SHIVAM KUMAR

I-JEPA — What If AI Learned to See by Imagining, Not Copying. ??? I simplified the research paper and explained the core ideas in an easy, technical way. Read here : shivam2003.com/posts/ijepa-pa… #ml #ai #cv #research

@ClaudeDevs Happy Happy Happy

Happy Friday! We've reset everyone's 5-hour and weekly rate limits.

xAI (@elonmusk ) open-sourced the algorithm behind X's For You feed. 7-stage pipeline. Grok-based transformer predicting 15 engagement signals. I broke down how it actually works . Algo : github.com/xai-org/x-algo… Read : shivam2003.com/posts/x-algori… #AI #LLM #Grok #xai

The latest 𝕏 algorithm has been published to GitHub github.com/xai-org/x-algo…

If you're serious about system design (in 2026), learn these 26 case studies: 1 How Stock Exchange Works: ↳ newsletter.systemdesign.one/p/stock-exchan… 2 How YouTube Works: ↳ newsletter.systemdesign.one/p/youtube-syst… 3 How Kafka Works: ↳ newsletter.systemdesign.one/p/how-kafka-wo… 4 How Google Docs Works: ↳ newsletter.systemdesign.one/p/how-does-goo… 5 How URL Shortener Works: ↳ systemdesign.one/url-shortening… 6 How WhatsApp Works: ↳ newsletter.systemdesign.one/p/whatsapp-sys… 7 How Airbnb Works: ↳ newsletter.systemdesign.one/p/airbnb-syste… 8 How Spotify Works: ↳ newsletter.systemdesign.one/p/spotify-syst… 9 How Slack Works: ↳ systemdesign.one/slack-architec… 10 How Reddit Works: ↳ newsletter.systemdesign.one/p/reddit-archi… 11 How Bluesky Works: ↳ newsletter.systemdesign.one/p/how-does-blu… 12 How Tinder Works: ↳ newsletter.systemdesign.one/p/tinder-archi… 13 How Twitter Timeline Works: ↳ newsletter.systemdesign.one/p/system-desig… 14 How Uber Finds Nearby Drivers: ↳ newsletter.systemdesign.one/p/how-does-ube… 15 How Pastebin Works: ↳ systemdesign.one/system-design-… 16 How Amazon S3 Works: ↳ newsletter.systemdesign.one/p/s3-architect… 17 How Do Apple AirTags Work: ↳ newsletter.systemdesign.one/p/how-do-airta… 18 How LLMs Actually Work: ↳ newsletter.systemdesign.one/p/llm-concepts 19 How Uber Computes ETA: ↳ newsletter.systemdesign.one/p/uber-eta 20 How Real Time Leaderboard Works: ↳ systemdesign.one/leaderboard-sy… 21 How ChatGPT Apps Work: ↳ newsletter.systemdesign.one/p/apps-in-chat… 22 How Nginx Works: ↳ newsletter.systemdesign.one/p/how-does-ngi… 23 How ChatGPT Works: ↳ newsletter.systemdesign.one/p/chatgpt-syst… 24 How Meta Serverless Works: ↳ newsletter.systemdesign.one/p/serverless-a… 25 How YouTube Was Able to Support 2.49 Billion Users With MySQL: ↳ newsletter.systemdesign.one/p/vitess-mysql 26 How Google Search Works: ↳ newsletter.systemdesign.one/p/web-crawler-… What else should make this list? === 👋 PS - Want my System Design Playbook for FREE? Join my newsletter with 200K+ software engineers now: → newsletter.systemdesign.one/join === 1 Save & RT to help other software engineers ace system design. 2 Follow @systemdesignone + turn on notifications.

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?

