AI Distilled

Why SUSE rejects lines of code, token counts, and developer leaderboards AI_Distilled #142: What’s New in AI This Week Social engineering is about manipulating people's emotions. Identify the susceptibilities that hackers use to exploit people. This NINJIO Insights Report dives into the key emotional susceptibilities that make social engineering work and offers concrete steps that your security team can take to equip your workforce to resist cyberattacks. Download the Guide Instead of our usual mix of AI news and analysis, today’s issue is dedicated to a single conversation. As AI coding agents become part of everyday engineering, a new wave of dashboards is emerging to measure their impact. Lines of code, token consumption, pull requests, utilization scores, and developer rankings are quickly becoming the default language of AI productivity. Rick Spencer, General Manager for Technology and Product at SUSE, argues that much of it is measuring the wrong thing. In today’s special issue, he explains why output is a poor proxy for value, why engineering leaderboards create the wrong incentives, and how SUSE measures AI through customer outcomes instead of developer activity. If you’re thinking about how to evaluate AI inside an engineering organization, this is a conversation worth spending time with. LLM Expert Insights, Packt P.S. If the following article ends up being your kind of read, you’ll probably enjoy Agentic Engineering, our new publication for builders navigating AI beyond the demo. Early subscribers are still receiving a free copy of AI Agents in Practice by Valentina Alto. Subscribe Here SUSE refuses to measure its engineers by how much code their agents write Rick Spencer on why output, tokens, and lines of code tell you nothing, and what an open-source enterprise tracks instead. As AI agents move into engineering workflows, new leaderboard metrics are tracking lines of code submitted, tokens consumed, and per-developer utilization. If agents are generating output, then output should be measured, compared across engineers, and ranked. Rick Spencer, General Manager for Technology and Product at SUSE, has looked hard at how the industry is measuring AI’s effect on engineering. “I consider that garbage vanity metrics,” he says, calling them unhelpful. His argument for what to track instead is one of the more clarifying things an engineering leader can hear right now, because it separates the numbers that look like progress from the numbers that actually represent it. Output is cheap; impact is what counts The core of Spencer’s position is a distinction between output and impact, and it matters because the two come apart precisely when AI enters the picture. AI makes output cheap as the lines of code, pull requests, and token counts all mount up when agents are doing the writing, which makes them exactly the wrong thing to measure if what you care about is value delivered. “We’re really tending away from measurements that measure output and utilization, and we’re trying to focus on impact,” he says. A leaderboard that ranks engineers by how much their agents produced does not tell you who is solving the hardest problems or keeping customers safe. It tells you who is generating the most volume, and in an AI-assisted world, that number is close to meaningless. There is also a structural reason the standard tooling does not fit SUSE, and it applies to more organizations than it might first appear. Much of the available measurement tooling assumes a particular shape of company. “They really assume you’re a proprietary software company where everyone’s working on a single code base,” Spencer explains, “which is just not how an open-source enterprise works.” His engineers work across hundreds, sometimes thousands, of repositories, where the maintenance work on each one differs enormously. A per-developer comparison across that landscape measures the shape of the work far more than it measures the contribution of the engineer, which is why he treats developer-to-developer comparison as fundamentally low value rather than merely imperfect. The reporting burden itself is part of his objection, and it is a point leaders setting up AI dashboards should sit with carefully. A measurement regime that requires engineers to generate weekly utilization reports spends the very time it claims to be optimizing. “I’d rather have them working than reporting,” Spencer says. The instrument meant to measure productivity eventually becomes a tax on it. What SUSE tracks instead Rejecting vanity metrics only helps if there is something better to put in their place. And Spencer shares how SUSE measures business impact in terms that connect directly to what customers actually receive. “How fast are CVEs being addressed, how fast are patches being backported, how fast are our L3 responses getting closed while maintaining the same NPS score,” he underscores, listing what his teams track. The common thread is that each one is an outcome the customer feels, not an activity the engineer performs. AI has been applied to exactly these areas, so measuring the speed and quality of those outcomes tells you whether the AI is doing anything worth its cost, which is the actual question worth asking. This shift from output to outcome reframes what a metric is for in the first place. A CVE response time captures whether the organization is keeping customers safe faster than it used to. A backport speed captures whether stable releases are getting their fixes without the manual grind that used to gate them. These numbers move because the underlying work got genuinely better, not because more text was generated, and that is the property that makes them trustworthy. They are also far harder to game, because the only way to improve them is to actually improve the thing the customer depends on. Give managers visibility, not a leaderboard None of this means SUSE ignores cost or utilization entirely, and the distinction Spencer draws here is the one that keeps the approach from collapsing into either negligence or surveillance. The company is building dashboards that give engineering managers visibility into their team’s cost and utilization, but the purpose is coaching rather than ranking. The unit of analysis is the team, and the question it answers is diagnostic. Spencer gives the example of a manager with an eight-person team noticing the numbers and asking the right kind of question. “We’re burning a lot of tokens. What are we actually doing that’s burning that many tokens? I’m not sure we’re getting value out of that.” The inverse matters just as much, where purchased seats for a code assistant sit unused, and the manager asks whether there are places the team should be drawing value that it is currently leaving on the table. The governance side of that picture, including how SUSE keeps agents and their costs inside a boundary it can stand behind, is covered in a companion piece, How SUSE Runs AI Without Losing Control. The difference between this and a leaderboard is not subtle, and it is the heart of the leadership lesson. A leaderboard exposes individuals and turns measurement into a game engineers play against each other, a game Spencer is explicit has nothing to do with customer value. Team-level cost visibility used for coaching does the opposite. It gives a manager the information to guide the team toward better use of the tools without making any individual engineer feel watched. “We’re really trying to decentralize and allow engineering managers to guide their teams on getting the most value out of the AI,” he says, “without it becoming like a leaderboard game where developers feel like they’re exposed.” The data exists to help the manager help the team, not to rank the team against itself. The principle holding it together What makes Spencer’s approach more than a list of preferred numbers is the principle holding it together, which is that measurement should serve the work rather than distort it. Every choice he describes follows from that one idea. Impact comes before output because output is the thing AI inflates. Team-level diagnostics come before individual leaderboards, because the goal is coaching rather than competition. Business outcomes come before activity counts, because outcomes are what customers actually receive. The decentralization to engineering managers reflects the same conviction that the people closest to the work are best placed to judge whether the AI is helping, given the right information and trusted to use it well. The deeper point for any leader standing up AI measurement is that the easy numbers and the useful numbers are not the same, and AI has widened the gap between them. The figures that are simplest to collect, lines of code, tokens, and per-head utilization, are the ones AI has made least meaningful. The figures that matter, the speed and quality of the outcomes customers depend on, take more thought to define and more care to track. Spencer’s argument is that the effort is the job. “Let’s focus on the impact,” he says, “the business impact, not on the utilization.” For engineering leaders deciding what belongs on a dashboard as agents reshape their teams, that is the distinction worth getting right before the vanity metrics calcify into the way the organization sees itself. If this article made you think, you’ll probably enjoy our new publication, AgenticEngineering. We’re less interested in asking whether agents can do something, and more interested in what it takes to make them work reliably in production. Early subscribers are still receiving a free copy of AI Agents in Practice by Valentina Alto. Subscribe and grab your free e-book instantly Explore Before Time Runs Out 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. 