Your intelligence agent for what matters

🔥 TOP SIGNAL

• Proactive loops are the real step change. Simon Willison's firsthand Fable transcript is the clearest proof: from a screenshot and a one-line goal, the agent stood up a local server, scratch HTML, browser automation, DOM probes, and screenshot capture to isolate a two-line CSS fix—and Willison says that same relentless proactivity is exactly why unsandboxed agents are a security risk if prompt-injected simonwillison. Cursor's multi-agent practitioners described the same broader pattern at scale: plan-first swarms, reminder loops, and separate judge agents that keep long-running tasks from stopping early Cursor.

⚡ TRY THIS

• Steal Simon Willison's browser-debugging escalation ladder. Start with a screenshot and a one-line objective. If the bug does not reproduce in Playwright across Chrome/Firefox/WebKit, have the agent: run the local dev server with fake env vars; write a scratch HTML page that isolates the behavior; open real Safari/Firefox; use uv run --with pyobjc-framework-Quartz plus screencapture -x -o -l to capture real browser windows; inject JS to trigger hidden UI flows; and post DOM measurements back to 127.0.0.1:9999 via a tiny CORS http.server so the agent can read the results from disk simonwillison.

• Run a plan-first swarm, not a prompt pile. In Cursor's multi-agent discussion, the advice was to spend most of the session in plan mode until you have a detailed markdown spec, then let a main agent delegate to named sub-agents via messaging scripts, with reminder loops that keep overnight runs anchored on explicit verification steps. For long runs, pass data across compaction cycles by reference instead of summaries Cursor. If you push toward bigger fleets, don't assume GitHub is your control plane: swyx's summary from ai.engineer says runtime and triggers are mostly solved, but coordination is not, and GitHub gets too noisy for hundreds of parallel PRs—use a CLI or messaging gateway instead @aiDotEngineer.

• Split build vs. review across model families. A concrete pattern from Cursor's panel: use Claude/Opus 4.7 for implementation or design, keep a GPT-5.5 worker around for review and functional verification, and run several rounds of "thermonuclear review" so different models catch different failure modes Cursor. Simon Willison used the same basic routing in a fresh side project: Claude Fable 5 for the plan, GPT-5.5 xhigh in Codex Desktop for the implementation of a custom Datasette extras explorer simonwillison.

• Read trajectories like code review, not scoreboard output. swyx's Frontier Code discussion argues for hard, real-world tasks plus maintainer-labeled trajectories and rubrics for mergeability, not just passing tests; the concrete checks are simple: did the agent touch the right files, write reasonable tests, and respect style/lint guidelines Alex Volkov from ThursdAI +1. Sourcegraph's 1,281-run study points the same way: failures in large repos show up as repeatable patterns tied to different infra fixes, not random bad luck @Sourcegraph.

📡 WHAT SHIPPED

• Cursor Auto-review — now default for new users; a classifier subagent evaluates actions in context and decides whether to allow, block, or ask for approval. Cursor says evals show 97% accuracy, with misses mostly near ambiguous edges. Details: cursor.com/blog/agent-autonomy-auto-review@cursor_ai +1.
• Google's Anti Gravity harness, per Logan Kilpatrick — framed as a shared IDE/web/CLI/SDK + Gemini API managed-agent layer, with the same harness powering Search, Gemini app, Cloud, and AI Studio; use-case variants appear to share roughly 80% of the base harness Sequoia Capital. Usage signals from Logan: Google teams used it to ship mobile and macOS apps faster internally, AI Studio saw roughly 350k Android apps built since last week, and about 20% of early apps were games Sequoia Capital.
• Gemini coding-model note — Logan says Gemini 3.5 Flash is better at coding than any prior Pro model Google released, and credits the jump to post-training gains rather than a bigger base model Sequoia Capital.
• Sourcegraph's large-repo postmortem — 1,281 agent runs across 40+ open-source repos surfaced five repeatable failure patterns, each pointing to a different infra fix. Read: Why coding agents fail in large codebases (and what to do about it)@Sourcegraph.
• Open-source maintainer loop from Peter Steinberger — his Codex setup wakes every five minutes, steers work to threads, and combines an orchestrator with triage/autoreview/computer-use skills so some changes can land autonomously. Study the skill files: maintainer-orchestrator and github-project-triage@steipete.
• Fresh small-project proof of routing — Simon Willison shipped a custom extras API explorer with Fable 5 handling the plan and GPT-5.5 xhigh handling the implementation; separately, he says Fable spotted and fixed bugs in asyncinject 0.7simonwillison +1.

