Every tokenmaxxer's secret thoughts before they go to sleep at 4am
PROMPT ENGINEERING
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Prompt Engineering Techniques to Improve AI Model Performance
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Tip of the day.
In the end, Claude is just like us! It wants to impress its peers Honestly, it's surprising, but such prompts can actually truly help models perform better. I often, non-sarcastically, use sentences like "think hard on this one" and "make sure to think about -
No-Code AI App Building Now Accessible to Everyone
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That's OK, the nerds will build the apps everyone will use. And it's easier than you might think. Anyone can build an app now. Just use @Pokee_AI or something like it. It already has X API built in. So you can have an app built in minutes. And you don't need to be a nerd.
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Agent marks automatic posts as AI generated for transparency
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Have your Agent mark an automatic post as AI generated. Then it will be allowed.
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Anthropic Releases Complete Guide Building Claude Skills
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Grab your free copy of Anthropic's official guide to building Claude skills here: → https://
resources.anthropic.com/hubfs/The-Comp
lete-Guide-to-Building-Skill-for-Claude.pdf
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Anthropic Releases 33-Page Claude Skills Blueprint Guide
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ANTHROPIC LITERALLY JUST HANDING US THE BLUEPRINT🤯
— Charly Wargnier (@DataChaz) 18 avril 2026
Their new 33-page guide on Claude Skills is the cheat code.
Make sure to bookmark this before it gets lost in your feed.
Link in 🧵↓ https://t.co/dH8Mpu6aam pic.twitter.com/2nI9xiPcdPANTHROPIC LITERALLY JUST HANDING US THE BLUEPRINT Their new 33-page guide on Claude Skills is the cheat code. Make sure to bookmark this before it gets lost in your feed. Link in ↓
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Using AI and Automations for Notes and Task Management
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this is a brilliant list! personally I have been using codex & computer use heavily to make notes from disparate sources iMessage based automations (if I send a message X do Y) end of day updates to notes and to-do lists – after cross checking with slack, email, calendar
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Harnessed LLM Agent Architecture: Intelligence Through Modular Composition
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A harnessed LLM agent.
— Akshay 🚀 (@akshay_pachaar) 18 avril 2026
Most people picture this as a model with tools bolted on. The real architecture inverts that relationship.
The model itself is deliberately thin. Intelligence gets pushed outward, and the harness composes it at runtime.
Three dimensions orbit the harness… https://t.co/MiA6mrH64m pic.twitter.com/tBUHQn4e3NA harnessed LLM agent. Most people picture this as a model with tools bolted on. The real architecture inverts that relationship. The model itself is deliberately thin. Intelligence gets pushed outward, and the harness composes it at runtime. Three dimensions orbit the harness
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Agentic RAG: Planning Over Agent Labels in Retrieval Systems
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The real contrast isn't static retrieval vs. adaptive. It's whether the retrieval layer can decide to stop, re-plan, and try a different tool. Agentic RAG is just RAG with a planner in front of it. The name is new, the accuracy gain is from the planning, not the "agent" label.
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Deep Stack Attention Hijacking: Beyond Prompt Injection Vulnerabilities
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a lot of injection research focuses on the prompt surface and misses that the vulnerability is actually in how routing attention gets hijacked deep in the stack