Jiss – A community-powered LLM API I built for open models

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I’ve been working on Jiss, a project to make open-source LLMs accessible without the massive cloud bills or the headache of managing your own 24/7 infrastructure. It’s a distributed network that routes inference requests to a pool of volunteer-run workers. If you have an app already using OpenAI, you can just swap the base_url and start running models like Llama 3 or Qwen3. The idea: Instead of relying on big providers, Jiss is powered by people donating idle compute. Whether you’re running Ollama on a Mac, a home server, or a workstation, you can connect your local setup to the network and help serve requests. I built this to see if we could create a useful, shared resource for the open-source AI community. The highlights: • Drop-in replacement: Use the standard OpenAI SDK—just change the base URL. • Streaming: Supports real-time token streaming (SSE). • Open: Anyone can join as a worker using a single script. It’s still early so I’d love to get your feedback or have you try running a worker!

Visit product View on Hacker NewsJan 27, 2026

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

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 · Strong signals: supports · Missing: reddit linkedin, podcasting, created

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

62%62% 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

38%38% 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

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

16%16% 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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