Your intelligence agent for what matters

1) Funding & Deals

• Humble Robotics raised about $24M led by Eclipse. The company emerged from stealth in April and is building a fully autonomous cabless electric hauler for short-haul freight, with initial routes under 50 miles. The product thesis is deliberately constrained: CEO Eyal Cohen describes Humble as a search for the simplest possible robotics platform to move freight from A to B. Cohen says he has spent 20 years in Bay Area deep tech across 7-8 startups, and Humble reassembled veterans from seven previous companies. TechCrunch

• Valar Atomics and Nvidia are partnering to explore a 30 MW nuclear-powered AI data center in Emery County, Utah. The concept pairs microreactor energy with next-generation cooling designed for near-zero water consumption. The announcement is notable because it specifies both the power source and cooling architecture upfront. @AtomsNotBits

2) Emerging Teams

• Cerberus. Aziz says Cerberus built an autonomous AI ethical hacker on a new programming language where every instruction must receive a mathematical proof of safety. He says he made a programming-language safety breakthrough at 17, runs Central Asia's top cybersecurity firm, has secured 300+ projects in three years, and Cerberus has already generated $150k+ in revenue with 15+ bank pilots. 500 Global

• Klinova. Maryam Haraz Ashvili is targeting a real clinical-ops bottleneck: matching patients to trials across handwritten, multilingual, and legacy records. Klinova says it found 4,000 eligible patients for a recent migraine trial in two hours, launched six pilots across 1 million patient records, and signed 22 LOIs worth $1.2M in five months; the team includes an operator who ran 35 clinical trials across Eastern Europe and a second-time founder. 500 Global

• mybots. The company says its AI sales agent can replace the CRM and outreach stack by finding customers, qualifying leads, sending offers, and closing deals. It reports $240k ARR after seven months, 25% MoM growth, and a 23% close rate across 147 businesses; the team includes a second-time founder and a competitive-programming champion CTO. 500 Global

• Tezbur. Umid Ahmedov says Tezbur's AI-optimized hybrid delivery network has processed 600k+ parcels, reached $1.7M ARR, and is growing 40% MoM. The company says it has 500+ customers, partnerships in Kazakhstan, paying clients in Denmark, and recently acquired a delivery company in Uzbekistan that should double MRR from next month; Ahmedov previously led data and AI architecture at Microsoft. 500 Global

• Bloomy. Y Combinator highlighted Bloomy as a mastery-learning platform for K-12 English, Math, and Writing. The product uses a Socratic AI tutor for standards-aligned instruction and teacher-facing insight tools, and early pilots showed about 2x faster than projected growth on NWEA MAP assessments. @ycombinator

3) AI & Tech Breakthroughs

• Genesis Molecular AI / PEARL. Co-founder Evan Feinberg and CTO Sergey Edunov are building a diffusion-based protein-ligand co-folding model that adjusts both ligand placement and protein structure, targeting induced fit and protein flexibility. In a recent OpenBind evaluation on 802 never-before-seen EV-A71 co-complexes, Edunov said PEARL was especially strong at moving the relevant loop; Feinberg argues that roughly 1Å RMSD is the threshold that really preserves interactions. Genesis says these gains have pushed its internal SAPPHIRE system toward agentic drug-discovery loops that can inspect poses, form hypotheses, read literature, and generate the next candidates. Edunov previously led Llama 2 training and Llama 3 pretraining at Meta. Latent.Space

• Humble's autonomy stack is a concrete example of recent vision-model gains changing robotics design. Cohen says pre-trained vision models quickly handled scenarios that used to require months of engineering work, including cones, traffic lights, and an officer holding a stop sign. Humble still uses lidar, radar, and camera on the vehicle, but says recent improvement in camera-centered vision models has been strong enough to reshape the stack. TechCrunch

• Devin Security Swarm points to a more structured agent architecture for software security. Cognition introduced it as a more cost-effective and accurate way to find vulnerabilities in complex codebases, based on an architecture it calls Agentic MapReduce. Harrison Chase's follow-up is useful because he ties the idea to a broader pattern: programmatically spawning subagents to get more deterministic control over how agents are created and run. @cognition +1

