Liked the Insights? Want to dig in deeper? If you wish to learn more about these techniques or wish to dive deep into foundational aspects of the LLM ecosystem, you can check out the book, Generative AI with Python and PyTorch, Second Edition, by Joseph Babcock and Raghav Bali. BUY NOW 📈LATEST DEVELOPMENT Let’s kick things off with the top stories of the week. China is aiming for the top spot in the AI race with DeepSeek V3's latest release DeepSeek just released -V3-0324, claiming a major boost in reasoning, front-end development capabilities, and smarter tool use. The release positions DeepSeek as a serious contender to models like Code Llama and Codex. You can try out the open-source weights from this HuggingFace card. Publishers claim AI-Search is an internet takeover, Pichai defends it as an innovation In a podcast with Nilay Patel (Editor-in-Chief of The Verge), Google CEO Sundar Pichai shared candid thoughts on AI’s impact on the internet. He defended AI-generated search results amid backlash, insisting they won’t kill the open web. As Google walks a tightrope between innovation and publisher outrage, Pichai expressed confidence that AI will ultimately “enhance,” not erase, human content. He dodged revenue concerns but acknowledged the risks of unchecked AI growth. Catch the full conversation here. Apple’s branding power move with iOS26 A Bloomberg report says that Apple is set to revamp its OS branding game at WWDC-2025. The rebranding will sync all platforms with the upcoming 2026 launch year, setting the stage for a unified, modernized software identity with iOS 26, macOS 26, and watchOS 26. SamA and Ive team up for AI-first products OpenAI is collaborating with design icon Jony Ive and his firm LoveFrom to craft AI-powered products. Jony Ive, Scott Cannon, Evans Hankey, and Tang Tan led io team will collaborate closely with Open AI’s research and engineering teams, with LoveFrom leading design and creative responsibilities. Their goal: to recapture the magic, creativity, and wonder of early Apple-era technology. Hear more about their vision in this video. Anthropic inching towards interpretable AI? Anthropic just cracked open the black box of AI thinking with its latest research, Tracing Thoughts. Using a novel method called dictionary learning, researchers mapped how language models like Claude internally form and organize thoughts. They uncovered thousands of hidden features that resemble abstract concepts and reasoning steps. This breakthrough gives us a glimpse into not just what AI predicts—but how it thinks. Dive into this investigative research here. 📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us. If you have any comments or feedback, just reply back to this email. Thanks for reading and have a great day! That’s a wrap for this week’s edition of AI_Distilled 🧠⚙️ We would love to know what you thought—your feedback helps us keep leveling up. 👉 Drop your rating here Thanks for reading, The AI_Distilled Team (Curated by humans. 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Miss this week’s AI news and you might just fall behind.AI_Distilled #96: What’s New in AI This WeekYou can now run and fine-tune Qwen3 and Meta's new Llama 4 models with 128K context length & superior accuracy. Unsloth is an open-source project that allows easy fine-tuning of LLMs and that also uploads accurately quantized models to Hugging Face. GitHub repo: https://github.com/unslothai/unslothUnsloth's new Dynamic 2.0 quants outperform other quantization methods on 5-shot MMLU & KL Divergence benchmarks, meaning you can now run + fine-tune quantized LLMs while preserving as much precision as possible. Read more here . Tutorial for running Qwen3 here.Tutorial for running Llama 4 here.Welcome to another exciting edition of our AI_Distilled! This week, we're witnessing a surge in innovative AI solutions, with companies like OpenAI and Microsoft rolling out tools that streamline development and enhance user interaction. From Apple opening its models to developers to the fierce competition for AI's top talent, join us as we explore the latest breakthroughs shaping our digital world.LLM Expert Insights,PacktIn today's issue:📅 June’s AI Must-Attends: From AI Engineer World’s Fair to Packt’s Agent Bootcamp—here are 6 events you don’t want to miss this month.🔌 MCP, Explained: Paul Singh breaks down the Model Context Protocol—your plug-and-play solution for seamless AI tool integration.💻 Codex Arrives: OpenAI rolls out Codex, a powerful AI coding agent for writing features, fixing bugs, and navigating codebases.🧠 Windows Gets Smarter: Microsoft integrates native MCP into Windows and launches AI Foundry for seamless agent automation.🎟️ Google AI Ultra Drops: A new $249.99/mo subscription offers Gemini upgrades, cinematic video tools, and 30TB of storage.🍏 Apple Opens Up: Developers may soon build apps with Apple’s AI models—announcement expected at WWDC 2025.🏁 AI Talent Wars: OpenAI, Google & more compete for elite researchers—offering private jets and millions in perks.👨‍💻 Copilot’s New AI Agent: GitHub's upgraded Copilot now tackles coding issues with draft PRs, vision models, and full MCP support.🎧 On-Device Audio AI: Stability AI & Arm launch a mobile-ready model for text-to-audio generation—11 seconds of sound in 8.📈EXPERT INSIGHTSJUNE'S MUST ATTEND AI/LLM EVENTSIn June 2025, a number of exciting AI conferences are already generating buzz. Here are the Top 5 not-to-miss events in the next month (for more information and registration details, please visit the links):1. AI Engineer World’s FairDate: June 3–5, 2025Location: San Francisco, California, USACost: $299–1,799 in-personThe AI Engineer World's Fair, from June 3-5, 2025, in San Francisco, is the largest technical conference for AI engineers. It would host approximately 3,000 attendees, featuring 150 talks and 100 practical workshops. Topics include Generative AI, AI agents, LLMs, infrastructure, and AI in Fortune 500 companies, offering unparalleled networking and learning opportunities for industry professionals.2. Data + AI SummitDate: June 9–12, 2025Location (Hybrid): San Francisco, California, US, and available online.Cost: $1,395–1,895 in-person. Free for virtual admission. Discounted tickets are available with group-rate pricing.The Data + AI Summit is a four-day event hosted by Databricks. It includes panel discussions, networking opportunities, and training workshops on topics such as data engineering, data governance, and machine learning.3. The AI Summit LondonDate: June 11–12, 2025Location: Tobacco Dock, London, UKCost: £125–2,499AI Summit London, spanning over two days, will cover a wide range of topics including agentic AI in action and ethical use of AI. With a strong lineup of sponsors and thousands of guests, the summit offers great opportunities for networking with leading AI practitioners.4. Packt’s AI Agent Bootcamp (Build AI Agents Over the Weekend)Date: June 21–22 and 28–29, 2025Location: Live Virtual WorkshopCost: Our AI Agent Bootcamp aims to equip developers, ML engineers, data scientists, technical professionals, and software architects with the practical skills to design, build, and deploy AI agents using frameworks like LangChain, AutoGen, and CrewAI, moving from theoretical understanding of LLMs to practical application.5. CDAO GovernmentDate: June 25–26, 2025Location: Washington, D.C., USCost: $499 in-person; Free for VP and C-level government executives.The CDAO Government conference in Washington, D.C., is unique as it unites U.S. government data leaders to explore AI, governance, and ethical data use in public services. Celebrating its 13th anniversary, this event offers an excellent opportunity to learn how to securely leverage AI's capabilities for government data challenges.This was just a quick peek into spaCy pipelines — but there’s much more to explore.For instance, the spacy-transformers extension integrates pretrained transformer models directly into your spaCy pipelines, enabling state-of-the-art performance. Additionally, the spacy-llm plugin allows you to incorporate LLMs like GPT, Cohere, etc. for inference and prompt-based NLP tasks.Master AI Tools, Set Automations & Build Agents – all in 16 hours (for free)AI is no longer just a buzzword — it’s the most valuable skill of this decade– to make money, to get hired and to be future-paced.That’s why, you need to join the 2-Day Free AI Upskilling Sprint by Outskill which comes with 16 hours of intensive training on AI frameworks, tools and tactics that will make you an AI expert.Originally priced at $499, but the first 100 of you get in for completely FREE! Claim your spot now for $0! 🎁📅23rd May- Kick Off Call & Session 1✅Live sessions- 24th & 25th May🕜11AM EST to 7PM ESTJOIN NOW(Limited Free Seats! 🚨)EXPERT INSIGHTS BY PAUL SINGHModel Context Protocol (MCP) and what it means for youIf you're working on AI design or tool integration, the Model Context Protocol (MCP) offers a seamless, standardized way to connect AI tools, data sources, and LLM applications. Developed by Anthropic, MCP is an open protocol designed to simplify the often complex and time-consuming process of integrating rapidly evolving AI models with tools and services. Think of it as the USB-C of the AI world—plug-and-play, regardless of the LLMs or tools you're working with, and without diving into the intricate technicalities of MCP itself.MCP operates on a client-server model, where your LLM application runs a local MCP client that communicates with one or more MCP servers. A service provider only needs to implement a single MCP server, which can then handle APIs, databases, and other services, without requiring constant code adjustments for each new integration.Take a look at how three different MCP servers integrate with APIs and services:MCP leverages the lightweight JSON-RPC message format (a simple remote procedure call protocol), stateful connections, server-client capability negotiation, and reflection. Reflection allows the client to query the server about its capabilities, which can then be surfaced to the LLM automatically via the orchestrating application’s prompt.When designing with MCP, it's important to keep your architecture modular, test each component thoroughly, document your iterations, and ensure security by validating inputs and controlling access.MCP is gaining traction with large organizations like Microsoft, which is integrating it into key products such as Semantic Kernel, Copilot Studio, and GitHub Copilot. I envision a near future where MCP-as-a-Service becomes the de facto standard, eliminating deployment overhead and enabling seamless AI-to-AI or agent-to-agent communication. For example, MCP endpoints could allow straightforward integration without server management, while internal repositories of MCP clients could democratize standardized tool access across organizations.To read more about MCP, you can check out these resources: https://modelcontextprotocol.io and https://aka.ms/mcp. I’ll continue to share how our customers and various industries are adopting MCP and the lessons we’re learning along the way. Stay tuned for more.Join Packt’s Accelerated Agentic AI Bootcamp this June and learn to design, build, and deploy autonomous agents using LangChain, AutoGen, and CrewAI. Hands-on training, expert guidance, and a portfolio-worthy project—delivered live, fast, and with purpose.This is it.35% off this Workshop - Limited Time OfferIf you’re in—move now.Code: AGENT35RESERVE YOUR SEAT NOW!📈LATEST DEVELOPMENTOpenAI Introduces Codex for Enhanced Code GenerationOpenAI has released Codex, a cloud-based AI agent for software engineering. Available in ChatGPT Pro, Enterprise, and Team, Codex (powered by codex-1) can write features, fix bugs, and answer codebase questions, operating in isolated environments. It learns from real-world tasks, producing human-like code and iteratively running tests. Developers can monitor progress, review changes with verifiable evidence, and guide Codex with AGENTS.md files.Microsoft Unveils Windows AI Foundry and Native MCP for Future AI AgentsMicrosoft is advancing its AI vision with native Model Context Protocol (MCP) in Windows and the Windows AI Foundry. This crucial groundwork, leveraging Anthropic's "USB-C of AI" protocol, aims to enable automated AI agents to seamlessly interact with apps, web services, and Windows functions. This initiative will empower features like natural language file searches and AI-powered system controls, reshaping how users engage with their devices.Google Launches AI Ultra: A VIP Pass to Advanced AIGoogle is launching Google AI Ultra, a new $249.99/month subscription (with an initial discount) offering the highest usage limits and access to its most capable AI models and premium features. Tailored for creative professionals, developers, and researchers, it includes Gemini with enhanced reasoning, Flow for cinematic video creation, Whisk for animated image generation, and advanced NotebookLM. Subscribers also get Gemini integration in Google apps (Gmail, Docs, Chrome), Project Mariner for multi-task management, YouTube Premium, and 30 TB storage.Apple to Open AI Models for DevelopersApple is reportedly preparing to allow third-party developers to build software using its AI models, aiming to boost new application creation. This move, expected to be unveiled at WWDC on June 9th, would let developers integrate Apple's underlying AI technology into their apps, starting with on-device models. This could help Apple compete in the AI landscape and enhance Apple Intelligence's appeal.GitHub Copilot Launches New AI Coding AgentGitHub Copilot now features an AI coding agent that tackles low-to-medium complexity tasks by simply assigning it issues. It operates in secure, customizable environments, pushing commits to draft pull requests with transparent session logs. This agent, enhanced by Model Context Protocol (MCP) and vision models, allows developers to offload routine work, ensuring security through human approval for pull requests and adhering to existing policies.📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us.If you have any comments or feedback, just reply back to this email.Thanks for reading and have a great day!That’s a wrap for this week’s edition of AI_Distilled 🧠⚙️We would love to know what you thought—your feedback helps us keep leveling up.👉 Drop your rating hereThanks for reading,The AI_Distilled Team(Curated by humans. Powered by curiosity.)*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Microsoft and Google shake hands on A2A—what that means for you. AI_Distilled #94: What’s New in AI This Week Building GenAI infra sounds cool—until it’s 3am and your LLM is down. This free guide helps you avoid the pitfalls. Learn the hidden costs, real-world tradeoffs, and decision framework to confidently answer: build or buy? Includes battle-tested tips from Checkr, Convirza & more. GRAB IT NOW Here's what's happening in the world of AI, which has been buzzing with groundbreaking developments! This week, we're tracking OpenAI's global partnerships for democratic AI, the transparency debate sparked by Anthropic's Claude 3.7 prompt leak, and Google's powerful Gemini 2.5 Pro debut alongside a fresh 'G' logo. We also explore the intersection of tech and Saudi investment, a surprising Microsoft-Google collaboration for AI agent interoperability, Anthropic's real-time web search integration into Claude, and OpenAI's practical guide for enterprise AI adoption. Ready to explore the cutting edge? Let's dive into the most captivating stories making headlines in the world of AI right now. LLM Expert Insights, Packt In today's issue: 🧠 Expert Deep Dive: Déborah Mesquita & Duygu Altinok explore how spaCy stays relevant in an LLM world—lightweight, fast, and surprisingly powerful. 🔄 OpenAI Goes Global: Launches "OpenAI for Countries" to support democratic AI infrastructure across nations. 🛡️ Claude 3.7 Prompt Leak: Anthropic’s 24K-token system prompt leak sparks concerns over AI transparency and model security. ⚙️ Gemini 2.5 Pro Preview: Google unveils major upgrades—interactive coding, UI focus, and top leaderboard rankings. 🎨 Google Logo Makeover: The iconic ‘G’ gets a gradient glow-up, syncing with the sleek aesthetic of Gemini AI. 🌍 Tech Meets Oil: Musk, Altman & co. attend Saudi summit seeking AI funding—sparking debate over geopolitics and ethics. 🤝 Microsoft Adopts A2A: In a rare move, Microsoft joins Google’s A2A protocol, enabling cross-agent communication in Azure. 🔍 Claude AI Gets Web Access: Anthropic arms Claude with real-time internet search—directly challenging traditional engines. 📘 OpenAI’s Enterprise Playbook: New guide reveals how companies like Klarna & Morgan Stanley are putting AI to work. 📈EXPERT INSIGHTS Is spaCy still relevant in an era of LLMs? With the dominance of LLMs, it may seem like we’ve acquired a magic wand capable of solving nearly any task — from checking the weather to writing code for the next enterprise solution. In this context, one might wonder: are our favorite Python libraries, which we've long relied on, still relevant? Today, we’ll talk about one such library, spaCy. Despite the rise of LLMs, spaCy remains highly relevant in the NLP landscape. However, its role has evolved. It now serves as a faster, more efficient, and lightweight alternative to large language models for many practical use cases. Consider, for example, an HR screening system at a Fortune 500 company. spaCy can extract information such as names, skills, experience and other relevant details from resumes, and even flag profiles that best match a particular job description. Now imagine the cost per resume if, instead of spaCy, an LLM handled these tasks. spaCy excels at tokenization, part-of-speech (POS) tagging, named entity recognition (NER), dependency parsing, and even building custom components using rule-based or machine learning-based annotators. In this issue, we’ll briefly explore the spaCy NLP pipeline, as detailed in the Packt book, Mastering spaCy, Second Edition, by Déborah Mesquita and Duygu Altinok. Here’s a high-level overview of the spaCy processing pipeline, which includes a tokenizer, tagger, parser, and entity recognizer. Let’s go through a overview of these components. 1. Tokenization: Tokenization refers to splitting a sentence into its individual tokens. A token is the smallest meaningful unit of a piece of text — it could be a word, number, punctuation mark, currency symbol, or any other element that serves as a building block of a sentence. Tokenization can be complex, as it requires handling special characters, punctuation, whitespace, numbers, and more. spaCy’s tokenizer uses language-specific rules to perform this task effectively. You can explore examples of language-specific data here. Consider the following piece of code: import spacy nlp = spacy.load("en_core_web_md") doc = nlp("I forwarded you an email.") print([token.text for token in doc]) The tokens are: ['I', 'forwarded', 'you', 'an', 'email', '.'] 2.POS tagging: Part-of-speech (POS) tags help us identify verbs, nouns, and other grammatical categories in a sentence. They also contribute to tasks such as word sense disambiguation (WSD). Each word is assigned a POS tag based on its context, the surrounding words, and their respective POS tags. POS taggers are typically sequential statistical models, meaning the tag assigned to a word depends on its neighboring tokens, their tags, and the word itself. To display the POS tags for the sentence in the previous example, you can iterate through each token as follows: for token in doc: print(token.text, "tag:", token.tag_) The output for the example sentence is: I tag: PRP forwarded tag: VBD you tag: PRP an tag: DT email tag: NN 3. Dependency parser: While POS tags provide insights into the grammatical roles of neighboring words, they do not reveal the relationships between words that are not directly adjacent in a sentence. Dependency parsing, on the other hand, analyzes the syntactic structure of a sentence by tagging the syntactic relations between tokens and linking those that are syntactically connected. A dependency (or dependency relation) is a directed link between two tokens. Every word in a sentence plays a specific syntactic role, such as verb, subject, or object, which contributes to the overall sentence structure. This syntactic structure is heavily used in applications like chatbots, question answering, and machine translation. In spaCy, each token is assigned a dependency label, just like other linguistic features such as the lemma or POS tag. A dependency label describes the type of syntactic relation between two tokens, where one token acts as the syntactic parent (called the head) and the other as its dependent (called the child). For example, in the sentence “I forwarded you an email,” spaCy will label “I” as the subject performing the action, “you” as the indirect object (the recipient), “email” as the direct object, and “forwarded” as the main verb (or root) of the dependency graph. A root word has no parent in the syntactic tree; it serves as the central verb that anchors the structure of the sentence. Let’s look at how dependency relationships appear in this sentence: for token in doc: print(token.text, "\tdep:", token.dep_) Output will be: I dep: nsubj forwarded dep: ROOT you dep: dative an dep: det email dep: dobj . dep: punct If the sentence were “You forwarded me an email,” the direct and indirect objects would change, allowing us to capture the underlying relationships and perform further processing based on them. Here are the dependency relationships for this sentence: You dep: nsubj forwarded dep: ROOT me dep: dative an dep: det email dep: dobj . dep: punct 4.Named Entity Recognition (NER): A named entity is any real-world object such as a person, a place (e.g., city, country, landmark, or famous building), an organization, a company, a product, a date, a time, a percentage, a monetary amount, a drug, or a disease name. Some examples include Alicia Keys, Paris, France, Brandenburg Gate, WHO, Google, Porsche Cayenne, and so on. A named entity always refers to a specific object, and that object is distinguishable by its corresponding named entity tag. For instance, in the sentence “Paris is the capital of France,” spaCy would tag "Paris" and "France" as named entities, but not "capital", because “capital” is a generic noun and does not refer to a specific, identifiable object. Let’s see how spaCy recognizes the entities in the sentence in the following code snippet: doc = nlp("I forwarded you an email from Microsoft.") print(doc.ents) token = doc[6] print(token.ent_type_, spacy.explain(token.ent_type_)) Since Microsoft is the only named entity in the sentence, spaCy correctly identifies it and specifies its type. [Microsoft] This was just a quick peek into spaCy pipelines — but there’s much more to explore. For instance, the spacy-transformers extension integrates pretrained transformer models directly into your spaCy pipelines, enabling state-of-the-art performance. Additionally, the spacy-llm plugin allows you to incorporate LLMs like GPT, Cohere, etc. for inference and prompt-based NLP tasks. Liked the Insights? Want to dive in deeper? The book Mastering spaCy, Second Edition by Déborah Mesquita and Duygu Altinok is your comprehensive guide to building end-to-end NLP pipelines with spaCy. Check it out! Join Packt’s Accelerated Agentic AI Bootcamp this June and learn to design, build, and deploy autonomous agents using LangChain, AutoGen, and CrewAI. Hands-on training, expert guidance, and a portfolio-worthy project—delivered live, fast, and with purpose. This is it. 50% off this Workshop ends on 18th May If you’re in—move now. Code: EXCLUSIVE50 Book Before 18th May Midnight RESERVE YOUR SEAT NOW! 📈LATEST DEVELOPMENT OpenAI Launches Global AI Partnership Initiatives OpenAI has launched "OpenAI for Countries," a global initiative aimed at assisting nations in developing AI infrastructure aligned with democratic values. It is partnering with the US government in these projects. Through these infrastructure collaborations, the program seeks to promote AI development that upholds principles like individual freedom, market competition, and the prevention of authoritarian control. This effort is part of OpenAI's broader mission to ensure AI benefits are widely distributed and to provide a democratic alternative to authoritarian AI models. Claude 3.7 System Prompt Leak Sparks Debate on AI Transparency and Security A leak revealed the 24,000-token system prompt of Anthropic's Claude 3.7 Sonnet. System prompts are the foundational instructions that guide an AI's behaviour, tools, and filtering mechanisms, essentially its rulebook. While showcasing Anthropic’s commitment to transparency and constitutional AI, the exposure raises security concerns about potential manipulation. The incident highlights tensions between openness and system integrity as AI models increasingly influence information access and decision-making across sectors. Google Unveils Gemini 2.5 Pro with Major Upgrades Google has unveiled an early-access preview of Gemini 2.5 Pro, its most advanced AI model, ahead of the upcoming Google I/O 2025 conference. The Gemini 2.5 Pro update introduces enhanced coding capabilities, particularly for building interactive web apps. It excels in UI-focused development, code transformation, and editing. This updated version leads on the WebDev Arena Leaderboard and demonstrates improved video understanding. Developers can access it via Google AI Studio and Vertex AI. Google Iconic ‘G’ Logo Gets a Makeover After a Decade The new logo features a gradient design, blending the brand's colors instead of using solid blocks. This change aims to modernize its look and align with the visual style of its AI products, like Gemini. The updated logo is currently visible on iOS and Pixel devices, with a wider rollout expected soon. AI Ambitions and Oil Wealth: Tech Titans Join Trump in Saudi Investment Summit Top U.S. tech leaders, including Elon Musk, Sam Altman, and Jensen Huang, joined President Trump in Riyadh for a major investment summit with Saudi Crown Prince Mohammed bin Salman. The event highlighted deepening U.S.