The next AI moat won’t be “I know how to prompt.” That’s already getting commoditized. The real moat is packaging how you think into a Skill people can run. Your edge becomes reusable.
Your process becomes a product.
Your brain starts earning without being in the room.
PROMPT ENGINEERING
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Packaging thinking into reusable AI Skills is the next moat
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Constrained Generation and Memory Patterns in AI
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That progression is a clean way to frame it. Constrained generation, constrained tool calling, constrained memory. Same pattern applied one layer deeper each time. The temporal piece is underrated. Most teams get to typed entities and stop, but fact invalidation is what keeps
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Concerns about infinite context windows and model memory
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Infinite context windows seem to present a very large problem to using AI. Today's models already leak too much old information into current responses, a distraction that is part of why they are cognitively exhausting to use I don't want to work with Borges's Funes the Memorious
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Why small conversational models differ from paid models
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Great question! A couple reasons:
1) they are not the same as paid models, they are small & built for fast conversation rather than real work
2) they are designed to be cheap to run, so they use minimal thinking and tool calls. Tool calls and thinking are big drivers of accuracy -
Helio Public Beta: Describe Goals, Get AI Team in 60s
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Helio moved to public beta, allowing anyone to describe a goal in plain language and get a working AI team up and running in under 60 seconds. > We set up an HR Manager, a Content Editor, and a Content Writer as AI teammates for TestingCatalog News. > Created a task and
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Microsoft paper: SkillOpt for self‑evolving agent skills
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One of the most important objects in agentic AI may turn out to be a Markdown file. Not the model weights.
Not the prompt. The skill document. A new Microsoft paper introduces SkillOpt: Executive Strategy for Self-Evolving Agent Skills. The thesis is sharp: If an agent’s -
Capafy: monetize AI workflows as products
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Your best prompt/workflow shouldn’t die in a folder.
— God of Prompt (@godofprompt) 26 mai 2026
Capafy turns Skills into products people can use while you get paid per run.
Closed-source.
Online.
Monetized.
This is where AI workflows stop being “cool demos” and start becoming assets. https://t.co/GsPxS3dXDrYour best prompt/workflow shouldn’t die in a folder. Capafy turns Skills into products people can use while you get paid per run. Closed-source.
Online.
Monetized. This is where AI workflows stop being “cool demos” and start becoming assets. -
Free ChatGPT Deep Dive Video & Article Breakdown
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I still can’t believe @karpathy released this 3.5-hour deep dive on how ChatGPT actually works… for FREE. 🤯
— Charly Wargnier (@DataChaz) 26 mai 2026
Easily the best video I've watched on the topic.
Swap your next Netflix binge for this.
Then read the ace article below from Codez breaking it all down 👀↓ https://t.co/5RYVSFckqq pic.twitter.com/c8spocCq5rI still can’t believe @karpathy released this 3.5-hour deep dive on how ChatGPT actually works… for FREE. Easily the best video I've watched on the topic. Swap your next Netflix binge for this. Then read the ace article below from Codez breaking it all down ↓
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Critique of Wired piece on AI fact-checking
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I found this Wired article on AI fact-checking frustrating. It could have been about why we continue to need human fact checkers (talk to people, use judgement, resolve conflict). Instead it is full of old info & stuff about free models GPT-5.5 Pro checked it (& I checked GPT)
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Typed Schemas for LLM Data Ingestion and Domain Context
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The hard part is that "what matters" is domain-specific and the LLM has no way to know that on its own. That is where typed schemas earn their keep. You encode what matters once, and every future ingestion inherits that judgment without the model having to rediscover it.