
builder. swe. prev quantumstudent @qubitbyqubit. gsoc redhat@project_flotta. member @ fossasia
Joined June 2017
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(1/n) just binged this YC podcast and it completely flipped how am thinking about enterprise AI deals
youtube.com/watch?v=DULfEcPR…
everyone's been doom posting about that MIT study saying 95% of AI projects fail but when you actually read it? it's basically a playbook for startups
Deependra Singh Shekhawat retweeted
This paper gives a fancy name to a problem you can already feel in your bones
"The Tragedy of the Cognitive Commons." Checking AI output requires deep expertise. Deep expertise comes from doing grunt work for years. And grunt work is the first thing AI eats
So we're building systems that need expert supervision while dismantling the only known process for making experts
The paper calls the shared pool of human expertise the "cognitive commons." Every profession drinks from it. Nobody's refilling it.
By eliminating junior roles, each company is acting totally rationally, and the collective result: a profession that can't catch AI's mistakes anymore because it never learned to do the work in the first place
Who checks AI's homework in 15 years?
Deependra Singh Shekhawat retweeted
Replying to @cailynyongyong
til founders and noble elements have a lot in common
theyre noble cuz they are unbother by outisde noise 💪🏻
Deependra Singh Shekhawat retweeted
If you are a mathematician, then you may want to make sure you are sitting down before reading further.
Deependra Singh Shekhawat retweeted
In a recent batch talk, YC General Partner @t_blom broke down how to build a self-improving, AI-native company.
He walks through how to create recursive, self-improving AI loops, and why founders who get this right will run companies that improve while they sleep.
00:00 — Companies Are Roman Legions
00:54 — Copilots Are the Wrong Mental Model
01:55 — Extract the Domain Knowledge
02:24 — The Recursive Self-Improving Loop
04:12 — The Holy Shit Moment at YC
05:50 — Self-Optimizing Product and Support Loops
06:29 — Burn Tokens, Not Headcount
07:23 — Middle Management Is Over
08:05 — Make Everything Legible to AI
09:40 — Regenerating the YC User Manual
11:19 — Software Is Ephemeral, Context Is Valuable
12:18 — Where Humans Still Matter
Deependra Singh Shekhawat retweeted
Claude pro tip: ask claude to explain something in 'grug mode' or use the github.com/juliusbrussee/cav… plugin.
It's stupid, but it works. And if it works, it's not stupid.
a company spends a percentage of their revenue on R&D to stay ahead
you need to do the same with your career to stay ahead
10% of your week should be spent on “personal R&D”
learning a new tool
talking to a mentor
building something
documenting the journey
reading docs from another part of the company
etc
it is the only way to stay ahead
and with the pace that things are moving
that is the bare minimum required imo
Deependra Singh Shekhawat retweeted
Being a founder is hard. Being a solo founder is much harder. Kennan did YC in S20 with Skio. Applied with one idea, pivoted during the batch, then pivoted again. Never gave up. The last pivot worked. Today Skio sold for $105M in cash.
There are very few straight lines to success. Congrats, Kennan!
couldn't agree more
I've worked with a few companies on this and spent a ton of time thinking about it
The hard part is if you want your employees using Claude, you don't have enough centralized control over their system prompt or skills, so no matter how well-organized your Notion or whatever is, the agent likely won't use it effectively
But everyone wants to use Claude and is very happy to be locked in by them, assuming they'll build whatever you need in a few months if it's not already there (probably correctly?)
Hard to find the right lane for this as a startup
Deependra Singh Shekhawat retweeted
Yann LeCun was right the entire time. And generative AI might be a dead end.
For the last three years, the entire industry has been obsessed with building bigger LLMs. Trillions of parameters. Billions in compute.
The theory was simple: if you make the model big enough, it will eventually understand how the world works.
Yann LeCun said that was stupid.
He argued that generative AI is fundamentally inefficient.
When an AI predicts the next word, or generates the next pixel, it wastes massive amounts of compute on surface-level details.
It memorizes patterns instead of learning the actual physics of reality.
He proposed a different path: JEPA (Joint-Embedding Predictive Architecture).
Instead of forcing the AI to paint the world pixel by pixel, JEPA forces it to predict abstract concepts. It predicts what happens next in a compressed "thought space."
But for years, JEPA had a fatal flaw.
It suffered from "representation collapse."
Because the AI was allowed to simplify reality, it would cheat. It would simplify everything so much that a dog, a car, and a human all looked identical.
It learned nothing.
To fix it, engineers had to use insanely complex hacks, frozen encoders, and massive compute overheads.
Until today.