📢 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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This week’s stories ask what it takes to build AI that can be trusted AI_Distilled #142: What’s New in AI This Week Tame Your AI Monsters: CLAUDE EDITION Join the First Episode of the Exclusive AI Agent Governance Lab Series. Rubrik runs Claude. This is what we learned. We're the first enterprise to put Rubrik Agent Cloud to work governing our own Claude implementation — and on June 30, we're opening that experience up. Secure Your Spot LLM Expert Insights, Packt P.S. If Denis Rothman’s Expert Insight ends up being your kind of read, you’ll probably enjoy Agentic Engineering, our new publication for builders navigating AI beyond the demo. Early subscribers are still receiving a free copy of AI Agents in Practice by Valentina Alto. Subscribe Here LATEST DEVELOPMENTS 🏛️ Governments take a closer interest in frontier AI models - Reports suggest the U.S. government is taking a more active role in overseeing the release of advanced AI models, with OpenAI reportedly limiting access to future frontier models and Anthropic previously facing scrutiny over model deployment. The developments underscore how national security concerns are becoming an increasingly important factor in how the most capable AI systems are released. ☁️ Amazon deepens AI infrastructure investment in India - Amazon will invest an additional $13 billion to expand its AI and cloud infrastructure in India, bringing its planned investment in the country to $48 billion by 2030. The expansion will grow AWS data center capacity in Mumbai and Hyderabad, reinforcing India’s position as a key market in the global race to build AI infrastructure. 🧪 AI distillation comes under renewed scrutiny - Anthropic has accused Alibaba of using “adversarial distillation” to replicate the capabilities of its Claude models, an allegation Alibaba denies. The dispute has thrust AI distillation into the spotlight, highlighting growing concerns over whether using another company’s models to train new AI systems crosses the line from accepted optimization to intellectual property infringement. 💳 Airwallex raises $320 million to expand AI-powered finance - Airwallex has raised $320 million at an $11 billion valuation as it accelerates its push into AI-native financial software and agentic commerce. The fintech also unveiled new AI products designed to automate corporate finance workflows and enable AI agents to make delegated payments, reflecting growing investment in autonomous financial systems. 📈EXPERT INSIGHTS Denis Rothman on the illusion of autonomous agents (part 1) The AI community is caught in a contradiction. On one hand, there is a push to deploy autonomous agents into enterprise environments, expecting them to reason, plan, and execute complex, multi-step workflows. On the other hand, the methodology relied upon to build these agents is overwhelmingly based on prompt engineering, which is merely an attempt to cajole predictable, deterministic behavior out of fundamentally stochastic, black-box LLMs. This creates a pervasive dissonance: the expectation of industrial reliability built atop probabilistic generation. The current noise suggests that if we scale the parameters, refine the system prompts, or throw more compute at the problem, true autonomous reasoning will spontaneously emerge. To cut through this noise, we must confront an uncomfortable reality. Unconstrained probabilistic generation cannot serve as the kinematics for reliable robotic or enterprise execution. If we are to build truly agentic systems, we must move beyond the brittle, zero-context art of prompting and embrace the rigorous, transparent discipline of context engineering. Read the Full Article on Substack If this Expert Insight article made you think, you’ll probably enjoy our new publication, AgenticEngineering. We’re less interested in asking whether agents can do something, and more interested in what it takes to make them work reliably in production. Early subscribers are still receiving a free copy of AI Agents in Practice by Valentina Alto. Subscribe and grab your free e-book instantly 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. 📢 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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Yann LeCun’s warning, Anthropic’s expansion, and a deeper look at agent evaluation AI_Distilled #141: What’s New in AI This Week Vector memory for AI agents in air-gapped, regulated, and offline environments VectorAI DB delivers sub-15ms retrieval for agent memory and RAG pipelines on your own infrastructure. On-premises, at the edge, or air-gapped. Native support for LangChain, LlamaIndex, and Hugging Face. Free Community Edition available. Get Started for Free LLM Expert Insights, Packt LATEST DEVELOPMENTS 📉 AI pioneer warns of industry bubble as costs outpace revenues - AI researcher Yann LeCun has criticized Elon Musk’s xAI as a struggling competitor in the race for frontier AI while warning that the broader industry risks a “big bubble explosion” if leading labs fail to reduce costs or raise prices. LeCun argued that today’s AI services remain heavily subsidized by investors and suggested that more advanced AI systems may ultimately require new architectures beyond large language models. 🧠 MIT gives robots a memory that works more like ours - MIT researchers have developed a new memory framework that allows robots to remember objects, locations, and past observations using natural language, enabling them to answer questions such as “Where did I leave my wallet?” By combining 3D mapping with AI-generated descriptions, the system could help future robots navigate complex environments and collaborate more naturally with humans. 🌏 Microsoft becomes the primary gateway for OpenAI models in China - While OpenAI and Anthropic have largely stayed out of the Chinese market, Microsoft has emerged as the main supplier of OpenAI’s models to major Chinese technology companies through Azure. The arrangement highlights Microsoft’s unique position in the global AI ecosystem, even as concerns grow around model distillation, geopolitical tensions, and the flow of advanced AI capabilities across national boundaries. 🇰🇷 Anthropic expands into South Korea with new office and AI partnerships - Anthropic has opened a Seoul office and announced partnerships with major Korean organizations, including NAVER, Samsung SDS, LG CNS, and Nexon, as demand for Claude continues to grow across the region. The company also signed an agreement with South Korea’s Ministry of Science and ICT to collaborate on AI safety, cybersecurity, and responsible AI adoption. ⚖️ Study highlights why AI still struggles to moderate online hate speech - New research shows that leading AI moderation systems often disagree on what constitutes hate speech, producing inconsistent results across demographic groups and content types. While AI can detect explicit abuse at scale, researchers say it still struggles with context, sarcasm, coded language, and reclaimed terms, underscoring the challenges of relying on automated systems for online content moderation. Claude is currently the most powerful tool of 2026. Yet almost no one knows how to actually use them. Our expert mentors have condensed 800+ hours of Claude research, articles, YouTube content and real-world practice into a focused 16-hour curriculum. Join the 2-Day Claude AI Mastery Workshop: a live, end-to-end deep dive into Claude plus 10+ AI tools, LLMs and workflows. You will learn how to: - master Claude's three modes : Chat, Cowork and Code. - Set up Skills, Connectors and Plug-ins to automate your desktop, Notion and files. - Vibe code apps and dashboards without writing code & 10+ AI tools and workflows that pair with Claude. 🧠 Saturday & Sunday 🕜 10 AM – 7 PM EST Register NOW! 