Ho

Hollow – serverless web perception for AI agents

Hacker News

Hollow – serverless web perception for AI agents

I wanted any AI Agent to access the web and perform tasks without costing me anything, which would normally require a browser running somewhere, but I didn’t want to run a headless browser or keep my laptop open. So I built an interface for agents that exists purely as a serverless function. The agent gets two primitives: ‘perceive’ and ‘act’. You POST a URL, get back a structured map, act on an element by ID. That's the whole interface. And it costs almost nothing, at approximately $0.00003 per page load. The LLM call is actually more expensive than the browsing itself. Works with Claude, GPT, Gemini, or any model that supports HTTP tool calls. The MCP server gives Claude Desktop native tools in three lines of config. Or grab the system prompt from the playground and paste it into any AI directly or deploy your own, it’s open source. Live playground: https://hollow-tan-omega.vercel.app/mirror GitHub: https://github.com/Badgerion/hollow

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Actual performance

2points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, claude · Missing: mac, macos, cursor
99%99% 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.
Indie HackersFits the IH revenue-focused audience · Strong signals: supports, gemini · Missing: reddit linkedin, podcasting, created
54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, open source, 000 · 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.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface, calls · Missing: plus, platform, intuitive
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
23%23% 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
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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