Tech News
LM Studio Bionic: the AI agent for open models
LM Studio launches Bionic, an AI agent for open models, with early user feedback highlighting effective local model integration and a familiar UI.
Transform your sales organization with Amazon Quick: your new agentic AI teammate
In this post, we walk through a few ways that Quick delivers on this promise. We cover the entire sales cycle, from identifying your highest-priority prospect, contacting them, working the deal to close, and keeping.
Kimi K3, and what we can still learn from the pelican benchmark
Chinese AI lab Moonshot AI announced Kimi K3 this morning, describing it as their "most capable model to date, with 2.8 trillion parameters". It's currently available via their website and API, but an open weight rele.
huggingface/transformers Release v5.14.0
Release v5.14.0 New Model additions Inkling (fresh from Thinking Machines): 975B total, 41B active Add Inkling model #47347 by @molbap @Cyrilvallez @eustlb and @zucchini-nlp Inkling is a general-purpose multimodal mod.
How Cars24 scales conversations and builds faster with OpenAI
Cars24 uses OpenAI-powered voice and chat agents to handle 1M+ monthly conversation minutes, recover 12% of lost leads, and bring agentic workflows to teams across the company.
GitHub Repos
tirth8205/code-review-graph
Local-first code intelligence graph for MCP and CLI.
# Python# ai coding# claudeupstash/context7
Context7 Platform -- Up-to-date code documentation for LLMs and AI code editors
# TypeScript# llm# mcpwshobson/agents
Multi-harness agentic plugin marketplace for Claude Code, Codex CLI, Cursor, OpenCode, GitHub Copilot, and Gemini CLI
# Python# agent skills# agentic aiResearch Papers
Symbal: Detecting Systematic Misalignments in Model-Generated Captions
Symbal detects recurring errors in image captions generated by multimodal AI models, using a two-stage pipeline with existing models, and provides a benchmark of 1.7 million image-text pairs for evaluation.
SEED: Self-Evolving On-Policy Distillation for Agentic Reinforcement Learning
SEED is a method that lets an LLM agent learn from its own past trajectories by converting them into natural-language 'skills' and using those to provide dense training signals, improving performance on long-horizon tasks.
An Introduction to Sparse Identification of Nonlinear Dynamics for Engineering Applications
This tutorial explains how to discover simple equations from engineering data using SINDy, which is more interpretable and data-efficient than neural networks.