HyperFrames – OSS Agentic HTML Video Framework for Agents

Hacker News

We built in HeyGen an open source framework specifically made for Agents solving our own pain point that we had when the agents tried to write Remotion. React is not agent-friendly at all, and Remotion is a custom framework where the agent has to first know how it works to make a video. That's changing with HyperFrames, it's just a superset of HTML, and agents know how to write HTML + GSAP by default in their training data, so it's more difficult to make failures with it! One cool thing is that in HyperFrames every web library can be used inside HyperFrames by default, we support all the Web, compared to Remotion where they need to make an adapter for it, and the agent would have to learn another primitive...

Visit product View on Hacker NewsApr 25, 2026

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Actual performance

3points

2comments

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, agentic · Missing: mac, macos, cursor

95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released

44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: friendly · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

25%25% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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