Researchers just dropped a paper called "LeWorldModel" (LeWM).
They completely solved the collapse problem.
They replaced the complex engineering hacks with a single, elegant mathematical regularizer.
It forces the AI's internal "thoughts" into a perfect Gaussian distribution.
The AI can no longer cheat. It is forced to understand the physical structure of reality to make its predictions.
The results completely rewrite the economics of AI.
LeWM didn't need a massive, centralized supercomputer.
It has just 15 million parameters.
It trains on a single, standard GPU in a few hours.
Yet it plans 48x faster than massive foundation world models. It intrinsically understands physics. It instantly detects impossible events.
We spent billions trying to force massive server farms to memorize the internet.
Now, a tiny model running locally on a single graphics card is actually learning how the real world works.
explained how to go viral on X.... then went viral the same day
hence proved
HOW TO GO VIRAL ON X: complete basics easy mode
with first guest as @minmax0501
00:00 Intro
00:50 @getyourmomo
01:16 First guest intro
02:16 Warm up account
02:51 Tip 1: tag communities
04:19 Tip 2: tag famous accounts
05:06 Understanding X audience
06:00 Things that didn’t work
06:28 Linkedin vs X
07:14 Tip 3: post everyday
08:20 Do videos work?
09:03 Tip 4: post successes
09:44 Do multiple channels work? What’s the difference?
10:40 Tip 5: wait for random viral posts
12:00 What’s your brand message?
13:10 Tip 6: Copy viral templates from competitors
13:25 Tip 7: Follow the trend
14:35 How to structure demo videos
15:10 1 second hook
16:13 First 5 seconds matter
17:48 1 feature / video
19:17 Story telling
21:32 Writing good script
22:47 Yeet
tl;dr Execute so well that your company becomes impossible to reject.
After 6 applications and 6 rejection emails, we finally got into Y Combinator.
Yes, read that one more time: 6 applications. 6 rejections.
We turned a rejection into an admission offer.
For a long time, every rejection led to the same question:
"Do we pivot, or keep going?"
We didn't think much of the first few rejections. Our reaction was mostly just: okay, back to building, apply again next time.
Honestly, the hardest one was the 5th rejection because we felt so close. It was the first time we got an interview. We believed we had a real shot.
But in the end, we got rejected… again.
Looking back, the decision was fair.
We were only doing around $300/month, and YC didn’t see a clear path to building a billion-dollar company through enterprise.
So we stopped guessing and started listening.
We did 20+ user interviews and realized something important: the people who really loved InsForge were not big enterprises. They were AI-native small teams and startups.
That fundamentally changed how we saw the company. We clarified who the product was actually for, doubled down on what was working, and kept building in public on X and LinkedIn.
We grew from 2,300 to 4,000+ databases in 2 months.
Then we applied again. Our second interview with YC. We really thought this would be the one. But once again, we were rejected.
That was the moment the question we had been asking ourselves after every rejection finally changed.
No longer:
“Do we pivot?”
Instead:
“How do we execute so well that the need for this product becomes impossible to ignore?”
After 30 days of hell, we launched @insforge Launch Week 1.
And it took off. Like, really took off!
→ 1.5M+ views on X
→ #1 on Product Hunt
→ #1 on GitHub Trending
→ 3K+ GitHub stars in one week
But here's the craziest part: after rejecting us, YC changed their mind. Here was our second chance.
We got an email from general partner Andrew Miklas (@amiklas), congratulating us on our launch and asking us to meet one more time.
We figured it would be another tough interview.
But the meeting was in two hours. No time to prepare.
We were so nervous up until the very end.
When we finally hopped on the call, he just said, “You guys have made huge progress. I want to work with you. Do you want to do YC?”
WTF?????????
Tony (@tonychang430) and I looked at each other.
We were so shocked, we didn't even know what to say.
Of course, the answer was yes.
This is when we learned:
Execute so well that your company becomes impossible to reject.
Every rejection forced us to clarify our vision.
The last one forced us to prove it.
Next stop: YC P26!! @ycombinator 🥳
( Read the full story below ⬇️ )
Deependra Singh Shekhawat retweeted
When expertise becomes an API, connecting dots becomes a superpower.
Deependra Singh Shekhawat retweeted
Most tech companies break out product management and product marketing into two separate roles: Product management defines the product and gets it built. Product marketing wires the messaging- the facts you want to communicate to customers- and gets the product sold. But from my experience that's a grievous mistake. Those are, and should aways be, one job.
There should be no separation between what the product will be and how it will be explained- the story has to be utterly cohesive from the beginning. Your messaging is your product. The story you're telling shapes the thing you're making.