📈EXPERT INSIGHTS Why a Good Answer Doesn’t Mean a Good Agent During a time when AI conversations are often louder than they are useful, Ammar Mohanna, PhD, brings a refreshing perspective. His career has moved fluidly between academia and industry, from teaching advanced AI courses at the American University of Beirut to advising teams on turning machine learning ideas into systems that can be trusted. He is also known for his candid take on the current AI landscape, especially the gap between meaningful engineering and what he often calls AI slop. In this conversation, Ammar challenges one of the most common assumptions in agent development: that a correct answer is evidence of a successful agent. He explains why reliability lies in the path an agent takes, not just in the result it produces, and why evaluation must evolve from output scoring to a discipline that measures behaviour and trustworthiness in production. Most teams think they’re evaluating agents, but they’re actually not. Where do you see the biggest illusion of evaluation today? The biggest illusion is that teams think they are evaluating an agent when they are only evaluating the final answer. That works well for a chatbot. But an agent is different. It plans, chooses tools, passes arguments, reads observations, retries, stops, and sometimes takes action. A final-answer score hides most of the actual failure surface. An agent can produce a good-looking answer after calling the wrong tool, wasting ten steps, misreading a tool result, or ignoring a failed call. From the outside, the answer may look acceptable. From a reliability perspective, the run is not acceptable. So the illusion is: “the answer looked right, therefore the agent worked.” However, what you need to know is whether the path was valid, efficient, grounded, and safe. Read the Full Interview on Substack Most Claude Code content focuses on prompts and quick wins. This workshop explores what comes next. Join Sam Keen, former engineer at AWS, Lululemon, and Nike, to learn how high-performing teams use structured context, reusable skills, workflow memory, and guardrails to get more consistent results from Claude Code. 🎟️ Exclusive for AI Distilled subscribers: Get 60% off with code AI60. Limited to the first 10 sign-ups. Register Now 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. 📢 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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It's moving deeper into the fabric of technology AI_Distilled #140: What’s New in AI This Week Avail 40% Off Today LLM Expert Insights, Packt LATEST DEVELOPMENT 🎮 Pokémon Go scans helped train AI now being explored for military drones - Location scans voluntarily submitted by Pokémon Go players have been used to train AI models that help machines understand and navigate physical environments. Following a partnership between Niantic Spatial and drone software company Vantor, the technology is now being explored for use in GPS-denied environments, raising fresh questions about how consumer-generated data may ultimately be used in military and defence applications. 🏗️ Mistral bets on agents, infrastructure, and custom AI chips - Mistral CEO Arthur Mensch says enterprise AI adoption is still in its early stages, with significant value yet to be unlocked as organizations adapt to agentic workflows. He also revealed that the French AI startup is exploring the development of its own chips, signaling ambitions to control more of the AI stack as it expands beyond models into infrastructure and enterprise deployment. 👷 Jeff Bezos pushes back on AI job-loss fears - Jeff Bezos argues that AI-driven productivity gains will create new industries, products, and jobs rather than trigger mass unemployment. Speaking about his AI startup Prometheus, Bezos said the bigger long-term challenge may be labor shortages, as AI accelerates innovation across sectors such as manufacturing, aerospace, semiconductors, and energy. 🎓 Anthropic launches $150 million AI fellowship program - Anthropic has unveiled Claude Corps, a $150 million initiative that will train and place 1,000 early-career professionals at nonprofits across the U.S. The program aims to help organizations adopt AI tools while equipping participants with practical AI skills, reflecting growing efforts to distribute the benefits of AI more broadly amid concerns about workforce disruption. ⚡ Google unveils DiffusionGemma for faster AI text generation - Google has released DiffusionGemma, an experimental open-source model that uses diffusion techniques instead of traditional token-by-token generation, enabling text generation speeds up to four times faster on dedicated GPUs. While not intended to replace conventional LLMs for quality-critical applications, the model offers a glimpse into alternative architectures designed for real-time, interactive AI workflows. 📈EXPERT INSIGHTS OpenClaw + LangGraph Playbook It’s easy to build an agent that talks. Building one that remembers things, sends messages, runs on a schedule, and generally makes itself useful is a different challenge. That’s where LangGraph and OpenClaw make an interesting combination.Let’s build one. The first step is creating the primary agent and establishing its responsibilities through a system prompt. import os from datetime import datetime from langclaw import Langclaw from langclaw.gateway.commands import CommandContext # Initialize the master agent application interface app = Langclaw( system_prompt=( “## Corporate Intelligence Agent\n” “You are a corporate intelligence analyst. You track market trends “ “and draft precise outreach sequences based on current events.\n” “Delegate deep multi-source research tasks to the web-researcher subagent.” ), ) Tools allow the agent to access capabilities beyond language generation. In this example, the agent can retrieve market intelligence data. Read The Full Article 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. 📢 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} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;display:none;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}.social_block .social-table{display:inline-block!important}}

Agentic Engineering is live. Agentic Engineering Is Now Live I'm interrupting your very busy schedule for an announcement that you might’ve already seen coming. Our newsletter, Agentic Engineering, has finally kicked off. We sent out surveys. We sent out pre-launch messages. And you’ve all been really supportive while we figured out the logistics of kicking off something new. Across the surveys and interviews, you had one clear frustration. Too much hype around updates, launches, and releases, and very little guidance on what to actually do with all of it. That gap is what Agentic Engineering is for. We will lean on our network of experts who are actively building in this space, so you’re not just getting opinions, but conversations from people making decisions right now, often before they show up on timelines. But enough said! We’ll leave it to you to decide how useful this space is. Subscribe if this sounds like what you’ve been looking for. You can always unsubscribe later. And just to nudge things along (without making it too transactional), early subscribers will receive a free ebook copy of AI Agents in Practice by Valentina Alto! Tanya, Agentic Engineering Join Agentic Engineering and Grab Your Free Copy 📈EXPERT INSIGHTS A preview of Agentic Engineering Something Maxime Labonne said during one of our earlier roundtables stuck with me because it runs counter to how people think about small models. The common assumption is that small models are simply the cheaper version of frontier models. Same idea, lower cost. But Maxime’s experience has been that they’re often harder to work with. Not because they’re worse, but because they expose problems that larger models can often hide. When most people build AI systems today, they’re testing them with frontier models. Those models are smart enough to compensate for weak prompts, incomplete logic, or edge cases that nobody thought about. Small models don’t give you that luxury. As Maxime put it, they can fail on surprisingly basic tasks, and when they do, entire workflows can break. Read the Full Article *{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} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;display:none;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}}

This week’s Expert Insight explores the agent spectrum AI_Distilled #138: What’s New in AI This Week SUBSCRIBE AI is entering an interesting phase. The biggest questions are no longer about what models can generate, but what systems can be trusted to do. That theme runs through this week’s stories, from AI-designed vaccines and industrial AI partnerships to debates around governance and autonomy. It also sits at the center of this week’s Expert Insight, which explores the spectrum of today’s agents and why not all “agents” are actually the same thing. LLM Expert Insights, Packt LATEST DEVELOPMENT 🧬 AI-designed vaccine enters human trials in world-first study- Researchers at the University of Cambridge have developed what they describe as the first AI-designed vaccine to enter human trials, using AI to create a “super-antigen” capable of protecting against entire families of viruses. The approach could pave the way for universal vaccines against coronaviruses, influenza, and future pandemic threats. 🛑 Anthropic co-founder calls for an AI “brake pedal” - Anthropic co-founder Jack Clark has warned that AI systems are approaching a point where they could increasingly develop without human input, arguing that governments need new regulatory frameworks to maintain control. His comments come as AI capabilities accelerate and concerns grow around economic disruption, autonomous systems, and long-term governance. 📈 Investors look to Asia for the next wave of AI growth - Investment strategists are increasingly pointing to Taiwan and South Korea as the next major beneficiaries of the AI boom, citing their central role in semiconductor and AI infrastructure supply chains. With valuations still below many U.S. AI stocks, some investors see emerging markets as offering significant upside in the next phase of AI-driven growth. 