𝘖𝘯𝘤𝘦 𝘶𝘱𝘰𝘯 𝘢 𝘵𝘪𝘮𝘦, 𝘵𝘩𝘦𝘳𝘦 𝘸𝘢𝘴 𝘢 𝘮𝘰𝘥𝘦𝘭 𝘵𝘩𝘢𝘵 𝘦𝘹𝘪𝘴𝘵𝘦𝘥 𝘰𝘯𝘭𝘺 𝘪𝘯 𝘸𝘩𝘪𝘴𝘱𝘦𝘳𝘴. Yesterday, @AnthropicAI released Claude Fable 5 — the first publicly available Mythos-class model. A new tier above Opus. A new chapter in the story. Fable, from the Latin fabula — "that which is told," the same root as Mythos. The naming is deliberate. This is not a point release. It is a lineage shift. 𝗪𝗵𝗮𝘁 𝗷𝘂𝘀𝘁 𝗰𝗵𝗮𝗻𝗴𝗲𝗱: → A new model tier above Opus exists. Haiku → Sonnet → Opus → Fable / Mythos. Fable 5 is the public model; Mythos 5 stays restricted under Project Glasswing. → SWE-Bench Pro: 80.3% vs GPT-5.5 at 58.6%. Andrej Karpathy called it "a major-version-bump-deserving step change" for long, difficult problems. → Vision and PDF parsing are now category-leading. An AI-first law firm said it "feels materially different" in blind contract reviews — precisely the territory I covered in my article on legal AI democratisation. → During early access testing, Stripe claimed to have migrated 50 million lines of Ruby code in a single day. 𝗧𝗵𝗲 𝗵𝗼𝗻𝗲𝘀𝘁 𝗽𝗮𝗿𝘁 — 𝘁𝗵𝗶𝘀 𝗰𝗼𝘀𝘁𝘀: → API: $10 / $50 per million input/output tokens — 2× Opus 4.8, the most expensive widely available model on the market. → On subscription plans, it counts as double usage. → But the nuance matters: complex tasks often complete in fewer turns. One physics research task finished in 36 hours using a third of the tokens GPT-5.5 needed over four days. 𝗪𝗵𝗲𝗿𝗲 𝗶𝘁 𝗲𝗮𝗿𝗻𝘀 𝗶𝘁𝘀 𝗽𝗹𝗮𝗰𝗲: → Long-horizon autonomous coding — deep codebase orchestration across agent fleets → Legal and financial document analysis — dense PDFs, cross-document reasoning → Complex research and analytics — vision-driven workflows, scientific data extraction This is not a daily driver. It is a scalpel reserved for the hardest problems. Available on Claude API, Claude Code, AWS Bedrock, Vertex AI, and Microsoft Foundry. Free on Pro / Max / Team plans through June 22. Usage credits required after that. PS: Field notes from real builds, not vendor marketing. #FromLabToLife #AI #ClaudeFable5 #Mytho #LegalAI #AgentOrchestration #ContextEngineering

Two years of building with AI coding agents. Six months of @COTRUGLI #ChasingJarvis workshop feedback. One methodology, now documented in depth. The article is published today. Three phases. One thread running through all of them. → Phase 1: Research, Design & Foundations Output: four markdown files — architecture, blueprint, UI/UX, security. Allow 3–7 days. This phase cannot be rushed. Phase 2 chaos is almost always a Phase 1 failure — not of effort, but of clarity. → Phase 2: The Build Phase Context engineering goes active. Watch the context window — act at 80%. Three layers of agent memory: markdown file libraries, RAG, and The Curator. Handoff files bridge what the model cannot remember between sessions. → Phase 3: Debug, Audit & Deploy Built ≠ finished. Manual testing first. Playwright MCP for automated coverage. Three-model security audit — Claude Opus, GPT-4o, Gemini Pro. Each model catches what the others miss. This process is tool-agnostic. @claudeai Code, @augmentcode, Codex, @opencode — the three phases apply across all of them. What it is not agnostic to is the discipline you bring to it. Full article links in the comments below. From Lab to Life | Article Collection #FromLabToLife #AI #ContextEngineering #CodingAgents #ChasingJarvis #AugmentCode #AIAgents

