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Desenvolver modelos de ML não precisa causar dor de cabeça. Com o OCI Data Science, a plataforma serverless e gerenciada pela Oracle Cloud Infrastructure, cientistas de dados podem focar no desenvolvimento de modelos, métodos e domínio. A infraestrutura fica por conta da nuvem!
Ask the starred-repo corpus questions in natural language: embed the query on-device, rank the stored vectors, and show the matching repos in the TanStack Start UI. This is where the earlier chapters pay off.
Stop Paying Your LLM to Flip Coins 9 design patterns for System One models like Jev and Laya, and how they make AI agents faster, cheaper, and safer Here’s an uncomfortable exercise. Open your …
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. - Release Release 5.18.0 · huggingface/transformers
TL;DR In only a few weeks, PyTorch Conference North America 2026 will begin in San Jose, bringing together the open source AI community to share ideas and collaborate. If you’ve...
I ran Laya, an open 421M-parameter decision model, in a browser tab and measured what it costs: download, speed and accuracy. In mid-September, TypeSafe AI released Jev, a “System One” model. It doesn’t write text.
Article URL: https://jevstiller.pages.dev/posts/the-guarantee/ Comments URL: https://news.ycombinator.com/item?id=49891769 Points: 43 # Comments: 6
Bank aliases for zero-downtime id changes, per-scope consolidation strategies, a refresh budget per mental model, multimodal dry-run extraction, streamed exports, CUDA for ONNX embeddings, and a long list of reliability fixes
A genuinely agent-native code-intelligence MCP server — open-source, local-first, broadly integrated — whose headline token-savings claims are asserted without a published, reproducible benchmark.
We rewrote cross-encoder inference in Rust, benchmarked it against Python, and on a small reranking model it came out only 1.1x faster at the median. That is not a sign of a slow Rust implementation.
The early rapid expansion of AI capabilities that focused on frontier models was largely ushered into the world by a few powerful, US-based AI labs. Open-weight models released from labs in China, early on from DeepSeek, and later from Moonshot, Z.ai, and others, have in part disrupted that…
CrowdStrike worked closely with Intel to introduce a new capability in Falcon Data Security that classifies sensitive data using language models that run on-device using dedicated hardware for AI. Learn more!
Machine learning model deployment moves trained models to production. Learn deployment patterns for ML models, CI/CD, and feature-flag rollouts.
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. - Release Release 5.17.0 · huggingface/transformers
You can finish three deep learning courses, build a CNN, and still find yourself stuck when the same model stops working on your own dataset. Maybe changing a few parameters does nothing, the validation loss starts behaving strangely, or the model simply refuses to learn.
Ente Photos now processes your photos up to ten times faster. For the first time, our ML runs on the GPU. And everything around the models is now lean Rust.
A reject decision must happen in milliseconds, cameras generate data continuously, and the line still runs when the WAN does not. This guide covers edge vision deployment for manufacturing, from hardware and model optimization to PLC integration and IT/OT controls.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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