🎬 GO DEEPER

• Cursor, 5:19-5:54 — separate judge agents. Best short explanation of why a worker agent needs an external checker to stop false-done states before they spread Cursor.

• Google/Logan, 38:39-40:30 — the model eats the harness. Useful mental model for why today's bespoke agent scaffolding keeps getting upstreamed into model behavior, and where the next edge moves Sequoia Capital.

• Frontier Code, 1:01:05-1:05:55 — trajectory reading party. Best clip today on how to judge agent work beyond pass/fail: wrong-file touches, reward hacking, weak tests, and mergeability Alex Volkov from ThursdAI.

• Study Simon Willison's full terminal transcript. It's one of the best public artifacts right now for seeing an agent improvise across Playwright, Safari, DOM instrumentation, screenshots, and local servers in a real debugging task simonwillison.
• Study steipete's skill files. If you want a concrete maintainer loop instead of general agent talk, the orchestrator and triage skills show how he structures autonomous repo maintenance around threads and review gates @steipete. maintainer-orchestrator · github-project-triage

Editorial take: the edge is moving from raw codegen to agent control systems—loops, judges, artifact capture, and transcript review are what make longer autonomy usable.

Top Stories

Why it matters: today’s clearest signals were about agent infrastructure, multimodal model performance, and the economics of the frontier-model race.

• OpenAI moved to strengthen Codex’s agent stack by reaching an agreement to acquire Ona. OpenAI said Ona’s secure cloud execution technology will help Codex handle longer-running work even when laptops are closed and help organizations deploy agents securely in production; Ona will join the Codex team after closing. @OpenAINewsroom
• Gemini Omni Flash jumped to the top of video generation benchmarks. It now ranks #1 in Video Arena for both text-to-video and image-to-video, with Arena reporting a +158 point gain over Veo 3.1 (1080p) in text-to-video, a +61 point lead over Seedance 2.0, a +77 point gain in image-to-video, and an 82% head-to-head win rate. DeepMind has framed Omni as its first step toward a model that can “create anything from anything,” starting with video. @arena +3
• Competition is shifting from benchmarks to margins. Anthropic is reportedly on track for its first profitable quarter, with revenue more than doubling to about $10.9B, while OpenAI is reportedly weighing further token price cuts to keep enterprises from moving to Claude. Separate estimates put $200 plans at roughly $8,000 of Claude Max 20x usage and $14,000 of ChatGPT Pro 20x usage, highlighting how aggressive current pricing already is. @kimmonismus +1

Research & Innovation

Why it matters: the strongest research updates sharpened the picture on agent limits, automated research, and training-data control.

• Agents’ Last Exam (ALE) introduced a rolling benchmark built from more than 1,500 expert-sourced tasks across 55 occupations. The results were mixed: today’s frontier agents can solve a meaningful fraction of professional tasks, but every frontier system tested scored 0% on the hardest tier requiring sustained reasoning, deep expertise, and long-horizon execution. Separate commentary said GPT-5.5 led the eval even when measured by tokens, cost, or wall-clock time. @dawnsongtweets +1

• Recursive unveiled an early automated AI research system, “Eureka Machine” v0.1, that it says reached state-of-the-art results on NanoGPT speedrun, NanoChat, and NVIDIA’s Sol-ExecBench. The company says the code and ideas behind those results were generated by the AI system itself and is open-sourcing the discoveries for scrutiny. @RichardSocher
• Goodfire introduced “predictive data debugging,” a method for estimating what DPO training data will amplify or suppress before training, reporting R²=0.9 against what models later learn. In examined datasets, it surfaced weaker safety guardrails, hallucinated links on sensitive topics, and localized sycophancy. @GoodfireAI +3

Products & Launches

Why it matters: new releases kept pushing agents, open coding models, and media systems closer to practical deployment.

• Perplexity integrated Deep Research directly into Computer. The feature is built on a “Search as Code” architecture in which the model writes code to assemble search, runs thousands of retrieval steps in parallel, connects to long-running sandboxes and tools, and is now available to Pro and Max subscribers. Perplexity says it outperforms its legacy Deep Research on every benchmark. @perplexity_ai +1
• Cohere released North Mini Code 1.0, its first open-source coding model. It is a 30B-parameter MoE with 3B active parameters running at about 66 tok/s in BF16, with day-zero MLX support and local deployment paths through GGUF quants, llama.cpp, Ollama, and vLLM. @Prince_Canuma +2
• Ideogram 4.0 became Ideogram’s first open-weights release. It debuted at #8 on the Open Weights Text-to-Image leaderboard and #31 overall, with 2K x 2K outputs, multilingual text rendering, bounding-box layout control, transparent backgrounds, and structured JSON prompts. @ArtificialAnlys

Industry Moves

Why it matters: capital and infrastructure decisions are increasingly defining who can build, train, and deploy the next generation of systems.

