Rege Aakaash @GatiFlowAPI

OpenAI released 700+ AI-generated math manuscripts. By its own count, only ~42% of top-line results are formalized in Lean, and 3 papers were withdrawn a day later. Generation is cheap. Verification is the scarce layer. What we saw in this week's GatiFlow Deep Dive: Attention fades fast. Our GitHub collector first tracked openai/math at 9,240 stars, about a day after the repo was created, and it reached 14,803 a week later (+60%). By day five the daily gain was roughly a tenth of day one. The checking work has barely started. A passing Lean proof checks the formal statement, not the claim people had in mind. More than a dozen proof checkers confirmed OpenAI's Navier-Stokes formalization, yet a new preprint argues it does not match the natural-language proof. The checker itself needs red-teaming. This summer an AI-assisted Collatz "disproof" got past Lean's kernel, and a hunt run with OpenAI's internal models found more routes to a proof of False. Patches kept shipping into late September. The labor market is already pricing the gap. A Lean engineering role on Mercor pays $90 to $110 an hour for part-time remote work reviewing AI-generated Lean statements and proofs, including statement fidelity. If you ship agentic code or research tools, the question for the next 2 to 4 weeks is narrower than "should we adopt formal methods": which outputs can be checked mechanically against a written spec, who owns that spec, and which checker, independent of the generator, sits on the path? Full Deep Dive with sources: gatiflow.io/deep-dive/veri…

Interrupt London is today. A good day to revisit the bet behind our July Deep Dive: the defensible part of an agent isn't how smart it is, it's how governable it is. "The Orchestration Layer Is Becoming the Product: What OpenWorker Reveals About the Current Architectural Cycle" What we found: → Andrew Ng and Rohit Prasad open-sourced OpenWorker on July 23: an agent that hands back finished work (a document, a Slack reply, a calendar update), not a chat transcript → Its permission engine sorts every tool call into four risk tiers before it runs: read, write_local, exec, external → Five modes decide what each tier may do: discuss, plan, interactive (default), auto, custom → Unattended mode doesn't raise the autonomy ceiling: a consequential action parks in an inbox until a human answers → The model is swappable (it runs on aisuite) and integrations come through MCP. The product is the governance surface Two answers to "finish the work, don't just chat": ChatGPT Work (July 9) runs in OpenAI's cloud, OpenWorker runs on your machine. The trade is speed to a demo versus what a security team can inspect. Our read: the market still treats the vertical application as the interesting bet. We think developer trust accumulates one layer down, in a permission model that survives a real enterprise security review. If you're evaluating agents for your team, three questions: 1. What can it do without a person signing off? 2. What happens when nobody is watching? 3. Can your security team inspect the permission contract itself, or only the vendor's description of it? On the calendar: AGNTCon + MCPCon North America (Oct 22-23), GitHub Universe (Oct 28-29). Making an agent smart is a modeling exercise. Making it governable is an architecture exercise. Full Deep Dive, with sources 👇 gatiflow.io/deep-dive/the-… GatiFlow turns 13 public developer sources, collected every six hours, into market and talent signals. One Deep Dive every Saturday: get the next one by email at the bottom of the page, no account needed.

A game-modding toolkit gained 77% in GitHub stars in 4 days. It's not a gaming story. It's a live example of a pattern VCs and CTOs should be watching: agent skills are spreading faster than anyone can verify them. This week's GatiFlow Deep Dive: "Distribution Is Outrunning Verification in the Coding-Agent Skills Layer" What the data shows: → universal-modder (skills + CLI + MCP server so AI agents can mod PC games): 2,917 → 5,170 stars in four tracked days, per our GitHub collector → Oct 1: Claude Code shipped Mods, plugins that run inside the agent with your permissions → One directory now lists 23,600+ Claude Code skills → Of 31,132 skills analyzed in one study, 26.1% had at least one vulnerability → SkillsBench: curated skills lift agent pass rates 16.2 points on average, but only 4.5 in software engineering vs 51.9 in healthcare Our read: the market is over-rotating on catalog size and under-pricing the verification harness and the curated knowledge behind a skill. If you build or buy dev tools, three questions for the next 2-4 weeks: 1. Who is allowed to put code on a developer machine? 2. Does every internal skill ship with its own verification against the live system? 3. Are you reading stars and directory counts as attention, or as quality? On the calendar: Interrupt London (Oct 13), AGNTCon + MCPCon North America (Oct 22-23), GitHub Universe (Oct 28-29). Full Deep Dive, with sources 👇 gatiflow.io/deep-dive/dist… GatiFlow turns 13 public developer sources, collected every six hours, into market and talent signals. One Deep Dive every Saturday: get the next one by email at the bottom of the page, no account needed.

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