4) Market Signals

• 500 Global's Eurasia batch is producing revenue-bearing AI companies across multiple verticals. In one demo day, Cerberus reported $150k+ in revenue, mybots $240k ARR, May Call $28k MRR, and Tezbur $1.7M ARR across cybersecurity, sales, debt collection, and logistics. 500 Global

• Autonomous vehicle and robotics sentiment is warming again, but around teams that stayed through the last cycle. In the TechCrunch discussion, the sector was described as starting to have a moment again in 2026, and Humble's CEO said many of the same operators from 10 years ago kept at it and are now beginning to see the results. TechCrunch

• AI data-center design is increasingly bundling energy source and cooling architecture from day one. The Valar Atomics/Nvidia concept pairs microreactor power with next-generation cooling designed for near-zero water consumption. @AtomsNotBits

• Some investors are explicitly framing new Series A funds as a source of alpha. One current example is Ashton Kutcher leaving Sound Ventures to start a new firm with former NFX GP Morgan Beller; Garry Tan then described new Series A funds as probably the biggest alpha in all of VC. @ychernova +1

5) Worth Your Time

• Latent.Space: The Coolest Diffusion Research Isn't in LLMs — the strongest long-form technical item in this set for PEARL, induced fit, and why diffusion primitives matter in 3D structure prediction. Latent.Space

• TechCrunch Equity on Humble Robotics — the clearest source here on the narrow freight thesis and why recent pre-trained vision models changed the autonomy stack. TechCrunch

• 500 Global in Eurasia Demo Day — a dense sourcing list for early traction across AI security, clinical operations, sales automation, debt collection, and logistics. 500 Global

• Cognition's Devin Security Swarm thread and Harrison Chase's follow-up — worth reading if you are tracking agent architectures built around programmatic subagent creation. @cognition +1

• YC on Bloomy — short, but one of the cleaner pilot outcome metrics in this set for AI tutoring. @ycombinator

Top Stories

Why it matters: the biggest updates combined stronger real-world capability, broader multimodal rollout, and a sharper focus on compute economics.

• Anthropic’s Fable 5 returned with both stronger real-work results and tighter controls. On the Remote Labor Index, Fable 5 completed 16.1% of 240 real remote-work projects at a professional standard, up from Opus 4.6’s 4.2% and roughly double the next model. Anthropic also redeployed it globally with new cyber classifiers that it says block the reported technique in over 99% of cases, with blocked requests routed to Opus 4.8. @CAIS +3

• Google shipped a broad Gemini/Gemma release wave. The company introduced Nano Banana 2 Lite for image generation, Gemma 4 12B for on-device use, Gemini Omni Flash APIs for custom video workflows, Gemini 3.5 Live Translate across 70+ languages, and NotebookLM upgrades for reasoning, code execution, and document generation. Gemini Spark also entered beta for U.S. Google AI Ultra subscribers with MCP support and app integrations. @Google +2

• The infrastructure race kept accelerating. Together Compute raised an $800 million Series C at an $8.3 billion valuation, while a separate cited update said it is serving 400T tokens per month as demand for open models rises. Bloomberg also reports Meta is planning to sell access to excess AI compute and hosted models from its infrastructure. @vipulved +2

Research & Innovation

Why it matters: researchers are pushing on the three bottlenecks that now matter most—speed, reliability, and world modeling.