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…

It is hard to communicate how much programming has changed due to AI in the last 2 months: not gradually and over time in the "progress as usual" way, but specifically this last December. There are a number of asterisks but imo coding agents basically didn’t work before December and basically work since - the models have significantly higher quality, long-term coherence and tenacity and they can power through large and long tasks, well past enough that it is extremely disruptive to the default programming workflow. Just to give an example, over the weekend I was building a local video analysis dashboard for the cameras of my home so I wrote: “Here is the local IP and username/password of my DGX Spark. Log in, set up ssh keys, set up vLLM, download and bench Qwen3-VL, set up a server endpoint to inference videos, a basic web ui dashboard, test everything, set it up with systemd, record memory notes for yourself and write up a markdown report for me”. The agent went off for ~30 minutes, ran into multiple issues, researched solutions online, resolved them one by one, wrote the code, tested it, debugged it, set up the services, and came back with the report and it was just done. I didn’t touch anything. All of this could easily have been a weekend project just 3 months ago but today it’s something you kick off and forget about for 30 minutes. As a result, programming is becoming unrecognizable. You’re not typing computer code into an editor like the way things were since computers were invented, that era is over. You're spinning up AI agents, giving them tasks *in English* and managing and reviewing their work in parallel. The biggest prize is in figuring out how you can keep ascending the layers of abstraction to set up long-running orchestrator Claws with all of the right tools, memory and instructions that productively manage multiple parallel Code instances for you. The leverage achievable via top tier "agentic engineering" feels very high right now. It’s not perfect, it needs high-level direction, judgement, taste, oversight, iteration and hints and ideas. It works a lot better in some scenarios than others (e.g. especially for tasks that are well-specified and where you can verify/test functionality). The key is to build intuition to decompose the task just right to hand off the parts that work and help out around the edges. But imo, this is nowhere near "business as usual" time in software.

Huge respect for Rakesh Rajendran, Co-founder of Nudgebee. What a speaker ! confident, precise, and deeply clear in his thinking. One of the standout talks at #KCDDelhi. Sharp product clarity. Strong execution mindset. Real depth. #AIOps #Kubernetes #CloudNative #CN

#KCDDelhi From AI-driven decision systems to GPU-native observability and vcluster use cases : strong technical depth. Great connecting with community leaders and builders. @kcddelhi #OpenSource #Kubernetes #AI

I created a Github repository to learn System Design, and I'm excited to share that it crossed 30k stars recently. The repository contains a collection of resources to study: - System Design Core Concepts - Networking and API Fundamentals - Database and Caching Fundamentals - Distributed Systems, Microservies and Architectural Patterns - System Design Tradeoffs - 40+ System Design problems categorized by difficulty level Check it out here: github.com/ashishps1/awes… If you find the repo valuable, consider giving it a ⭐️ and share with others. Thanks to everyone who has starred or forked the repository!

Can AI agents design better memory mechanisms for themselves? Introducing Learning to Continually Learn via Meta-learning Memory Designs. A meta agent automatically designs memory mechanisms, including what info to store, how to retrieve it, and how to update it, enabling agentic systems to continually learn across diverse domains. Led by @yimingxiong_ with @shengranhu 🧵👇 1/

When I created Claude Code as a side project back in September 2024, I had no idea it would grow to be what it is today. It is humbling to see how Claude Code has become a core dev tool for so many engineers, how enthusiastic the community is, and how people are using it for all sorts of things from coding, to devops, to research, to non-technical use cases. This technology is alien and magical, and it makes it so much easier for people to build and create. Increasingly, code is no longer the bottleneck. A year ago, Claude struggled to generate bash commands without escaping issues. It worked for seconds or minutes at a time. We saw early signs that it may become broadly useful for coding one day. Fast forward to today. In the last thirty days, I landed 259 PRs -- 497 commits, 40k lines added, 38k lines removed. Every single line was written by Claude Code + Opus 4.5. Claude consistently runs for minutes, hours, and days at a time (using Stop hooks). Software engineering is changing, and we are entering a new period in coding history. And we're still just getting started..

I believe this new model in Claude Code is a glimpse of the future we're hurtling towards, maybe as soon as the first half of next year: software engineering is done. Soon, we won't bother to check generated code, for the same reasons we don't check compiler output.

@iyoushetwt Fortran

What was the first programming language you learned ? Mine : C++

where a change that isn’t properly understood causes a worse problem during peak production load, and a need to back out the change.

One night, a police officer sees a drunk searching the ground under a streetlight. He asks if he lost his keys, but the drunk says he did. The officer can’t find them, so he asks again, “Are you sure you lost them here?” The drunk replies, No, but this is where the light is best.

Metrics best suited for resource analysis are 1. IOPS 2. Throughput 3. Utilisation 4. Saturation

The values of tunable parameters, are valid only at a specific point in time . #performaceengineering

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