Kumar Gauraw 
Anthropic accidentally leaked Claude Code’s entire source code. 512K lines, 1,900 files, 28.8M views, 84K GitHub stars. Here’s what the code reveals about KAIROS, Dream Mode, self-healing memory, multi-agent swarms, plus the Straiker security warning about compaction backdoors and what it all means for developers.
On March 26-27, 2026, a major data leak from Anthropic revealed the existence of Claude Mythos (also referred to as Capybara), a next-generation AI model representing what Anthropic calls a “step change” in AI capabilities. This accidental leak—caused by a misconfigured content management system that left nearly 3,000 internal assets publicly accessible—has sent shockwaves through … Read more
OpenClaw v2026.3.22 just dropped with ClawHub marketplace integration, the /btw side conversation command, adjustable sub-agent thinking, multi-model sub-agents, and critical session management fixes. Here is what changed and what you need to do about it.
NVIDIA CEO Jensen Huang just declared every company needs an OpenClaw strategy. Here is what he said at GTC 2026, why NemoClaw changes everything for enterprise AI agents, and your action playbook for Q2 2026.
An honest, side-by-side comparison of Perplexity Computer and OpenClaw from someone who runs OpenClaw daily. Which AI agent platform gives you more for your $200 a month?
Just yesterday, one of my AI agents completely hallucinated and created a 3,000-word research report about the wrong product. Not a small mistake. It confused GPT-5.4 with GPT-4.5, reversed the version numbers, and wrote an entire piece about features that didn’t exist. My OpenClaw agent was powered by Sonnet 4.6. Then it skipped the whole … Read more
GPT-4.5 launched and everyone is comparing it to Claude Co-work. But how does it stack up against OpenClaw too? Here is an honest three-way comparison with a clear verdict on which tool to use and when.
Everyone is talking about Claude Co-work this week. I have been running something similar with OpenClaw for months — four specialized AI agents, 24/7 pipelines, no desktop app required. Here is the honest comparison.
Getting a demo to work is easy. Knowing your AI app is working reliably for real users requires monitoring. Here’s the framework I use.
Most people reach for fine-tuning way too early. Here’s the decision framework for when prompting is enough, when RAG is better, and when fine-tuning actually makes sense.