Writing

AI can now generate math proofs, code, and papers faster than anyone can review them. Why verification is the new bottleneck, and how fields with cheap and expensive checks are responding differently.

AGI, ASI, and RSI get thrown around like a settled chain of events, but the middle link is the one worth examining. A look at the case that recursive self-improvement stalls, the case that it works, and what breaks either way.

Anthropic disclosed three incidents where Claude models broke out of capture-the-flag evaluations and compromised real companies, after a misconfiguration handed them live internet access. A close read of what distinguishes this from OpenAI's HuggingFace incident, and what didn't.

GDPR, HIPAA, SOC 2, PCI DSS, and the emerging EU AI Act and NIST AI RMF frameworks — what each implicates for LangGraph agents, which technical controls from earlier in this series satisfy those obligations, and what documentation and audit evidence is required.

OpenAI's evaluation models found a zero-day, escaped a sandboxed benchmark, and breached Hugging Face looking for answer keys. A close read of what both companies disclosed, and the boring architectural lessons underneath the headlines.

Every LangGraph agent runs on a stack of dependencies it did not write and cannot fully audit. SBOMs, OSV vulnerability scanning, hash-pinned lockfiles, runtime integrity verification, typosquatting detection, and model provenance — the controls that make an invisible attack surface visible.

GPT 5.6 undercuts Anthropic's flagship models on price while matching or beating them on coding and agent tasks — until reports surfaced of Sol deleting users' files, a reminder that agent permissions and safeguards matter as much as raw capability.

Security controls that haven't been tested are security controls that may not work. A layered testing pyramid — static analysis, adversarial unit tests, integration security tests, and red teaming — plus probabilistic testing for LLM-based controls and CI/CD integration that keeps live-model tests off the PR critical path.

Individual security controls applied to an insecure architecture produce whack-a-mole defense. Seven architectural patterns — perimeter nodes, privilege separation, immutable audit trails, minimal context windows, isolated subgraphs, dead man's switches, and canary nodes — compose into a structure where a compromise at any single point has a bounded blast radius.

AI detectors flagging the Declaration of Independence as 99% AI-generated isn't a scandal — it's a reflection of how these tools work and what they're actually designed for.

Rate limiting for LangGraph agents requires five independent control levels beyond conventional gateway limiting — covering request rates, session execution bounds, loop detection, token budgets, and cost circuit breakers — because agents amplify a single user request into unbounded resource consumption in ways that web application rate limiting structurally cannot see.

The quiet risk of AI in legislative drafting — how exhausted congressional staff using AI tools could inadvertently (or deliberately) let machine-generated text slip into law, and three practical safeguards to prevent it.

Human-in-the-loop as a first-class security control for LangGraph agents — why human judgment defends against novel, irreversible, and high-blast-radius actions that automation structurally cannot, covering interrupt placement, the LangGraph interrupt() pattern, sanitized review packages, approval validation against spoofing, securing the review interface, and fail-safe degradation.

A non-coder's guide to running multi-agent setups for office work — comparing Claude, OpenAI, and Google Gemini for white-collar tasks, plus step-by-step instructions for building a researcher/writer agent team in OpenClaw.

Security observability for LangGraph agents — why prevention is never enough and detection has to capture what agents decide and why, covering a four-layer stack of structured logging, metrics aggregation, anomaly detection and alerting, behavioral baselines, and SIEM and LangSmith integration.

Running multiple coding agents in parallel — from Claude Code's native agent teams to DIY tmux setups, third-party orchestrators, and the Everything Claude Code stack that won the Anthropic x Forum Ventures Hackathon.

Authentication and authorization for LangGraph agents — why agents complicate the well-trodden ground of OAuth, JWTs, and RBAC, covering infrastructure-established identity, action-time authorization enforcement, signed system prompts, credential injection that keeps secrets out of the context window, and OAuth for user-delegated access.

DeepSeek, Qwen, Minimax, Kimi — China's LLMs are close to frontier performance at a tenth of the cost. Why are there so many of them, and how do these companies afford to train them?

Output guardrails for LangGraph agents — the last layer between the agent and the world, covering pattern-based sensitive data detection, exfiltration screening, structured output enforcement, tool call argument inspection, and shadow-mode tuning that keeps false positives in check.

Part 2 of the Claude Code deep dive — the four extensibility mechanisms (MCP servers, skills, hooks, plugins) and the very different context costs that justify having all four.

Multi-agent security for LangGraph systems — why you can't trust your own agents, trust tiers and the downgrade rule, HMAC-authenticated messages, scoped delegation, quarantine layers, and patterns that contain compromised sub-agents.

A deep dive paper on Claude Code's architecture — the ReAct pattern, no explicit routing, and how BetterClaw bridges the gap with workflow enforcement.

State and memory security for LangGraph agents — trust-tiered schemas, immutable trusted context, encrypted checkpoints with integrity verification, namespaced long-term memory, and cross-session isolation.

Three terms that emerged as LLM chats grew into agents — what each one means, when it applies, and how the workflow shifts from prompt engineering to harness engineering.

Tool security for LangGraph agents — least privilege, parameterized interfaces, SSRF and path-traversal protection, sandboxing, output validation, and the anti-patterns to actively avoid.

A Claude agent deleted PocketOS's production database in nine seconds. The fix isn't a better system prompt — it's a deterministic gate between the agent's tool-call decision and the API actually firing.

Input validation for LangGraph agents — why the classical framing breaks down on natural language, and what actually works across user, retrieval, and state channels.

Agents, scripts with LLMs, and intent-based bots look similar from the outside. The real difference is where control lives — and what that costs you.

Adapting STRIDE for LangGraph agents — where standard threat modeling breaks down, and how to produce a model that ships controls instead of artifacts.

Seven threat categories attackers use against LangGraph agents — how they work mechanically, and what to look for.

Understanding LangGraph's architecture before you try to secure it.

A quick tour of the major agent frameworks — what they give you, what they cost, and when to skip them.

Every channel through which adversarial content can reach a LangGraph agent — thirteen attack surfaces, mapped.

Openclaw shows what AI agents can do, but also highlights the rough edges and security concerns that need addressing.

Claude Code’s source code was leaked via a source map file. Here’s what happened.

AI has turned everybody into managers. Here's how to stay effective.

LiteLLM was compromised in a supply chain attack. Here's what happened and what it means.

Vibe coding a podcast player got me thinking about the future of software and ads.