Cadenya – An Agent Runtime

Hacker News

My name is Robert, and I'm the founder of Cadenya, an agent runtime. Cadenya is not like other managed solutions. Instead, it uses your API or MCP servers as the tools in your agentic loop, which also is managed. Cadenya is also agnostic to the model provider. Wire up OpenRouter, or any OpenAI compatible endpoint, and it will use it for inference. It also supports agent variations - where Cadenya will select the agent's with the best feedback using Thompson Sampling. And finally, Cadenya can be used in the frontend. Widgets receive their own hostname with CORS settings, and you can leverage the widgets-sdk and react components to build an agentic experience in the browser, without any complex backend logic. Email support@cadenya.com with the Subject "Saw on HN" and get a promocode that unlocks a free account with $20 of credit.

Visit product View on Hacker NewsSep 10, 2026

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

95%95% 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, compatible · Missing: reddit linkedin, podcasting, created

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: widgets · Missing: mobile apps, ios, personal

55%55% 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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

13%13% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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