I learned story telling from Steve Jobs. I learned product management from Greg Joswiak. Joz, a fellow Wolverine, Michigander, and overall great person, has been at Apple since he left Ann Arbor in 1986 and has run product marketing for decades. And his superpower- the superpower of every truly great product manager- is empathy. He doesn't just understand the customer. He becomes the customer.
So when Joz stepped into the world with his next-gen iPod to test it out, he fiddled with it like a beginner. He set aside all the tech specs- except one: battery life.
The numbers were empty without customers, the facts meaningless without context.
And, that's why product management has to own the messaging. The spec shows the features, the details of how a product will work, but the messaging predicts people's concerns and finds way to mitigate them.
- #BUILD Chapter 5.5 The Point of PMs
just keep moving forward
You have no experience.
You’ve never started a company.
You’ve never had a full time job.
Nike is going to kill you.
You’re a kid.
You don’t have technical skills.
You shouldn’t build hardware.
Apple is going to kill you.
You can’t build hardware.
You can’t measure heart rate non-invasively.
Athletes don’t care about recovery.
Under Armour is going to kill you.
It won’t be accurate.
You don’t listen.
You’re an ineffective leader.
You can’t recruit great talent.
You’re going to have to pay every athlete.
You can’t measure sleep non-invasively.
It’s too expensive to research.
Athletes are a small market.
The product costs too much to make.
The product costs too much to sell.
Your valuation is too high.
Consumers aren’t going to want it.
Hardware is too hard.
You should measure steps.
Fitbit is going to kill you.
You can’t build a marketing engine.
You can’t raise enough money.
You need a real CEO.
Google is going to kill you.
You can’t be a subscription.
You can’t build a brand.
You can’t do consumer in Boston.
Your valuation is too high.
You shouldn’t make accessories.
You shouldn’t make apparel.
Lululemon is going to kill you.
You can’t predict Covid.
Stay in your niche.
You are going to run out of money.
You can’t build a health platform.
Amazon is going to kill you.
You can’t measure blood pressure.
You can’t get medical approvals.
The market is too small.
You don’t understand AI.
The market is too competitive.
It won’t work internationally.
The supply chain is too complicated.
You can’t build an AI.
You can’t raise enough money.
It’s too competitive.
Healthcare isn’t going to want it.
…
Just keep going ✌️
Deependra Singh Shekhawat retweeted
Exceptional pre-seed founders wanted.
Low wages.
Consistent doubt.
Hazardous journey.
Ferocious competition.
Long months wandering pre-PMF.
Honor and recognition in case of success.
Camaraderie regardless.
finally an explanation for my 6 open chrome windows n uncountable tabs
its not bad management its just low activation energyy
zero warmup just doing
People always ask me how I have time to do so much. I never thought it's about time management, but I couldn't articulate what it was.
Today I gave my last 7 days of Chrome browsing history & Google products activity to Claude Code. It came up with a better explanation then I ever could myself:
"Time management is about organizing a fixed resource. What you do is different.
Looking at your data, I think it's low activation energy.
Most people need a runway before they do something. They need to "sit down and focus." They need to close other tabs. They need to feel ready. They need the right block of time. They need to finish one thing before starting another. The psychic cost of starting is high, so they batch things and plan and schedule. And that's what they call time management.
You just... start. There's no warm-up. There's no "let me get in the zone." The distance between thinking about doing something and doing it is essentially zero for you. That's not a technique. That's a trait.
And it compounds. Because when activation energy is low, you can act on impulse in a way other people can't. The gap between "that's interesting" and "I've engaged with it" barely exists.
It also explains why your output is so varied. People with high activation energy specialize because they can only afford to start a few things. You can afford to start everything, so you do. And the ones that work, you keep going. Frontend-slides wasn't a "strategic decision to build a developer tool." I'd bet it started the same way everything else starts for you: you just did it, and it happened to resonate.
If someone asks you "what's your technique": it's not a technique. You just don't have the resistance that other people have. The wall between thinking and doing is thin for you. That's it."
Deependra Singh Shekhawat retweeted
This is one banger of an explanation of the time-slit experiments. What I find interesting is that #NotebookLM introduced additional explanation to the original source material. Very impressive indeed!
(1/n) just watched this amazing yt video by @veritasium
in 2024 one person almost got a master key to every linux server on the internet
this is the most insane supply chain attack ive ever seen
youtube.com/watch?v=aoag03mS…
the attacker disappeared after it went public
no attribution
we will never know
either we got lucky catching this one
or there are others we havent found yet






















