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PrismCat – Local transparent proxy and debugging console for LLM APIs

Hacker News

PrismCat – Local transparent proxy and debugging console for LLM APIs

Hi HN, I built PrismCat because I got tired of not knowing what LLM SDKs (like LangChain or various OpenAI/Anthropic wrappers) were silently injecting into my prompts behind the scenes. It's often hard to debug why a streaming request froze, or why a function-calling agent returned malformed JSON, without diving deep into SDK code. PrismCat is a single-binary, self-hosted proxy and UI designed to run silently 24/7 as an LLM "black box" logger. How it works: It uses subdomain routing to achieve transparent proxying. For example, if you add an upstream named `openai`, PrismCat exposes ` http://openai.localhost:8080 `. Modern operating systems and browsers resolve `*.localhost` to `127.0.0.1` automatically, so you don't even need to edit your `/etc/hosts` file. You just change one line in your code—the `base_url`—and everything gets captured transparently (including Server-Sent Events/streaming). Key features: - Fully local-first: written in Go, stores data in SQLite, never sends your keys or prompts to a third party. - Interactive Playground: Tweak and replay failed requests in the UI without re-running your scripts. - SSE capture: Captures and reconstructs SSE chunks so you can inspect the raw flow. - Request Override (Opt-in): Intercept and rewrite outgoing requests/headers without code changes. I'd love to hear your feedback on the implementation (especially the subdomain routing approach) and what features you'd like to see next!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, openai, single · Missing: mac, agents, macos
93%93% 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: including · Missing: supports, reddit linkedin, podcasting
86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io, including · Missing: https docs, excited, just released
73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
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.

Incorrect prediction on native model

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