Tech News
Go is an ideal language for AI-assisted software engineering
A Google blog post argues Go is ideal for AI-assisted software engineering, citing its simplicity, strong tooling, and readability.
Stealing Reasoning Traces from Proprietary LLM APIs
A vanity domain name ( stolen-thoughts.com ) for a neat paper: Anthropic, OpenAI, and Google return encrypted chain-of-thought blocks to clients that can be replayed across sessions, users, and models.
NVIDIA Nemotron 3.5 Lightning Delivers Fast, Accurate Specialized Task Execution for Long-Running Agents
Long-running AI agents spend most of their time on high-volume execution: tool calls, result validation, and subagent delegation. Using a frontier reasoning.
Daybreak models are now available on AWS
OpenAI and AWS are making Daybreak cybersecurity capabilities available through Amazon Bedrock to support enterprise security workflows.
How ONESTRUCTION built the Ishigaki-IDS foundation model with AWS GenAIIC
ONESTRUCTION, with technical advisory from the AWS Generative AI Innovation Center, built Ishigaki-IDS, a foundation model specialized for construction and BIM workflows.
GitHub Repos
NanmiCoder/cc-haha
Local-first cross-platform desktop workspace for Claude Code / agents: multi-agent, Git worktrees, code diffs, skill marketplace, multi-model, Computer Use, task-aware desktop...
# TypeScript# agent# aiunslothai/unsloth
Local UI to run and train LLMs and diffusion models, including Kimi K3, MiniMax-H3, Gemma 4, Qwen3.6, DeepSeek-V4, FLUX and more.
# Python# agent# chatgptjrswab/axe
A lightweight cli for running single-purpose AI agents.
# Go# ai agents# automationResearch Papers
Test-Time Augmentation for LLMs: When Input Diversity Beats Output Diversity at Matched Compute
This paper compares adding input diversity (e.g., rephrasing questions) versus output diversity (sampling multiple reasoning paths) for improving LLM accuracy at the same compute budget, finding that input diversity via semantic rephrasing is often more cost-effective.
CARE: Confidence-Aware Reasoning for Reliable Medical VQA
This paper introduces CARE, a framework to make medical visual question answering models more accurate and better calibrated by training them to express confidence that matches their actual correctness, using a two-stage process of supervised fine-tuning and reinforcement...
A Hybrid Nested Harness for Decoupling Structure and Parameters in LLM-Driven Optimization
This paper introduces a hybrid optimization method that combines language models with traditional numerical optimizers. The LLM proposes a structural sketch with numeric gaps, and a separate optimizer fills in the numbers, improving efficiency and performance across various...