AI Tools

Ever wanted to fine-tune an LLM but bounced off the three-hour tutorial on YAML configs, Python environments, and CUDA toolkit versions? Yeah, me too. And I’ve been tracking this space long enough to know most tools assume you’re an ML engineer who dreams in transformer architectures. But LlamaFactory? And it takes a different approach. Here’s what it is: a zero-code LLM fine-tuning framework with over 72,830 GitHub stars, supporting 100+ models and 20+ training methods. And its claim to fame? A Gradio-powered Web UI called LLaMA Board that turns the whole process into a visual workflow — pick your model, load your dataset, hit Start. ...

Your AI agent just wrote 200 lines of code. But it has no clue Issue #42 exists. Has no idea the last CI run failed. Can’t see the three open PRs you need reviewed. So you Alt-Tab out, open gh CLI, check manually, paste results back into the agent, and continue the slow dance. So that’s the gap GitHub MCP Server fills — and it’s been sitting at 30,924 stars since GitHub open-sourced it. I’ve been running it for a week across Claude Code, Codex, and Cursor. Let me show you what it actually does. ...

Ever found yourself copy-pasting SQL results from pgAdmin back into your chat with Claude Code, hoping it understands the schema? Yeah, me too. The whole “AI agent can’t see your database” problem has been the single biggest productivity gap in the MCP workflow — until now. Google just dropped MCP Toolbox (formerly genai-toolbox), an open-source MCP server that gives AI agents direct access to 15+ databases. Not a wrapper. Not a middleware. Native database connectivity through the MCP protocol, zero boilerplate. ...

Ever started a fresh Claude Code session and spent the first 50 tokens explaining the same project context you laid out yesterday? Me too. And it’s the cold-start tax — on a paid subscription those losses add up fast. So I’ve tried CLAUDE.md (manual upkeep), –continue (full transcript replay, token-heavy), claude-mem (my earlier review), and external memory tools (API calls, data leaves your machine). All work, but none solve the problem cleanly. Then I stumbled on recall — a 3-day-old project with 216 stars. Here is a Claude Code plugin that auto-captures your session and summarizes it locally, zero LLM calls and zero outbound traffic. Worth testing. ...

You’ve got a local LLM setup — Ollama, LM Studio, whatever. Now which model do you actually run? That’s the question nobody’s really answering well. HuggingFace shows you download counts. Ollama search tells you what fits in VRAM. But “fits” and “best” are two very different things. I’ve spent way too many afternoons downloading model after model, testing them one by one, only to wonder if there’s something better I missed. ...