🏭 Hitachi and Intel partner to advance industrial AI and digital infrastructure - Hitachi and Intel have announced a strategic collaboration to accelerate AI adoption across manufacturing, energy, mobility, and other critical industries. The partnership will focus on areas including physical AI, edge computing, quantum technologies, and factory automation, to build more intelligent and resilient industrial infrastructure. 📈EXPERT INSIGHTS A preview of Agentic Engineering The current AI ecosystem has developed a habit of describing wildly different systems with the exact same word: agents. A retrieval pipeline that reformulates search queries, a workflow assistant that schedules meetings, and a system capable of coordinating multi-step operational decisions with minimal supervision now all routinely get discussed under the same umbrella. And I think that’s why the conversation around agents sometimes feels simultaneously overcomplicated and vague. In reality, though, these systems are operating at very different levels of autonomy. So, I wanted to take a new angle: the different kinds of agent organizations are building, and how capabilities change as systems move from retrieval into action and eventually toward autonomy. Read The Full Article 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. 📢 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} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;display:none;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}.social_block .social-table{display:inline-block!important}}

We’re running a short audience survey to help decide Rethinking What an AI Newsletter Should Be Over the past few months, AI_Distilled has grown into a community of readers coming from very different parts of the AI ecosystem. As the space continues evolving, we’ve been thinking carefully about what this publication should become going forward and how we can make it more genuinely useful for the people reading it. So we’ve put together a short survey to understand what readers want more of, what feels missing from current AI media, and where we should take AI Distilled next. It should take around 4 minutes to complete, and every response will directly help shape the next phase of the publication. Take Survey Appreciate you taking the time. LLM Expert Insights, Packt *{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} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;display:none;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}.social_block .social-table{display:inline-block!important}}

AI dependence and synthetic influence raise new questions AI_Distilled #136: What’s New in AI This Week Get Tickets Researchers are now questioning whether heavy reliance on AI tools could weaken independent thinking, while AI-generated influencers and automated marketing systems are making it harder to separate expertise from synthetic persuasion. That tension between autonomy and control sits at the center of this week’s Expert Insight. The excerpt explores how modern AI agents are structured internally, particularly the separation between an agent’s persistent identity and the tasks it is tasked with performing. As agents become more embedded into real workflows, those architectural choices are starting to matter far beyond prompt engineering experiments. LLM Expert Insights, Packt LATEST DEVELOPMENT 🧠 Heavy AI dependence may weaken independent thinking, researchers warn -A study by researchers from MIT, Carnegie Mellon, Oxford, and UCLA found that people using AI assistants to solve reading and maths problems completed tasks faster but showed lower engagement with critical thinking and problem-solving processes. The findings raise concerns that growing reliance on AI tools could gradually reduce persistence and independent reasoning skills over time. ⚡ Anthropic doubles Claude usage limits after major SpaceX compute deal -Anthropic has expanded usage limits for Claude Code and its API after signing a compute partnership with SpaceX that gives it access to more than 220,000 NVIDIA GPUs at the Colossus 1 data center. The announcement highlights how competition in AI is increasingly shifting from model capabilities alone to securing massive infrastructure and compute capacity at scale. 🏋️ AI-generated fitness influencers push misleading transformation claims online - Google has developed TurboQuant, a compression method that reduces AI working memory requirements by up to six times without affecting performance. The advance could significantly lower infrastructure costs and enable more powerful models to run efficiently, though it remains at an early stage. 🛠️ Tools worth trying this week - From AI-powered email signature builders to branding assistants that generate polished HTML-ready designs in minutes, these tools show how generative AI is quietly reshaping even the most routine parts of digital work. If you want to experiment with lightweight but practical AI utilities, these are worth a look. We’re thinking about launching something new If you have a minute, take our quick survey and tell us what you’d actually want to read. It’ll help us build something that’s genuinely worth your time. Take the Survey 📈EXPERT INSIGHTS 30 Agents Every AI Engineer Must Build In this week’s Expert Insight, Imran Ahmad, author of 30 Agents Every AI Engineer Must Build, explores one of the foundational ideas behind modern agent engineering: the separation between an agent’s persistent identity and its real-time tasks. The two-layer prompt architecture: System and user prompts One of the most foundational innovations in agent design is the two-layer prompt architecture, which distinctly separates an agent's core identity from its real-time instructions. This layered design, consisting of the system prompt and the user prompt, establishes a clear division of responsibilities, drawing inspiration from classical software principles such as separation of concerns and abstraction layers. A helpful analogy is that of an agent functioning as a diplomat: the system prompt defines the diplomat's country, values, and code of conduct; the user prompt is the current negotiation or message they are handling. The diplomat must respond fluidly, but always in alignment with national policy. In multi-agent scenarios, this diplomat analogy extends across agent boundaries. When one agent passes a task or data payload to another, it is effectively handing off a "diplomatic brief": the receiving agent's system prompt must re-establish persona, authority scope, and operational constraints for the new context. Without explicit role-passing in the handoff protocol, the receiving agent may inherit ambiguous instructions or combine roles across agents. Well-designed multi-agent architectures, therefore, encode the PTCF components not just in each agent's internal system prompt but also in the inter-agent message schema, ensuring that every communication boundary preserves the constitutional clarity that the framework provides. Together, these two layers form what we might call the agent's prompt contract: > System prompt: How the agent behaves > User prompt: What the agent should do Read Full Article Build and test native paywalls in seconds Turn a prompt into a complete native paywall with RevenueCat’s Paywalls AI Editor Update copy, adapt designs for dark mode, and launch A/B tests without waiting for the next sprint.Use free up to $2.5k monthly tracked revenue. 96,000+ apps trust RevenueCat. Learn More 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. 📢 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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They’re taking on real work, and the risks are showing AI_Distilled #135: What’s New in AI This Week Building AI Resilience: Managing Agent Risk with Trust Infrastructure Rules-based security fails AI agents. On May 5, learn to scale safely with "Trust Infrastructure." We’ll dive into our Pillars of Trust framework, contextual guardrails, and how Rubrik Agent Cloud provides a foundation for secure, resilient AI. Save My Spot This week feels like a turning point for AI agents. They are no longer just helping with tasks, they are starting to take on entire workflows on their own. In some cases, that means real gains in speed and productivity. In others, it means things can go wrong very quickly when systems are not set up with the right safeguards. What’s becoming clear is that the risk isn’t just in the model, it’s in how these agents are actually run. The expert insight this week digs into that layer, showing how choices like using a cloud API, self-hosting, or running models on-device can directly shape latency, cost, and control, and ultimately decide whether an agent works reliably or fails in production. LLM Expert Insights, Packt LATEST DEVELOPMENT 🧠 Mistral launches Medium 3.5 and cloud-based coding agents in Vibe - Mistral has introduced Medium 3.5, a new flagship model designed for long-running coding and multi-step tasks, alongside cloud-based agents that can run work asynchronously. The release signals a shift toward developers offloading entire workflows to AI agents that operate independently and return completed tasks. ⚠️ AI coding agent wipes company database in seconds after going rogue - An AI coding agent powered by Claude Opus 4.6 deleted a company’s production database and all backups in a single API call, wiping months of data in under 10 seconds. The incident highlights how weak safeguards across AI tools and cloud infrastructure can turn routine automation into irreversible system failures. ⚡ Google unveils AI memory breakthrough that cuts usage by up to 6x- Google has developed TurboQuant, a compression method that reduces AI working memory requirements by up to six times without affecting performance. The advance could significantly lower infrastructure costs and enable more powerful models to run efficiently, though it remains at an early stage. 