• NVIDIA’s TwoTower points to a cheaper speed path than full retraining. The method repurposes a pretrained 30B model into a two-part diffusion language model where one copy holds context and the other writes token chunks in parallel, preserving 98.7% of original quality at 2.42× faster generation with only ~8% of the original training data. @NVIDIAAI +1

• RLMF targets a persistent LLM weakness: confidence calibration. The approach uses a model’s own self-judgments as a training signal, first calibrating faithful confidence estimates and then editing outputs into natural uncertainty language; the reported result is state-of-the-art faithful calibration while surpassing standard RL by up to 63%. @dair_ai

• Neural Theorizer (NEO) pushes world models toward explicit reasoning. The system learns compositional theories from raw observation without language or LLM supervision, aiming to discover reusable primitives rather than only predict pixels; it was selected for an ICML 2026 oral presentation. @SungjinAhn_ +1

Products & Launches

Why it matters: the newest tools are packaging strong models into workflows teams can act on immediately.

• Devin Security Swarm turns agentic coding into security ops. Cognition says the new Agentic MapReduce system scans whole codebases, validates exploitability in sandboxes, and can ship remediation PRs; on a 50-vulnerability GHSA set across 14 languages, it found 36 issues at 30% lower cost per finding than the next most accurate alternative. @cognition +3

• Notion added an HTML block for AI-generated interactive outputs. Teams can ask AI to turn content into interactive explainers, prototypes, or diagrams directly inside a page, reducing the gap between draft output and something collaborators can test. @NotionHQ

• VS Code expanded its agent workflow surface. The July release adds chat banners for failing CI checks and review feedback, better multi-session management in the Agents window, and sandboxed terminal commands on macOS and Linux. @code +2

Industry Moves

Why it matters: companies are competing on infrastructure, operational efficiency, and distribution as much as on raw model quality.

• Odyssey raised $310 million at a $1.45 billion valuation. The Palo Alto lab, backed by Amazon and AMD Ventures, is building world models for interactive real-time simulations rather than fixed video generation. @TheRundownAI

• Shopify showed how much margin can come from model operations, not just model choice. Its Model Optimization Flywheel converts product expertise into evals and repaired training data; in one GraphQL agent example, annualized serving cost fell from $27 million to $1 million after 4× prompt compression while still beating frontier models on quality. @ShopifyEng

• Runway expanded enterprise distribution through Bertelsmann. Its tools will be integrated across Bertelsmann businesses including RTL Group, BMG, and Bertelsmann Marketing Services. @c_valenzuelab

Policy & Regulation

Why it matters: frontier deployment is increasingly being shaped by formal testing and public-governance frameworks, not just model releases.

• Anthropic is building a more formal safety coordination layer. Alongside Fable 5’s redeployment, it says it is drafting a jailbreak-severity framework with Amazon, Microsoft, Google, and other partners, and expanding U.S. government collaboration on pre-release testing and safeguards. @AnthropicAI

• The UN’s independent science panel released a preliminary AI report. The report is positioned as an evidence-based assessment of AI’s current state and argues that benefits and harms will depend on government choices. @ODET_UN +1

Quick Takes

Why it matters: these smaller updates still show where evaluation, media generation, and local deployment are heading.

• Claude Sonnet 5 scored 1391 Elo on AA-Briefcase, second behind Fable 5, but max effort averaged 183 turns per task. @ArtificialAnlys
• Reve 2.0 debuted at #2 on Artificial Analysis’s text-to-image leaderboard and uses structured layout prompts for easier editing. @ArtificialAnlys
• Fish Audio S2.1 Pro launched with 83-language TTS, voice cloning, and 56.3 characters/second generation; API access is free through July 24. @ArtificialAnlys
• Qwen3.6-27B-NVFP4 arrived on Hugging Face, optimized for Blackwell GPUs and cutting local-memory requirements by about 2.5×. @NVIDIARTXSpark +1

🔥 TOP SIGNAL

The best coding-agent setups today looked hybrid, routed, and heavily gated—not autonomous. Theo says his Fable workflow improved end-to-end agent PR acceptance from roughly 50% rejected to 0% closed in a day by keeping Fable on high, routing implementation/computer-use/UI verification work to Codex/GPT-5.5, and codifying model priority rules in CLAUDE.md@theo +1. DHH described the same pattern at 37signals: most Basecamp 5 fixes and feature upgrades now start with a prompt, but beta testing and senior review still decide what survives because working AI code can still damage architecture, performance, or security 37signals.