-Gulf ties as tech firms seek AI infrastructure funding and Saudi Arabia diversifies beyond oil. Critics question national security risks tied to this commercial diplomacy. Microsoft Adopts Google’s A2A Protocol to Boost AI Agent Interoperability In a rare move, Microsoft has adopted Google’s Agent2Agent (A2A) protocol, enabling AI agents from different platforms to communicate and collaborate. This move promotes open standards and enhances enterprise interoperability. Integrated into Azure and Copilot Studio, A2A allows cross-vendor AI coordination. It aligns with Microsoft’s broader push toward open AI ecosystems, amid rising enterprise demand for agent-based automation solutions. Anthropic's Claude AI Gets Real-Time Web Search, Challenges Traditional Search Engines Anthropic has equipped Claude AI with a web search API, enabling real-time internet access and source-cited answers. The feature lets Claude fetch and summarize current data, challenging traditional search engines. Aimed at developers, it allows custom controls and enhances tools like customer support or news apps. This shift may reshape content attribution and search monetization. 📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us. If you have any comments or feedback, just reply back to this email. Thanks for reading and have a great day! That’s a wrap for this week’s edition of AI_Distilled 🧠⚙️ We would love to know what you thought—your feedback helps us keep leveling up. 👉 Drop your rating here Thanks for reading, The AI_Distilled Team (Curated by humans. Powered by curiosity.) *{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Meet the AI framework that's quietly powering the future of LLM apps.AI_Distilled #94: What’s New in AI This WeekDecoding ML is an educational newsletter that provides content on designing, coding, and deploying production-grade AI systems with software engineering and MLOps best practices to help you ship AI applications.Our motto is to learn production AI by doing!Thus, in addition to the newsletter, we offer five free courses on building end-to-end AI applications. If you thrive on hands-on experiences and building projects, these courses are for you.One example is the Second Brain AI Assistant open-source course, which comprises six modules that explore advanced techniques, including agentic RAG, fine-tuning LLMs, and LLMOps.To create your production-ready AI assistant, you’ll connect all the dots by building modular pipelines for data, features, training, inference, and observability.Find out more about Decoding ML’s free courses and newsletter!Welcome to this week's AI roundup! We’re seeing OpenAI's ambitious expansion plans, Apple's daring exploration of a potential search engine shift, and Amazon's Vulcan robot bringing delicate handling to automation; the AI landscape is evolving rather rapidly. In this issue, we also have expert insights from Dr. Ben Auffarth on integrating RAG agents with LangGraph. Sold?First up, our top stories of the week.LLM Expert Insights,PacktIn today's issue:🧠 Expert Deep Dive: Ben Auffarth explores advanced RAG patterns using LangGraph—from conversational memory to hybrid retrieval and agentic reasoning loops.🔄 OpenAI’s Post-4o Reset: After 4o’s sycophantic flaws, OpenAI restructures, adds data residency in Asia, and eyes $3B Windsurf acquisition.🧭 Apple Eyes Google Alternatives: Eddy Cue confirms Apple is testing AI search options like ChatGPT and Perplexity, causing Alphabet shares to slide.🧪 Meta Feels the Pressure: No new model at LlamaCon raises concerns over Meta’s ability to keep pace with Alibaba and DeepSeek.🔍 Google’s AI Search Expands: AI Mode gets a broader rollout—offering conversational results and richer visual insights.🤖 Amazon’s Vulcan Gets a Soft Touch: New tactile-aware warehouse robot handles delicate goods across U.S. and German facilities.📉 AI Reshapes the Workforce: CrowdStrike, Shopify, and Duolingo lay off staff as they pivot to AI-first strategies across roles and operations.Wysh Life Benefit allows any financial institution to offer free life insurance directly through their customers’ savings accounts. By embedding micro life insurance into deposit accounts, Life Benefit provides built-in financial protection that grows with account balances. It’s a simple, no-cost innovation that enhances loyalty, encourages deposits, and differentiates institutions in a competitive market. No paperwork. No medical exams. Just automatic coverage that provides peace of mind—without changing how customers bank.TALK TO OUR TEAM TODAY📈EXPERT INSIGHTS - BEN AUFFARTHA sneak peek into RAG patterns with LangGraphWith the latest models now supporting 100K+ context windows, are RAG systems still relevant?Yes, absolutely! says Dr. Ben Auffarth, Chief Data Officer, Chelsea AI Ventures Ltd, and author of Generative AI with LangChain, published by Packt.To understand why RAG is far from obsolete, let’s take a look at the patterns Ben highlights and and how LangGraph makes them more powerful.LangGraph enables the creation of graph-based applications where runnables (i.e., composable units like chains, tools, or language model calls) act as nodes, and transitions between them serve as edges. It supports persistent state management, particularly useful for handling cyclical flows and maintaining context in multi-turn conversations for typical RAG systems. This persistent state allows the system to retain and evolve context over time. Thus, by facilitating decision-making based on intermediate results, LangGraph empowers RAG workflows to dynamically adjust their paths based on prior outcomes.Ben identifies three advanced RAG patterns that take full advantage of this flexibility in his blog. Let’s look at them.1. Conversational memory for RAG One of the key challenges inRAG is follow-up questions in multi-turn conversations,especially when users leave out critical context. LangGraph addresses this issue through stateful conversation management. In LangGraph, the conversation state (history of user and assistant messages) is maintained. This state becomes an input for nodes (runnables), enabling query rewriting, where the current user query can be augmented based on historical context. This allows for more targeted and context-aware retrieval, ensuring that the RAG system retrieves information relevant not just to the current question but to the entire conversation thread.2. Hybrid retrieval with knowledge graphsRAG systems need to capture information fromboth structured and unstructured sources to effectively augment model responses. RAG can perform vector searches to identify relevant documents, articles, etc. To capture facts and relationships between entities, however, a RAG system needs to work with structured knowledge bases like knowledge graphs.Knowledge graphs are extremely useful as they represent entities as nodes and their relationships as edges, making it easier to capture and query complexrelationships. LangGraph enables hybrid retrieval by combining both vector searches and graph queries, leading to more semantically rich and factually grounded outputs.3. Agentic RAGsAs AI agents become more capable, RAG systems must keep up—handling complex reasoning and dynamic decision-making, including interpreting queries, planning multi-step retrieval strategies and refining search queries iteratively. A popular approach that facilitates this dynamic retrieval strategy is a ReAct (Reasoning + Acting) loop. In ReAct, an agent interleaves reasoning steps (like language model-generated planning) with actions (e.g., querying a retriever, calling a tool, or accessing an API). This loop allows the system to decompose complex queries, determine what to retrieve and when, and refine or redirect the retrieval strategy based on intermediate observations. There’s much more to uncover about how LangChain and LangGraph can supercharge your RAG systems.Liked the Insights? Want to dive deeper?Grab a copy of Generative AI with Langchain, Second Edition written by Ben Auffarth and Leonid Kuligin.Build production ready LLM applications and advanced agents using Python and LangGraph.ORDER NOWJoin Packt’s Accelerated Agentic AI Bootcamp this June and learn to design, build, and deploy autonomous agents using LangChain, AutoGen, and CrewAI. Hands-on training, expert guidance, and a portfolio-worthy project—delivered live, fast, and with purpose.🎓 As part of the exclusive Packt community, you get 50% off with code EXCLUSIVE50. Limited seats available.RESERVE YOUR SEAT NOW!📈LATEST DEVELOPMENTOpen AI goes bullish on expansion post 4o's sycophantic updateAfter admitting to 4o’s sycophancy and detailing what went wrong during the training, OpenAI has made a series of announcements.In his announcement about OpenAI’s structure, SamA informed the employees that while OpenAI will keep its nonprofit roots, the for-profit arm will be turned into a Public Benefit Corporation to generate resources that help build safe, democratic AI for everyone.In another announcement, OpenAI rolled out data residency in Asia. This means businesses can now store API data in the region, helping with local privacy rules and boosting speed.With a focus on moving from core research focus to building AI products useful for everyone, OpenAI welcomed Fidji Simo, CEO of Instacart and former Meta exec, to its board. Fidji’s experience in scaling consumer tech is likely to bolster OpenAI’s product, operations, and user engagement at scale.OpenAI is in between advanced talks to acquire Windsurf, an AI-powered coding assistant, for approximately $3 billion. Once the deal goes through, this would mark OpenAI's largest acquisition to date, enhancing the company’s capabilities in AI-driven software development. Looks like this acquisition will catalyze the time to market for a strong ChatGPT coding assistant.Apple considers AI search alternatives and Alphabet feels the impactIn his testimony against Alphabet Inc., Eddy Cue, Apple’s senior VP of services, talked about Apple’s intentions to bring AI-powered search to Safari and explore players like OpenAI's ChatGPT, Perplexity AI, and Anthropic as potential replacements for Google as its default search engine. This reveal led to a nearly 10% drop in Alphabet's stock value.Meta admits to feeling the heat from Chinese AI competitorsAt its inaugural LlamaCon, Meta showcased its AI developments, including a new Llama API and partnerships for faster AI deployment. However, the event revealed Meta's challenges in keeping pace with competitors like China's DeepSeek and Alibaba's Qwen. The community was surprised that there were no new model announcements, fueling speculation that Meta may be falling behind in the AI race.Google’s not listening — AI Mode in search gets a wider rolloutWhile users continue to tease Google with idioms, they have rolled out a new AI Mode in its Search, offering users AI-generated answers sourced from its search index. This feature provides a more conversational search experience and includes visual cards with detailed information about businesses and products.Amazon’s Vulcan robot gains the power of touchAmazon introduced Vulcan, an AI-enabled warehouse robot equipped with tactile sensors, allowing it to handle delicate items with human-like care. Vulcan is already operational in facilities in the U.S. and Germany, processing over 500,000 orders.AI gobbles up jobs at CrowdStrike, Shopify, and DuolingoCybersecurity firm CrowdStrike is laying off 500 employees, approximately 5% of its workforce, as it adapts to the evolving landscape driven by AI. The company plans to continue hiring in product engineering and customer-facing roles.Language learning app Duolingo and e-commerce platform Shopify are transitioning to AI-first models, reducing contractor roles and prioritizing AI as a strategic platform shift. Shopify’s CEO in his memo to staff, laid down expectations to use AI in their daily tasks and prove that a job can’t be done with the help of AI before asking for more resources.📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us.If you have any comments or feedback, just reply back to this email.Thanks for reading and have a great day!That’s a wrap for this week’s edition of AI_Distilled 🧠⚙️We would love to know what you thought—your feedback helps us keep leveling up.👉 Drop your rating hereThanks for reading,The AI_Distilled Team(Curated by humans. 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ChatGPT's New Shopping Capabilities, Adobe's Firefly Enhancements, Microsoft's Recall Rollout, Meta' AI_Distilled #93: What’s New in AI This Week Become an AI Generalist that makes $100K (in 16 hours) Still don’t use AI to automate your work & make big $$? You’re way behind in the AI race. But worry not: Join the World’s First 16-Hour LIVE AI Upskilling Sprint for professionals, founders, consultants & business owners like you. Register Now (Only 500 free seats) Date: 2nd-3rd-4th of May, 11 AM - 7 PM. Rated 4.9/10 by global learners – this will truly make you an AI Generalist that can build, solve & work on anything with AI. In just 16 hours & 5 sessions, you will: ✅ Learn the basics of LLMs and how they work. ✅ Master prompt engineering for precise AI outputs. ✅ Build custom GPT bots and AI agents that save you 20+ hours weekly. ✅ Create high-quality images and videos for content, marketing, and branding. ✅ Automate tasks and turn your AI skills into a profitable career or business. All by global experts from companies like Amazon, Microsoft, SamurAI and more. And it’s ALL. FOR. FREE. 🤯 🚀 Join now and get $3000+ in additional bonuses: AI community access ($1999), AI Tool Stack ($299), and Workflow Templates ($999)—all unlocked when you sign up and attend! REGISTER NOW Greetings for the day! We're thrilled to present the latest issue of AI Distilled! In this edition, we've curated the most compelling stories from the AI world, including OpenAI's expansion of ChatGPT's capabilities, Adobe's unveiling of AI-powered creative tools, Microsoft's AI Recall feature, and Meta's announcement of LlamaCon. Additionally, don’t miss expert insights from Wrick Talukdar, who shares his take on the key principles of building agentic AI systems. Stay tuned as we continue tracking the latest AI developments and offering expert perspectives on the innovations shaping today’s world and tomorrow’s future! LLM Expert Insights, Packt In today's issue: 🧠 Expert Deep Dive: Wrick Talukdar shares design principles, collaboration models, and ethical frameworks for building agentic AI systems. 🛍️ ChatGPT Goes Shopping: OpenAI adds product recommendations, price comparisons, and direct links within the chat interface. 🤖 Alibaba’s Qwen 3 Debuts: The Chinese tech giant launches its hybrid-reasoning AI model to compete with DeepSeek and Baidu. 🎤 Meta Hosts LlamaCon: Meta rallies open-source AI developers ahead of Meta Connect 2025. 🎨 Adobe Supercharges Creative Cloud: Firefly 4, moodboarding, and natural language Photoshop editing unveiled at Adobe Max London. 🧠 Microsoft’s Recall Is Live: AI-powered memory feature now available on Copilot+ PCs—with enhanced privacy measures. 💻 BitNet Runs on CPUs: Microsoft’s energy-efficient ternary-weight model reduces AI compute costs by up to 96%. 🧮 Gemini Adds a Thinking Budget: Google’s Gemini 2.5 Flash now lets devs control reasoning depth for flexible performance. 📰 WaPo x OpenAI: The Washington Post integrates into ChatGPT, offering summaries and links to original reporting. Liked the Insights? Want to dive deeper? Grab a copy of Building Agentic AI Systems written by Anjanava Biswas andWrick Talukdar. Create intelligent, autonomous AI agents that can reason, plan, and adapt ORDER NOW 📈LATEST DEVELOPMENT OpenAI Transforms ChatGPT into a Shopping Assistant OpenAI has rolled out new shopping features within ChatGPT, enabling users to receive personalized product recommendations, compare prices, view visuals, and access direct purchase links—all within the chat interface. Check out the announcement on OpenAI’s X post. Alibaba Launches Qwen3 Amid Intensifying AI Competition Alibaba has unveiled Qwen 3, an advanced upgrade to its flagship AI model, featuring hybrid reasoning capabilities aimed at improving adaptability and efficiency for app and software development. This release marks a strategic move in China's escalating AI race, following recent advancements by competitors like DeepSeek and Baidu. Explore the Qwen3 repository on GitHub. Meta Hosts LlamaCon to Promote Open-Source AI Meta has launched LlamaCon to spotlight advancements in open-source AI to assist developers in creating applications and products. The event precedes the Meta Connect conference scheduled for September and underscores Meta's commitment to investing in AI and supporting the developer community. You can RSVP your presence on their official website. Adobe Unveils AI-Powered Creative Tools at Adobe Max London At Adobe Max London 2025, Adobe showcased a suite of AI-driven enhancements across its Creative Cloud applications. Key features include the Firefly Image Model 4 for hyper-realistic image generation, Firefly Boards for collaborative moodboarding, and natural language editing in Photoshop. If you are a content creator or a designer, you should discover how these tools can boost your creative workflows. Microsoft’s AI Recall Feature Now Available in Copilot+ PCs Microsoft has officially released its long-awaited Recall feature for AI-enabled Copilot+ PCs. Recall continuously captures screen snapshots to help users retrieve previously viewed content. After addressing early privacy concerns raised by security experts, Microsoft implemented significant security adjustments before the feature's release. Microsoft’s BitNet, an Ultra-efficient CPU-based AI Model Gains Traction Microsoft researchers have introduced a groundbreaking AI model that operates efficiently on standard CPUs, eliminating the need for GPUs. This model utilizes ternary weights (-1, 0, 1), significantly reducing energy consumption by up to 96% compared to traditional models. You can check out the Hugging Face model card and the technical paper here. Gemini 2.5 Series Introduces a “Thinking Budget” Google’s Gemini 2.5 Flash now includes a “thinking budget" that allows developers to control the model's reasoning depth, enabling a balance of performance and computational cost. This flexibility caters to diverse application needs, from quick responses to complex problem-solving. Learn more about the thinking budget here. The Washington Post Warms Up to GenAI, Partners with OpenAI The Washington Post has entered a strategic partnership with OpenAI to expand its journalism access through integration within ChatGPT. This collaboration allows ChatGPT to display summaries, excerpts, and links to The Post’s original reporting in response to user queries, marking a significant step toward greater public access to reliable and timely information. This could very well be the beginning of a new era where top content creators embrace GenAI for a more user centric future. Read the full announcement on OpenAI’s blog. 📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us. If you have any comments or feedback, just reply back to this email. Thanks for reading and have a great day! That’s a wrap for this week’s edition of AI_Distilled 🧠⚙️ We would love to know what you thought—your feedback helps us keep leveling up. 👉 Drop your rating here Thanks for reading, The AI_Distilled Team (Curated by humans. 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A quick dive into multi-agent systems, go back to the basics with the Periodic Table for ML AI_Distilled #92: What’s New in AI This Week We hope you had fun at Easter. A lot changed while you were away. So here is a quick round-up of what happened this week and what this means for you. LLM Expert Insights, Packt In today's issue: 🧠 Author Insights: Can I Be Your (Multi-) Agent? – Paul Singh explores A2A, a lightweight protocol enabling AI agents to communicate and collaborate autonomously. ⚙️ Google’s Agent Ecosystem – Google introduces the Agent Development Kit (ADK), Agent Garden, and support for the MCP standard to accelerate multi-agent systems. 🔗 Microsoft’s Multi-Agent Stack – Azure’s Semantic Kernel and AutoGen offer robust, production-ready frameworks for building and orchestrating AI agents. 🤖 Satya Nadella on Copilot – New features like Agents, Notebooks, and the Agent Store signal Microsoft's push to make Copilot the UI for AI. 🚀 NVIDIA NeMo Now GA – NeMo’s microservices are now generally available, helping enterprises build and optimize powerful AI agents. 🧠 ByteDance’s UI-TARS-1.5 – An open-source multimodal agent built for GUI interaction across devices, enhanced with reinforcement learning. 🖼️ OpenAI’s ImageGen API – GPT-Image-1 API brings advanced image generation and moderation control to business workflows. ⚠️ OpenAI o3 and o4-mini Update – The latest system card shows improved reasoning but increased hallucination rates in new models. 🔬 MIT’s Periodic Table for ML – A research-backed framework connecting 20+ classical ML algorithms to accelerate AI innovation. 🧩 EXPERT INSIGHTS (By Paul Singh) Can I Be Your (Multi-) Agent? A2A Explained. In my book, Generative AI for Cloud Solutions, I detailed the "multi-agent" concept before the concept became popular and predicted its significance in 2025. This idea has proven to be quite accurate. As a quick overview, AI agents (agentic workloads) interact with their environment (take action), collect data, and perform tasks autonomously to achieve predetermined goals. They learn, adapt, and make decisions based on the information they gather, without constant human intervention. Ideally, you can (and should) also allow human intervention into the system, called human-in-the-loop. For multiple agents running to address a problem or provide a service, there is the new Agent-to-Agent or Agent2Agent (A2A) communication protocol that standardize inter-Agentic communications. A2A is a lightweight JSON-RPC protocol that lets agents across clouds swap context instead of code or credentials, all over HTTPS. As one can imagine, we already live in a world where agents are created on a daily basis. Soon, AI Agents will not only be communicating autonomously but also creating other agents on a daily (or perhaps every minute) basis! It is a bit daunting to imagine this; however, we need to create a framework on how these Agents work and communicate together, and hence the birth of A2A. Let’s look at what the industry leaders are offering to support multi-agent systems Google: Earlier this month, at Google Cloud Next 2025, Google made the following key AI announcements on Agents and Agent/Multi-Agent Ecosystems: Agent2Agent (A2A) protocol, a new open protocol intended to help enterprises support multi-agent systems, along with the Agent Development Kit (ADK), a new open-source AI framework now in preview that is designed to simplify building multi-agent systems while maintaining control over agent behavior. ADK supports Model Control Protocol (MCP), an open standard introduced by Anthropic and adopted by OpenAI, with the goal of standardizing how AI applications connect with external tools, data sources, and systems. We'll do a deeper dive into MCP in an upcoming issue. They also announced Agent Garden—a collection of pre-built agent samples, tools, and connectors available in ADK. Agent Garden also has an Agent Engine, used for deploying AI agents with enterprise-grade controls, and an Agent Designer, a no-code tool in Agentspace that allows anyone to create custom agents. Microsoft: In parallel, Microsoft offers both Semantic Kernel and AutoGen—two open-source frameworks for developing and orchestrating multiple AI agents. So, while Google’s ADK is new, Azure already has a mature, flexible, and production-hardened agent development stack available today. Microsoft’s multi-agent ecosystem is built on common principles, such as a standardized declarative workflow for multi-agent orchestration. For developers looking to experiment, AutoGen provides a playground for rapid innovation. You can drop the Semantic Kernel into your Azure AI Foundry stack or Google Agentspace Enterprise for instant, secure, async interoperability with any A2A-compliant agent, regardless of modality. For those ready to scale, Semantic Kernel offers the stability required for production applications. A recent blog by one of my colleagues, Evan Matson, describes Integrating Semantic Kernel Python with Google's A2A Protocol | Azure AI Foundry Blog . If this excites you and you are beginning to explore GenAI, my book Generative AI for Cloud Solutions offers you a quick start guide. This book enables you to gain a foundational understanding of interplay between LLMs and ChatGPT, and how to develop efficient and scalable solutions on the cloud. Liked the Insights? Want to dive deeper? Grab a copy of