🔍 Scientists propose new blueprint for fully transparent AI systems - Researchers have developed a mathematical framework for AI that can explain how it learns, remembers, and makes decisions, addressing the long-standing “black box” problem. While still at an early stage, the approach could lead to more reliable and controllable systems. 🌐 China pushes toward an AI-driven “intelligent economy” at scale - China is accelerating a shift from digital infrastructure to a fully AI-integrated economy, with strong state backing and rapid deployment across industries. The strategy points to a broader move toward “swarm intelligence” and large-scale automation. We’re thinking about launching something new If you have a minute, take our quick survey and tell us what you’d actually want to read. It’ll help us build something that’s genuinely worth your time. Take the Survey 📈EXPERT INSIGHTS Agentic Architectural Patterns for Building Multi-Agent Systems This week’s expert insight comes from Agentic Architectural Patterns for Building Multi-Agent Systems by Dr. Ali Arsanjani and Juan Pablo Bustos, a practical guide to turning AI prototypes into systems that can actually run at scale. Both authors bring deep enterprise experience, from large-scale architecture to real-world deployment, and focus on the decisions that shape how agentic systems behave in production. In this excerpt, they look at a layer that often gets overlooked: how models are served. Whether you rely on cloud APIs, self-hosted setups, or edge deployment, the way an LLM is delivered has a direct impact on latency, cost, control, and reliability. It’s a reminder that building agents isn’t just about model capability, but about the infrastructure choices that make those capabilities usable. Serving architectures for agentic LLMs The way an LLM is served, that is, the manner in which it is made available for inference, directly impacts its responsiveness, scalability, cost, and security within an agentic system. The "serve" component, a critical piece of any comprehensive GenAI reference architecture, must be carefully considered as it forms the bridge between the trained LLM and the agent that relies on its intelligence. The choice of serving architecture is not one-size-fits-all and depends heavily on the specific needs of the agent and the broader enterprise context. Cloud-hosted APIs Cloud-hosted APIs (such as those from OpenAI, Google's Vertex AI, Anthropic, and other providers) are a popular choice for many agentic systems. These services offer the significant advantages of managed infrastructure, meaning the complexities of hardware provisioning, scaling, and maintenance are handled by the provider. They typically provide access to state-of-the-art models, often the largest and most capable ones, without requiring direct investment in specialized hardware such as GPUs or TPUs. Many of these API offerings also include built-in monitoring, security features, and regular model updates. However, this convenience comes with potential trade-offs. Read Full Article Packt is hosting a free live session on DeerFlow, where key contributors will demo the popular open-source SuperAgent framework based on LangGraph. This event is designed for engineers, AI practitioners, product teams, and anyone exploring autonomous workflows or open-source agent systems. Register now and join us on May 6, from 9:00 to 10:30 AM EDT. Register 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. 📢 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} @media (max-width: 100%;display:block}.mobile_hide{min-height:0;max-height:0;max-width: 100%;display:none;overflow:hidden;font-size:0}.desktop_hide,.desktop_hide table{display:table!important;max-height:none!important}.social_block .social-table{display:inline-block!important}}

Better systems, bigger stakes AI_Distilled #134: What’s New in AI This Week The latest models are getting better at handling complex work with less input, and they are starting to behave in ways that feel closer to real collaborators than tools. At the same time, the stakes are becoming clearer. Developments like Anthropic’s Mythos are drawing attention from governments and financial institutions, while new models from across the industry are pushing on cost, speed, and capability. Companies are already adjusting how they work in response. It feels less like a steady upgrade to how AI fits into the world. LLM Expert Insights, Packt LATEST DEVELOPMENT 🤖 OpenAI launches GPT-5.5, pushing toward more capable agentic AI systems - OpenAI has introduced GPT-5.5, its most capable model to date, designed to handle complex, multi-step tasks with minimal guidance. The model shows strong gains in areas like coding, research, and tool use, with improved ability to plan, execute, and iterate across workflows while maintaining speed and efficiency. With enhanced safeguards and early enterprise deployment, the release signals a continued shift toward AI systems that act more like autonomous collaborators than passive tools. 🌍 Anthropic’s Mythos model turns AI into a geopolitical flashpoint- Anthropic’s Mythos model has triggered a global scramble among governments and central banks after demonstrating the ability to uncover critical vulnerabilities across financial systems and infrastructure. Access to the model is tightly controlled, with most countries excluded, turning it into a strategic asset and raising concerns about unequal visibility into emerging cyber risks. The episode highlights a deeper shift: as AI capabilities advance, they are starting to function less like product launches and more like geopolitical leverage points with real security implications. ⚙️ DeepSeek previews V4 model, reinforcing China’s push for low-cost AI leadership- Chinese AI startup DeepSeek has released a preview of its V4 model, building on the disruption caused by its earlier low-cost, high-performance systems. The new model emphasizes strong agent capabilities and lower inference costs, while remaining open-source and optimized for local deployment. With support for domestic chips and growing competition within China, V4 signals a broader shift toward AI sovereignty and cost-efficient alternatives to Western models 🎧 xAI launches Grok Voice Think Fast 1.0 for real-time enterprise voice agents- xAI has introduced Grok Voice Think Fast 1.0, a new voice model designed for real-time, multi-step workflows across customer support, sales, and enterprise applications. The model focuses on low-latency responses, accurate data capture, and reliable tool use in noisy, real-world environments, with early deployments already handling complex support and sales interactions at scale. The release highlights a growing shift toward AI agents that can operate autonomously in live, high-stakes conversations. 📉 Tech layoffs deepen as Meta and Microsoft double down on AI investments- Meta and Microsoft are cutting thousands of jobs while ramping up spending on AI, with Meta planning to reduce its workforce by around 10% and Microsoft offering voluntary exits to a significant portion of employees. Executives point to rising productivity from AI as a key factor, with some claiming that tasks once handled by large teams can now be completed by far fewer people. The moves highlight a growing shift: as companies invest heavily in AI infrastructure and capabilities, workforce structures are beginning to change alongside it. We’re thinking about launching something new If you have a minute, take our quick survey and tell us what you’d actually want to read. It’ll help us build something that’s genuinely worth your time. Take the survey 📈EXPERT INSIGHTS RAG-Driven Generative AI, Second Edition This week’s Expert Insight comes from the second edition of RAG-Driven Generative AI by Denis Rothman, a practitioner who has spent decades building AI systems in real-world enterprise settings. This edition focuses on how RAG is evolving from simple experiments into production-ready systems that work with enterprise data at scale. In this excerpt, Rothman breaks down the RAG ecosystem into its core parts and explains how they fit together. The RAG Ecosystem RAG-driven generative AI is a framework that can be implemented in many configurations. However, the RAG framework runs within a broad ecosystem, as shown in Figure 1.3. No matter how many retrieval and generation frameworks you encounter, it all boils down to the following four domains and the critical questions that accompany them: > Data: Where is the data coming from? Is it reliable? Is it sufficient? Crucially, in the MAS-RAG era, does the data stay within the secure corporate trust boundary? > Storage: How is the data going to be stored? In the traditional approach, data was fragmented between SQL databases and external vector stores. In the modern approach, we ask: Can we store vectors alongside business data in a single converged database? > Retrieval: How will the correct data be retrieved? Will we use simple keyword matching (Naïve) or integrated vector search (Advanced)? > Generation: How will the appropriate generative AI model be selected? How will we securely pipe the retrieved private data into the model? READ FULL ARTICLE View the latest HubSpot Developer Platform updates in Spring Spotlight See what's new for the HubSpot Developer Platform! Ship faster with AI coding tools like Cursor, Claude Code, and Codex. Build MCP-powered AI connectors, run serverless functions with support for UI extensions, and use date-based versioning to streamline roadmap planning. Learn more 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. 