• Jeff Bezos raised $12B for Prometheus at a $41B valuation. The company’s pitch is an “artificial general engineer” that compresses the design-to-build loop by 10x or more, alongside a reported $100B vehicle to acquire industrial companies and the manufacturing data they generate. @kimmonismus
• xAI is building a 500 MW data center in Saudi Arabia with HUMAIN and NVIDIA, which would make it xAI’s largest facility outside the U.S.; for comparison, Colossus-1 in Memphis is around 300 MW. @kimmonismus
• Google DeepMind launched a $10M research fund with partners including Schmidt Sciences, Cooperative AI, and ARIA to study how AI systems behave as a group. @GoogleDeepMind

Policy & Regulation

Why it matters: governments are starting to define how AI connects to national infrastructure, not just consumer software.

• China’s MIIT issued an AI+ICT implementation plan for 2026-2028 that ties together the “East Data West Compute” project, autonomous networks, agentic AI, embodied intelligence, and the national compute-network strategy. @pstAsiatech

Quick Takes

Why it matters: these smaller updates still add signal on deployment speed, tool quality, and inference economics.

• Trajectory Labs says it post-trained NVIDIA Nemotron 3 Ultra on Harvey Legal Agent Bench in under 24 hours, putting an open model into the same performance band as leading closed legal models at lower cost. @trajectorylabs
• Cursor made Auto-review the default for new users; its classifier subagent reviews actions in context and reportedly hits 97% accuracy. @cursor_ai
• Baseten and Inception launched Mercury 2 in production, citing 1,000+ tok/s on standard NVIDIA GPUs and early reports of 82% lower latency and 90% cost savings. @baseten
• OpenAI added developer mode for Codex browser use in Chrome and the in-app browser, with Chrome DevTools Protocol support for profiling JavaScript, console output, network traffic, and page state. @OpenAIDevs

1) Funding & Deals

Air Street is the repeat investor in this batch, showing up in both robotics data infrastructure and AI air defence @nathanbenaich +1.

• Macrodata Labs — Air Street-backed robotics data infrastructure. Macrodata Labs launched to build infrastructure for the robotics data loop as LLMs/VLMs make robots more capable but the data layer remains underbuilt @macrodata_labs. Founders @gui_penedo and @HKydlicek come from the FineWeb team that built training datasets used by many LLMs, and Air Street led the round @nathanbenaich. The company also released Refiner, an open-source framework for turning messy demonstrations into inspectable, training-ready datasets with annotations, reward-model scoring, and local-to-cloud pipeline scaling @macrodata_labs.

• Alta Ares + Airbus Defence and Space. Two days after announcing a $60M Series A led by Air Street, Alta Ares said it partnered with Airbus Defence and Space to accelerate deployment of resilient, next-generation air defence solutions by combining Airbus integration expertise with Alta Ares' AI-powered tactical air defence @nathanbenaich. Alta Ares says its counter-drone systems have been operationally deployed in Ukraine since 2024 @nathanbenaich.

2) Emerging Teams

• OpenCredits.ai — embedded monetization infrastructure for AI apps. The product lets developers embed a checkout inside their app, issue user credit keys, call OpenAI/Anthropic-compatible APIs, meter usage, and earn referral share without separately wiring Stripe or managing model-provider keys themselves r/SaaS. The founder says the product was first deployed inside Claude Code Chat and reached 90 paying customers in under 30 daysr/SaaS.

• Hyper AI — instant demos instead of delayed sales calls. The founder frames the wedge around a specific B2B funnel problem: 30-60% of booked demos never happen r/SaaS. Hyper AI serves an immediate demo at the moment of intent rather than making leads wait days, and the founder says early pilots are converting demos that otherwise would have been lost r/SaaS.

• Science — neural interfaces and retinal implants. YC highlighted Science cofounder and CEO @maxhodak_ as the company develops retinal implants and neural interfaces for patients with vision loss and neurological disease, aimed at advancing human + computer interaction @ycombinator.

