Multiplayer, a debugging agent to run locally next to your coding agent

Hacker News

We built Multiplayer because we kept running into the same problem: coding agents connected to existing observability stacks inherit all the limitations those stacks were built with. Sampled traces, aggregated metrics, context that stops at service boundaries, missing request/response content from deep within the system. The PRs they produce look plausible and fail in production (i.e. “PR slop”). Multiplayer runs locally alongside Claude Code (Codex, Copilot, and Cursor coming soon) and captures full-stack, unsampled session data across your entire system. We collect everything from frontend user actions to backend traces and logs, including request/response content and headers. It’s all the things most observability tools either sample out or don't capture at all. We only save data when something goes wrong, so you're not paying to store everything your system produces around the clock. When an issue is identified, Multiplayer deduplicates it locally before anything reaches your coding agent. The same bug appearing across a hundred sessions becomes one issue, one prompt, one PR. Your agent works from a complete, correlated picture of what actually broke rather than a partial signal from a sampled trace. We tried to make it as easy as possible to get started, so it’s just one command line to install: `npm install -g @multiplayer-app/cli && multiplayer` Happy to get into the architecture, the data model, or how we handle the local-first approach to data privacy.

Visit product View on Hacker NewsMay 28, 2026

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

99%99% 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, including · Missing: supports, reddit linkedin, podcasting

69%69% 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: exist, existing, ide · Missing: https docs, excited, just released

46%46% 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 · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

26%26% 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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