
member of technical staff @lyzr__ai
earth 19999
Joined February 2017
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Krish Soni 𝕏 retweeted
Finally summarized what I've been sending to friends in DMs View article
uber’s greyball and god view are wild examples of engineering at scale.
one shows how systems can be used to outsmart regulators, the other how real-time data can give you a live view of an entire city.
there’s so much to learn from breaking down how these systems actually worked.
/anatomy Explain how Redis caching machine works
Introducing Anatomy
Describe a machine in one line. Claude brings it to life.
- Interactive isometric wireframes you can rotate, inspect and explore
- WebGL shaders for fire, water, plasma and more
- Real 3D motion, with individual parts that rotate and move
Install it as an agent skill. Fully open source.
Replying to @instagram
@instagram , hear me out:
let me set restricted words for specific 1:1 dms.
if someone tries to send a message with one of them, show:
“Some words are restricted in this chat.”
don’t let the message send until they remove the word.
need this feature immediately 🤠
cc @Meta
Every git bisect run comes down to one thing: the number your script exits with.
0 is good. 1 to 127 (except 125) is bad. 125 is "skip this commit." Anything else stops the search.
Git halves the range after each answer and prints the first bad commit when one is left.
If your agent's behavior lives in a repo, as it does with GitAgent, the same trick finds the commit that changed it.
Krish Soni 𝕏 retweeted
Inference and databases make up so much of the future of compute.
Highly recommend you git gud at one of these two things.
transferred 10k whatsapp chats from android to ios.
whatsapp really made this impossible than it needed to be 😭
thanks babyy, @claudeai.
Krish Soni 𝕏 retweeted
AI Engineering Day - 01: Attention
Spent today understanding how attention actually works in LLMs. The idea is simpler than the formula makes it look.
Take "The animal didn't cross the road because it was too tired."
To figure out what "it" means, the token "it" sends out a Query: what am I looking for?
Every other token holds a Key: here's what I contain.
The best matches get a high score, and the model blends their Values into a new representation of "it".
That's the whole trick. Softmax just turns the scores into weights that add up to 1.
I wrote my notes on this with diagrams, including self vs cross vs causal attention and multi-head. If you're learning this too, it might save you some time:
learn.ratishfolio.com/llms/a…
Krish Soni 𝕏 retweeted
I made my first crore when I was 21 in college.
It came entirely from remote work, not YouTube. That financial baseline changed my life and gave me the total freedom to explore the tech industry on my own terms.
I will always advocate for expanding your horizons beyond local borders. Expanding your network globally, especially from India, is life-changing.
Always remember: folks who are actually making good money won't push overpriced courses on you or seek validation online.
Focus on what works: build in public, contribute to open source, touch grass, lower your screen time, connect with people, and be kind.
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Krish Soni 𝕏 retweeted
Jev is now available to everyone. No waitlist.
Start using it here: console.typesafe.ai
this is the kind of innovation i find interesting.
pocket fm isn’t just using models to generate stories they’re building a whole system around long-form storytelling: narrative memory, story planning, continuity, feedback, and localization. that’s a much harder problem than just making an llm write better prose.
we actually got to build in this space through their zero to one hackathon, where we built pocket-taste a layer for recommendations, creator insights, similarity detection, and story generation.
seeing pocket fm now push this kind of infrastructure further is pretty cool.
Introducing Sherpa: the most advanced fiction writing AI
We accelerated from $250M in ARR to $500M because Sherpa helped increase content production by 1200% in 1 year
Sherpa was trained on 5.5B hours of playtime with minute by minute dynamic retention data.
550K+ creators have produced 2.6M hours of content annualised using it
Pocket FM is like Netflix for audio-only dramas, with our own pool of one-person studios.
10% of eligible writers on Pocket FM make >$200K
One blockbuster produced >$100M in revenue
3 writers have become millionaires in <2 yrs
We built Sherpa to enable anyone to make >$1M by writing world-class fiction stories:
1. The Idea: Drop a 1-2 sentence concept. Sherpa interrogates it like a veteran editor on tension, stakes, and psychology
2. World & Characters: It builds out the complete lore, tone, and character psychologies
3. Sub-Plot planning: Breaks the premise into arcs, arcs into episodes, and episodes into scenes
4. Scene-by-Scene Generation: Outlines and drafts entire episodes, with you able to steer, rewrite, or override anytime
5. Editorial Review: Stress-tests every draft for pacing, engagement drop-offs, prose, and coherence before it locks
6. One-Tap Production: Pick a voice, convert to audio drama, and publish directly to Pocket FM’s millions of listeners
7. Global Scale & Monetization: Revenue-share on performance, with automatic localization so you earn across international markets
Test Sherpa for free here: pocketfm.com/sherpa
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Generic LLMs fail at serialized fiction because they lack a long-horizon narrative reward function.
Sherpa solves this through three core technical leaps:
1. Narrative World Model (State Tracking & Retrieval): Context windows degrade over long runs. Sherpa constructs an evolving semantic knowledge graph tracking character states, secrets, and plot dependencies. High-speed retrieval surfaces exact context on demand, maintaining zero continuity decay across hundreds of episodes
2. Hierarchical Story Planner: When writing a 500-episode story like Naruto, you need to plan 100s of sub plots. Rather than generating linearly, Sherpa decomposes narrative across discrete levels: season -> arc -> sequence -> episode -> scene.
Rather than generating everything upfront, like a generic LLM, Sherpa uses progressive planning and dynamic replanning. As the story evolves, it identifies what changed, traces the downstream impact, and replans only the affected parts.
3. Prose Engine (Trained on series' retention data): LLMs write robotically, but serial fiction needs emotion, tension, pacing, and dialogue that sounds like real people.
Sherpa's Prose Engine was designed specifically for storytelling. It was built on 1B+ tokens of Pocket's own stories, trained by learning from what listeners engage with, where they drop off, and what keeps them hooked.
Feedback is taken from specialized evaluator models that measure every scene against a 40-item checklist. (Evaluator models were benchmarked against human reviewers and matched them 80–90% of the time.)
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Owning distribution and creation puts us in a very unique spot.
More shows -> More data -> Sherpa becomes better -> more creator success -> more creators -> more shows
Pocket FM has already seen one $100M IP. I believe Sherpa will soon lead to dozens of single-person studios creating billion-dollar shows.
Most people are scared of AI but I think it'll unlock more human creativity, help creators earn more, and bring the next great IPs to life. This will create millions of jobs and new income streams.
What does a universal control plane for enterprise agents actually look like?
It accepts every framework, LangChain, CrewAI, Google ADK. It works with whichever coding agent your team already uses, Claude Code, Codex, Copilot, Cursor, Devin. It connects to the enterprise platforms running their own agents, Salesforce, SAP, ServiceNow, Databricks, Snowflake. It routes to any model, frontier or open-weight. It deploys on any cloud.
And it tracks every agent that shows up across all of them, the ones built with no code and the ones built by an engineer typing into a terminal.
That is Opencontroller.
last week i built a self-hosted on premise meeting bot.
ecr-registered images for dedicated meeting workers, realtime webhooks to trigger bots, and an event-driven backend to manage the whole flow.
for transcription:
streaming audio → vad → speech-to-text → translation, with spanish support.
dealt with the fun parts too: concurrency, retries, session state, and scaling meeting workers.
i cooked
