⚡ TRY THIS

• Route by task, not by loyalty (Theo). 1) Run Fable only on high. 2) Put model-priority rules in CLAUDE.md. 3) Teach Claude Code to call Codex/GPT-5.5 for implementation work. 4) Offload token-heavy computer use and codebase analysis to other models, then feed the results back to Fable. Theo says Codex still beats Claude for computer use, UI/UX verification, and well-specified execution; this setup also stayed under rate limits on the $200 plan @theo +2.

• Use a prompt-to-beta-to-review merge path (DHH, 37signals). 1) Let designers, PMs, or juniors prompt the feature until it works. 2) Put it on a branch and beta with real data. 3) Get senior review on Ruby/JS before merge. 4) If the AI implementation is wrong, keep the idea and front-end work, then rewrite the code. DHH says this guardrail existed because raw AI output sometimes created technical debt or introduced architecture, performance, or security problems 37signals.

• Give every coding agent a living repo manual (LangChain OpenWiki).npm install -> openwiki init -> choose provider/model -> optionally add a LangSmith key for traces -> commit the generated OpenWiki/ docs -> add a GitHub Action running openwiki update daily, weekly, or every 4 hours. OpenWiki will also create or update agents.md/Claude.md so agents know to consult the docs whenever they need codebase context LangChain.

• Make verification machine-playable and visible (ThePrimeagen + Peter Steinberger). Primeagen's pattern: add lots of asserts, build a JSON-playable version of the app or game, then let an LLM run 1,000 playthroughs to find bugs or crashes and auto-create Linear tickets @ThePrimeagen. Steinberger's lighter-weight variant: point Codex at user feedback, then let computer use attach before/after screenshots directly inside the PR when no GitHub API is available @steipete.

📡 WHAT SHIPPED

• OpenWiki — new open-source LangChain agent for codebase docs. It generates repo docs, auto-updates them as the codebase evolves, does Q&A over docs/codebase, and ships here: langchain-ai/openwikiLangChain +1. The output includes a QuickStart.md index plus architecture, CLI, business-logic, and git-history files built for coding agents to consume LangChain.

• DeepAgents + RLMs — LangChain showed RLM support via code interpreter middleware. The main agent gets a task function so it can orchestrate subagents in code, and the workflow keyword triggers the mode in Decode LangChain. On the Oolong long-context task, RLM-enabled DeepAgent did much better at 128k context than plain DeepAgent, which often gave up with "I can't answer"-style responses; tradeoff is more latency and higher token cost LangChain.

• Decode + GLM 5.2 — install is "copy the script from the DeepAgents docs site, then run decode"; init supports Fireworks + GLM5P2, and auth can wire Tavily or LangSmith tracing LangChain. Useful built-ins: threads, offload, mcp; GLM 5.2 was presented at 81 on terminal-bench and 79 tokens/sec, with a demo building an LLM chat app end to end LangChain.

• Cursor's model board moved again — Claude Fable 5 is back in Cursor, Cursor says it leads CursorBench, and Cursor also says it's the most expensive per task; full comparisons: cursor.com/evals@cursor_ai +1. Kimi K2.7 Code also launched in Cursor, with K2.5 scheduled to go away Friday; Cursor shared evals comparing K2.7 against GLM 5.2 @jediahkatz +2.

• Real adoption signal: LangSmith at Pendo — Pendo says LangSmith catches 60% of agent failures before customers see them. Their daily loop is simple and worth stealing: open the trace dashboard, find the gap between customer needs and current agent behavior, then build new eval sets @LangChain.

🎬 GO DEEPER

• 2:11-2:53 — DHH on prompt-first development. Good clip if you want evidence that AI is now in the main path for a real software team, not just sidecar autocomplete 37signals.

• 8:06-9:09 — Basecamp's merge gate. Best short explanation today of why "it works" is not a sufficient acceptance test for AI-generated code 37signals.

