Wanted to share a skill I built for designing and reviewing the prompts that run AI agents. Most prompt engineering advice for agents still treats the system prompt as a text block: "write a clear role, add examples, be specific." That helps with a chat answer, but agents fail in ways text-block advice doesn't cover. I kept watching the same three failures: an agent with overla
🛠 ChatGPT
Saw this breakdown on X discussing Andrej Karpathy's perspective on agent architecture: "Two Autonomous Agent loops made Karpathy's loop 1000x better with Graph Engineering." The core idea is that moving from a single sequential execution loop to a connected graph topology gives agents vastly better context and task orchestration. From an engineering standpoint, this makes comp
🛠 ChatGPT
With the help of Chatty (that’s what I call ChatGPT), I built a simple system for managing long-term projects without depending too much on ChatGPT’s built-in memory. The problem was pretty simple: ChatGPT was good at remembering things like how I prefer to work, but project information eventually became outdated. “I like to discuss the architecture before writing code” is usef
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Jeff Dean, co-creator of MapReduce, BigTable, TensorFlow, and Google Brain, sat down at the 2026 Frontier & Pioneer Symposium for a rare retrospective on 27+ years of systems architecture, and his new venture, Discovery Loop. This is the kind of talk that's dense enough to warrant a slow re-watch; here's the signal, stripped of the noise. Key Takeaways: MoE before it was cool:
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Anthropic's latest technical insights for Claude Fable 5 highlight its remarkable capacity for nuanced storytelling, rich multi-character reasoning, and intricate instruction-following. However, getting Fable 5 to consistently sustain complex narrative worlds and strict logical constraints without drifting off-track requires a tailored structural framework. Digging through page
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