pandeyji retweeted
the challenges if you're curious :)
fly.io/dist-sys/1/
pandeyji retweeted
WEB3 Builders is back.
Space 11: @prashant__sha hosts @kirat_tw.
Everyone asks Harkirat how to land the remote job. We ask what comes after: how you get paid from India, in dollars and in crypto.
Thu 15 Oct, 8 PM IST. Set a reminder:
xitter.cf/i/spaces/1qJDzWpoaNkKV
Anthropic engineer:
“90% of our engineers were using self‑improving loops. Now everyone shifted to building agentic Graphs"
“No more prompting.”
In just 10 minutes, she builds her entire Claude Code setup and workflow live from a blank terminal.
This is more valuable than most $1000 agentic courses.
Watch this video, Then save the article below if you want to become a graph architect before everyone else catches up.
pandeyji retweeted
I’m the actual human who made every single one of these videos they keep gluing together without asking. They love calling me ‘ex-Google engineer’ or ‘this guy’. Follow this guy and let’s actually chat. Original post is right here:
xitter.cf/shenseanchen/status/20…
Want the real AI Agent Harness System (Loop + Memory + Eval) instead of the mash-up? Grab my open-source Waku Agent (almost 1k stars): GitHub.com/shenseanchen/waku…
Original YouTube channel: yt.seanchen.io
(Better for your eyes. Trust me:))))
I also run a community where I host live biweekly Q&As and share all the original system-design files:
whop.com/sean-ai-community-d…
@X @elonmusk @benjitaylor @singhai @dinkin_flickaa please fix the theft on X.
@vattybear thanks so much for the constant support my friend! 🔥
THIS GUY MAKES COMPLEX AI AGENT CONCEPTS RIDICULOUSLY EASY TO UNDERSTAND.
No jargon wall. No assuming you already know what a reward model or an evaluation harness is.
Just a straight line from "I have no idea how agents actually work" to "oh, that's genuinely simple."
The best explainers do not simplify by cutting corners. They simplify by finding the one analogy that makes the whole thing click.
pandeyji retweeted
best way to learn docker is by using it. Use Docker for everything you are running right now, unless there is a really specific reason why that is impossible.
The clue for me of really understanding the use case, was being confronted in production with configurations that could take hours or generate very complex automation setups. Dockerizing the setup is so powerful in that use case that you learn how to use it and what it's strong points are right away.
If you do not have a complex production setup or are just learning on your own, try to create something like that. Set up an elasticsearch cluster with kibana, set up a prometheus / grafana, set up some SQL databases, ... Just things that you would do in real life. You will be confronted with everything you need to run a complex setup in production and you will learn really quickly how much more easy the Dockerized setup usually is vs the native.
pandeyji retweeted
Every backend engineer should know how distributed systems work and their basics.
If you want to actually master Distributed Systems, stop hoarding tutorials and watch Martin Kleppmann’s free lecture series.
It completely changes how you think about building scalable, fault-tolerant backends.
Link in the replies. 👇
pandeyji retweeted
yoo chat
if you are looking for any repositories to dive deep on the topic of docker image optimization than have a look at this one .... hope it helps you
github.com/ag-chirag/ai-dock…
pandeyji retweeted
Single ML project flooded my DMs with opportunities!
In Feb, I created a project called STOCK-AGENT-OPS
> github.com/kmeanskaran/stock…
It has data analytics, feature engineering, Feast, model training, evaluation, MLflow, FastAPI backend components, Redis cache, a LangChain agent, AWS Bedrock, LLM optimization, Prometheus, Grafana, Docker, Kubernetes, AWS EC2, EKS, ECR, and more.
I took all these noisy tools and steps and plugged them into a clean architecture, and delivered two articles as well:
- Dev Phase: substack.com/@kmeanskaran/no…
- Prod Phase (AWS): substack.com/@kmeanskaran/no…
Just building a single project brought me so much freelance work and job opportunities. I'll share the interview process, but more than that, the foundation this project gave me helped me answer and explain things in interviews.
I got rejected in the 4th round by an SF Series A startup, and on the same day, I got an offer from @Syngenta as an MLOps Engineer.
In short: you have to build systems in your personal projects and fix the boring parts, like caching, latency, reducing GPU time, and batching, rather than making the same flashy projects over and over.
Trust me, you just need one quality project to completely change your trajectory in learning and career.
I'll be posting more about interview prep and the importance of building in public for AI/ML.
Cheers!
Career Update!
I've joined @Syngenta as an MLOps Engineer in a global AI team.
Excited to ship projects into production!
pandeyji retweeted
If you are really out of ideas for this weekend I would suggest you to build parsers TOML, Markdown, HTML and YML parsers are pretty well known to build ....
I would suggest to build some Network protocol parsers like HTTP/2 frame parser, DNS packet parser, TNS record layer parser this will cover your most of the networking theory part ...
second if you want to go deep dive into binary formats and serialization then build parser for your ZIP file, a git packfile parser (the most interesting one) ....
the one I am currently working on are a DWARF parser which generally parses the .debug_info and .debug_line sections for source level debugging
pandeyji retweeted
Recently got a lot of DMs asking how I got a remote job. So here's what worked for me.