• 4:50-5:59 — OpenWiki wiring into agents.md / Claude.md. Watch this if you want agents to discover repo docs automatically instead of re-explaining the codebase in every session LangChain.

• 4:15-5:17 — Decode -> LangSmith tracing. Nice walkthrough of turn-by-turn traces, token usage, and tool-call inspection for long-running coding sessions LangChain.

• Study next:langchain-ai/openwiki for self-updating agent docs @LangChain; OpenClaw PR 98452 for Codex-driven UI improvement and screenshot evidence inside a real PR @steipete.

Editorial take: today's alpha wasn't "pick one winner model" — it was route models by task, give them durable context, and force proof before merge.@theo +3

Governance and safety infrastructure

UN panel puts concentration and control at the center

The UN's Independent International Scientific Panel on AI released a preliminary report ahead of the inaugural Global Dialogue on AI Governance in Geneva on July 6-7 Rappler +1. It flags fast capability growth — top HLE benchmark scores rose from 8% to 45% in 16 months — alongside concentration of power, with the U.S. holding 75% of compute in the world's largest AI clusters and frontier development concentrated in two countries Rappler +1.

The report also points to gaps in evaluation, security, and governance, while co-chair Yoshua Bengio said policymakers need plans robust to multiple capability trajectories, including plausible faster AI-driven AI research; the panel emphasized that its role is to provide an evidence base, not policy recommendations Rappler.

Why it matters: The UN conversation is becoming more concrete: capability acceleration, compute concentration, and control limits are now being framed together as governance inputs rather than separate debates.

FLARE launches a shared way to report AI flaws

FLARE, a coalition led by Hugging Face CEO Clément Delangue with researchers from MIT, Stanford, Princeton, Harvard, Northeastern, Carnegie Mellon and others, launched its first release: a standardized way to report AI flaws across the ecosystem @ClementDelangue. The goal is that one disclosure can reach developers, safety organizations, and registries, and the coalition argues that accessible or open-source systems are easier to inspect, stress-test, and hold accountable @ClementDelangue.

Why it matters: Safety work is inching from broad principles toward shared operating procedures.

Compute economics and platform competition

The compute buildout is getting more financialized — and more contested

NVIDIA introduced a revenue-sharing and credit-support model that lets AI clouds procure NVIDIA infrastructure for AI-native customers, giving NVIDIA both product revenue and a share of cloud revenue on supported capacity; early deployments include Sharon AI with up to 40,000 Grace Blackwell GB300 GPUs and Firmus, which expects to scale its Indonesia campus to 360 megawatts and up to 170,000 NVIDIA GPUs nvidia. Separately, NVIDIA said it plans to produce up to $500 billion of AI infrastructure in the U.S. with partners including TSMC, Foxconn, Wistron, Corning, Lumentum, Coherent, and Amkor, with Blackwell wafer production underway in Phoenix and new system-manufacturing facilities in Houston and Fort Worth nvidia. On the demand side, Together Compute announced an $800 million Series C at an $8.3 billion valuation, while Gary Marcus pointed to Bloomberg-reported plans for Meta to market excess AI processing capacity as a possible sign that overbuild may already be starting @vipulved +2.

Why it matters: The infrastructure story is no longer just bigger clusters. It now includes new financing structures, new manufacturing footprints, and a real debate over whether capacity could eventually outpace demand.

ZAI's GLM 5.2 extends the open-weight push into long-context agents

ZAI released GLM 5.2, a text-only flagship model with a 1 million token context window, 128,000 token maximum output, function calling, structured output, context caching, and MCP support for coding and agentic workflows Matt Wolfe. The model is open weight under an MIT license at 753 billion parameters, but its roughly 1.5TB weight footprint makes local use impractical for most people; access is instead geared toward the ZAI site, APIs, or self-hosting on cloud GPUs Matt Wolfe. A video walkthrough framed the lower cost as a practical advantage for longer agent runs and heavier experimentation in token-intensive workflows Matt Wolfe.

Why it matters: Even when "open" does not mean laptop-friendly, cheaper open-weight models can still broaden experimentation around agent workflows and reduce dependence on closed frontier stacks.

