Openvisor – A Session Explorer for OpenCode

Hacker News

Hi HN! I have been playing around with Opencode lately, and wanted a way to dig through my sessions. So I built one! I love the detailed tracing of LangSmith and this is heavily inspired from it - you can dig into every turn and inspect the tool calls, subagent use and other associated stuff. OpenVisor is fully local-first - nothing ever leaves your machine. After you drop the exported JSON into OpenVisor, it is stored locally in the browser's IndexedDB. Go check it out - I have included a sample session to play around as well! PS: Claude Code support is coming soon! Github Link: https://github.com/anomitra/openvisor

Visit product View on Hacker NewsJul 27, 2026

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AppSumoStrong fit for a featured deal · Strong signals: soon, calls · Missing: plus, platform, intuitive

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntUnlikely to reach the leaderboard · Strong signals: mac, agent, claude · Missing: agents, macos, cursor

49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · 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.

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

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

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

32%32% 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

20%20% 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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