Sawyer Ruhl

On March 10, 2026, OpenAI quietly shipped the upgrade every STEM student has wanted for years: interactive math and science tools baked directly into ChatGPT. No more bouncing between ChatGPT for explanations, Desmos for graphs, and Wolfram Alpha for computations. For the first time, you can manipulate variables in real-time, watch equations update live, and […]

Most people install OpenClaw, connect it to Telegram, set up a cron job or two, and call it a day. That’s fine. But the users who get the most out of it — the ones running full business operations on autopilot — have figured out the skills system. Skills are how you teach OpenClaw to

When Z.ai announced GLM-5-Turbo on March 16, 2026, the headline was “faster and cheaper” — but the number that actually matters is buried in OpenRouter’s provider telemetry: a 0.67% tool call error rate. Compare that to the base GLM-5 endpoints, where error rates run from 2.33% to 6.41%, and you start to understand why Z.ai

Mistral just made the “which model do I deploy?” question obsolete — at least for their product line. Mistral Small 4, released March 16, 2026, is a 119B-parameter Mixture-of-Experts model that collapses four previously separate products (Mistral Small, Magistral, Pixtral, and Devstral) into a single deployment target with configurable reasoning effort. The kicker: it’s fully

Anthropic quietly shipped something on March 12, 2026 that most AI coverage buried in a changelog — Claude can now generate charts you can actually click, hover, and interact with directly inside the chat window. Not a static PNG. Not a code block you have to run yourself. A live, reactive visualization that responds to

Picture this: your OpenClaw assistant can search the web, read files, and run cron jobs right out of the box. That’s useful. But what if it could also query your database directly, pull live data from any API, read your calendar, execute code in a sandbox, search your entire codebase, and talk to dozens of

TL;DR VERDICT 8.6 / 10 Gemini Embedding 2 is the most significant leap in embedding model architecture in years. Google didn’t just make a better text embedder — they collapsed four separate retrieval pipelines into one, and the benchmarks back it up. With a 68.16 MTEB score, 3072-dimensional vectors, native multimodal support for text/images/audio/video, and