My New Article is Live "Context is the Code: The Complete Three-Phase Process for Building with AI Agents" "So context engineering is Phase One. What happens to it in Phases Two and Three?" A student in my Chasing Jarvis MBA workshop asked that. Context engineering is not a phase. It is the practice that runs through all three. I had described it as a starting condition. It is a continuous discipline. The three phases, clarified: → Phase 1: Research, Design & Foundations Output: foundational markdown files (architecture.md, blueprint.md, ui_ux.md, security.md). Allow 3–7 days. This phase determines everything that follows. Phase 2 chaos is almost always a Phase 1 failure — not of effort, but of clarity. → Phase 2: The Build Phase Context engineering goes active. Watch the context window — act at 80%, beyond which hallucinations increase. Maintain CLAUDE.md after every milestone. When the session approaches its limit: write a handoff file. It bridges what the model cannot remember itself. → Phase 3: Debug, Audit & Deploy Built ≠ finished. Manual testing first. Playwright MCP for automated coverage. Three-model security audit: Claude Opus GPT Gemini Pro. Each model has different blind spots. Run at least two. The article also introduces three layers of agent memory: → Layer 1 — Markdown files (CLAUDE.md / AGENT.md): active sprint context. Start here on every project. → Layer 2 — RAG: for large document corpora only. Do not use because it sounds sophisticated. → Layer 3 — The Curator: long-term, compounding knowledge via wiki graph and MCP. Not alternatives. A stack. This methodology is tool-agnostic. The three phases work equally across Claude Code, Augment Code, Codex Open Code, and others. What it is not agnostic to is the discipline you bring to it. Full article — links in the comments: 👇 → Medium → Substack #FromLabToLife #AI #ContextEngineering #CodingAgents #ChasingJarvis #AIAgents #AugmentCode

From insightful moments to unforgettable memories 🔬✨ The NAMS Week’26 Symposium/Career Talk was nothing short of impactful 🙌 Here’s a glimpse of how we moved From Lab to Life 💡🧫 #NAMSWeek2026 #FromLabToLife #BigWaveLedAdministration

✨ FROM LAB TO LIFE — SYMPOSIUM HIGHLIGHTS 🔬📸 Thank you to everyone who attended and made today impactful, inspiring, and memorable 🙌❤️ 📸 Event pictures: [photos.app.goo.gl/cBirNeuURV…] #NAMSWeek2026 #FromLabToLife #BigWaveLedAdministration

Tomorrow, we move from Lab to Life 🔬✨ Get ready for learning, networking, fun activities, and unforgettable moments at NAMS Week 2026 🔥 📅 May 20th, 2026 Be present. Be involved. Be remembered. #NAMSWeek2026 #FromLabToLife #BigWaveLedAdministration

I asked my second brain to map the evolution of everything I've written. Not "summarise my articles." Not "find content about X." I asked it to draw me a timeline — with titles, summaries, and the key message I was communicating at each point in time. What came back stopped me for a moment. 50 articles. 7 distinct phases. A narrative arc I hadn't consciously seen before — from Web3 infrastructure (2016) through AI leadership research, through the From Lab to Life series, through orchestration, all the way to Two Worlds of Code published last month. The question worth asking: 𝘄𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝘁𝗵𝗶𝘀 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗳𝗿𝗼𝗺 𝗷𝘂𝘀𝘁 𝗱𝘂𝗺𝗽𝗶𝗻𝗴 𝗺𝘆 𝗮𝗿𝘁𝗶𝗰𝗹𝗲𝘀 𝗶𝗻𝘁𝗼 𝗮 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝘄𝗶𝗻𝗱𝗼𝘄? Two things. → 𝗦𝗰𝗮𝗹𝗲. All my articles combined exceed 200K or even 1M tokens — the upper limit of most context windows. Static ingestion simply doesn't work. Something has to be done with the content before retrieval. → 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝘃𝘀. 𝘀𝘁𝗼𝗿𝗮𝗴𝗲. When you load static documents, you get the text. When you load a knowledge graph, you get the relationships between the text. My second brain doesn't store articles — it stores entities, concepts, summaries, and the connections between them. Every article is a node. Every idea links to every other idea it touches. When I ask "show me the evolution," the system traverses a graph, not a pile of files. The result isn't a list of articles. It's a map of how my thinking developed — including patterns I hadn't named and connections I'd made unconsciously. 𝗧𝗵𝗲 𝘀𝗲𝘁𝘂𝗽: The Curator app ingested my articles over time, decomposing each one into a structured knowledge graph stored as plain markdown. My Curator MCP then connects that graph directly to Claude. That's the entire stack. No vector database. No embeddings pipeline. No cloud dependencies. Static content tells you what you wrote. A second brain tells you what you were thinking. → #TheCurator & #MyCuratorMCP are open source. → The Framework is available on GitHub. → The articles are at medium.com/@talirezun #SecondBrain #KnowledgeGraph #AI #FromLabToLife #TheCurator #ContextEngineering