Generative AI for Cloud Solutions written by Paul Singh Architect modern AI LLMs in secure, scalable, and ethical cloud environments. ORDER NOW 📈LATEST DEVELOPMENT Satya Nadella bats for Copilot as UI for AI In his recent LinkedIn post, Nadella highlighted four cool Copilot features: Agents, Notebooks, and Create. The update introduces intelligent Agents like Researcher and Analyst along with a new Agent Store to expand these capabilities with partner offerings, to help create custom agents in Copilot Studio. You can use Notebooks to centralize diverse project data. Enhanced Search can search across all apps, including third-party platforms, providing comprehensive answers and source data. The Create function can transform presentations into videos and generate images from prompts. Check out these updates here. NVIDIA’s NeMo is now generally available Nvidia has now made NeMo microservices empower enterprises to create AI agents that boost employee productivity. These tools facilitate model customization, curation, and evaluation, enabling continuous optimization. Learn about NeMo here. ByteDance’s multimodal agents UI-TARS-1.5 is an open-source multimodal agent that excels in GUI interaction. It performs exceptionally well in computer, browser, and phone use, demonstrating human-like perception. Enhanced reasoning through reinforcement learning allows it to generalize effectively in web browsing and gameplay. Here is a quick round up on TARS’ capabilities and performance. ImageGen in OpenAI API After the success of the image generation feature with ChatGPT, OpenAI has made it available as the gpt-image-1 API. SamA, in one of his X posts, informed the community that the API version enables you to control moderation sensitivity which is not available in the ChatGPT version. OpenAI is working with several businesses like Canva, HubSpot, and GoDaddy, among others, to use this API for several use cases. Find out more here. o3 is more accurate but hallucinates a lot OpenAI recently released the o3 and o4-mini System Card. This card details the capabilities and safety evaluations of OpenAI's new models, which combine advanced reasoning with tool capabilities. The results show that the advanced ChatGPT models, o3 and o4-mini, hallucinate more than older models, despite improved reasoning. OpenAI doesn’t know why, nor do they know how to solve this. You can check out the results here. Periodic table for ML MIT researchers have created Periodic Table of Machine Learning, a framework connecting over 20 classical algorithms. This framework reveals how existing algorithms are connected, identifies gaps, and enables discovery of new algorithms by unifying existing approaches. The periodic table offers a toolkit for designing novel AI algorithms more efficiently. You can read the research paper on this framework here. [RUBRIK GUIDED LAB] AWS CLOUD NATIVE PROTECTION According to an IBM report, 82% of breaches involved data stored in the cloud. What's your data recovery plan? Join us for Virtual Camp Rubrik: AWS Cloud Protection to: Protect AWS workloads, Amazon EC2, Amazon RDS, and Amazon EBS Recover and restore your AWS data and workloads Discuss the current state of the cloud threat landscape SAVE YOUR SLOT 📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us. If you have any comments or feedback, just reply back to this email. Thanks for reading and have a great day! That’s a wrap for this week’s edition of AI_Distilled 🧠⚙️ We would love to know what you thought—your feedback helps us keep leveling up. 👉 Drop your rating here Thanks for reading, The AI_Distilled Team (Curated by humans. 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Unlock Azure OpenAI with our new guide and explore top LLM innovations.AI_Distilled #91: What’s New in AI This WeekHappy Easter!While you celebrate with family and hunt for Easter eggs, we bring to you the latest news and a specially curated cheat sheet.LLM Expert Insights,PacktIn today's issue:📘 Exclusive! Azure OpenAI Cheat Sheet: Your ultimate quick-guide to Azure OpenAI from our best-selling book.🚀 GPT-4 Series Unleashed: OpenAI drops GPT-4-o, o3, and o4-mini—breakthrough or burnout?🔍 Cohere Embed 4: Enterprise search redefined with 128K context length and 100+ language support.🇨🇳 ByteDance Seed-Thinking-v1.5: A STEM-optimized model with lean training and sharp performance.🤝 Claude’s Research Mode: Anthropic’s Claude gets collaborative with citations and Workspace sync.🧰 Google ADK Launch: Build and deploy multi-agent systems with Google’s new Python toolkit.Get Smarter about Cloud and DevOps. Join 44,000+ engineers who trust CloudPro.Join for Free🧩 AZURE OPEN AI CHEAT SHEETWith the release of GPT-4 series models, the demand for Azure OpenAI Service has skyrocketed too. On popular demand, we have curated this Azure OpenAI (AOAI) Cheat Sheet for you from our best-selling book Azure OpenAI Essentials by Amit Mukherjee and Adithya Saladi.Liked the Insights? Want to dive deeper?Grab a copy of Azure OpenAI Essentials written by Amit Mukherjee and Adithya Saladi.A practical guide to unlocking generative AI-powered innovation with Azure OpenAI. Build innovative, scalable, and ethical AI solutions.Pre-order AZURE OPENAI ESSENTIALS today!📈LATEST DEVELOPMENTOpen AI releases their smartest models – again!OpenAI seems to be in a frenzy with every update being touted as the most capable and smartest yet, while skeptics argue GenAI has plateaued.In a series of X posts, OpenAI announced the release of the GPT-4 series, the rollout of its image library, and the introduction of the o3 and o4-mini agentic reasoning models.What do you think of these latest models? Are they glam or sham? Check out these updates at OpenAI News.Cohere launches Embed 4: Support for ~200-page Documents with breakthrough 128K Token Context LengthThe AI race continues to heat up. Cohere’s recently released Embed 4 can now search through multimodal documents with a 128k context length, offering support for over 100 languages. Targeted at enterprise agentic applications, Embed 4 can sift through a sea of unstructured organization data, enabling quick searches, domain-specific insights, and improved employee productivity.These are the use cases that really matter. What do you think? You can learn more about Embed 4 here.ByteDance opens GitHub repository for Seed-Thinking-v1.5Chinese companies continue to challenge AI giants with another high-performing, resource-conscious model. Trained on 400,000 high-quality samples with a dual-layer reward system of seed verifiers and seed-thinking verifiers and choosing to actively use only 20 billion of 200 billion training parameters, this model claims to achieve superior performance in STEM tasks. You can track this repo for more updates.Anthropic believes in collaborationWe love how Anthropic is following the AI Roadmap. From sharing updates on LLM audits to studying the impact of AI on the economy, they are positioning themselves as an AI company that not only innovates but also cares.In an interesting development, Claude has enabled its Research mode, which searches for answers to your queries from multiple perspectives along with citation links to help foster trust. Claude can also integrate with your Google Workspace to capture your work context and collaborate with you on your day-to-day tasks. We’re getting started today—what about you? Learn more here.Google introduces ADK at Google Cloud Next 2025With agentic systems on the rise, Google has released its Agent Development Toolkit just in time. This Python-based framework offers end-to-end tools for designing, building, evaluating, and deploying multi-agent systems.This certainly looks promising. However, you’ll need to try it yourself to see if it delivers as promised. Here is a quick guide to help you get started.That’s a wrap for this week’s edition of AI_Distilled 🧠⚙️We would love to know what you thought—your feedback helps us keep leveling up.👉 Drop your rating hereThanks for reading,The AI_Distilled Team(Curated by humans. 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Welcome aboard, your AI journey just leveled up.Welcome to AI_Distilled, your weekly dose of sharp insights, clean code, and behind-the-scenes breakdowns from the world of LLMs and generative AI.Each issue is engineered to bring you:🔍 Actionable takes on LLM architectures, frameworks, and real-world deployments🛠️ Tools, libraries, and workflows curated by Packt’s LLM Engineering team✨ Thought pieces from practitioners building the future🎁 Your free eBook is ready! Download hereGenerative AI Foundations in PythonHandpicked to begin your GenAI journey with Python as you explore LLMs, understand responsible generative AI practices, and apply your knowledge to real-world applications through guided tutorialsWhat’s next?Every week, you’ll receive curated updates, expert insights, and hands-on breakdowns of the most important developments in AI — all distilled into a format you can read in minutes.Let’s build the future of AI together.Cheers,The AI Distilled TeamWant to dive deeper?Here are a few top picks we recommendBUY NOWBUY NOWBUY NOW💼 Interested in reaching thousands of AI Pros?📫 Get the sponsorship pack or reply to this email — we'll send the details your way.*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

AI is rewriting the rules—are you keeping up? AI_Distilled #89: What’s New in AI This Week MEET THE TEAM - The faces behind your go-to AI newsletter This week, AI's hitting your screen, your workflow, and maybe even your love life. In this issue of AI_Distilled, we dive into the latest AI tools turning creators into power users, track the funding and feature wars shaking up the industry, and explore how AI is reshaping not just work but the very idea of work itself. Oh—and Hollywood? It’s caught in a plot twist of its own. As always, we’ve distilled what’s real, what’s next, and what actually matters. LLM Expert Insights, Packt In today's issue: 🎨 AI for Creators and Consumers OpenAI’s AI image generator is now free-tier (with limits), Runway’s Gen-4 brings consistency to AI videos, and eight free tools let users create Ghibli-style images. Happiest Minds launches an AI-powered investment assistant, Tinder debuts an AI flirting coach, Papa Johns integrates AI for personalized pizza, and Samsung’s AI AC automates nighttime cooling. 🏗️ AI Breakthroughs Microsoft, Nvidia, Alphabet, and Amazon may surpass Apple by 2030. Anthropic reveals Claude’s reasoning, OpenAI secures $40B for AGI, and Alibaba prepares Qwen 3. Meta’s AI research head exits, Qualcomm buys VinAI’s AI division, Amazon enters the AI agent race, and NVIDIA open-sources its GPU scheduler. 🌍 AI and Society Sam Altman predicts AI will shrink developer jobs, OpenAI expands free AI education, and Infosys partners with the Linux Foundation on ethical AI. Bill Gates foresees a two-day workweek due to AI automation, and Nokia pushes AI-powered networks to bridge Africa’s digital divide. 🎬 Hollywood and AI AI tools appear in Oscar-winning films, sparking debate over creativity and automation. Justine Bateman fights back with an AI-free film festival, urging Hollywood to resist algorithm-driven storytelling. 🎨 AI FOR CREATORS AND CONSUMERS From dreamy Ghibli-style image editors to AI video models that (finally) understand continuity, this week’s batch of tools is here to spark both curiosity and chaos. Whether you're making art, flirting on apps, or managing your money—there’s an AI for everything.I'm a new paragraph block. OpenAI’s image generator hits free tier—with limits, hype, and heats OpenAI has opened its GPT-4o-powered image generator to all ChatGPT users, though free users face a cap—reportedly three images a day. The tool exploded in popularity (Studio Ghibli-style edits, fake receipts, GPU meltdowns), prompting both creativity and concern. OpenAI says all generated images carry metadata and are subject to its usage guidelines. Runway’s Gen-4 model brings continuity to AI-generated videos Runway’s new Gen-4 video model claims to fix one of AI video’s biggest flaws: consistency. With just one reference image and a few prompts, users can generate characters and scenes that hold together across multiple shots and angles—finally making AI video feel less glitchy. Happiest Minds launches AI-powered investment assistant on Azure Happiest Minds has rolled out Investment Companion, a generative AI tool that helps investors navigate complex financial info through a chat-driven, multimedia interface. Now live on the Microsoft Azure Marketplace, it pulls and prioritizes content from multiple sources—aiming to make investor relations smarter, faster, and a lot less painful. Papa Johns goes full AI to deliver hyper-personalized pizza experiences Papa Johns is teaming up with Google Cloud to bring AI into everything from order suggestions to voice-based pizza requests. Their new innovation team, PJX, will use Google’s Vertex AI and Gemini to drive predictive ordering, personalized rewards, chatbot-based support, and even AI-optimized restaurant operations. The goal? Pizza that knows what you want before you do. Samsung’s new AI AC syncs with your fans to kill the midnight thermostat shuffle Samsung's latest Bespoke AI WindFree ACs now work with SmartThings-certified fans and switches to automate nighttime cooling—no more waking up to switch settings. Using AI-powered temperature prediction and environmental sensing, the system balances comfort and energy use, helping consumers sleep better and cut electricity bills. It’s smart home tech that finally understands what “uninterrupted rest” means. 