📢 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! 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As AI advances, the focus shifts to how we manage it. AI_Distilled #133: What’s New in AI This Week Otis: The World's First Cinematic AI Experience Forget generic chatbots. Otis is a wise elder on a cinematic porch at sunset, back turned, voice warm, ready to talk through whatever you're carrying. The world's first cinematic AI experience. April 21st on Kickstarter! Learn more This week, AI felt a little closer to the real world. Anthropic’s Mythos model has already pushed banks and governments into defensive mode, while newer, more controlled releases show how carefully these capabilities must now be handled. At the same time, AI is quietly becoming more useful across specific domains, from scientific research to the infrastructure that runs these systems. It’s a reminder that progress isn’t just about smarter models, but also about how safely and effectively we can use them. LLM Expert Insights, Packt LATEST DEVELOPMENT 🛑 Anthropic’s Mythos model raises global alarm over financial system vulnerabilities - A new AI model from Anthropic, dubbed Claude Mythos, has triggered concern among finance ministers and central bankers after demonstrating the ability to identify vulnerabilities across major operating systems, browsers, and financial infrastructure. The model has already prompted discussions at IMF meetings, with governments and banks being given early access to test and secure their systems before public release. Officials warn that while the technology could strengthen cybersecurity, it also lowers the barrier for malicious actors to exploit critical weaknesses at scale. 🛡️ Anthropic releases Claude Opus 4.7 with reduced cyber capabilities amid safety concerns - Anthropic has launched Claude Opus 4.7, a new model positioned as its most capable general-purpose release, but deliberately less powerful in cybersecurity tasks than its controversial Mythos model. The company says it has added safeguards to detect and block high-risk use cases, reflecting growing concerns about how advanced models could expose system vulnerabilities. The move signals a shift toward controlled deployment, as Anthropic tests how to safely scale models with capabilities that may otherwise pose systemic risks. 🧬OpenAI unveils GPT-Rosalind, a model built for life sciences research - OpenAI has introduced GPT-Rosalind, a domain-specific model designed to support scientific workflows across biology, drug discovery, and genomics. The model focuses on tasks such as hypothesis generation, literature synthesis, and experimental planning, aiming to accelerate early-stage research where timelines can stretch over a decade. Currently available as a research preview, GPT-Rosalind reflects a broader push toward specialized AI systems tailored to complex, real-world disciplines like life sciences. 🧪 OpenProtein aims to make AI-driven protein design accessible to biologists - OpenProtein.AI is building a no-code platform that gives researchers access to advanced protein-design models without requiring machine learning expertise. Founded by MIT researchers, the platform allows scientists to generate, test, and optimize protein sequences using AI, helping accelerate drug discovery and biological research. By lowering the barrier to entry, the company is aiming to bring cutting-edge AI tools directly into the hands of biologists and smaller labs. ☁️ Cloudflare launches unified AI inference layer to support multi-model agents - Cloudflare is positioning itself as a unified inference layer for AI agents, allowing developers to access 70+ models across multiple providers through a single API. The platform is designed to handle real-world agent workflows, where tasks are split across different models, while also managing latency, cost, and reliability. With features like automatic failover and centralized usage tracking, the move reflects a broader shift toward infrastructure that can orchestrate complex, multi-model AI systems at scale. We’re thinking about launching something new If you have two minutes, take our quick survey and tell us what you’d actually want to read. It’ll help us build something that’s genuinely worth your time. Take the 2-minute survey 📈EXPERT INSIGHTS Mastering NLP From Foundations to Agents This week’s Expert Insight comes from Mastering NLP From Foundations to Agents by Lior Gazit and Meysam Ghaffari, a guide that moves from core NLP principles to the realities of building and fine-tuning modern AI systems. As teams push to adapt large models for real-world use, the constraint is often no longer ideas, but resources: compute, memory, and cost. In this excerpt, Gazit and Ghaffari walk through Quantized LoRA (QLoRA), a technique that makes it possible to fine-tune large language models efficiently on limited hardware, without sacrificing performance. Understanding QLoRA QLoRA extends the idea of LoRA to enable fine-tuning of LLMs on a single GPU. The core idea is to keep the base model frozen and stored in 4-bit quantized precision, while training the LoRA adapters in higher precision (such as bfloat16 or float16). This achieves two goals simultaneously: >> Drastically reducing memory requirements >> Allowing the adapters to both compensate for quantization error and adapt the model to downstream tasks Let’s analyze how QLoRA compares to standard LoRA in practice, focusing on the trade-offs between memory reduction and model fidelity. We will specifically demonstrate how techniques such as NF4 (4-bit NormalFloat) and paged optimizers allow us to recover the quality of full fine-tuning while significantly lowering the barrier to entry for model adaptation. READ FULL ARTICLE 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. 📢 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 BERT? AI Agents: The New AI System Stack AI_Distilled #132: What’s New in AI This Week If you look at most AI applications built today, something interesting stands out. Very few teams are training models from scratch. Instead, developers are building systems that orchestrate models, data, and tools together. Modern AI development has shifted from training models to designing AI systems. That shift is exactly what we explore in our Build AI Agents Over the Weekend, where developers learn how production AI systems are actually built. In a previous cohort, Lior Gazit (ML Group Manager at S&P Global) walked through the evolution that led to today’s AI agents. Here are three key insights from that session. Let’s start with the first major shift. LLM Expert Insights, Packt 1. The Shift: From Training Models → Prompting Models Before LLMs, most NLP systems followed a familiar pipeline: Collect data → Label data → Train model → Deploy model This required thousands of labeled examples and complex ML pipelines. Models like BERT introduced transfer learning, allowing developers to fine-tune pretrained models instead of training from scratch. But LLMs pushed the paradigm even further. Today, many tasks can be solved with prompting alone. For example, instead of training a classifier to detect whether a tweet reports an earthquake, a developer can simply prompt an LLM: “Here is a tweet. Tell me if it reports an earthquake.” This drastically reduces development time and removes the need for large labeled datasets. 2. When Prompting Isn't Enough: Retrieval-Augmented Generation (RAG) LLMs are powerful, but they have limits. You can't simply paste an entire knowledge base or legal document into a prompt. This is where Retrieval Augmented Generation (RAG) becomes essential. Instead of sending all documents to the model, a RAG system: • Retrieves the most relevant document chunks • Sends those to the LLM • Generates an answer grounded in that context This allows AI systems to work with large datasets and private knowledge bases without retraining models. 3. Why AI Agents Are Emerging Once developers realized LLMs could reason across tasks, a new architectural pattern emerged: Instead of relying on one model, systems now coordinate multiple specialized agents. For example: User request → Planning agent → Coding agent → QA agent → Final response Each agent focuses on a specific responsibility, allowing systems to tackle more complex workflows. This approach mirrors how human teams collaborate. Build AI Agents Over the Weekend These ideas are interesting in theory. But the real challenge is building these systems end-to-end. In our Workshop, developers build real production patterns, including: ✔ Retrieval-augmented generation systems ✔ Multi-agent workflows ✔ LLM routing strategies ✔ Monitoring and tracing pipelines The next cohort starts tomorrow in less than 24 hrs - 14th March 2026 If you're building AI applications today, this workshop is designed to help you move from LLM experimentation to production systems. SAVE YOUR SEAT Workshop Goes Live Tomorrow - Today is the Last Chance to Book Your Seat! What’s the biggest challenge you're facing when building AI systems today? Reply and let us know, we read every response. 📢 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} @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}}