4 days. 1 vision. Endless possibilities. 🔬✨ NAMS Week 2026. where ideas leave the lab and come to life. Future microbiologists. Future game changers. 📅 May 20th – 23rd Be present. Be involved. Be remembered. #FromLabToLife #NAMSWeek2026 #BigWaveLedAdministration

"The intelligence bottleneck is solved. The infrastructure bottleneck has just begun." For three years, everyone asked the same question: are AI models good enough? That question is closed. 1 million token context windows. Open-source models rivalling the closed frontier. Reasoning that handles legal documents, codebases, entire project histories in a single pass. The brain is ready. And yet — I still spend an hour every week manually posting content across platforms my agent cannot touch. Social media APIs are locked. The open web is actively closing to agent access. The moment I connect an agent to data that actually matters, privacy becomes a design problem nobody has solved cleanly. The bottleneck didn’t disappear. It moved. The next two years won’t be won by the lab that builds the smartest model. They’ll be won by whoever builds the best body for it — the infrastructure, the access layer, the trust framework that lets an agent actually reach the data it needs to be useful. That race is already on. And it’s more interesting than the model race ever was. Full article link in the comments 👇 #FromLabToLife #AI #AIAgents #FutureOfWork #ContextEngineering

📰 NEWS | #กรมวิทยาศาสตร์การแพทย์ ประกาศความพร้อมสูงสุด ใช้ห้อง BSL-3 ตรวจวินิจฉัย #Hantavirus อย่างปลอดภัย shorturl.asia/2ZGwk #FromLabToLife 🌏 #ข่าวกรมวิทยาศาสตร์การแพทย์ 🧪 #ข่าวกระทรวงสาธารณสุข

The Brain Is Ready. The Body Is the Problem. This is where we are in May 2026. ─────────────────────── For three years, the question was: are AI models good enough? That question is answered. Context windows hit 1 million tokens. Open-source models now rival the closed frontier. The intelligence we need to automate our daily work genuinely exists. The new question is harder: How do we build an AI assistant that actually does the things we spend hours doing manually on our computers every day — while keeping our data safe? ─────────────────────── This is the direction the entire industry is moving right now. @AnthropicAI built Dispatch — message Claude from your phone, come back to find the work done on your Mac. @OpenAI rebuilt Codex as a full desktop agent — it sees your screen, clicks, types, and runs tasks in the background while you keep working. @openclaw went from zero to 347,000 GitHub stars in 5 months — the most-starred software repo in history — because one developer built what everyone actually wanted: an AI assistant that lives in the messaging apps you already use and works for you while you get on with your life. The direction is clear. The obstacles are not. ─────────────────────── → Social media APIs are locked. You cannot automate posting to X, Facebook, or Instagram. → The open web is closing. Platforms are actively blocking AI agent access. → Cloud agents need your data — but your most sensitive data cannot leave your environment. → Local models are almost capable enough. But not yet for most daily tasks. We have the brain. Building the right body for it — one that can reach the data it needs, through channels that are increasingly locked, without compromising the privacy of what it touches — is the defining infrastructure challenge of the next two years. ─────────────────────── In my new From Lab to Life article, I document exactly where we stand: ✦ Why 1M token context was the silent prerequisite for everything ✦ What OpenClaw proved — and why the big labs are now building the same thing ✦ Anthropic vs OpenAI: two architectures, one race ✦ The API barrier and the privacy dilemma — neither resolved cleanly yet ✦ A practical framework for connecting your agent to your data safely ✦ What open-source models mean for data sovereignty Links in the first comments. #FromLabToLife #AI #AIAgents #FutureOfWork #ContextEngineering #Privacy #OpenSource #Automation

موضوع مثر للأهتمام جدا دايما مبدعين يا طلاب المختبرات ☝🏼⭐️ #FromLabToLife

I am grateful to have participated as a speaker at the #FromLabToLife event, and also to be a member of the greatest club.✨

شعور الفخر بعد انجاز اليوم 💥🧡 #FromLabToLife

سُعدت بالمشاركة كمتحدثة في فعالية From Lab To Life 🔬 ممتنة لهذه التجربة وللحضور المميز 🌟 #FromLabToLife #AMSC

#FromLabToLife #uqulabmed Finally we did it 🤍🔬🧪

🩵 #FromLabToLife | #From_lab_to_life

#FromLabToLife Every step I take is a step closer to my dreams.🏆💚

#FromLabToLife Proud to be part of this day 🔬🥼🌟

🔬 Coming Soon... رحلة بدأت من المختبر… لتصنع أثرًا في الحياة. #ComingSoon #FromLabToLife #MedicalLab