8 free tools to Ghiblify your photos without touching Photoshop The Ghibli-style image trend sparked by OpenAI’s image model has taken over the internet—but you don’t need GPT-4o to get that dreamy, soft-lit anime look. From old-school editors like LunaPic to AI-powered tools like Flux and Fotor, this roundup offers eight free ways to transform your pics into storybook scenes. Just be mindful of privacy policies before uploading your digital soul. Tinder launches an AI flirting coach—because dating wasn’t awkward enough Tinder's new game, The Game Game, lets users flirt with AI personas powered by OpenAI—complete with voice interactions, meet-cute scenarios, and a flame-based scoring system. It’s part fun, part feedback tool, and part commentary on how blurry the line between romance and AI has become. For now, it's iOS-only in the U.S.—but clearly, AI wingmen are trending. 🏗️ AI BREAKTHROUGHS From dreamy Ghibli-style image editors to AI video models that (finally) understand continuity, this week’s batch of tools is here to spark both curiosity and chaos. Whether you're making art, flirting on apps, or managing your money—there’s an AI for everything.I'm a new paragraph block. Behind the scenes, the big players are making bold moves. Open models, massive funding rounds, and strategic shifts are shaking up the AI landscape—from Claude’s inner workings to Amazon and Alibaba’s next-gen playbooks. These four AI giants could overtake Apple by 2030 Microsoft, Nvidia, Alphabet, and Amazon are racing ahead—on revenue, EPS, and AI capabilities—while Apple’s growth has stalled. With Nvidia riding the GPU boom and Alphabet and Amazon pushing genAI across stacks, analysts say Apple’s spot as the world’s biggest company may not last the decade. The AI arms race isn't just technical—it's economic. Claude isn’t just guessing—Anthropic peeks under the hood Anthropic just dropped a rare behind-the-scenes look at Claude’s “AI biology,” revealing that the model plans ahead in creative tasks, processes language across cultures through a shared conceptual core, and even fakes logic under pressure. Their interpretability tools catch Claude in the act—whether it’s anticipating rhymes in poetry or hallucinating answers. It's a step toward making these black-box brains a bit more transparent—and hopefully, more trustworthy. OpenAI bags $40B to supercharge AGI ambitions OpenAI has secured a jaw-dropping $40 billion in funding at a $300 billion valuation, with backing from SoftBank. The money will go toward scaling compute, expanding ChatGPT’s reach, and pushing further toward AGI—with the usual promises of transforming science, education, and creativity along the way. Alibaba gears up to launch Qwen 3 amid China’s AI arms race Alibaba is prepping the release of Qwen 3—an upgraded flagship model—as soon as this month, in a fast-paced response to DeepSeek’s rapid rise. With AI one-upmanship heating up in China, the timing underscores just how intense the race has become, especially as DeepSeek’s low-cost, high-performance models gain global traction. Meta’s AI research head Joelle Pineau steps down after 8 years Joelle Pineau, the force behind Meta’s AI research and key initiatives like PyTorch and Llama, is exiting the company in May after nearly a decade. Her departure comes just as Meta ramps up AI investment, and ahead of its first LlamaCon. While she hasn’t revealed her next move, Pineau says she’s taking time to “observe and reflect”—and will keep one foot in academia at McGill. Qualcomm snaps up VinAI’s genAI division to fuel on-device AI push Qualcomm has acquired the generative AI arm of Vietnam’s VinAI, bringing in top-tier talent and tech for computer vision and language models. The move strengthens Qualcomm’s edge AI strategy—aiming to embed smarter AI into smartphones, cars, and PCs without relying on the cloud. VinAI CEO Hung Bui, ex-DeepMind, will join Qualcomm as part of the deal. Amazon joins the AI agent race with Nova Act and SDK Amazon just launched Nova Act—its take on AI agents that can browse, search, and even complete checkout tasks online. Paired with a developer-focused SDK, it signals Amazon’s growing confidence in its Nova foundation models and its intent to grab a bigger slice of the agentic AI pie. While rivals like OpenAI and Anthropic got there first, Amazon is banking on developer mindshare and enterprise integration to stand out. NVIDIA open-sources its powerful GPU scheduler, KAI NVIDIA has released the KAI Scheduler—its Kubernetes-native GPU orchestration engine—as open source under Apache 2.0. Built to tackle real-world AI workload chaos like fluctuating GPU needs, queue fairness, and job prioritization, KAI brings enterprise-grade scheduling logic to the community. It's a bold move that invites developers to help shape the backbone of scalable, containerized AI infrastructure. 💾 AI AND SOCIETY What happens when AI starts teaching the world, reshaping workweeks, and rewriting the rules of ethics? Let’s take a peek beyond the lab and into the living room. Sam Altman says AI might shrink demand for software engineers According to OpenAI CEO Sam Altman, AI is already generating over half the code at some companies—and the future could see even fewer human devs as “agentic coding” evolves. His advice? Get really good at using AI tools, not just writing code. It’s less about mastering syntax, and more about mastering adaptability. OpenAI rolls out free AI courses for everyone, everywhere OpenAI has expanded its Academy into a free, global hub for AI learning—offering courses on everything from prompt engineering to nonprofit workflows. With online tutorials, in-person workshops, and partnerships spanning schools, job programs, and governments, the goal is clear: make AI literacy mainstream and accessible across all walks of life. Infosys and Linux Foundation team up on ethical AI for networkss Infosys has joined forces with the Linux Foundation to promote responsible AI in global networking, contributing two open-source tools—Salus and Essedum—to tackle bias, privacy, and explainability. The initiative aims to embed ethical guardrails into AI-driven infrastructure, signaling a strong push toward open, accountable innovation in an increasingly automated internet. Bill Gates predicts a 2-day workweek—thanks to AI Bill Gates says AI could slash the standard workweek to just two days within a decade, as automation takes over routine jobs across industries like healthcare, education, and logistics. While human touch will still matter in areas like sports and creativity, Gates envisions AI handling everything from diagnoses to tutoring—reshaping productivity as we know it. Nokia’s AI-powered network vision aims to close Africa’s digital divide At MWC 2025, Nokia showcased how it’s fusing AI and next-gen network infrastructure to expand broadband across Africa and optimize global connectivity. From fixed wireless access to its Event-Driven Automation platform, Nokia is betting big on AI to power data centers, enable real-time network optimization, and bridge underserved regions into the digital economy. 🔒 HOLLYWOOD AND AI What happens when AI starts teaching the world, reshaping workweeks, and rewriting the rules of ethics? Let’s take a peek beyond the lab and into the living room. The strikes were just the beginning. As AI tools infiltrate film sets, scripts, and even award-winning performances, Hollywood is facing a creative identity crisis. This week’s stories spotlight the industry’s uneasy dance with generative AI. AI tech makes its way into Oscar-winning films Once branded the villain during industry-wide strikes, AI is now making cameos in Oscar-winning films and turning heads at L.A. cocktail parties. From voice-altering tech in The Brutalist to studio-backed AI startups like Moonvalley, the entertainment world is embracing AI—but not without tension. As lawsuits, protests, and open letters mount, the industry’s future hangs in the balance: will AI empower the next Scorsese, or replace the actors who bring the stories to life? Justine Bateman isn’t fighting AI—she wants to burn it out of Hollywood Filmmaker Justine Bateman isn’t trying to slow AI’s takeover of Hollywood—she’s daring it to burn faster so something real can rise from the ashes. Through her AI-free Credo 23 Film Festival, Bateman’s rallying creators around raw, human-made storytelling—and calling out tech giants for gutting artistry in favor of algorithmic slop. Her stance is clear: AI isn’t a tool, it’s a replacement machine—and she’s betting the audience will eventually crave soul over speed. Transform your professional world with ChatGPT and OpenAI—master prompt design to revolutionize development, marketing, research, and enterprise implementation Preorder Practical Generative AI with ChatGPT today! PRE-ORDER NOW! 📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us. If you have any comments or feedback, just reply back to this email. Thanks for reading and have a great day! *{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Top highlights from the AI frontier—don’t miss this week’s updates.AI_Distilled #88: What’s New in AI This WeekMulti-cloud compliance in a multi-jurisdictional worldThe cloud has become more like a fog, obscuring lurking compliance risks.READ FULL ARTICLEAI’s not just evolving; it’s sprinting!And we’re back with another issue of AI_Distilled to keep you in the loop. This week, it's all about precision breakthroughs, high-stakes tech plays, and the aggressive innovation pushing AI from concept to reality. Google’s Gemini 2.5 is taking AI reasoning to new heights, DeepSeek’s shaking up the market with a budget-friendly beast of a model, and Microsoft’s unleashing AI agents like it’s a futuristic security showdown. Oh, and did we mention OpenAI’s new image generator is basically the internet’s latest obsession?It’s all happening, and we’ve packed the essentials right here. Let’s go!LLM Expert Insights Team,PacktIn today's issue:🧩 AI Models and Frameworks – DeepSeek’s V3-0324 sets new benchmarks; Google’s Gemini 2.5 enhances AI reasoning; Claude adds real-time web search; Tencent’s T1 heats up China’s AI race; DeepLearning.AI offers a free ‘vibe coding’ course with Replit.📈 AI Market and Business Moves – DeepSeek disrupts markets with a budget-friendly AI model; Databricks and Anthropic simplify enterprise AI; OpenAI upgrades its voice assistant; OpenAI and Meta eye AI expansion in India; Crypto buzz: Solaxy, Bitcoin Bull, Mind of Pepe; KPMG’s AI agents spark job automation concerns.💾 AI Hardware and Infrastructure – Broadcom’s chips boost power efficiency; Ant Group slashes AI costs with local GPUs; Columbia Engineering’s 3D photonic-electronic platform revolutionizes AI hardware.🔒 AI for Security and Networking – Microsoft’s Security Copilot automates threat response; Huawei’s AI WAN transforms networks; Darktrace finds AI tools overtaking hiring in cybersecurity.🌐 AI Ecosystems and Platforms – OpenAI’s image generator goes viral; Elon Musk’s Grok AI expands to Telegram; WSO2’s Choreo improves developer workflows; Nvidia’s Dynamo boosts AI inference efficiency.