→ Pick one niche and go deep. Something you actually like, that you can talk about without running out of things to say.
→ Be consistent. Do what you're doing everyday, even if it's just for an hour. It builds muscle memory.
→ A few places to find these companies: YC jobs, Wellfound, Remote OK, We Work Remotely. Look for the ones that recently raised funding, they're hiring fast.
→ Find the founder or their employees. Get their contact, X, or email, and message them directly.
→ Keep it simple. Who you are, what you do, one line on why this company. Know about the company first, don't message blindly.
→ If you only want remote, say it upfront in the first message. Don't let it come up later.
→ Send proof of work, not just a resume. Make a Notion doc, write a bit about yourself, and drop the links to your best projects. Add a 2-3 minute Loom video walking through them, it shows the actual effort. If you have open source work, add that too.
→ Don't wait to feel qualified. Just start. But have a few good projects ready before you do.
→ Even if someone rejects you, keep the connection. Say thank you, end it well. I actually got referred to another company by a founder who rejected me.
→ Post your work on X. Doesn't matter how much reach you get, it still increases visibility. I was getting clients even without much reach.
→ It's a numbers game. Do cold DMs every day, make it a habit, not something you try when you're desperate.
→ Keep the consistency and the hard work. You don't know when the opportunity will come.
pandeyji retweeted
hello internet stranger,
read this book 2 times
would love to talk about this book ... if you are junior or just starting with this book just for interview purposes go through chapter 3, 5, 6, 8 and 9 thoroughly although you will find the book a little hard for the first but sticking with it will be worthwhile
If you read it really carefully and very slowly, the content is fantastic and can’t be faulted.
I have ordered the second addition but I had to cancel my order on halfway will see next time :)
pandeyji retweeted
Part 1 in my series on Low Level System Design in Rust is live now - This part covers:
• how to lay out a shared Rust struct by who-touches-what
• zoning fields by write owner and frequency, then
• padding the cross-core ones to avoid false sharing and prefetcher-induced contention
Cache-Conscious Data Layout in Rust: Field Zoning, False Sharing, and the 128-Byte Rule - debasishg.github.io/blog/par…
pandeyji retweeted
Rust 8 years ago. 🦀
- Rustlang,
- cargo,
- std,
- tokio
Rust today. 🦀
- Actix web
- Axum
- Rocket
- Hyper
- Reqwest
- Tonic
- Sqlx
- Diesel
- sea orm
- tokio postgres
- Pollers
- Vello
- iced
- Egui
- Tauri
- Trunk
- wasm bindgen
- yew
- leptos
- bevy
- macroquad
- serde
- rayon
- crossbeam
- Regex
- ltertool
- log
- cargo edit
- cargo deny
- cross
And many more....
pandeyji retweeted
Saw this video after 2 years..these were the projects
Text Editor
2D Game - Space Invaders
Compiler - Tiny BASIC
Mini Operating System
Spreadsheet
Video Game console emulator
Dope list tbh
pandeyji retweeted
Shubham dropped another hermes masterclass
How I built an AI Agent team that runs 24/7 explained in 19 minutes.
OpenClaw is where the squad started. Hermes is where it's evolving. YES, I use both of THEM just via Telegram.
4 things that makes my agent team work like humans:
- Automated Cron: Agents run pro-actively on schedules and heartbeats
- Memory: They keep context about my preferences, audience, past performance, and decisions.
- Self-improving review loops: Each agent reviews its own work monthly. Monica reviews the entire squad bi-weekly and grades them.
- Escalation: They do the work, but I still make the final decision.
These agents manage the open-source Awesome LLM Apps repo with 115k+ GitHub stars and help me stay up-to-date with pro-active daily signals.
I operate like a CEO reviewing a squad from my phone. The agents keep the work moving until I have to step in.
pandeyji retweeted
Top 10 system design resources I still point working devs to:
1) Designing Data-Intensive Applications (Kleppmann) — real tradeoffs: logs vs DBs, consistency, stream processing.
2) Software Architecture: The Hard Parts (Ford/Richards) — how to choose, not just patterns; good on modularity and coupling.
3) Site Reliability Engineering (Google) — SLIs/SLOs, error budgets, incident response, capacity planning.
4) System Design Interview (Alex Xu) — fast reps; good for sketching APIs, storage, caching, sharding under time pressure.
5) Designing Distributed Systems (Brendan Burns) — Kubernetes patterns explained as general distributed system building blocks.
6) AWS Well-Architected Framework — concrete checklists for reliability, security, cost; useful even if you run on-prem.
7) Martin Fowler’s architecture articles (martinfowler.com) — bounded context, evolutionary architecture, integration patterns.
8) High Scalability blog (highscalability.com) — teardown posts; learn why teams picked queues, caches, and DBs in prod.
9) Jepsen analyses (jepsen.io) — teaches failure modes you only find at 3am: split brain, stale reads, bad retries.
10) Practice project: build a mini SaaS and run it — REST+DB, add Redis cache, background jobs, metrics/tracing, load test, then break it with chaos/fault injection

































