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Open-source multi-modal LLM chat agent

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Open-source multi-modal LLM chat agent

Hi everyone, I’m learning LLM and AI. And I’m building a multi-modal full stack LLM chat agent. [0] Using semantic-router for dynamic conversation routing, and LiteLMM for model providers. It was lots of fun to learn and build. Here is the full list of large language models. I will update more models in the future. [1] And, of course you can use Llama 3 via Ollama locally! I will be adding function calling support (tools use) for the models to have it more capable, like an agent, in the future. Hope this project helps everyone to try out using multi-modalities LLM providers agent! [0] GitHub: https://github.com/vinhnx/VT.ai [1] List of LLM models currently supported: https://github.com/vinhnx/VT.ai/blob/main/src/vtai/llms_conf...

Visit productView on Hacker NewsApr 22, 2024
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, model, models · Missing: mac, agents, macos
92%92% 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.
Hacker NewsStrong engagement from HN community · Strong signals: llama, ide, io · Missing: https docs, excited, just released
56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
44%44% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
39%39% 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
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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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