
We are hosting a prediction market meet up in San Francisco this week with @numinous_ai and @Radion_trade this Saturday! Come join if you want to chat about the future of forecasting & markets!
amiropensourcefan τ
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@amirsrtp
👨💻👷♂️ big fan of #DeAI and #Blockchain industry, the infinite trend 📈

We are hosting a prediction market meet up in San Francisco this week with @numinous_ai and @Radion_trade this Saturday! Come join if you want to chat about the future of forecasting & markets!







Introducing GPT-Rosalind, our frontier reasoning model built to support research across biology, drug discovery, and translational medicine.







How much of your AI provider's stack can you read? OpenAI gives you a privacy policy. Their inference engine, load balancer, and the code that handles your plaintext are all closed. Chutes is open source top to bottom: → Python SDK: chutesai/chutes → API server: chutesai/chutes-api → Inference engines (vLLM and SGLang forks): chutesai/vllm, chutesai/sglang → E2EE proxy with post-quantum crypto: chutesai/e2ee-proxy → Claude Code proxy: chutesai/claude-proxy → Codex proxy: chutesai/responses-proxy → OAuth SDK: chutesai/Sign-in-with-Chutes → GPU verification library: chutesai/graval Fork it and audit it before you sign a contract. OpenAI publishes a privacy policy. We publish the source code. github.com/chutesai What's the one part of your AI provider's stack you wish you could read?





“We want to orchestrate the world’s compute, in the same way Bitcoin did, but to train models that can rival ChatGPT” @macrocrux on the @twistartups podcast, sharing the motivations behind @IOTA_SN9: to train frontier models efficiently and at scale, in an age where CapEx for centralized training is reaching unsustainable highs.