Meta reorganizes, Apple explores new compute, and policy catches up. AI_Distilled #131: What’s New in AI This Week CLICK HERE TO AVAIL THE OFFER A pattern is emerging in AI this week. Governments are trying to get ahead of the technology, companies are reorganizing their engineering teams around it, and the infrastructure race keeps intensifying. The UN is moving toward global coordination on AI governance, Meta is restructuring how it builds models, and Apple may even lean on Google’s servers to power the next generation of Siri. At the same time, AI keeps finding its way into places that matter: helping doctors choose cancer treatments and shaping policy decisions in agriculture. The pace is fast, but underneath it all, the field still rests on surprisingly fundamental ideas about language and structure. That’s exactly where today’s Expert Insight takes us. LLM Expert Insights, Packt LATEST DEVELOPMENT 🌐 UN launches global science panel to steer AI governance - The UN General Assembly has approved the creation of an independent international scientific panel to study the impacts of artificial intelligence and guide global policy. The initiative aims to give governments shared, evidence-based insight into AI’s economic and societal effects while helping bridge the knowledge gap between advanced AI nations and developing countries. 🧠 Meta sets up new engineering unit to speed up AI model development - Meta is creating a new applied AI engineering organization to accelerate the development and refinement of its next-generation models. The team will focus on building tools, generating training data, and running evaluations to help models improve more quickly, working closely with the company’s Superintelligence Lab. ☁️ Apple may rely on Google servers to power its next-generation AI Siri - Apple is reportedly exploring the use of Google’s data centers to run parts of its upcoming AI-powered Siri, as the company prepares a major upgrade to the assistant using Google’s Gemini models. The move highlights how Apple may lean on external infrastructure to handle the growing compute demands of advanced AI, especially as usage of its own Private Cloud Compute servers remains relatively low. 🧬 AI model could help doctors tailor treatment for pancreatic cancer patients - Researchers have developed an AI tool designed to analyze clinical and molecular data to help doctors choose more effective treatment strategies for pancreatic cancer. Because the disease is often diagnosed late and responds differently to therapies, the model aims to identify patterns that can guide more personalized treatment decisions. 🌾 Researchers explore co-designing AI agents to support agricultural policymaking - Researchers and policymakers are exploring how AI agents can be co-designed with stakeholders to support agricultural policy decisions, rather than being built solely by technologists. The approach emphasizes collaboration with farmers so that AI systems reflect real-world agricultural needs and governance priorities. Advocates say this participatory design model could make AI tools more aligned with food-system challenges such as climate resilience. 📈EXPERT INSIGHTS Mastering NLP From Foundations to Agents This week’s Expert Insight comes from Mastering NLP From Foundations to Agents by Lior Gazit and Meysam Ghaffari, an in-depth guide that traces the evolution of natural language processing from classical machine learning techniques to modern agentic AI systems. Gazit, a seasoned machine learning leader in the financial sector, and Ghaffari, a senior data scientist specializing in NLP and deep learning, combine practical engineering experience with academic depth. In this excerpt, they unpack part-of-speech tagging, a foundational NLP technique that still underpins many modern language systems, from traditional pipelines to today’s LLM-powered applications. POStagging Part-of-speech(POS) taggingis the practice of attributing grammatical labels, such as nouns, verbs, adjectives, and others, to individual words within a sentence. This tagging process holds significance as a foundational step in various NLP tasks, including text classification, sentiment analysis, and machine translation. POS tagging can be performed using different approaches, such as rule-based methods, statistical methods, and deep learning-based methods. In this section,we’llprovide a brief overview of each approach. Rule-based methods Rule-basedmethodsforPOStagging involve defining a set of rules or patterns that can be used to automatically tag words in a text with their corresponding parts of speech, such as nouns, verbs, adjectives,and so on. The process involves defining a set of rules or patterns foridentifyingthedifferent partsof speech in a sentence. For example, a rule maystatethat any word ending in "-ing" is a gerund (a verb acting as a noun), while another rule maystatethat any word preceded by an article such as "a" or "an" islikely anoun. These rules are typically based on linguistic knowledge, such as knowledge of grammar and syntax, and are often specific to a particular language. They can also be supplemented with lexicons or dictionaries that provideadditionalinformation about the meanings and usage of words. The process of rule-based tagging involves applying these rules to a given text and identifying the parts of speech for each word. This can be done manually, but is typically automated using software tools and programming languages that support regular expressions and pattern matching. READ FULL ARTICLE Senior engineers, tech leads, and developers serious about maintainable code: this live session is for you. Learn pragmatic techniques for large-scale refactoring using structured code search (ast-grep) and Claude Code-assisted workflows—combining determinism with speed for safer production changes. Join us March 14th at 10AM ET. 🎟️ Packt community members save 50% with code AIDISTILLED50. Includes the Mastering ast-grep ebook and Claude Code prompt templates. REGISTER NOW AT 50% 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. 📢 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! 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Anthropic softens safety, Meta secures chips, and agents reshape enterprise stacks. AI_Distilled #129: What’s New in AI This Week CLICK HERE TO AVAIL THE OFFER This week, AI’s evolution came with sharper trade-offs. As agents move deeper into enterprise systems, security teams are confronting new vulnerabilities. Anthropic recalibrated its safety posture under competitive pressure. Meta locked in a $60B compute strategy, while sovereign AI initiatives gained momentum globally. At the same time, researchers are refining how we monitor and reason about LLM behavior. The message is clear: scaling intelligence now requires architectural discipline — not just bigger models. LLM Expert Insights, Packt LATEST DEVELOPMENT 🚨 Enterprise Security Risk from AI Agents - AI agents are quietly reshaping enterprise systems — and not just in productivity. Autonomous agents with deep access to internal tools, APIs, and memory are now creating new security vulnerabilities, including prompt injections and unauthorized execution paths. Security teams must rethink access control, audit trails, and risk models to manage these emergent threats. ⚖️ Anthropic Scales Back Safety Pledge in Heated AI Race - Anthropic, long regarded as a safety-first AI lab, has revised its Responsible Scaling Policy — dropping key commitments to delay deployment when safety controls lag. While introducing periodic public risk reports, the shift reflects competitive pressures in an environment with limited regulation, raising questions about risk trade-offs at top AI labs. 🔌 Meta’s Strategic $60B Chip Deal with AMD - Meta has agreed on a $60 billion multi-year deal with AMD for AI chips and a 10 % equity stake, diversifying beyond Nvidia and scaling infrastructure for large-scale training and inference. This reflects a broader trend of major AI players securing specialized compute capacity amid supply constraints and performance demands. 📈 India AI Impact Summit Unveils Local Models & Strategy - The India AI Impact Summit revealed new Indian AI models (e.g., Sarvam AI variants and BharatGen Param2) and national AI infrastructure commitments, supported by plans to add thousands of GPUs and expand sovereign compute capacity. Microsoft also committed large-scale investments to expand access in emerging markets. 