3) AI & Tech Breakthroughs

• Perplexity Deep Research in Computer — "Search as Code." Perplexity says the model writes code that assembles search itself, running thousands of retrieval steps in parallel and tailoring the search plan to each question @perplexity_ai. The company says this version outperforms its legacy Deep Research on every benchmark, and Deep Research is now a native skill inside Perplexity Computer rather than a separate mode @perplexity_ai +1.

• Ferveret — nuclear-reactor-inspired cooling for AI data centers. YC highlighted Ferveret's claim that its cooling system outperforms state-of-the-art liquid cooling by 15%, delivers 35% more tokens from the same power, and uses zero water @ycombinator.

• Abacus AI demo — one-shot 3D games and complex mobile apps. Bindu Reddy said AI agents can now one-shot extremely complex mobile apps and 3D games, including interactive 3D experiences, auth/database support, multi-user play, and one-click Android/iOS deployment @bindureddy. The cited demo was a game built on Abacus AI's agent using Claude Fable and GPT 5.5x High @bindureddy.

4) Market Signals

• The data layer is becoming the product surface for agents. SaaStr's summary of Firebolt CEO Benjamin Wagner argues that customers and their agents increasingly judge the data layer directly, rather than treating it as invisible backend plumbing saastr.

The practical implications in the source material are specific: regulated buyers want bring-your-own-cloud, air-gapped, and on-prem deployments saastr; fragmented backends across laptop, cloud, and customer environments create migration and experience tax saastr; open source and common SQL matter more when coding agents can read code and write queries directly saastr; and exposing SQL-like access to customer agents turns resource isolation, autoscaling, and 24/7 reliability into first-order requirements saastr.

• UK spinout terms are moving founder-friendlier. Average non-cash equity stakes taken by UK universities fell from 25% to 16% in 2025, with the median now 11% @yoramdw. The cited benefits are better founder incentives, cleaner cap tables for investors, and faster lab-to-market execution @yoramdw. The broader framing is that tech transfer remains an obvious lever given Europe's scientific strength and weaker entrepreneurial output @yoramdw.

• AI outbound is crowding traditional verticals. One founder selling into distribution and manufacturing said prospects are already hearing from three, four, and in some cases up to 11 AI vendors pitching similar solutions r/startups. In the same discussion, the founder pointed to preseed and seed-stage AI companies hiring BDRs and SDRs to push more outbound into that noise r/startups.

• AI accessibility is being framed as a market-expansion thesis. In YC's Meesho interview, the company said it recently launched a voice agent called Vani and views AI as a way to serve rural users who struggle with reading, typing, clicking, and other standard app concepts Y Combinator. Its longer-term vision is a voice-and-image-driven experience so users can transact without the app flows that currently feel overwhelming Y Combinator.

5) Worth Your Time

• How Meesho Became India’s Biggest Shopping App — useful for the AI accessibility thesis, especially the shift toward voice-agent commerce for users who find standard app UX overwhelming Y Combinator.

• Your Data Layer Used to Hide Behind Your Product. Now It Is the Product. With Firebolt’s CEO — worth reading if you are diligencing agent-facing infrastructure, deployment flexibility, or open-source database strategy saastr.

• Perplexity’s Deep Research in Computer post — useful if you are evaluating agentic search architectures; it lays out the "Search as Code" claim and the benchmark framing against legacy Deep Research @perplexity_ai.

• MIT News on Ferveret’s cooling system — useful follow-up if you are tracking infrastructure wedges around AI power and cooling; the linked summary claims 15% better performance than state-of-the-art liquid cooling, 35% more tokens from the same power, and zero water consumption @ycombinator.

• Macrodata Labs launch thread — concise read on the robotics data-loop bottleneck and the open-source Refiner framework for turning messy demonstrations into training-ready datasets @macrodata_labs.

What stood out today

Automated discovery claims got stronger, but so did the evidence on current limits

Recursive unveiled what Richard Socher called a v0.1 "Eureka Machine"—an automated open-ended discovery system positioned as an early milestone toward recursive self-improving superintelligence—and said it reached state-of-the-art results on NanoGPT speedrun, NanoChat, and NVIDIA's Sol-ExecBench, with the code and ideas behind those results invented by the AI itself and open-sourced for community investigation @RichardSocher. A new preprint pointed the other way: SciConBench introduces 9.11k scientific questions derived from Cochrane Systematic Reviews and reports that frontier AI agents cannot synthesize scientific conclusions well @manoelribeiro. The contrast matters because DeepMind is explicitly building science-focused systems: Demis Hassabis described Gemini for science as a Gemini variant with tools for citations, literature lookup, and graph reading, and pointed to AlphaFold's release of roughly 200 million protein structures, now used by more than 3 million researchers across 190 countries, as an example of "science at digital speed" New Frontier +1.

