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Six issues ago, this newsletter didn't exist yet. But now, we have 20k+ folks on itStarting with the next edition,you’llreceive this newsletter directly from our Substack, so keep an eye out for it there.A bit of history before we get into today's issue.When we conducted the Build Intelligent Networks with AI in January, we had 150+ attendees from 30+ countries and came out as AutoCon4's highest-rated workshop. That response is the reason The AI Network Engineerexists. There was clearly an appetite for this conversation, not just for four hours in a workshop, but every week.Sowe started writing.We understood that engineers wanted to see how these ideas actually work against real network environments. How MCP skills are built. How agents should behave when they have access to tools. How to keep automation reusable instead of creating another pile of scripts only one person understands.Six months later, the question has changed. It's no longer "can this work", it's "how do I make this work reliably, without it falling over the first time something unexpected happens."In the upcoming session, we're moving from raw MCP tools to composable, reusable skills, using spec-driven development to actually constrain agent behavior, and designing agentic loops that detect when they're stuck and recover safely, all hands-on with OpenClaw and NetClaw as production frameworks,ENGINEERING AGENTIC NETWORK OPERATIONSJune 30th | 9 AM EDT50% off with code SPECIAL50 (ENDS SOON)John CapobiancoandWilliam Collinsare running this one live. If their names sound familiar, it's because they're the same two voices behind most of what we've covered: John's trust ramp from Issue #1, the RAG-is-your-vars-file and MCP-is-your-Galaxy-role explainers from Issue #4, William's "spider-web it out" approach to agent rules from Issue #3.Cohort 2 picks up where those conversations leave off. The honest version of where most teams get stuckisn'tbuilding an MCP server,it'srunning one in production without it becoming another thing to babysit.That'swhat this session is built around:MCP skills that are reusable across your environment, instead of one-off integrations you write once and thenmaintainforever. Spec-driven development, which is really the production version of the "markdown files that define what the agent can and can't do" idea from Issue #3,and the five-line job description from Issue #6, role, trigger, tools, job, escalation, except now you're writing the spec that an agentic loop actually follows. And agentic loopsdesignedto recover when they get stuck, rather than failing in a way nobody can diagnose.All of it hands-on, in a live lab, with OpenClaw,NetClaw, Selector AI,Containerlab,FastMCP, and AristacEOS. Same digital-twin philosophy John described back in Issue #1: let the agents break things here, where itdoesn'tcost you anything, beforethey'reanywhere near production.If Issue #4 made MCP click for you on paper, this is where you build the thing.Use code SPECIAL50 to get 50% offBOOK YOUR SEAT NOWALSOWORTH A LOOK THIS MONTHLinux,SysAdmin, and AI Workflows with Imran AfzalJune 27 | 11:30 AM EDT | 40% off with code EARLY40A quick second one, because it lines up with the "collection job" idea fromIssue #5. Imran Afzal, one of Udemy's best-selling Linux instructors, is running a hands-on session on ChatGPT CLI, Shell Genie, Aider, and Goose CLI, generating commands and scripts from plain English, explaining logs, and reviewing what these tools produce before you run it. If Cohort 2 is the network and agent layer, this is the Linux layer everything underneath actually runs on.Use code EARLY40 to get 40% offRESERVE YOUR SEAT NOW The Network Automation Accelerator is a new 12-week DevNet Academy program that helps traditional network engineers build practical automation skills through hands-on labs, mentoring, code reviews, and a final real-world automation project. Register your interest to learn more.Register hereThe reason we keep coming back to these sessions is simple.AI in infrastructure is no longer just a curiosity. But it also cannot be treated like a magic layer you place on top of production and hope for the best.The teams that get value from it will be the ones that know how to scope the work, define the boundaries, test in a lab, review the output, and keep humans in control where it matters.That was the lesson from our first Networking event, with the second one we're just getting startedHope to see you there.–PacktConferencesP.S.Engineering Agentic Network Operationsis happening onJune 30 at 9 AM EDT. Use codeSPECIAL50 while the flash sale is live. Goes out in <48 Hours*{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}}

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}}