Tech News
Meta caps internal AI token spending
Meta is capping internal AI token spending after costs approached billions in 2026.
Run NVIDIA Nemotron and OpenAI GPT OSS models on Amazon Bedrock in AWS GovCloud (US)
We're excited to introduce US-based frontier open-weight models in AWS GovCloud (US). With this release, Amazon Bedrock now supports OpenAI’s open-weight GPT OSS models (120B and 20B) and NVIDIA Nemotron (Nano.
Mastering Agentic Techniques: AI Agent Reinforcement Learning
Reinforcement learning (RL) is central to aligning language models, from reinforcement learning with human feedback (RLHF) within AI assistants to newer.
The latest AI news we announced in June 2026
June Pixel Drop hero.
Simplify model selection in Amazon Bedrock with the open source Model Profiler
The Amazon Bedrock Model Profiler is an open source tool that aggregates model metadata from multiple AWS APIs and external sources into a single, searchable interface. In this post, you’ll learn what the Model Profil.
GitHub Repos
wshobson/agents
Multi-harness agentic plugin marketplace for Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot, and Gemini CLI
# Python# agent skills# agentic ailobehub/lobehub
🤯 LobeHub is your Chief Agent Operator, organizing your agents into 7×24 operations by hiring, scheduling, and reporting on your entire AI team.
# TypeScript# agent# agent collaborationcode-yeongyu/lazycodex
The one and only agent harness for complex codebases.
# TypeScript# ai# ai agentsResearch Papers
When LLMs Develop Languages: Symbolic Communication for Efficient Multi-Agent Reasoning
Communicative Language Symbolism Routing (CLSR) is proposed, a test-time framework in which multiple LLM agents autonomously invent, evolve, and share compact Language Symbolism Frameworks (LSFs), while a latent-free router adaptively selects and composes these languages per...
AGC-Bench: Measuring Artificial General Creativity
Creativity research has debated whether creativity is domain-specific (e.g., visual, writing, science), and if it is psychometrically separable from general intelligence. Both questions now apply to LLMs, but a unified benchmark of AI creativity remains elusive.
QuasiMoTTo: Quasi-Monte Carlo Test-Time Scaling
Scaling inference compute, by generating many parallel attempts per problem, is a costly but reliable lever for improving language model capabilities. By default these attempts are generated independently, wasting inference compute on redundant solutions.