LLMs are very two-sided for security. On the one hand, long-latent bugs are finally surfacing. On the other hand, before you choose to use a product, you can get your own code review done for incredibly low cost*! In the short term it’s incredibly bad, in the long-term it democratizes code review greatly. “Don’t trust, verify” finally becomes an actual possibility for normal people! * of course LLMs do still make up issues when asked, push any such review to write PoCs to check their analysis!

LLMs are all terrible at humor. Maybe in 20 years we’re all just going to be high paid comedians with robot audiences. Imagine selling out MSG and it’s all just Optimus and that weird sock robot with the googly eyes chugging WD-40 and laughing at your jokes. I’m actually totally here for it

LLMs are great consensus detectors. @donnelly_brent explains why AI-generated analysis often reflects the institutional average, not a differentiated view. But consensus framing tells you what is already priced and what everyone else is probably looking at.

🤝 "LLMs for ambiguity, deterministic agents for hard policy"—a contract-compliance ADK sample that takes role separation seriously. Title: Contract Compliance Pipeline (GoogleCloudPlatform/generative-ai) URL: An auditable demo that uses Google ADK's A2A protocol to coordinate a Python (intake) agent and a Go (validation) agent. Three highlights worth your attention. 🧠 Separating LLM from deterministic work Instead of routing everything through an LLM, ambiguous extraction goes to the LLM/parser side while hard policy enforcement runs in a deterministic Go engine. Rules like "value ≤ $500k" or "term ≤ 5 years" get checked in a repeatable, identical-every-time way. 🔌 Cross-language A2A handoff ADK's RemoteA2aAgent discovers the Go service via its Agent Card (/.well-known/agent.json) and sends a JSON-RPC 2.0 SendMessage. A Python agent (FastAPI:8000) and a Go agent (net/http:8888) collaborate using only a standard protocol. 📜 Auditable policy and artifacts Policies (value cap, term limit, minimum insurance, required exit clause, no unlimited liability, etc.) are passed as custom_policies and swappable per audit. Results are visualized with an execution trace, and the system even auto-generates compliance certificates and parameter sheets. It reads like a template you could lift straight into real regulated/review-heavy systems. #AIAgents# #GoogleADK#