Llumen – Lightweight LLM chat app that runs in <1s with OpenRouter

Hacker News

Hey HN, I've been tinkering with self-hosting LLMs and got frustrated with bloated setups needing a dozen API keys for chat, search, OCR, and more. So I built llumen: a dead-simple, full-stack chat web app that gets you chatting with models out-of-the-box. Why it might interest you: - Effortless Start: Drop in a single OpenRouter API key—no juggling endpoints. Docker image or binaries ready for Linux/Windows (prebuilts on release). - Blazing Fast & Tiny: Boots in under 1s, <100MB disk. - Smart Features: Markdown with code/math rendering, normal chat mode, web-search integration and WIP agentic/deep-research modes. - Opensource: MPL 2.0 licensed. Screenshots and a quick demo GIF in the README to see it in action. Quick Docker spin-up: <pre><code>docker run -it --rm -e API_KEY="your-key" -p 80:80 -v $(pwd)/data:/data ghcr.io/pinkfuwa/llumen:latest</code></pre> Default login: admin / P@88w0rd.

Visit product View on Hacker NewsSep 27, 2025

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

96%96% 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

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

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

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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat, smart · Missing: web3, crypto, cryptocurrency

7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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