Microsoft Favors Anthropic over OpenAI for Visual Studio Code
Microsoft's Visual Studio Code now features an auto AI model selector that prioritizes Claude 4 over GPT-5, indicating a strategic shift towards Anthropic's AI capabilities for enhanced coding performance.
IBM Technology Atlas
IBM's Technology Atlas outlines six strategic roadmaps aimed at revolutionizing performance and efficiency in IT and business, focusing on areas like AI, Quantum, and Hybrid Cloud.
GPT‑5-Codex and upgrades to Codex
GPT-5-Codex is a fine-tuned variant of GPT-5, specifically designed for AI-assisted programming tools, enhancing code review capabilities and integrating with existing platforms like VS Code and Codex Cloud.
Show HN: Pyproc – Call Python from Go Without CGO or Microservices
pyproc enables seamless integration of Python functions into Go applications, allowing for zero network overhead and true parallelism by utilizing Unix Domain Sockets for inter-process communication, thus avoiding the limitations of traditional methods like CGO and microservices.
Building towards age prediction
🤗LeRobotDataset: Bringing large-scale datasets to lerobot
LeRobotDataset:v3.0 introduces a file-based structure that consolidates multiple episodes into single files, enhancing scalability and efficiency for large datasets in robot learning.
AMQ: Enabling AutoML for Mixed-precision Weight-Only Quantization of Large Language Models
AMQ (Automated Mixed-Precision Weight-Only Quantization) optimally balances model quality and memory usage for Large Language Models (LLMs) by assigning layer-wise quantization bit-widths, enabling broader deployment under strict constraints.
RAGs to Riches: RAG-like Few-shot Learning for Large Language Model Role-playing
The RAGs-to-Riches framework reformulates LLM role-playing as a text retrieval problem, enhancing few-shot learning by incorporating curated reference demonstrations to improve response authenticity and character consistency.
SpecVLM: Fast Speculative Decoding in Vision-Language Models
SpecVLM introduces a novel approach to speculative decoding in vision-language models (VLMs), achieving 1.5–2.3x speedups over traditional autoregressive inference through an innovative elastic visual compressor that optimizes performance based on input characteristics.
HARP: Hallucination Detection via Reasoning Subspace Projection
HARP (Hallucination detection via reasoning subspace projection) introduces a framework that effectively separates semantic and reasoning information in LLMs, enhancing hallucination detection by projecting hidden states onto a reasoning subspace.