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BixRouter – a non-linear AI chat interface for OpenRouter

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BixRouter – a non-linear AI chat interface for OpenRouter

Using chat interfaces often spawns many different thoughts, and while the AI and it's agents are okay at keeping track of it, I'm not always there. I needed to be able to see where my thoughts diverged, and retrace them back to the source. While mulling over a better interface, and trying to get memories into the OpenRouter CLI, I decided to roll it all into one. BixRouter is a tool for visualising your thoughts and conversations. - Every response is a node. Ask a follow-up from any of them and the conversation forks. - Memory and context follow each branch back to the root, so that your conversations can diverge completely and maintain a foundational root. - Long branches can be compacted, and the context window will start again from the latest compacted node in the graph. - Highlight tips spawn threads with context-driven definitions, so you can maintain direction without getting hung up on a misunderstood term. - By default, requests run off a shared OpenRouter key, which is limited to cheap, non-thinking models. Your own key can be added locally, which routes requests to your own account and gives you access to all models (this key attaches to your requests and I never see it). - No accounts, storage, or signups. Chats are stored in your localstorage, and can be exported as text (playfully dubbed .bixgraph files!) I'd love your feedback on my little pet-project tool. Just for the big-screens right now, I'll adapt it for little-screens later!

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor
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 · Missing: supports, reddit linkedin, podcasting
54%54% 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
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
41%41% predicted probability of success on TrustMRR, 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
19%19% 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
15%15% 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 · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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