Asimov – Context Manager for Coding Agents

Hacker News

Hey HN, I built https://asimov.mov . an MCP for coding agents to search from latest docs of APIs, repos or basically anything. It can index and retrieve docs + repos very fast to search from them and can do it on-the-go (like something launched yesterday and you indexed it right away, from your agent). The indexed docs would only be available to you and would be stored on your own little server (we manage that!) Hope Asimov can make your Coding Agents hallucinate less (by not implementing 1000-year old Stripe API or Cloudfare) :) Happy to chat about the features and feedback + giving some Pro tiers (for free!) if anyone wants to try more!

Visit product View on Hacker NewsOct 21, 2025

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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, mcp · Missing: mac, macos, cursor

97%97% 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 · Missing: supports, reddit linkedin, podcasting

51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: 000 · Missing: https docs, excited, just released

41%41% 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 · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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