Same Prompt. Different Answers Every Time. Here's How I Fixed It.
This is Part 3 of our AI verification series. Part 1: Three AIs analyzed our product. None passed the truth filter → Part 2: Human in the loop doesn't scale. Human at the edge does. → Same prompt. Same AI. Different sessions. Different outputs. Post 1 showed three different AIs diverging on the same question. That's expected. Different training, different weights, different answers. But we didn't stop there. We re-ran the same AI on the same prompt in a new session. We got materially different outputs again. Both looked authoritative. Neither warned us they disagreed with each other. What the same AI said twice Prompt: "Forecast Korea's AI industry in 2027." Session 1 produced: Market size: $10–15B at >25% CAGR Global positioning: "Global AI G3 powerhouse" Hardware claim: "All Korean elect
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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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