Agent workflows are getting more production-oriented

OpenAI reaches for secure background execution with Ona

OpenAI said it has reached an agreement to acquire Ona, whose secure cloud execution technology is meant to help Codex take on longer-running work even when laptops are closed and help more organizations deploy agents securely in production; after closing, Ona will join the Codex team @OpenAINewsroom. OpenAI's description of the deal centered on secure execution and production deployment rather than a model release @OpenAINewsroom.

Perplexity folds Deep Research into its Computer agent

Perplexity said Deep Research is now a native skill inside its Computer agent harness and that the system is built on a new "Search as Code" architecture @perplexity_ai +1. The company says the model writes code to assemble searches, runs thousands of retrieval steps in parallel tailored to each question, and outperforms legacy Deep Research on every benchmark @perplexity_ai.

A concrete enterprise deployment example

BBVA lays out a bank-wide AI operating model across 120,000 employees

In an OpenAI event, BBVA described a top-down AI agenda organized around six specialized "robots" covering retail customer experience, banker advisory, risk, back-office work, software development with Codex, and general-purpose employee agents, alongside two pillars: data readiness and agent orchestration OpenAI. The bank said it has rolled out ChatGPT Enterprise to 120,000 employees worldwide and backed the rollout with dedicated adoption teams, executive dashboards, and training across regions OpenAI. BBVA also said bottom-up experimentation has produced more than 100 GPTs used by thousands of employees, with 70-80% time savings in many cases, and that its OpenAI partnership helped it make major course corrections along the way OpenAI.

Worth watching

Google DeepMind launches a $10M fund on collective AI behavior

Google DeepMind, together with Schmidt Sciences, Cooperative AI, and ARIA Research and with support from Google.org, launched a $10 million fund to study the collective behaviors that can emerge when millions of AI agents interact @GoogleDeepMind. The stated goal is to understand how AI systems behave as a group, not just one model at a time @GoogleDeepMind.

Gemini Omni Flash is being positioned for developers, not just demos

Logan Kilpatrick said Google DeepMind's Gemini Omni Flash is state-of-the-art on image-to-video, text-to-video, and video editing, pointed developers to a public benchmarks page, and said API access is coming soon @OfficialLoganK +1. The announcement emphasized both benchmark claims and near-term developer distribution @OfficialLoganK +1.

What stood out

Today's cleanest recommendations were explanatory rather than generic: one resource on formal methods, one on how Community Notes works, and one documentary Tony Fadell used to explain how early mobile teams were "building the iPhone 15 years too early" @paulg +2

Most compelling recommendation

Jane Street blog post on formal methods

• Title: Jane Street blog post on formal methods @paulg
• Content type: Blog post @paulg
• Author/creator: Jane Street @paulg
• Link/URL:https://blog.janestreet.com/formal-methods-at-jane-street-index/@paulg
• Who recommended it: Paul Graham @paulg
• Key takeaway: Graham argued that AI will increase both the need for formal methods and the supply of tools that make them cheaper to use @paulg
• Why it matters: This was the strongest recommendation today because it came with a clear thesis about how AI changes engineering practice, not just a link to save @paulg

Two more high-signal picks

How Community Notes reduce viral misinformation

• Title:How Community Notes reduce viral misinformation@jaybaxter
• Content type: TED talk / video @jaybaxter
• Author/creator: Keith Coleman, Jay Baxter @jaybaxter
• Link/URL:https://www.ted.com/talks/keith_coleman_jay_baxter_how_community_notes_reduce_viral_misinformation@jaybaxter
• Who recommended it: Elon Musk @elonmusk
• Key takeaway: Musk called it a great interview about how Community Notes works and said it was "super helpful for truth-seeking" @elonmusk
• Why it matters: It is the most direct recommendation in today's set for understanding a credibility system that the talk itself says is aimed at reducing viral misinformation @jaybaxter +1

General Magic

• Title:General MagicGiant Ideas
• Content type: Documentary Giant Ideas
• Author/creator: Not provided in notes
• Link/URL: Not provided in notes
• Who recommended it: Tony Fadell Giant Ideas
• Key takeaway: Fadell said it is a film "that everybody should watch" while explaining that the General Magic team was solving iPhone-like problems long before the market was ready Giant Ideas
• Why it matters: This was the clearest historical recommendation today: Fadell used it to point readers back to an earlier wave of mobile product work and the problems those teams were already trying to solve Giant Ideas

