LiteChat: A t3.chat cloneathon competitor [video]

Hacker News

I Started a couple month back and stopped because it was getting out of hand... then came the cloneathon. https://github.com/DimitriGilbert/LiteChat https://litechat.dbuild.dev/ 100% Client-Side with history and multi providers, including Ollama and LMStudio (works best with openrouter though) Auto titles, multimodal attachements, regen, fork and sumarize. Editable responses and codeblock with versions history. You can race multiple models and even combine their responses. A virtual filesystem with git support plus their tools and conversation sync through git. Support for MCP server, STDIO support with the provided bridge. Extended block support with MermaidJS, react-flow and Formedible support ( https://dimitrigilbert.github.io/Formedible/ ) And because monkey copy/paste/replace is a chore, a prompt library, agents (with their tasks) and sequential workflow using prompts from the library/tasks and transform steps. Each step automatically bring the output format for the next one (if it needs) in its system prompt. TLDR : I wanted to work with AI HARD, I missed features on https://t3.chat So I forged my own set of wheels ! It is not the prettiest to say the least but it works :) why => https://github.com/DimitriGilbert/LiteChat?tab=readme-ov-fil...

Visit product View on Hacker NewsJun 19, 2025

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

64%64% 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: started, including · Missing: supports, reddit linkedin, podcasting

55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

27%27% 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

20%20% 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.

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