
engineer, inventor, migrant. building @CRED_club ; lesser half to @monagandhi ; @virginia_tech alum. đźđł
India
Joined November 2008
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miten sampat retweeted
the silliest way to lose a lot of money in 2026 is to hold onto stupid priors for dear life
the second silliest is to make âsafeâ bets behind prestige vs. first principles evaluation of tech/business. if a company has fancy pedigree but is just losingâŠdonât fight the tape
programmable money combined with the powers of artificial intelligence will create massive companies.
bigger than the scale of fintechs weâve seen globally thus far.
miten sampat retweeted
Mona Gandhi, Co-Founder & CEO of Kello, will be joining us LIVE on the show today at 4 PM.
miten sampat retweeted
Indiaâs credit card market could be on track for 3x growth.
On Fintech Baatcheet, hosted by @ndtv, Miten Sampat (@miten), Interim CEO, @CRED_club, pointed to the headroom ahead: credit cards are currently held by just 4.5â5% of Indians, while total credit card spending could eventually grow from around $300 billion to $1 trillion.
In conversation with our President & CEO
@ShwetaRKohli, Sampat added that as credit adoption expands, the opportunity for fintechs will be to make credit simpler, smarter, and more engaging.
aim higher than a single market
@Kuvera_In
kuvera.in/us-stocks
After 9 years. 16K+ family homes. 500M sqft inside real homes covered, we know home robots are being built in wrong order.
So we built Matic the way nature grows a child.
Finally, sharing our vision:
1.â â Why robots get stuck at the demo
2.â â Why labs can't ship robots
3.â â Why we started with eyes, not hands
4.â â The 99.999% rule in robotics
5.â â Where Matic is going next
1. Why most robots get stuck at demos
Robot demos look brilliant in a room arranged around its limitations: familiar lighting, predictable furniture, nothing troublesome on the floor.
A familyâs home makes no such accommodations. There are cables, toys, shifting rugs and, occasionally, dog poop. Yes, humanoids will need to avoid that too.
Repeating a task under familiar conditions of uncluttered floors is not the same as handling an unfamiliar home with all its chaos.
2. Why labs can't ship robots
SLAM (Simultaneous Localization and Mapping) is how a robot maps its surroundings and locates itself within them. Ask most academics and they'll tell you it's a solved problem.
But real homes are the most chaotic spaces that exist.
There are glass doors that look like open doorways, mirrors, stairs, rugs, charging cables, and Legos placed all around.
So ask yourself: if indoor mapping and navigation is "solved," where are the robots? Why aren't airports, hotels and grocery stores full of them?
Calling it âsolvedâ misses the question families actually care about: can I leave this thing alone and trust it?
3. Why we started with eyes, not hands
Nature doesn't give birth to a fully working human. We took our cue from how nature develops humans: capabilities built on one another.
Children learn to see before they learn to grab, and to grab before they learn to plan and handle the unexpected.
Perception comes first. Manipulation follows. More complex responsibilities come after that. At every stage, the robot must earn the next job by doing its current one well enough.
Before asking a robot to pick up a sock, we wanted it to understand where it was, where the sock was, and how to reach it.
Most of the industry started at the top, with humanoids. We started at the bottom, with a robot whose job is to see and move through a home precisely with 1cm accuracy in any lighting condition.
Floor cleaning gave that foundation an immediately useful job. It forced us to confront navigation, clutter, privacy and everyday reliability before reaching for more complex chores.
The floor cleaner isnât a detour from our larger ambition. It is how weâre building toward it.
4. The 99.999% rule in robotics
At 90%, one in ten decisions is wrong, and a robot makes thousands in a single clean. That's the robot that bumps, gets lost, and falls down the stairs.
Every extra nine is a new mountain. The failures get rarer, harder to find, and exponentially harder to fix. They barely even happen until they happen in your home.
Matic's visual SLAM runs at 99.999% in 16K+ homes. Weâre the ONLY unsupervised home robot at scale with pure vision-only full autonomy. If you ask us, the gap between 80% and 99.999% is where our 9 years of engineering went.
We close that gap inside real homes. When Matic handles something imperfectly, it saves a short clip and keeps it on the device. It only leaves if the family chooses to share it. If they do, it gets labeled, fed back into the model, and every Matic gets smarter, including theirs.
Families control what leaves their homes. We do the work of making the product better.
Better robots earn trust > trust brings more homes > and more homes teach the robot more.
5. Where Matic is going next
Phase one was perception and it's nearly done. Matic has covered 500 million square feet, 400K miles in thousands of real lived-in homes.
Now phase two is going to be about manipulation. And our idea is to build a robot that doesn't just move through your home, but acts in it to eliminate even more chores.
Each step must be useful today, not justified by something we promise tomorrow. The evolution of Matic is the revolution.
The goal isnât to put the most impressive humanoid in your living room.
Itâs giving your family time and energy back through robots that earn your trust, protect your privacy, and help without becoming another responsibility.
More time for each other. Less work getting in the way.
Thatâs Matic.
Get yours today at maticrobots.com
reading a book written in 1895
its discourse is indistinguishable from todayâs realities
range of human affairs is fairly constant
form factors change.
miten sampat retweeted
The obvious is missing.
So we built Sol - hellosol.app
Sol finds the work itself, does it, and comes back for your approval.
Every day in our emails we say "Iâll shareâ, "I'll reviewâ, "I'll get back" - then repeat the exact same thing to an AI. Why?
Sol finds everything you said youâd do & gets them started for you.
It does the research, creates the doc, builds the slides, finds the time, connects the dots across multiple emails, doing everything it takes to get the job done - but doesnât send, schedule, or share anything until you approve.
Sol runs on its own computer, uses a browser, and has a library of skills that automatically get assigned to the work that needs to get done. No setup. It just starts working.
We've raised $4M from General Catalyst, Nexus Venture Partners, DeVC, PeerCheque, Kunal Shah, and a few others.
Extending early access now.
@generalcatalyst @nexusvp @DeVC_Global @peercheque
@neerajarora @b_jishnu @kunalb11 @miten @RTinkslinger @Rahul_J_Mathur @AkarshS27 @SiddhantD06 @RajatAgarwal167