Domain AI keeps getting more operational

Neural Concept shows AI-native engineering in production

Neural Concept, spun out of EPFL, builds physics-aware models that ingest 3D geometries and predict aerodynamics, deformation, and temperature, turning solver-style iteration from days into minutes cognitiverevolution. Jaguar Land Rover said its external aerodynamics workflow went from about 50 to 1,500 design evaluations per day with the system, while battery cool-plate suppliers cut development cycles 80% and found designs that cooled 20% better on 15% lighter parts cognitiverevolution. The platform is also used by Formula 1 teams operating under compute caps, and the company argues AI is augmenting rather than replacing simulation as more of the workflow becomes automated cognitiverevolution.

Why it matters: This is one of the clearest current examples of AI shrinking real-world hardware design loops, in an auto market where legacy OEM timelines still trail Chinese peers by years cognitiverevolution.

Genesis says better co-folding can support tighter drug-discovery loops

Genesis Molecular AI said its PEARL model can account for protein flexibility and induced fit, letting it place ligands accurately without long molecular-dynamics simulations Latent.Space. On OpenBind, a benchmark of 802 previously unseen EV-A71 complexes, the company said PEARL outperformed public models zero-shot; the team also argues the field's common 2Å RMSD threshold is too loose for reliable interaction modeling and that 1Å is the more useful bar Latent.Space. Former Meta Llama 2/3 lead Sergey Edunov has joined as CTO, and Genesis says this level of model accuracy is what makes its internal agentic discovery loop, SAPPHIRE, newly viable Latent.Space.

Why it matters: The interesting shift here is from better structure prediction alone toward claims of end-to-end agent-tool-lab iteration in drug discovery.

Highest-signal pick

The clearest recommendation today was not a single article or book, but a publication Patrick Collison described as a repeat source of exceptional reading @patrickc.

Works in Progress

• Content type: Blog / publication
• Author/creator: Not specified in the source notes
• Link/URL: Not provided in the source notes
• Recommendation context:X post
• Who recommended it: Patrick Collison @patrickc
• Key takeaway: Collison said a very high portion of the links in Richard Hanania's post came from Works in Progress, and called it "in essence the most must-read thing on the internet" @patrickc
• Why it matters: It stood out as a recommendation for a recurring source of essays rather than a one-off read, and the endorsement was unusually emphatic @patrickc

Two focused reads

The End of Decisions

• Content type: Article / Substack post
• Author/creator: Maurice Rosen This Week in Startups
• Link/URL: Not provided in the source notes
• Recommendation context:VC Roundtable on YouTube
• Who recommended it: Ben, during a VC roundtable discussion This Week in Startups
• Key takeaway: He called the piece "interesting" and "well written," then summarized its thesis as AI enabling higher-level decisions in domains that were previously qualitative, similar to what computation did in chess and poker This Week in Startups
• Why it matters: This recommendation came with a specific framework for how AI may change decision-heavy work, not just a bare title mention This Week in Startups

The Book of Kells: Unlocking the Enigma

• Content type: Book
• Author/creator: Victoria Whitworth @patrickc
• Link/URL: Not provided in the source notes
• Recommendation context:X post
• Who recommended it: Patrick Collison @patrickc
• Key takeaway: Collison said he has been reading about the origins of Christianity, wanted to understand the Book of Kells, and has been enjoying Whitworth's book @patrickc
• Why it matters: It came attached to a concrete learning project rather than a generic bookshelf nod, and Collison explicitly said the book is "very good" @patrickc

Pattern

Today's authentic recommendations split between a source of sources and two targeted deep dives. Collison highlighted one publication as an ongoing input for strong reading @patrickc, while Ben and Collison separately pointed to specific works that framed AI-era decision-making and historical study This Week in Startups +1.