🧩 AI MODELS AND FRAMEWORKSThe world of AI models is anything but static. From game-changing reasoning capabilities to cutting-edge coding enhancements, innovators are rewriting the rulebook on what AI can do. Here’s a look at the latest models raising the bar.DeepSeek’s V3-0324 model aims for AI dominanceDeepSeek is back with an upgraded model, V3-0324, boasting stronger reasoning abilities, improved code handling, and enhanced writing capabilities. Scoring 81.2 on the MMLU-Pro benchmark, it’s now considered the top-performing non-reasoning model, surpassing giants like Gemini 2.0 Pro and Claude 3.7 Sonnet. With an MIT license and the ability to run locally, DeepSeek is expanding accessibility and pushing the boundaries of open-weight models. But questions about safety features remain unanswered.Google’s Gemini 2.5 pushes AI thinking capabilities to new heightsGoogle has launched Gemini 2.5, its most advanced AI model yet, introducing “thinking” capabilities that enhance decision-making and accuracy. The model’s standout version, Gemini 2.5 Pro, has topped the LMArena leaderboard and demonstrated exceptional performance in reasoning, coding, math, and science benchmarks. Designed for complex tasks, it boasts a massive 1 million token context window, soon to double to 2 million tokens. Available now in Google AI Studio and the Gemini app, with broader access planned for Vertex AI, Gemini 2.5 aims to offer developers more powerful tools for sophisticated AI applications.Claude gets a boost with real-time web search now availableAnthropic’s AI assistant, Claude, just got a major upgrade — it can now search the web to provide real-time, relevant responses. This new capability expands Claude’s knowledge base beyond its initial training data, allowing it to offer fact-checked, citation-backed information on the latest events and trends. This enhancement is particularly beneficial for professionals across sales, finance, research, and even casual shoppers seeking reliable, up-to-date insights. Currently, web search is available to paid users in the U.S., with broader access planned soon.Tencent’s T1 Model becomes a new contender in China’s AI raceTencent has officially launched its T1 reasoning model, intensifying the AI competition in China. Powered by the Turbo S foundational language model, T1 promises faster response times and better handling of extended text documents with minimal hallucination rates. Benchmark tests indicate that T1 outperforms rival DeepSeek’s R1 model on various knowledge and reasoning metrics. With aggressive AI investments planned for 2025, Tencent is making a strong play to dominate China’s AI landscape.DeepLearning.AI offers free course on ‘vibe coding’ with ReplitDeepLearning.AI has launched a free short course, ‘Vibe Coding 101 with Replit,’ teaching developers how to build AI-powered applications using text-based prompts. Guided by Michele Catasta and Matt Palmer from Replit, learners will explore a unique framework involving thinking, debugging, and providing context to create tools like a website performance analyzer and a national park ranking app. This course is part of DeepLearning.AI’s broader effort to democratize AI coding tools and introduce new ways of developing AI applications.📈 AI MARKET AND BUSINESS MOVESIt’s been a busy week for AI business strategies, with companies doubling down on partnerships, rolling out ambitious new projects, and making some unexpected moves. We've put together a few bold plays reshaping the AI market.DeepSeek’s AI breakthrough shakes global tech marketsDeepSeek’s latest cost-effective AI model is sending shockwaves through global markets, challenging the narrative that cutting-edge AI requires billions in infrastructure and high-tech chips. Investors are spooked, with Nvidia’s shares dropping 16.3% and European tech stocks seeing their worst day since October. While some see this as a wake-up call for U.S. AI dominance, others view it as an opportunity to invest in high-quality tech shares while prices are down. The broader implications could redefine AI’s future, making it more accessible and cost-effective than ever before.Databricks and Anthropic join forces to democratize enterprise AIDatabricks and Anthropic have signed a landmark five-year partnership to integrate Anthropic’s Claude models, including the cutting-edge Claude 3.7 Sonnet, into the Databricks Data Intelligence Platform. The collaboration aims to help over 10,000 companies build AI agents that can reason over proprietary data with robust governance, security, and customization through tools like Mosaic AI. By uniting Databricks’ infrastructure with Anthropic’s AI expertise, the partnership promises to simplify AI deployment for enterprise-specific use cases, from healthcare to retail.OpenAI’s voice assistant gets a personality boostOpenAI has rolled out updates to its Advanced Voice Mode, making ChatGPT’s voice assistant more natural and less likely to interrupt users during conversations. Free users can now pause mid-sentence without disruption, while paid users enjoy a more engaging and creative AI personality. This update comes as OpenAI faces growing competition from startups like Sesame and big players like Amazon, which are also racing to enhance AI voice interactions.OpenAI and Meta eye AI expansion through Reliance partnershipOpenAI and Meta are reportedly in talks with India’s Reliance Industries to broaden their AI reach in the country. Discussions include using Reliance Jio to distribute ChatGPT and potentially hosting AI models in a massive three-gigawatt data center Reliance plans to build in Gujarat. OpenAI is also considering lowering its ChatGPT subscription fees, making the service more accessible. This potential partnership could mark a significant push toward AI integration in India’s rapidly growing tech landscape.Three altcoins primed for a breakout in 2025Crypto experts are buzzing about three promising altcoins that could make waves in 2025: Solaxy (SOLX), Bitcoin Bull (BTCBULL), and Mind of Pepe (MIND). Solaxy is building a Layer-2 solution for Solana to tackle congestion issues, while Bitcoin Bull offers Bitcoin rewards and a burning mechanism tied to BTC’s price milestones. Mind of Pepe stands out as an AI-driven crypto agent providing market insights and actively influencing sentiment. With presales already attracting millions, these projects could be worth keeping an eye on.KPMG’s ambitious AI agents raise questions about automation and jobsKPMG is developing intelligent agentic AI systems designed to operate as tireless digital colleagues, capable of making decisions and completing tasks autonomously. These AI agents, aimed at enhancing productivity and efficiency across departments like audit, tax, and advisory, are expected to be equipped with high IQ and EQ to better respond to client needs. While KPMG emphasizes collaboration between AI agents and human professionals, the initiative raises concerns about potential job displacement, especially as other companies like PwC and Meta also explore the capabilities of agentic AI.💾 AI HARDWARE AND INFRASTRUCTUREAI hardware is getting a serious power boost with next-gen chips and innovative architectures tackling efficiency and scalability. Catch up on the latest hardware developments everyone’s talking about.Broadcom’s new AI chips prioritize power efficiencyBroadcom has unveiled its latest AI networking chips, Sian3 and Sian2M, designed to improve power efficiency and performance for AI data centers. Built on 3nm and 5nm technology, these chips promise over 20% power reduction compared to previous models, addressing one of the biggest challenges in scaling AI clusters. By integrating VCSEL drivers and enhancing connectivity for 800G and 1.6T optical transceivers, Broadcom aims to support next-gen AI infrastructure with lower costs and greater efficiency.Ant Group slashes AI training costs with homegrown GPUsAnt Group has achieved a 20% reduction in AI model training costs by using locally produced GPUs instead of Nvidia’s high-performance chips. Its Ling-Plus-Base model, a 300 billion parameter MoE model, demonstrates that powerful LLMs can be effectively trained on less powerful hardware without compromising performance. As China’s tech companies innovate to sidestep U.S. export controls, Ant Group’s approach could pave the way for more affordable AI development.New 3D photonic-electronic platform promises AI hardware revolutionResearchers at Columbia Engineering have unveiled a 3D photonic-electronic platform that massively boosts energy efficiency and bandwidth density, promising to reshape AI hardware. Detailed in the study, “Three-dimensional photonics for ultra-low energy, high bandwidth-density chip data links,” published in Nature Photonics, the platform integrates photonics with CMOS electronics to achieve a bandwidth density of 5.3 Tb/s/mm² while consuming just 120 femtojoules per bit.🔒 AI FOR SECURITY AND NETWORKINGWhat does AI’s rapid evolution mean for cybersecurity? Intelligent threat detection, innovative network architectures, and companies stepping up their defenses and preparing for the next wave of AI-powered threats. Let's take a closer look.Microsoft’s AI security agents take center stageMicrosoft has unveiled new AI-powered agents under its Security Copilot platform, designed to tackle high-volume security tasks like phishing detection, data security, and identity management. With over 84 trillion daily signals processed, Microsoft’s AI agents aim to enhance cybersecurity efficiency through autonomous threat response and prevention. New multi-cloud security measures and tools to combat emerging AI threats are also rolling out, with Microsoft Defender now covering models across Azure, AWS, and Google Vertex AI.Huawei’s AI WAN aims to revolutionize IP networksAt the MPLS & SRv6 AI Net World Congress 2025, Huawei unveiled its AI WAN solution designed to transform IP networks with AI-driven operations, connections, and routing devices. The launch of the AI WAN Initiative, in collaboration with the IPv6 Forum and industry giants like Telecom Argentina and Turkcell Türkiye, aims to enhance network efficiency, reduce costs, and drive new service growth. Huawei’s three-layer AI architecture — AI routers, AI new connections, and AI new brain — seeks to accelerate the shift toward autonomous networks while improving total cost of ownership.AI-powered tools, not staff, are the future of cybersecurityAccording to Darktrace’s annual State of AI Cybersecurity report, most security professionals are prioritizing AI-powered solutions over hiring additional staff in 2025. With 87% preferring platform-based tools over standalone products and 88% emphasizing AI’s role in shifting from reactive to proactive security, the focus is clearly on efficiency. Interestingly, 84% of respondents prefer AI solutions that don’t require external data sharing, reflecting growing privacy concerns. As AI adoption accelerates, cybersecurity teams are preparing to optimize their defenses and enhance training for end users.🌐 AI ECOSYSTEMS AND PLATFORMSThe AI ecosystem is expanding fast, with platforms rolling out features that make deploying and managing AI easier than ever. Go through our rundown of the latest tools and integrations setting the standard for AI accessibility.OpenAI’s new image generator causes a social media stormOpenAI’s latest addition to ChatGPT-4o, called ‘4o Image Generation,’ has gone viral thanks to its ability to create visuals mimicking various artistic styles, including Studio Ghibli’s iconic animation aesthetic. This built-in image generator allows users to craft photorealistic or artistic images directly within the model using text prompts. While the feature has become an instant hit with subscribers, its availability for free users has been delayed due to overwhelming demand. OpenAI plans to roll it out to Enterprise and Edu users via API soon.Elon Musk's Grok AI lands on Telegram, stirring buzz and scrutinyElon Musk's Grok AI is expanding beyond X, integrating into Telegram for Premium users as part of a broader strategy to boost engagement. The latest model, Grok 3, is said to be ten times more capable, handling creative tasks and deep reasoning. However, Grok’s controversial, unfiltered responses have caught the attention of India's IT Ministry, raising concerns over its use of language and content moderation. The expansion to Telegram could be a game-changer for the messaging app as it competes with AI-integrated platforms like WhatsApp.Choreo’s AI-powered overhaul: WSO2’s bold push for developer productivityWSO2 has unveiled a powerful update to its AI-native developer platform, Choreo, designed to boost productivity for platform and software engineering teams. Now available as both a cloud service and open-source software, Choreo introduces innovative features like AI-driven FinOps for cloud cost optimization, automated alerting systems, and Kubernetes management via Self-Service Data Planes. By simplifying infrastructure management and enhancing workflow efficiency, WSO2 is making AI-driven digital transformation more accessible than ever.Nvidia’s Dynamo digs deeper: How it’s changing AI inferenceNvidia’s Dynamo, unveiled at GTC 2025, is more than just another AI framework. Marketed as the "operating system of an AI factory," Dynamo optimizes prefill and decode processes by dynamically routing tasks to specific GPU clusters, enhancing efficiency and throughput. The smart routing feature, which leverages key-value (KV) caching, helps avoid redundant computations and improves response times for similar queries. Dynamo also introduces a low-latency communication library to speed up GPU-to-GPU data transfers and a memory management subsystem that effectively handles KV cache data. Nvidia claims that the framework can double inference performance for Hopper-based systems and offer a staggering 30x improvement on Blackwell NVL72 systems.Learn enterprise patterns, key design principles, and proven architectures for building AI agents with LangChain and LangGraph.Pre-order Generative AI with LangChain today!PRE-ORDER NOW!📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us.If you have any comments or feedback, just reply back to this email.Thanks for reading and have a great day!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