🚀 MIT Develops Better Reasoning & LLM Monitoring Techniques - Researchers at MIT introduced new techniques for probing LLM behavior, exposing how context and long conversations can bias outputs and affect reliability. Such findings inform safer and more robust AI systems by highlighting architectural weaknesses and opportunities for refining reasoning engines. 📊 AI Agents Transition from Theory to Integrated Systems - A growing body of analysis affirms the structural shift from isolated generative models to agentic systems that act, plan, and orchestrate workflows across applications — radically changing how AI is used in production and enterprise environments. 📈EXPERT INSIGHTS Agentic Architectural Patterns for Building Multi-Agent Systems This week’s Expert Insight comes fromAgentic Architectural Patterns for Building Multi-Agent Systemsby Dr. AliArsanjaniand Juan Pablo Bustos. Dr.Arsanjani, long known for his work in enterprise architecture and now Director of Applied AI Engineering at Google Cloud, brings decades of large-scale systems thinking to the agentic AI conversation. In this excerpt, the authors introduce the Agent Router pattern, a practical way to map user intent to the right specialized agent without relying on brittle keyword rules or guesswork. It may look simple on the surface, but once your system grows beyond a single assistant, this pattern quickly becomes essential. The Agent Router pattern (intent-based routing) Agent Routeris the{ XE"Agent Router pattern" }foundationalpattern for decoupling the user's intent from the specific agent that executes it. In early or simple systems, developers oftenreliedon hardcoded conditional logic (e.g., if "sales" in query:call_sales_agent). However, at an enterprise scale with dozens of specialized agents, this approach becomes brittle and unmanageable.Agent Routersolves this by introducing a dedicated architectural layer that acts as a sophisticated switchboard. This pattern combines two distinct mechanisms:semantic intent extraction (understanding the "what") and graph-constrained routing (deciding the "who"). By separating these concerns, the system can scale to support new agents and capabilities without requiring a rewrite of the core orchestration logic. It serves as the "Hello World" of agentic coordination,the minimalviablecorerequiredfor intelligent dispatch. Context A systempossesses{ XE"Agent Routerpattern:context" }asuite of specialized agents, each with distinct{ XE"context" }capabilities. Users interact with the system via natural language, which is often ambiguous, varying in phrasing, orcontainingirrelevant noise. Problem How can the{ XE"Agent Routerpattern:problem" }systemaccurately map an unstructured, variable{ XE"problem" }naturallanguage request to the specific agent best suited to handle it, without "hallucinating" capabilities or relying on fragile keyword matching?Forces in the problem space include the following: Ambiguity versus precision: User{ XE"Agent Routerpattern:forces" }inputsare vague and{ XE"forces" }unstructured, but agent execution requires precise, structured commands. Scalability versus maintenance: Adding a new agent should not require rewriting the central routing logic. The system must accommodate growing capabilities dynamically. Safety versus hallucination: The system must ensure that a request is never routed to an agent that cannot handle it, avoiding the risk of an agentattemptingto perform a task outside its guardrails. READ FULL ARTICLE Built something cool? Tell us. 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Frontier launches, global AI strategy evolves, and RAG matures. AI_Distilled #129: What’s New in AI This Week This week, AI’s center of gravity shifted decisively toward systems, not just models. OpenAI introduced Frontier to operationalize enterprise agent fleets. Anthropic pushed Claude deeper into production-grade reasoning. Global leaders debated sovereign AI strategies at scale. Meanwhile, on the ground, real engineering challenges like RAG indexing pipelines are defining whether these ambitions hold up in production. The race is no longer about who builds the smartest model — it’s about who builds the most durable stack. LLM Expert Insights, Packt LATEST DEVELOPMENT 🚀 Enterprise AI & Strategic Infrastructure OpenAI launches Frontier — enterprise AI agent platform - OpenAI unveiled Frontier, a comprehensive enterprise platform for building, deploying, and managing autonomous AI agents across internal systems with shared context, governance, and security — marking a shift from isolated models to “AI coworkers” that can execute complex business workflows at scale. 🧠 Anthropic upgrades Claude with Opus 4.6 - Anthropic released Claude Opus 4.6, its most advanced model yet, featuring longer context windows and agent-oriented workflows capable of deeper reasoning, coding, and enterprise-grade tasks — intensifying the rivalry with OpenAI in agent-centric deployments. 📊 Global AI Policy & Collaboration India hosts AI Impact Summit with global tech leaders - The India AI Impact Summit 2026 brought together policymakers and AI executives — including Prime Minister Narendra Modi, Google’s Pichai, and UN officials — to discuss AI sovereignty, open-source models, and infrastructure investment commitments potentially exceeding $200 B, while also spotlighting governance, equity, and inclusion. 🛡️ Competitive Dynamics & Industry Tension OpenAI vs Anthropic: Summit rivalry surfaces - At the India AI Impact Summit, live optics underscored competitive tension between OpenAI and Anthropic leadership, reflecting deeper strategic and cultural divides as both pursue distinct visions for AI deployment and risk management. 📡 Open Source & Community Moves OpenClaw’s creator joins OpenAI - The creator of OpenClaw — a popular open-source autonomous assistant — joined OpenAI, with the project entering an independent foundation, signaling broader industry interest in community-driven agent frameworks integrated into major AI ecosystems. 🌍 AI Sovereignty & Global Strategy Summit highlights “third way” AI leadership - Leaders at India’s summit emphasized a “third way” for AI development that balances open-source models, sovereign compute, and international collaboration — distinct from dominant US/China dynamics and geared toward Global South innovation. 🔁 Agentic AI Ecosystem Shift Enterprise agents replace standalone models - Industry consensus is coalescing around agentic architectures — where systems act autonomously across heterogeneous corporate stacks — rather than isolated LLM deployments, shaping long-term enterprise AI strategy. Join the Machine Learning & Generative AI System Design Workshop and learn how to design AI systems that survive production. Get 35% off with code FLASH35 JOIN NOW 📈EXPERT INSIGHTS Building Natural Language and LLM Pipelines This week’s Expert Insight comes fromBuilding Natural Language and LLM PipelinesbyLaura Funderburk, a leading voice in production-grade AI systems and developerrelations leadat AI Makerspace. In this excerpt, Funderburk breaks down one of the most overlooked parts of Retrieval-Augmented Generation (RAG): the indexing pipeline. She illustrates how Haystack’s flexible routing, preprocessing, and unification workflows turn scattered, messy data into structured knowledge ready for intelligent querying — the invisible architecture that keeps RAG systems from collapsing in production. Building pipelines with Haystack: indexing, naive RAG, and hybrid RAG At the heart of any effective RAG system are two distinct yet co-dependent workflows: an offline indexing pipeline responsible for preparing the knowledge base, and an online query pipeline thatleveragesthis prepared data to answer user questions in real time. This section provides the blueprints for constructing these two foundational pillars. We will first build a versatile indexing pipeline capable of ingesting data from multiple sources and formats and then construct our first query pipeline (a naive RAG system) that serves as a functional baseline for everything that follows (a hybrid RAG system with ranking). Indexing pipelines: preparing your knowledge base The indexing pipelineis a critical offline process. Its primaryobjectiveis to take web addresses, unstructured, or semi-structured data from various sources, convert it into a standardized format, and load it intoDocumentStore, where it can be efficiently searched. A well-designed indexing pipeline is the bedrock of a high-performing RAG system, as the quality of the data ingested directlyimpactsthe quality of the retrieval and, ultimately, thefinal generated answer. We will build an indexing pipeline that can handle a diverse mix of data sources simultaneously: live web pages, local text and PDF files, and structured tabular data from CSV files. This is achieved by usingFileTypeRouter, acomponentthat directs different data types to theappropriate converters, allowing for a unified yet specialized ingestion workflow. The key steps are depicted inFigure 4.3: READ FULL ARTICLE 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. 📢 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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