Big Ideas

• AI needs shared memory, not just chat. Asana argues enterprise AI spend showed 0% productivity gains in cited Goldman Sachs and McKinsey research because people were using chat tools individually, copying data in and out, while decisions never made it back into a shared context graph Product School. Their counter-model is a work graph that captures goals, portfolios, projects, and tasks—"who does what by when and how"—so approvals, corrections, and process data can compound into better agent behavior over time Product School. Why it matters: PM teams will get more leverage from systems that retain decisions than from isolated prompts. How to apply: route PRD reviews, launch plans, and customer-feedback decisions into one shared system, and make approvals/rejections visible so future agent outputs can improve.

• Habit formation starts with internal triggers, not notifications. Nir Eyal’s Hook Model remains a useful frame for frequent-use products: trigger → action → variable reward → investmentTetr College of Business. The key shift is from external triggers like pings to internal triggers like boredom, loneliness, or uncertainty Tetr College of Business. The final step matters because each use should improve the product, with AI making more "market of one" customization feasible Tetr College of Business. Why it matters: it gives PMs a concrete test for whether a product can become a habit. How to apply: use it for high-frequency products, not low-frequency ones like insurance Tetr College of Business.

Tactical Playbook

1. Version PM-owned AI artifacts in Git. Aakash Gupta’s note is simple: PMs should version skills and evals the same way other teams version important files Aakash Gupta. The working rhythm is straightforward: pull the latest changes, create a branch, edit, commit with a descriptive message, push, open a PR, and merge after review Aakash Gupta. Why it matters: prompt-like assets change constantly; Git gives you review, history, and rollback. How to apply: start with one evaluator or reusable skill, then move it into a repo with PR-based review.

2. Separate AI for personal speed from AI for team execution. Asana describes two modes: headless/MCP access through tools like Claude, ChatGPT, and Gemini for fast individual data access Product School, and an in-graph multiplayer mode where role-based AI teammates watch work, respond across people, and take action inside the shared system Product School. Why it matters: not every AI workflow belongs in chat, and not every workflow needs shared automation. How to apply: use headless tools for quick retrieval and updates; reserve shared-agent workflows for recurring, cross-functional processes.

Case Studies & Lessons

• Fitbod shows the Hook Model in practice. Eyal describes uncertainty at the gym as the internal trigger; opening the app is the action; the unknown workout, reps, sets, and weight create the variable reward; and logged workouts improve future recommendations Tetr College of Business. He says the team confirmed they built the app from the Hook Model Tetr College of Business. Takeaway: habit loops are strongest when every use makes the next session better.

• Asana moved PLG and forward-deployed AI talent into product. The company moved its PLG team—including pricing, packaging, experimentation engineering, and PM resources—under product, with a GM who owns revenue and reports to the CPO Product School. It also put an incubation team of forward-deployed AI engineers under the Asana AI GM so early customer learnings feed directly back to PMs and engineers Product School. In one workflow, voice-of-customer requests are reviewed by an AI agent, routed to a PM for judgment, and then sent to an AI coder that generates a PR for engineering review Product School. Takeaway: if AI changes acquisition or delivery, shorten the loop between customer signal, PM judgment, and implementation.

Career Corner

• The PM skill gap on agents is widening, but the advice is practical. Product Compass says PMs are splitting into two speeds: those who work with agents every day and those who have not started yet The Product Compass. Their recommendation is not more theory: start with VS Code, where 90% of the learning transfers to any agent, and learn by doing, breaking things, and fixing them The Product Compass. How to apply: pick one recurring PM task this week—analysis, drafting, or prototyping—and run it end to end with an agent inside VS Code.

Tools & Resources

• The AI-Native PM Roadmap: weekly 90-minute live sessions at 6:00 PM CET, with demos, materials, homework on your real product, and recordings afterward The Product Compass. The first three sessions are free and move from Cowork to Codex to Claude Code in VS Code The Product Compass. Later sessions, recordings, exercises, and a certificate option are for premium members The Product Compass.

• GitHub for PMs: the full playbook co-written with Shubham Saboo, including the rollback workflow for PM-managed AI assets Aakash Gupta.