Ano Mass @hardtakealways

When projects like Vercel, and Atlassian start adopting DESIGN. md, it’s probably more than a trend.

Ozmen @nozmen

a week ago

Official DESIGN.md adoption is happening: @vercel , @resend , @clerk @mintlify , @nuxt_js and @Atlassian all publish first-party design systems that AI agents can read. We're tracking them all in one place →

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A couple of before / after examples of the skills I've added last week.

Jakub Krehel @jakubkrehel

2 weeks ago

I’ve added 4 new skills to the /better collection. /better-accessibility Finds accessibility issues and helps fix them. /better-layout Improves layout, spacing, hierarchy and responsiveness. /better-writing Makes product copy clearer, more concise and consistent.

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@iNishant4 @Wangchuk66 As a nationalist what are you doing to prevent corruption

@Shruti314547 @Wangchuk66 10th me kitne bar fail kiye ho?

#EXCLUSIVE || How does @thekiranbedi see what happened yesterday in the heart of Delhi? Many are calling it a militarised response by the police against defenceless students and activists, rather than policing by consent. Tune in to know what the Former IPS has to say! #Delhi #NewsToday #StudentProtest #JantarMantar #Trending #Delhi #DelhiPolice @SardesaiRajdeep

⚠️ What Happens If the Government Doesn't Listen by 12 PM Tomorrow? Article 32 Explained. 🚨👇🏻

अगर ऐसी तस्वीरें और वीडियो आने लगे तो दिल्ली पुलिस वालों और देश के लिए गलत होगा पुलिस वालों को भी छात्रो के साथ बरबरता करने से पहले सोचना होगा घर उन्हें अकेले ही जाना है?

You are the most vile, most pathetic, most disgusting person @sudhirchaudhary I always agree to disagree, never hate anyone but you are really a bad person. You are now labelling innocent students for your propaganda. Shame on you!

it’s official, vibe coding done by legend himselff😂

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This really works good, I’ve started to run: /ui-ux-pro-max /make-interfaces-feel-better /simplify After every major design changes, works like a charm.

Jakub Krehel @jakubkrehel

4 months ago

/make-interfaces-feel-better npx skills add jakubkrehel/make-interfaces-feel-better

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PROMPT // Create a 14-page horizontal-swipe editorial magazine as a single HTML file. E-ink aesthetic: monochrome only, Playfair Display for headlines, Source Serif for body, IBM Plex Mono for metadata. Page 1: Dark cover. Page 14: Dark back cover. Pages 2-13: The 12 most trending GitHub repos in the last [CHANGE TO DAY or WEEK] (search for them). Alternate light/dark pages. Each repo slide is a 55/45 split layout. Left: giant ghosted rank number, category tag, editorial headline, repo path, description. Right: star count at 6.5vw+, language tag, then a visible callout box (background tint + 4px left border) with a personalized "How to use this" note explaining how I should think about applying this repo to my work. Rules: 100vw x 100vh pages, CSS transform-based horizontal pagination, no scroll Arrow key + swipe + wheel + dot navigation Huge fonts. Headlines at 5vw+. Star counts at 6.5vw+. Body text at max(17px, 1.5vw). Nothing small. High contrast on dark pages. Body text at .85 opacity minimum. Callout text at .8. Nothing overlapping. Generous margin-bottom between all elements. 10vh bottom padding. Full-bleed edge-to-edge. No floating cards. No padding boxes. Think Monocle magazine.

1.Download Obsidian → create a new vault (folder) 2.Download Claude Desktop (Claude Code) 3.Point Claude to the path of your vault 4.Paste the prompt from the article > Just 4 steps and 10 minutes of your time The face I made when I realized I had been suffering all this time, not knowing such simple things

Defileo🔮 @defileo

4 months ago

x.com/i/article/2041…

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🚨 You need to see this. @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 🤯 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: → Define - refine ideas, write specs before a single line of code → Plan - decompose into small, verifiable tasks → Build - incremental implementation, context engineering, clean API design → Verify - TDD, browser testing with DevTools, systematic debugging → Review - code quality, security hardening, performance optimization → 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: ✦ Claude Code ✦ Cursor ✦ Antigravity ✦ ... 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 🧵↓

bro created an AI job search system for Claude Code that scored 700+ job applications and actually got him a job. AND IT'S NOW OPEN-SOURCE. It scans multiple company career pages, rewrites your CV per job, and even fills application forms. The repo has: > 14 skill modes (evaluate, scan, PDF, ...) > Go terminal dashboard > ATS-optimized PDF generation via Playwright > 45+ companies pre-configured (Anthropic, OpenAI, ElevenLabs, Stripe...) GitHub: github.com/santifer/caree…

🚨 Someone reverse-engineered the design systems of Apple, Spotify, Airbnb, and 30+ billion-dollar companies. Packed each one into a single file. Free. It's called Awesome Design MD. Drop one file into your project. Your AI agent builds UI that looks like Spotify. Or Apple. Or Airbnb. Instantly. Not screenshots. Not Figma links. A single DESIGN .md file that captures every color, font, spacing value, button style, and layout pattern from a real website. In a format AI agents read and reproduce. Here's the difference: Tell Claude Code "build me a landing page" and it gives you generic UI. Tell Claude Code "build me a landing page" with Spotify's DESIGN .md in your project and it gives you Spotify. Here's what's inside: → Apple. Premium white space, SF Pro typography, cinematic imagery. → Spotify. Vibrant green on dark, bold type, album-art-driven layout. → Airbnb. Warm coral accent, photography-driven, rounded UI. → Linear. Ultra-minimal, precise spacing, purple accent. → SpaceX. Stark black and white, full-bleed imagery, futuristic. → BMW. Dark premium surfaces, precise German engineering aesthetic. → NVIDIA. Green-black energy, technical power aesthetic. → Uber. Bold black and white, tight type, urban energy. → Sentry, PostHog, Raycast, Cursor, ElevenLabs, and 20+ more. Here's how to use it: → Pick a design system from the collection → Copy the DESIGN .md file into your project root → Tell your AI agent to use it → Get UI that matches the design language of a billion-dollar company That's it. One file. Your AI agent now has the design taste of a $200/hour design consultant. Designers charge $5,000+ for a custom design system. Companies spend $50,000+ building one from scratch. This is free. 31 design systems. Copy. Paste. Ship beautiful UI. Works with Claude Code, Cursor, Codex, and any AI coding agent that reads project files. 100% Open Source. MIT License.

Wow, this tweet went very viral! I wanted share a possibly slightly improved version of the tweet in an "idea file". The idea of the idea file is that in this era of LLM agents, there is less of a point/need of sharing the specific code/app, you just share the idea, then the other person's agent customizes & builds it for your specific needs. So here's the idea in a gist format: gist.github.com/karpathy/442a6… You can give this to your agent and it can build you your own LLM wiki and guide you on how to use it etc. It's intentionally kept a little bit abstract/vague because there are so many directions to take this in. And ofc, people can adjust the idea or contribute their own in the Discussion which is cool.

Andrej Karpathy @karpathy

4 months ago

LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating

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LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

Here's how to install the skill, or just to copy-paste it: aihero.dev/s/gmZDqb

Someone on Github Uploaded Company wise Interview Questions 😭😭 I'm going to cancel my leetcode premium now 😭🙂