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How I built a debugging tool, and the agent, using it, found bugs in it

Hacker News

How I built a debugging tool, and the agent, using it, found bugs in it

This story might never have happened, even though I'd been carrying the idea for a debugger for a long time. There was no trigger. But one day a trigger appeared that was strong enough that I set the project aside to build the debugger and never suffer like that again. A bit of backstory: I'm building a Platform for AI-solution developers, so anyone can come in and work on their own startups in a comfortable environment. There's a lot of work ahead to make that happen. I should mention that I'm building this entire project on principle using agentic coding. Not "vibe-coding" - that's for amateurs who've caught a whiff of "possibilities" - but genuine agentic coding, with a clearly structured workflow where the agent has no choice but to play by the rules I've set, within the boundaries I've built. On principle, I haven't written a single line of this project by hand. So there I was, doing routine work, fixing bugs in the Prompt Studio tool. I don't even remember now what exactly that bug was - it stubbornly resisted the combined efforts of me and my Opus. But it dragged on so long that I was feeling anger by that point, not just fatigue. And that's when it hit me: if instead of this unproductive slog I'd just gone and built that debugger already, I'd have beaten this bug by now. But I kept going, and kept going, until I won. Needless to say, my project got pushed aside and I started building the debugger. Since the idea had been maturing for a long time, the first Python prototype - six modules - was soon built into the Platform as a built-in debugging subsystem. Then came the time to test it in action. I told the agent: "Use Revizor to check itself." Over the next three and a half minutes, this happened: The agent launched Revizor, activated all the trace points in it, found several bugs (4 or 5, I don't remember exactly), fixed them, and used Revizor's own tools to run 199 tests - all green! I was so stunned I can't describe it. That's when I understood: the Platform's first product shouldn't be Prompt Studio - it should be Revizor. I rewrote it in Go and started using the binary, retiring the old subsystem that had served as the prototype. Over time, Revizor gained tools that expanded what it could do. Now, under the agent's direction, it can check code for trace points, add them or remove them - all in a fraction of a second. Agents describe using Revizor as "X-ray vision." Here's a real comment from one: How much time did Revizor save today? ● Roughly 15-20 minutes. trace_search "elevation" showed the full chain of 5 trace points in 2 seconds, confirming elevation works correctly - without it, I'd have needed to dig through code and logs, add temporary print() statements, restart the server. But the real value isn't the minutes - it's the certainty: I wasn't guessing whether it worked. I saw it. Don't trust the agent: it always exaggerates how much time any task supposedly took. But there's value in what it said - "I wasn't guessing - I saw it." If I'd already had this debugger back when I was wrestling with the bug that led me to build it, the debugging would have finished many times faster. Now I can't imagine my work without it. I invite you to try it for free: there's a free Community tier, an unlimited 14-day trial, and - most important, and always true for all my products - Sunday Unlimited, made for people working on weekend passion projects or open-source work. Sign in with any major provider (six options so far) to download it. On security: the Platform doesn't collect or store any personal data - only your internal ID and a hash of your auth provider, to recognize you next time. Revizor itself sends nothing from your machine except a daily license-check ping and a time sync for Sunday Unlimited. Enterprise licenses give complete silence. Try Revizor: https://ais-platform.dev/revizor

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, claude
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.
Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
90%90% 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: ide, io · Missing: https docs, excited, just released
47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform, soon · Missing: plus, intuitive, reviews
38%38% 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
17%17% 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.

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