19 | Code • Build • Ship | DM for work/Collab
Joined November 2023
- Tweets12.7K
- Following510
- Followers2.6K
- Likes54.7K
Anurag Jha retweeted
500K people online in a server. Someone sends “hello”.
500K deliveries? No. About 500. Publish once per gateway, fan out locally in memory.
Also covered:
Snowflake IDs for ordering
Time-bucketed Cassandra partitions
Bitmask permissions
Do read-
Anurag Jha retweeted
Visit and bookmark it first and thank me later toki.achintyakj.tech
Anurag Jha retweeted
Scribely is getting thousands of organic visits, and now your product can get some of that visibility too.
opened up 4 exclusive sponsor spots for fellow founders.
your logo + product name + clickable link, featured right on Scribely's homepage.
Get your product discovered 👀
scribely.site
Anurag Jha retweeted
be Tim
> post your iPhone Duo review on X
> the video blows up
> 50M views and 200K+ followers in less than 48 hours
> give away 100 iPhone 18 Pros
>Get the title of Chinese MrBeast
> collab with the actual MrBeast
Anurag Jha retweeted
Today, I'm launching llmdojo.dev:
- The hands-on way to learn AI engineering: LLMs, RAG, agents, evals and inference.
- 50+ interactive labs. Tweak tokenizers, attention, embeddings and KV caches live.
- Train a neural network in your browser and watch it learn.
- Build agentic workflows with tool calls, memory, guardrails and bounded autonomy.
- Ship RAG that works: chunking, hybrid search, reranking, and LLM-as-a-judge evals.
- Production AI: model routing, fallbacks, quantization, caching and cost per request.
- 490+ interview questions, reported from OpenAI, Anthropic, Google, Meta, Amazon, Microsoft, Mistral and 50+ more.
- Every answer is cited from papers and books, or measured on real models.
Anurag Jha retweeted
Winner announcement in 24 hours! We'll randomly draw 100 eligible winners with help from @grok. Make sure you follow us, like & repost to be in the running. Good luck! 🍀
Anurag Jha retweeted
If you want your product to blow up, just pay @anuragdotdev to tweet about it.
Every product he tweets about gets anywhere from 10K to 500K impressions on X.
Contact him if you want to promote your product and get it in front of thousands of people.
Anurag Jha retweeted
The 'One Piece' anime premiered 27 years ago today. 🏴☠️
And the journey is still far from over.
▪️ 1,180 Anime Episodes
▪️ 600M+ Manga Copies Sold Worldwide
▪️ 1,196 Manga Chapters
▪️ 15 Animated Films
▪️ 2 Live-Action Seasons
▪️ 100+ Manga Volumes
▪️ 40+ Video Game Titles
▪️ 80+ Countries Reached
▪️ 20+ Anime Seasons
▪️ 27 Years of Adventures, Dreams & Legacy
Created by Eiichiro Oda, One Piece has become one of the biggest manga and anime franchises in history.
From East Blue to the Grand Line, from Alabasta to Wano, and wano to elbaf, generations of fans have grown up with the Straw Hats.
And after all these years, we're still chasing the same dream.
The One Piece is real. 🏴☠️
Anurag Jha retweeted
For those of you who are new, I have written a comprehensive article that's basically a deep dive on Redis, from first principles to production.
Most people know Redis as “that fast cache.” It is so much more. Here is what the full article covers:
Foundations: what Redis is and why in-memory changes everything
Data structures: Strings, Lists, Sets, Sorted Sets, Hashes, Bitmaps, HyperLogLog, Streams, Geo
Core features: TTL, eviction, transactions, pipelining, Lua, Pub/Sub
Persistence: RDB, AOF, and the hybrid mode
Scale: replication, Sentinel, and Cluster with its 16,384 hash slots
Security and ops: ACLs, TLS, memory tuning, monitoring
Real-world patterns: caching, rate limiting, distributed locks, queues, leaderboards
Modern Redis: JSON, search, time series, Bloom filters, vector search
If you work on backend systems, this is the one resource that takes you from “SET and GET” to designing a production-grade Redis setup.
Anurag Jha retweeted
Sometimes the answer is right and I still have no idea what ai just said. Instead of noticing that, it just hands me another wall of text which still make no sense to me
Imagine if ai could tell I was lost and try a completely different way of explaining it not more words but a completely different angle. I'd actually pay to use that
I can see a lot of potential here and honestly, in the race to make AI that can code, we should be thinking about other ways people actually want to use it too for people other than developers
Yesterday we previewed Griffin, a Human Interaction Model capable of seeing, hearing, sounding, and looking like a human does. There have been a lot of questions, so I wanted to take a moment to share our thoughts.
By way of introduction: Tavus is a research lab focused on enabling machines to meet us where we are, and to understand the nuances of how we communicate beyond words.
Griffin, our latest model, isn’t publicly available yet. Yesterday’s announcement was a limited research preview to demonstrate what the model is capable of. With AI progressing so quickly, we prefer to share breakthroughs openly and in real-time as we work on a safe public release.
Face-to-face is how we evolutionarily communicate, it carries the most meaning and intent, and we want computers to be able to help with work that benefits from that emotional understanding, expression, and immersion. Some examples of the kinds of use cases we care deeply about:
- A tutor that can build understanding of how a student learns, see exactly when there is confusion or disengagement, and adapt the lesson to fit them.
- A health expert that can answer any questions about your upcoming appointment or prescription, at the pace you want, at any time you need, even on a weekend.
- A language coach that you can practice speaking with, that can correct your movement and pronunciation, and help build confidence to have real conversations
- Or the perfect assistant for everyone, that understands intent, knows how you work and remembers what matters.
We’re working with partners on safeguards and systems for disclosure, as well as inviting discussions with officials around wider regulation and safe use. People will always know they’re interacting with AI, while providing an interface that removes the need to ‘speak computer’.
We believe in a future where computers understand us well enough to make technology more accessible, more useful, and make us more capable as humans. That is the world we want to build, and we understand the responsibility to do so safely.
Anurag Jha retweeted
Built a semantic code search from scratch with my local llm based on this article
- indexes my local repository with embedding
- built a c++ engine to store those embedding with the same compact tricks as Uber uses (6-byte key hashes, 5-byte value offsets its actually a 11 byte per entry design)
- uses local 4B model to summarize results
-- for now it doesn't supports the reasoning and I am working on it :D
this was such a banger read by uber on perf optimizations and serving huge read heavy workloads that too from a small memory
uber.com/us/en/blog/translat…
Anurag Jha retweeted
Ever browsed 6 component libraries just to find out none of them have what you need?
Fixed that.
designeng.tools now has a Components Index:
→ search any component
→ see every library that has it
→ jump straight to it
find it at designeng.tools/components
Got tired of saving hundreds of websites across bookmarks, Notion, and random tabs just to find them again later.
So I built designeng.tools
A growing collection for design engineers:
• Website inspiration
• UI & app screenshots
• Design inspiration
• OG image inspiration
• Logos & branding
• Design + dev tools
• Resources
• Creators & people worth following
And a lot more.
Still adding new categories, tools, resources, and people regularly.