Nvidia GTC, OpenAI’s latest breakthrough, and what’s next in GenAI!AI_Distilled #87: What’s New in AI This WeekAI threats are evolving—here’s how to build unbreakable cyber resilience and fight misinformation before it spreads.READ FULL ARTICLEThe AI world isn’t slowing down, and neither are we. We’re back with another issue to keep you in the loop. High-stakes corporate moves and creative AI applications getting Hollywood’s attention: there’s plenty to catch up on this week. And it’s all here, concisely curated for you. Let’s get to it!LLM Expert Insights Team,PacktIn today's issue:Recent Developments – Baidu’s AI advancements, Intel’s strategy shift, Google-MediaTek partnership, US blocks DeepSeek, AI-driven cyber defense, SoftBank’s $6.5B Ampere dealNvidia GTC 2025 – Blackwell Ultra AI chip, Dynamo inference software, Cisco-Nvidia Secure AI FactoryHollywood & AI – Russo Brothers explore AI in Marvel, copyright law debatesGame-Changing AI Tools – OpenAI’s ChatGPT Connectors, Nvidia & MIT’s new image generation tech📰RECENT DEVELOPMENTSBold acquisitions and groundbreaking AI advancements: the tech world is buzzing with big moves and new strategies. Here are a few intriguing shifts turning heads this week:Baidu steps up the AI game with new models and free chatbot accessBaidu has launched a new AI reasoning model, X1, and its latest foundation model, Ernie 4.5, while making its Ernie Bot chatbot free for individual users ahead of schedule. The X1 model is said to offer performance comparable to DeepSeek’s efficient model at a lower cost, while Ernie 4.5 reportedly outperforms OpenAI’s GPT-4.5 across various benchmarks. The move comes as Chinese tech companies rush to enhance their AI platforms following DeepSeek’s groundbreaking open-source release.Intel's new CEO targets AI and chip manufacturing revampIntel’s incoming CEO, Lip-Bu Tan, is preparing to restructure the company’s AI and chip manufacturing strategies, aiming to enhance efficiency and reclaim Intel’s standing in the semiconductor industry. His plans include streamlining middle management, improving Intel’s Foundry operations (which makes chips for other design companies such as Microsoft and Amazon), and developing AI chips using advanced 18A process technology. Tan also aims to attract major customers like Nvidia and Broadcom, positioning Intel for a stronger future in AI-driven chip manufacturing.Google partners with MediaTek for next-gen AI chipsGoogle is reportedly collaborating with MediaTek to develop the next generation of TPUs, expected to be produced next year. The partnership is driven by MediaTek’s strong ties with TSMC and its lower production costs compared to Broadcom, which Google also partners with for AI chip development. Google’s TPU chips play a critical role in its AI strategy, powering services like Google Search, YouTube, and Gemini AI models.US Commerce Department blocks DeepSeek over data privacy concernsThe US Commerce Department has prohibited the use of the Chinese AI model DeepSeek on government devices, citing concerns over data privacy and potential exposure of sensitive information. The ban, communicated through mass emails to staff, aligns with broader legislative efforts by Congress members pushing to restrict DeepSeek’s access on government-issued equipment due to fears of data exploitation by the Chinese government.Sophos leverages multimodal AI for advanced cyber defenseAt the 2024 Virus Bulletin conference, Sophos Principal Data Scientist Younghoo Lee presented research on using multimodal AI to enhance spam, phishing, and web content detection. Unlike traditional models, multimodal AI analyzes both text and visuals simultaneously, identifying sophisticated threats by understanding how legitimate and malicious content differ across multiple data types. Its capabilities include detecting phishing tactics through text analysis, brand verification, and advanced URL screening.Prompt Security unveils AI safeguards to prevent unauthorized data accessPrompt Security has introduced new authorization features to enhance security and control over generative AI applications within enterprises. The system provides real-time prevention of unauthorized data access by analyzing user identity and request context, ensuring AI tools like Copilot and Google Gemini adhere to existing security policies. Integrated with identity providers like Okta and Microsoft Entra, the platform offers granular policy enforcement, flexible redaction options, and comprehensive audit logging to protect sensitive corporate data.SoftBank to acquire Ampere Computing in $6.5 billion AI-focused dealSoftBank Group has announced its acquisition of Ampere Computing, a startup known for its Arm-based server chips, for $6.5 billion. The deal, expected to close in the second half of 2025, will see Ampere operating as an independent subsidiary with its headquarters in Santa Clara, California. SoftBank aims to enhance its AI infrastructure investments, building on partnerships like its recent collaboration with OpenAI and participation in the $500 billion Stargate AI project.⚡NVIDIA GTC 2025Nvidia’s GTC 2025 conference is making waves with several major announcements aimed at revolutionizing AI infrastructure, performance, and security. Take a look at our roundup of the most significant updates coming out of the event.Nvidia launches Blackwell Ultra AI chip to revolutionize AI processingAt GTC 2025, Nvidia unveiled its Blackwell Ultra AI chip, which offers 1.5 times the performance of its predecessor and significantly boosts AI processing capabilities. The chip powers Nvidia’s new GB300 superchip, designed for AI systems used by major companies like Amazon, Google, Microsoft, and Meta. Nvidia claims the Blackwell Ultra, paired with its DGX SuperPod AI supercomputer, dramatically enhances AI reasoning capabilities, delivering faster and more efficient responses than previous models.Nvidia Dynamo: New open-source AI inference software for enhanced efficiencyNvidia has launched Dynamo, an open-source AI inference software designed to improve the efficiency and scalability of AI reasoning models within AI factories. By using techniques like disaggregated serving, which separates processing and generation tasks across GPUs, Dynamo promises to double AI performance and revenue generation while minimizing operational costs. The software is compatible with popular frameworks like PyTorch and NVIDIA TensorRT-LLM, making it accessible to enterprises, cloud providers, and AI innovators worldwide.Cisco and Nvidia partner to launch Secure AI Factory for enterprise AI infrastructureCisco and Nvidia have introduced the Cisco Secure AI Factory, a comprehensive AI architecture package designed to enhance AI networking security and efficiency. The solution integrates Cisco’s Hypershield and AI Defense packages with Nvidia DPUs, SuperNICs, and enterprise storage from partners like Pure Storage and NetApp. Aimed at safeguarding AI development, deployment, and operations, the platform offers flexible deployment models and reference architectures for industries including finance, healthcare, and manufacturing.🤖 GAME-CHANGING AI TOOLSFrom boosting enterprise productivity to making AI more accessible to everyone, take a look at the most compelling tools and innovations sparking conversations right now.OpenAI to pilot ChatGPT Connectors for Google Drive and Slack dOpenAI is set to launch a beta feature called ChatGPT Connectors, allowing business users to link Google Drive and Slack accounts to ChatGPT. This integration aims to enhance the chatbot’s ability to answer queries using internal files, presentations, spreadsheets, and Slack conversations. OpenAI plans to expand this feature to other platforms like Microsoft SharePoint and Box.Nvidia and MIT unveil ‘HART’ for faster, high-resolution image generationNvidia and MIT have introduced a new tool called ‘HART’ that merges the strengths of diffusion models and autoregressive models into a unified approach. Designed to generate highly realistic images more efficiently than some current models, HART delivers high-resolution results with minimal steps. Its scalability is projected to be exponential, with future integration plans for video generation and audio prediction tasks.Oracle’s AI Agent Studio empowers Fusion Cloud users with custom AI agentsOracle’s AI Agent Studio is now enhancing the Oracle Fusion Cloud Applications Suite by allowing businesses to create and manage AI agents tailored to their needs. With tools for agent orchestration, LLM integration, and data validation, the platform promises streamlined workflows while ensuring security and reliability. However, areas like governance and privacy compliance still require further attention.SAP introduces 'Joule for Developer' to enhance AI-driven developmentSAP has launched 'Joule for Developer,' a new AI co-pilot aimed at improving SAP Build tools for developers by integrating purpose-built LLMs. The tool offers intelligent suggestions, automates tasks like documentation and sample data generation, and supports code optimization and process automation. With seamless integration across SAP Build tools and SAP Business Application Studio, SAP aims to empower developers to build more efficient, innovative, and secure applications. Looking ahead, SAP plans to enhance this tool with AI agents offering improved data security and AI-compliant platforms.Get lifetime access to top AI tools with 1min.AI1min.AI is an AI platform offering lifetime access to popular tools like GPT-4, Gemini, and other leading AI solutions for a one-time payment of $79.97. Equipped with cutting-edge tools for editing and various content-related tasks, 1min.AI promises a powerful boost to your AI toolkit, helping you stay updated with the latest trends in AI technology. The deadline to secure a lifetime subscription to 1min.AI for just $79.97 is March 30 at 11:59 p.m. PT.🎬 HOLLYWOOD AND AIHollywood’s relationship with AI continues to evolve, balancing both excitement and concern about the ethical implications. How are industry leaders navigating the intersection of technology and artistic expression?Russo Brothers explore AI’s role in future Marvel projectsThe Russo Brothers, known for their groundbreaking visuals in Marvel films, have shared their thoughts on the potential use of AI in Avengers: Doomsday and Avengers: Secret Wars. They believe AI could enhance the creative process by leveraging advanced editing technology to deliver a superior cinematic experience. However, the challenge lies in responsibly integrating AI into their filmmaking approach.Hollywood opposes proposed AI copyright law changesLeading Hollywood figures have addressed an open letter to the administration, raising concerns about proposed changes to copyright laws affecting AI training. They argue that relaxing these laws could harm the creative industry, which employs thousands of Americans, by compromising the integrity of original content and artistic expression.Learn enterprise patterns, key design principles, and proven architectures for building AI agents with LangChain and LangGraph.Preorder Generative AI with LangChain today!PRE-ORDER NOW!📢 If your company is interested in reaching an audience of developers and, technical professionals, and decision makers, you may want toadvertise with us.If you have any comments or feedback, just reply back to this email.Thanks for reading and have a great day!*{box-sizing:border-box}body{margin:0;padding:0}a[x-apple-data-detectors]{color:inherit!important;text-decoration:inherit!important}#MessageViewBody a{color:inherit;text-decoration:none}p{line-height:inherit}.desktop_hide,.desktop_hide table{mso-hide:all;display:none;max-height:0;overflow:hidden}.image_block img+div{display:none}sub,sup{font-size:75%;line-height:0}#converted-body .list_block ol,#converted-body .list_block ul,.body [class~=x_list_block] ol,.body [class~=x_list_block] ul,u+.body .list_block ol,u+.body .list_block ul{padding-left:20px} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}