👋 Hello, Anthropic team!
Thanks for checking out my blog. I'm excited about the opportunity to work with you on building safe, beneficial AI systems.
Feel free to explore the posts on AI alignment, verification theory, and software engineering.
— James
We've known since 1936 that universal verification is impossible. Now we're trying it on AI systems that adapt to detection.
For any detector f, it is possible to construct a program g that can bypass or defeat it. Any alignment test becomes a signal that says, "Humans are watching."
I got tired of MCPs that proxy calls through another LLM when I'm already using the LLM, I want to use. It drives me crazy - creates unnecessary complexity, breaks conversation flow, and prevents real-time prompt modification.
So I built the Pattern MCP Server. Simple concept: expose prompt content directly Instead of executing it through a middleman.
While building it, I did a deep dive into the popular Fabric's 215 patterns.
✅ A-tier (15%): Security patterns like analyze_malware are genuinely excellent
❌ D/F-tier (15%): find_female_life_partner reduces relationships to algorithms
(icky)
The bigger issues I found: - Cargo cult prompt engineering ("think with 1,419 IQ") - Over-rigid constraints ("write exactly 16 words per bullet") - 90% lack examples despite examples being the most powerful instructional tool - Anxiety-driven repetition ("DO NOT COMPLAIN" x3)
Added some notes around how to fix said issues.
Anyway: Everyone needs their prompt library. The best prompts are ones you've refined for your specific workflow.
The Pattern MCP Server gives you direct access to prompts - both Fabric's collection and your custom patterns - without execution overhead. Mix, match, and modify on the fly.
Model collapse isn't an AI Habsburg chin, but rather the possibility of AI becoming the McDonald's we secretly crave. While researchers worry about vanishing tail distributions and recursive training loops, the underlying tragedy lies in the fact that we are creating monotonous machines for a market that prioritizes predictability over surprise. The models won't "collapse." They'll converge on exactly the mediocrity we deserve.
Two camps emerge: Nilenso says AI amplifies the skills of skilled developers. Fly.io says stop fetishizing craft—we're problem solvers, not artisans.
The bottleneck has shifted from writing code to knowing what to build.