Large language models reflect the ideology of their creators - Nature
<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTFB0OEpobDhiSlBIX2xVUkhla1NoaDFTMjdCbEdMRlZfYWtxMndqd29fWDRueTBXYzlQQkVjWjZXa083dFl6WnlBNmZGaXRqbVpINUg0cHA4aGpvZW1hdWE4?oc=5" target="_blank">Large language models reflect the ideology of their creators</a> <font color="#6f6f6f">Nature</font>
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v4.3
Changes ik_llama.cpp support : Add ik_llama.cpp as a new backend: new textgen-portable-ik portable builds, new --ik flag for full installs. ik_llama.cpp is a fork by the author of the imatrix quants, including support for new quant types, significantly more accurate KV cache quantization (via Hadamard KV cache rotation, enabled by default), and optimizations for MoE models and CPU inference. API: Add echo + logprobs for /v1/completions . The completions endpoint now supports the echo and logprobs parameters, returning token-level log probabilities for both prompt and generated tokens. Token IDs are also included in the output via a new top_logprobs_ids field. Further optimize my custom gradio fork, saving up to 50 ms per UI event (button click, etc). Transformers: Autodetect torch_dtype fr
1.13.0
What's Changed Features Add RuntimeState RootModel for unified state serialization Enhance event listener with new telemetry spans for skill and memory events Add A2UI extension with v0.8/v0.9 support, schemas, and docs Emit token usage data in LLMCallCompletedEvent Auto-update deployment test repo during release Improve enterprise release resilience and UX Bug Fixes Add tool repository credentials to crewai install Add tool repository credentials to uv build in tool publish Pass fingerprint metadata via config instead of tool args Handle GPT-5.x models not supporting the stop API parameter Add GPT-5 and o-series to multimodal vision prefixes Bust uv cache for freshly published packages in enterprise release Cap lancedb below 0.30.1 for Windows compatibility Fix RBAC permission levels to m
Show HN: MicroSafe-RL – Sub-microsecond safety layer for Edge AI 1.18µs latency
I built MicroSafe-RL to solve the "Hardware Drift" problem in Reinforcement Learning. When RL agents move from simulation to real hardware, they often encounter unknown states and destroy expensive parts. Key specs: 1.18µs latency (85 cycles on STM32 @ 72MHz) 20 bytes of RAM (no malloc) Model-free: It adapts to mechanical wear-and-tear using EMA/MAD stats. Includes a Python Auto-Tuner to generate C++ parameters from 2 mins of telemetry. Check it out: https://github.com/Kretski/MicroSafe-RL Comments URL: https://news.ycombinator.com/item?id=47621536 Points: 1 # Comments: 0
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Show HN: MicroSafe-RL – Sub-microsecond safety layer for Edge AI 1.18µs latency
I built MicroSafe-RL to solve the "Hardware Drift" problem in Reinforcement Learning. When RL agents move from simulation to real hardware, they often encounter unknown states and destroy expensive parts. Key specs: 1.18µs latency (85 cycles on STM32 @ 72MHz) 20 bytes of RAM (no malloc) Model-free: It adapts to mechanical wear-and-tear using EMA/MAD stats. Includes a Python Auto-Tuner to generate C++ parameters from 2 mins of telemetry. Check it out: https://github.com/Kretski/MicroSafe-RL Comments URL: https://news.ycombinator.com/item?id=47621536 Points: 1 # Comments: 0
Google Gemma 4: Everything Developers Need to Know
Google dropped Gemma 4 on April 2, 2026, A full generational jump in what open models can do at their parameter range and the first time in the Gemma family's history that one ships under Apache 2.0, meaning commercial use without permission-seeking. Some context: since Gemma's first generation, developers have downloaded the models over 400 million times and built more than 100,000 variants. Four Models, One Family Gemma 4 is a family of four, each aimed at a different point in the hardware spectrum. E2B : Effective 2 billion active parameters. Runs on smartphones, Raspberry Pi, Jetson Orin Nano. 128K context window. Handles images, video, and audio. Built for battery and memory efficiency. E4B : Effective 4 billion active parameters. Same hardware targets, higher reasoning quality. About
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