Umair

Building Flowcore, scaling Cloudusk, and running SkipLine. Always chasing harder problems, bigger opportunities, and the next thing worth building. Keep cooking.

For a specific LLM agent workload, Flowcore avoided almost all model calls by reusing cached deterministic results.

Translation: if you’re a advanced researcher at OpenAI on the O1 visa (extraordinary ability) and you apply for a green card, they’ll make you go back abroad and wait years for an appointment. Of course, by then you probably no longer work at OpenAI.

embrace tokenmaxxing as glitch for rapid startup growth for s26 companies, so apply by may 25 to ace

Good morning from your Premier League champions 👋

Applied to 11 startup programs between May 1–20. Currently refining and building Flowcore while awaiting application outcomes.

Flowcore's intro

5. Another problem: agents execute too conservatively. The planner might: - search the web - query memory - run code Today agents wait to decide. Future systems will execute branches speculatively in parallel, then discard unused work. Like CPU speculative execution.

4. The solution to previous problem is persistent KV-cache infrastructure. Instead of recomputing everything. Base KV |---- planning delta |---- retrieval delta |---- coding delta Shared prefixes + branch-local deltas.

Feels like we’re still in the “single-threaded web server” era of AI agents. A lot of the next breakthroughs will come from systems + infrastructure, not prompts. @sdianahu

Did you catch the Moon-Venus conjunction? On the night of May 18, our Moon and Venus had a celestial meetup, called a conjunction. Their position in the sky made them appear close together, despite being millions of miles apart in space.

There should be clear visual feedback for application status and progress in startup accelerator dashboards. It would make the experience much simpler and easier for founders.

Data centers in space is an ambitious endeavor.

Building the infrastructure layer for AI agents: GPU orchestration, speculative execution, and adaptive inference routing. Turns out optimizing agent workflows eventually pulls you into hardware.

Rediscovering my love for hardware while working on inference infrastructure for AI agents.

We asked a dozen DevTool founders from companies like @RevenueCat, @greptile, @firecrawl, @infisical, @ollama, @resend, @mintlify, @UnslothAI, @porterdotrun, and @recallai, about the state of AI agents and the future of software engineering. In this episode of Founder FAQ, we covered everything from agents as customers and the end of coding, to advice for founders starting out and what they're most excited about going forward. Their answers might surprise you. 00:00 – Meet the Founders 03:00 – Building for Agents First 04:22 – Biggest Early Mistakes 07:15 – Do Founders Still Write Code? 09:22 – Most Unexpected AI Discoveries 12:09 – What's Underrated Right Now 14:38 – Predictions & What's Next