We made a VS Code extension to recreate a debugger experience from logs

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A month ago [1], we made an MCP server so Cursor can debug Node.js on its own. We emailed every person that starred our repository [2] and learnt that frontend devs really want to give Cursor access to browser logs, and that backend devs (our intended audience) do not use debuggers nearly as much as we thought. We interviewed friends across startups and discovered that they use logs to debug, because they can’t run services locally on their machine. The services (1) require too much disk, RAM, or CPUs to run locally, (2) have too many service dependencies (think microservices), or (3) are a faff to instantiate locally with a debugger. Instead, our friends instrument their services, deploy them to staging environments via Kubernetes, and then query the logs via data stores (think Grafana, Axiom.co, Google Cloud Logging, etc) or directly (think Kubernetes logs). We thought: "What if we could recreate a debugger-like experience from logs?". That would save them from browsing logs and trying to make sense of them outside the context of the code base. We looked into it and made a VS code extension that lets you (1) import logs, (2) go to the line of code associated with a log, and navigate up/down the probable call stack associated with a log. It's a prototype, but if you're interested in trying it out, we'd love some feedback! GitHub: github.com/hyperdrive-eng/traceback --- References: [1]: https://news.ycombinator.com/item?id=43446659 [2]: 140 Github stars, 69 emails sent (the rest were bots), 19 responses received (= 28% response conversion), 4 meetings held (= 21% meeting conversion).

Visit product View on Hacker NewsApr 16, 2025

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

92%92% 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.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

63%63% 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

41%41% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

14%14% 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.

Incorrect prediction on native model

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