Llmcanvas.chat Tree-based LLM chat on an infinite canvas

Hacker News

No one thinks linearly, yet current LLM interfaces still force you to, and it sucks. So I built llmcanvas.chat. The name says it all; you can branch, regenerate across models and compare: all in one infinite canvas. Every prompt and response is a node in the canvas and it brings ultimate flexibility on how you interact with the LLM, while keeping the old-school way of chatting linearly. Bring your own keys, 4 providers supported: Anthropic, OpenAI, Gemini, and OpenRouter. Try it: llmcanvas.chat. I'd love to hear your feedback.

Visit product View on Hacker NewsAug 26, 2026

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

89%89% 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: gemini · Missing: supports, reddit linkedin, podcasting

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: interface · Missing: plus, platform, intuitive

55%55% 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 · Missing: https docs, excited, just released

43%43% 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 · Strong signals: way · Missing: mobile apps, ios, personal

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

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

28%28% predicted probability of success on BetaList, based on ML models trained on real launch data.

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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Correct prediction on native model

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