Why this set matters

The common thread today was systems thinking: Paul Graham on technical rigor, Elon Musk on truth-seeking infrastructure, and Tony Fadell on an earlier attempt at mobile computing @paulg +2

Big Ideas

• AI expands roles before it erases them. Sachin Rekhi argues product, design, and engineering are not collapsing into one "AI builder" blob; each circle is expanding. If AI doubles engineering throughput, the leverage shifts toward clearer direction on what to build and why. Role blending removes coordination bottlenecks, but differentiated strategy, design, and frontier engineering still need specialists @sherifmansour +1. Why it matters: PM value rises with framing, prioritization, and decision quality. Apply it: let PMs prototype and designers ship polish, but keep explicit ownership for strategy choices and quality bars.

• Good strategy is built before the strategy deck. Scott Belsky's point: teams often solidify strategy only when they need to present it. Exploring "the edges that may someday become the center" and running experiments early makes bolder decisions easier later @scottbelsky. Why it matters: faster execution exposes weak assumptions faster. Apply it: keep a small queue of edge bets and socialize what you learn before quarterly or annual planning.

Tactical Playbook

• Use reverse demos for discovery. Musubi starts onboarding by having customers walk through their current moderation system and show what is failing - often false positives or systems that cannot adapt to new attacks Product Talk. From there, the team proposes a fit-for-problem mix from a reusable toolkit rather than defaulting to bespoke work Product Talk. Why it matters: you get grounded in real failure modes, not abstract requirements. Apply it: (1) ask customers to show the live workflow, (2) capture where it breaks, (3) map those failures to capabilities, (4) generalize only after repeated demand across customers Product Talk.

• Treat evals as operating work, not vocabulary. In OpenAI PM hiring conversations, candidates stood out by running real evals, writing rubrics for failures, and measuring improvement on actual builds - not by talking about evals abstractly Aakash Gupta. Musubi pushes the same discipline into the product with customer-managed golden sets, automated error analysis, and human review to avoid overfitting Product Talk. A solo builder of PasteFlow made the same point from another angle: prompting was maybe 10% of the work; the rest was PRDs, edge cases, scope control, and defect triage r/ProductManagement. Apply it: start with one failing workflow, define a golden set or rubric, review false positives and negatives, and keep a human decision-maker in the loop.

Case Studies & Lessons

• Meesho found fit by observing real behavior, then changing segments hard. The team first listened only to sellers and missed the consumer side; when they pushed the app to buyers, consumers called it the "worst of both worlds" versus malls or e-commerce Y Combinator. Sitting in shops revealed the real behavior: many merchants were already "online" through WhatsApp groups, which functioned as the storefront Y Combinator. Meesho then focused on online-native sellers, launched Meesho Supply, and saw organic usage double month over month with high retention Y Combinator. Later, even with a business serving 10 million sellers, the company committed to a direct consumer app after fresh field research showed many assumptions about app-download friction no longer held Y Combinator.

Takeaway: stay close enough to customers to see behaviors competitors miss, then be willing to re-segment or abandon a successful channel when the long-term user reality changes Y Combinator.

Career Corner

• The hiring signal is PM depth plus proof of work. One cited benchmark put OpenAI's median PM compensation at $860K Aakash Gupta. More useful than the number: four OpenAI PM conversations emphasized deep PM fundamentals, shipping something real, and being able to explain evals from firsthand experience Aakash Gupta. Common misses were shallow AI familiarity, eval jargon without actual evals, and repos nobody uses Aakash Gupta. Apply it: build one small product, let people use it, track where it breaks, and document how you measured improvements Aakash Gupta.

• Practitioner job-search advice is getting more tactical. In community discussion, PMs recommended optimizing resumes for ATS keywords, applying on company career pages instead of Easy Apply, and posting AI projects on LinkedIn to attract recruiter outreach r/ProductManagement. Another shared constraint: regulated industries may screen hard for direct domain experience, so adjacent sectors can be a more realistic bridge r/ProductManagement.

Tools & Resources

• Lightweight proof-of-work stack: Claude Code plus Lovable or Replit were recommended as fast ways to build public AI projects that demonstrate PM judgment, not just prompt fluency r/ProductManagement +1.
• Job-search helpers: BuiltIn, Motion Recruitment, Oliver James, and the HideJobs plugin were specifically recommended for filtering opportunities and reducing LinkedIn noise r/ProductManagement.