json is so token inefficient it hurts these days man, these braces and quotes are costing me real $$
PROMPT ENGINEERING
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Turning kids into cartoons with Freepik Spaces and Seedance 2.0
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Turning my kids into cartoons is super fun
— Linus β¦ Ekenstam (@LinusEkenstam) 12 avril 2026
Here I'm experimenting with Freepik Spaces and JSON prompting using Seedance 2.0
Full prompt below π pic.twitter.com/ecQ0GtjlqzTurning my kids into cartoons is super fun
Here I'm experimenting with Freepik Spaces and JSON prompting using Seedance 2.0 Full prompt below -
Claude.md File Reaches 15K Stars with AI Coding Guidelines
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A single ππππ¨ππ.πΊπ± file just hit 15K GitHub stars. (derived from Karpathy's coding rules) Andrej Karpathy observed that LLMs make the same predictable mistakes when writing code: over-engineering, ignoring existing patterns, and adding dependencies you never asked for. If you've used AI coding assistants, you've hit all of these. But here's the thing: If the mistakes are predictable, you can prevent them with the right instructions. That's exactly what this ππππ¨ππ.πΊπ± does. You drop one markdown file into your repo, and it gives Claude Code a structured set of behavioral guidelines for your entire project. This is a big deal. – Built entirely around prompt engineering for AI coding assistants – No framework, no complex tooling, just one .md file that shapes behavior Developers are moving past "use AI to write code" and into "engineer the AI's behavior so the code is actually good." The Claude Code ecosystem is growing fast, and the best tools in it aren't always software. Sometimes they're just well-crafted instructions. 100% open-source. I've shared a link to the GitHub repo in the next tweet!
β View original post on X β @akshay_pachaar, 2026-04-12 17:02 UTC
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Top AI Papers: Agents, LLMs, and Coding Automation
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The Top AI Papers of the Week (April 6 – 12) – Memento
– Neural Computers
– The Universal Verifier
– Agent Skills in the Wild
– Memory Intelligence Agent (MIA)
– Single-Agent vs Multi-Agent LLMs
– Scaling Coding Agents via Atomic Skills Read on for more: -
Debating optimal markdown files for AI agents likely temporary
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It is notable that we are all debating exactly which markdown files are most important to feed AI (skills, memory, tool instructions) and in which order to feed them to get the best output. Feels that this is likely a temporary state of affairs in the development of agents
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Hermes Agent: Self-Improving AI with Cross-Session Memory
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The self-improving AI agent from Nous Research! Hermes Agent is a self-improving AI agent that builds skills from your work, improves them over time, and remembers across sessions. Most AI agents reset every conversation. You teach them your codebase structure, they forget. You
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AI Scaling Bad Targeting: Context Matters More Than Prompts
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The hardest part of AI is not about writing the perfect prompt.
The hardest part is knowing who youβre actually talking to. AI wonβt fix bad targeting.
It just helps you scale it faster. Automated βAI-generated outreachβ fails for the same reason this text did: β No context -
Prompt that Forces LLMs to Be Clear and Direct
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Muchos se quejan de que ChatGPT divaga demasiado. Un tipo en Reddit encontrΓ³ el prompt definitivo que hace que ChatGPT, Claude o Gemini respondan claro, preciso y directo al grano. Abajo tienes el prompt completo
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Corey Ganim builds first managed agent with Claude Code
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this guy is @coreyganim. Full video (including the Google Doc you hand to Claude Code to build your first managed agent) here: piped.video/watch?v=nAOyErphβ¦
β View original post on X β @datachaz, 2026-04-12 08:50 UTC
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Agent Skills: AI Coding Agents with Engineering Best Practices
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If you found this useful, a like or RT goes a long way π¦Ύ Follow me β @datachaz for insights on LLMs, AI agents, and data science! Charly Wargnier (@DataChaz) π¨ ICYMI @addyosmani from Google just dropped his new Agent Skills and it's incredible. It brings 19 engineering skills + 7 commands to AI coding agents, all inspired by Google best practices π€― AI coding agents are powerful, but left alone, they take shortcuts. They skip specs, tests, and security reviews, optimizing for "done" over "correct." Addy built this to fix that. Each skill encodes the workflows and quality gates that senior engineers actually use: spec before code, test before merge, measure before optimize. The full lifecycle is covered: β Define – refine ideas, write specs before a single line of code β Plan – decompose into small, verifiable tasks β Build – incremental implementation, context engineering, clean API design β Verify – TDD, browser testing with DevTools, systematic debugging β Review – code quality, security hardening, performance optimization β Ship – git workflow, CI/CD, ADRs, pre-launch checklists Features 7 slash commands: (/spec, /plan, /build, /test, /review, /code-simplify, /ship) that map to this lifecycle. It works with: β¦ Claude Code β¦ Cursor β¦ Antigravity β¦ … and any agent accepting Markdown. Baking in Google-tier engineering culture (Shift Left, Chesterton's Fence, Hyrum's Law) directly into your agent's step-by-step workflow! `npx skills add addyosmani/agent-skills` Free and open-source. Repo link in π§΅β β https://nitter.net/DataChaz/status/2043246635996807300#m
β View original post on X β @datachaz, 2026-04-12 08:35 UTC