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OmiAI – A highly opinionated AI SDK with auto-model selection

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OmiAI – A highly opinionated AI SDK with auto-model selection

I missed the good old days when gpt-3.5 was the best model in the market. You had one library, one "framework" and one standard. Now we have awesome models like R1 that rival o1 being 100x cheaper, gemini flash 8b with a 1m context length and support for basically every file type, then we have sonnet 3.5 that peaks at coding tasks! Rather than using a framework that makes it easy to switch models and manage schemas, I built a tool that automatically combined the best of all models with a suite of powerful tools like AI scrapers, OCR, web search, etc How it works? On each execution the tool will first auto-select the best model based on your prompt objective then it will tool call the relevant tools if required for more context or to perform a specific action. Then it will automatically decide to run all this through a reasoning model (R1) depending on the complexity of the prompt. Lastly, it will run all outputs combined on the auto-selected LLM and return the response. View all models available which in my opinion are some of the best in the market right now: https://github.com/JigsawStack/omiai/blob/94ea56a9f953b4d229... View the repo here: https://github.com/JigsawStack/omiai Tech used: - Vercel's AI SDK as the underlying framework - JigsawStack for the AI tools and embedding model Note: I built this in 24 hours, so It's still pretty early and expect breaking changes The goal is to keep it opinionated meaning never giving the ability to pick models and limit the amount of configurations/tools. We'll only replace models with better ones and are unlikely to add. Your opinions matter too, so if you think something can be made even simpler or a config setting needs to be added, feel free to open a discussion/pr and we can take it from there!

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, context · 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.
Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting
88%88% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive
60%60% 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
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 · Missing: mobile apps, ios, personal
33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% 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
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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