yiğit

Introducing Aval - a new open source format for interactive video on the web. It has a built-in state machine, frame accurate transitions, and packed alpha transparency. This is probably the craziest thing I’ve ever built with Codex. I’d been dreaming about this technology for years. Before AI, building it would have taken months of work. I could never justify that investment for a noncommercial open source project. Then, a little over a year ago, Airbnb created Lava for almost exactly the same purpose. That gave me hope. But Lava was never released as open source. So I decided to build my own, with AI. Aval comes with a compiler and a web renderer. Together, they give you: • a deterministic state graph - named states, authored triggers, and routing where the latest trigger wins. • frame accurate routes with transitions that begin on authored content frames using portals, finishes, cuts, and reversals. • small file size and low CPU overhead, perfect for small icons designed and animated in Blender. • seekless loops so the decoder timeline keeps moving forward across loop seams instead of seeking. • packed alpha transparency. transparent prerendered motion composited with WebGL2. • a web native runtime: decoding with WebCodecs and rendering with WebGL2. • progressive fallback: Host-owned fallback markup remains available for unsupported and reduced motion contexts. Now it exists, it’s open source, and it’s available today as a technical preview. I’ll be polishing it further over the next few days.

Introducing Composer 2.5, our most powerful model yet. It's more intelligent, better at sustained work on long-running tasks, and more reliable at following complex instructions. For the next week, we’re doubling the included usage of the model.

I wasn’t fully convinced until a few months ago when we hired a 27-year-old analyst who’s on Claude all day. Our 7-person company is now functioning like a 15-person company, and we’re just scratching the surface. Every single task he works on starts with Claude. If you’re not there, you’re in for a shock. If you are, you’re already in shock at how fast the world just changed.

We raised $1M dollars to reinvent how people read. Introducing Mark II - a $159 AI bookmark. Thread below

Yesterday, PHBench launched on Product Hunt. It’s one of the most interesting Product Hunt-related studies I’ve seen. It’s a new benchmark. The question behind it is simple: Can Product Hunt launch signals predict which startups will raise a Series A? They analyzed 67,292 featured Product Hunt launches from 2019–2025 and matched them with Crunchbase funding data. The main lessons: >Only 0.78% of featured Product Hunt launches raised a Series A within 18 months. >a featured launch does not mean “future unicorn.” >Product Hunt signals do contain statistically meaningful predictive information. >Upvotes, comments, daily rank, maker team size, maker network, timing, and category all help estimate which startups have a higher chance of raising a Series A. The strongest signals were not just “more upvotes.” The strongest signals were: >large maker team × strong community engagement >strong Product Hunt rank >maker/team network >B2B categories >API, Payments, Fintech, Sales, Meetings, SaaS, Web App, and Developer Tools topics One of the biggest takeaways: >B2B launches convert much better. >API, Payments, and Fintech products converted to Series A at roughly 2.4–3.0%, about 3x the baseline. A viral consumer launch can get attention. But a strong B2B launch often signals something VCs care more about: >clear buyer, clear market, clearer path to revenue. Another interesting finding: >Classic ML beat zero-shot LLMs on this task. >They tested Gemini models on anonymized numerical launch signals. >The best Gemini model still did not beat the logistic regression baseline. >For structured, tabular launch data, engineered ML still beats a general-purpose LLM without product context. There was also a strong market-cycle effect. >Launches from 2020–2021 had a much higher chance of reaching Series A because of the VC boom. >After 2022–2023, conversion dropped with the funding market contraction. In a nutshell: Product Hunt is not a crystal ball. But it is also not just vanity metrics. A strong launch can become a market signal, especially when the product is B2B, the team is credible, the category is fundable, and the engagement is real. Product Hunt does not cause Series A funding. But Product Hunt launch signals can correlate with future fundraising outcomes. That’s a big deal for founders, investors, and anyone who treats launch data seriously.