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A better way to handoff web bugs to AI agents

Hacker News

A better way to handoff web bugs to AI agents

Hi HN, Zidan here. I’ve been experimenting with AI-assisted debugging and noticed a recurring gap: most tools optimize for agent-led exploration (ex: giving claude code a browser to click around and try to reproduce an issue). But in many cases, I've already found the bug myself. What I actually want is a way to hand the agent the exact context I just saw - without retyping steps, copying logs, or hoping it can reproduce the behavior. So we built FlowLens, an open-source MCP server + Chrome extension that captures browser context and lets coding agents inspect it as structured, queryable data. The extension can: - record specific workflows, or - run in a rolling “session replay” mode that keeps the last ~1 minute of DOM / network / console events in RAM. If something breaks, you can grab the “instant replay” without reproducing anything. The extension exports a local .zip file containing the recorded session. The MCP server loads that file and exposes a set of tools the agent can use to explore it. One thing we focused on is token efficiency. Instead of dumping raw logs into the context window, the agent starts with a summary (errors, failed requests, timestamps, etc.) and can drill down via tools like: - search_flow_events_with_regex - take_flow_screenshot_at_second It can explore the session the way a developer would: searching, filtering, inspecting specific points in time. Everything runs locally; the captured data stays on your machine. Repo: https://github.com/magentic/flowlens-mcp-server

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, model
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
60%60% 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: io · Missing: https docs, excited, just released
36%36% 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: way · Missing: mobile apps, ios, personal
36%36% 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
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: recurring · Missing: arr, mrr, revenue
22%22% 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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