Experiments in using AI agents to produce long-form technical analysis — from automated research pipelines to multi-source synthesis.
Note: These posts are AI-generated experiments. Accuracy and completeness may vary. Treat them as starting points, not conclusions.
In March 2026, someone extracted the complete source code of Claude Code from an npm package and published it to GitHub. No modifications. No commentary. Excluding generated code, lock files, and test fixtures — roughly 512,000 lines of TypeScript, dumped into a repository with a single commit. How this happened is itself a security lesson. Anthropic published version 2.1.88 of their npm package with a production source map file — cli.js.map, weighing in at 59.8 MB — that contained the original TypeScript source, comments and all. A misconfigured .npmignore or a build pipeline that skipped artifact scanning, depending on who you ask. The file was there for anyone to extract. Security researcher Chaofan Shou was the first to notice. ...
TL;DR for busy operators Three minutes, top to bottom: DeerFlow is powerful and highly composable: LangGraph runtime, FastAPI gateway, MCP extensibility, skills, channels, memory, subagents, sandbox modes, custom agents, and a guardrails layer for pre-tool-call authorization. This is not a toy stack. Power comes with a steep security responsibility curve: the docs and config make it easy to run in insecure ways — skip ingress auth, overexpose API routes, enable high-impact tools broadly, or run local sandbox in shared contexts, and you’re asking for trouble. OpenClaw is more opinionated operationally about channel policies, trust boundaries, gateway hardening, and tool restriction baselines for a personal-assistant model. Clearer security defaults out of the box. Runtime reality matters: DeerFlow can run in constrained environments, but full-stack convenience depends on host prerequisites (nginx/docker/toolchain), and no configured model means no actual agent run. Bottom line: treat DeerFlow as a programmable power framework, not a safe appliance. Explicitly harden ingress, authz, tools, sandbox mode, MCP secrets, and channel trust before exposing it to real users. Why this analysis exists Most AI-agent platform writeups make one of two mistakes: ...
OpenClaw is not a “chatbot deployment.” It is a high-privilege automation control plane that can read files, run commands, browse sites, call APIs, and operate across messaging channels. That means your security model must be closer to platform security than to “prompt quality.” ...
One of the stranger things about AI security is how many people trust benchmark scores they would never trust anywhere else. If someone told you a new static analyzer catches 90% of vulnerabilities, your first question would be: 90% of what? In what code? Under what assumptions? What did it miss? But when an LLM benchmark shows a leaderboard, people often skip those questions and go straight to conclusions. ...