Narendra Santhosh

@igormomentum @karpathy @THDX @rauchg @mitchellh @dhh @addyosmani Good X starter pack for devs

Here are some best accounts to follow for original content on AI, engineering and design: @karpathy — on llms @thdx — opencode creator @rauchg — vercel ceo @mitchellh — ghostty, ex-hashicorp founder @dhh — ruby on rails creator, 37signals/basecamp cto @addyosmani — google cloud ai lead @zeeg — sentry founder @jarredsumner — bun founder @BHolmesDev — astro/dev educator @boristane — led cf workers observability @karrisaarinen — linear founder @kepano — obsidian founder @trq212 — claude code updates @bcherny — claude code creator @lennysan — product management / interviews @jasonfried — 37signals/basecamp ceo @leerob — OG educator devrel (cursor, next.js) @ctatedev — vercel labs @Shpigford — serial maker/founder Design engineering: @shadcn — shadcn creator @emilkowalski — emilkowal .ski @joshpuckett — interfacecraft .dev @jakubkrehel — jakub .kr @raphaelsalaja — userinterface .wiki @nandafyi — design @ cloudflare @benjitaylor — design @ twitter, agentation @mengto — founder aura build, educator @jayneildalal — designer interviews @jh3yy — design eng breakdowns Engineering media and news: @GergelyOrosz — youtube/pragmaticengineer @theo — youtube/t3dotgg @ThePrimeagen — youtube/ThePrimeTimeagen @Rasmic — youtube/rasmic @atmoio — youtube/atmoio DB people: @jamwt — convex CEO @jamesacowling — convex CTO @glcst — turso CEO @samlambert — planetscale CEO -- Who else would you add to this list?

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.

Today we're excited to announce NO_FLICKER mode for Claude Code in the terminal It uses an experimental new renderer that we're excited about. The renderer is early and has tradeoffs, but already we've found that most internal users prefer it over the old renderer. It also supports mouse events (yes, in a terminal). Try it: CLAUDE_CODE_NO_FLICKER=1 claude

Today learnt more about indexing and it's value with my colleague

@md_kasif_uddin Notepad++

Be honest, Which one was your first code editor?

Computer use is now in Claude Code. Claude can open your apps, click through your UI, and test what it built, right from the CLI. Now in research preview on Pro and Max plans.

I love this rapid development phase, we shipped 10+ features for @edithlyai last weekend We did run tests, it's getting an inch better every time

Yup, we may see new framework but will we be seeing a good compiler from AI Can LLMs even make some of it

Idea to reality in hours

any good product showcase tool for marketing and feature reveal

This gives a good relief

Are you also annoyed with AI Coding the CTAs, clickable link fails to have cursor pointer instead just cursor

Vibe Code ❌ Binge Code ✅

You can now enable Claude to use your computer to complete tasks. It opens your apps, navigates your browser, fills in spreadsheets—anything you'd do sitting at your desk. Research preview in Claude Cowork and Claude Code, macOS only.

lately I am realising, users doesnt care you do RAG, AI Memory or any fancy tech stuff, it should solve one of their problem or add a value

We invited Claude users to share how they use AI, what they dream it could make possible, and what they fear it might do. Nearly 81,000 people responded in one week—the largest qualitative study of its kind. Read more: anthropic.com/features/81k-i…

@GeoffreyHuntley @coreyepstein Glad to be in 0.1 % 😅

@coreyepstein I'd go for as far as saying 99.9% of the world does not understand it or heart of it.

gm

Niceeee

It’s a big week for Stitch!🚀 We’re so excited to show you what we’ve been building, starting today with the official Stitch TypeScript SDK. npm i @​google/stitch-sdk We designed our SDK for both humans and their agents to drive Stitch designs. Call stitch from your code with simple methods like stitch.createProject() and screen.edit(). Use an agent-centric API stitch.listTools(), stitch.callTool("create_project", { }) and an AI SDK adapter for agentic workflows. Live on GitHub and npm. Works seamlessly with Node.js and Bun. Links in 🧵. Happy building! 🛠️