Nilkanth Patel @nilkanth
coffee, design, software, rinse & repeat. cto @trypallet. past: @askherald (YC W19) @quantcast @nytgraphics, @newyorker, @columbia. forever: @BAPS love. nilkanthpatel.com New York, NY Joined March 2009-
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I joined Pallet because of @SushanthRaman's conviction. Starting over when the business is growing and almost no one agrees with you is a hard call to make. He made it because he believed we could build something much bigger. Glad he’s sharing this story.
Two years ago, @TryPallet was growing fast, but I decided we needed to start over. It was a period that I haven't shared much about until now. Deep down, I knew we had built a good business, but not an outlier company. Almost no one agreed. My teammates lost faith and left. One
Zero tokens were used in this build
Two million shipments a year at 99% accuracy is a distributed systems problem wearing an AI costume. Under the hood: typed, functional, event-driven plumbing. Immutable versions. Every shipment is data, every side effect is a liability. Boring on purpose. Proud of this one.
We're proud to partner with @OneLineage, the world's largest temperature-controlled logistics company. Today, @TryPallet AI agents process two million of their shipments a year at 99% accuracy, helping them scale without compromising the high-touch service they're known for.
this new genre of videos featuring robots doing objectively hilarious shit saved humanity
WATCH: A humanoid robot training for the “Robot Olympics” in Beijing runs too fast, fails to stop, slams into a safety cushion, and breaks at the waist
One thing I often tell people is that the only two time horizons we should care about are: 1 month and 5 years (which maybe should be updated to 2 years in the age of AI)
"no i just prefer using claude code in the terminal." "what about emacs" "what's that?"
we’re getting warmer…
OWN YOUR INTELLIGENCE Last year, building on open-weight models was primarily a cost rationalization exercise. Slightly worse performance for a much cheaper price. Now, it is increasingly an existential and strategic topic for our portfolio. Intelligence is the product.
I wonder what Jeff Dean born 20 years later would look like. I’m sure there are plenty of folks with the same depth but I feel like today’s startup monoculture discourages people who like to go deep and solve problems down to the “bits.” We like confuse “agency” with “shallow but quick.”
Tomorrow will be my last day at Google after 27 years, and watching it grow from 25 people to 190,000+ has been an amazing journey. Below is a note I shared with many people internally at Google today. An excerpt is: It has been an absolute pleasure to work with you and to help
boy if i was chairman of the openai board this would be music to my ears
Rent the intelligence, own the relationship - how Sierra's Context Engine helps you build a competitive advantage in AI sierra.ai/blog/context-e…
Getting PG blessup means you've made it @dakshgup
I asked the founders if I could say which company this is, and they said ok. It's Greptile, which incidentally also has one of my favorite names of any YC co.
Supply chains broke my mental model of enterprise software. I'd spent years thinking enterprise software was about helping a company optimize its own business, then I started building for supply chains and realized there isn't a single owner of the system you're trying to optimize. Decisions depend on suppliers, carriers, customers, ports, weather, tariffs, and events no one controls. Before you can optimize a supply chain, you first need to understand how decisions ripple across the entire network, so we built Atlas to see the end-to-end picture. Check out @vidhur2k's deep dive.
Startups often conflate FDE with just being close to customers in the early days. Being forward-deployed when you're starting out makes a lot of sense because you get signal on what to build. FDE (in the original Palantir sense) exists to bridge the last mile in a $20m contract because the customer has bespoke needs and environments. Unless you are closing those contracts, FDE should not be a crutch to avoid doing the hard work of building a scalable product. Our engineers have always been very customer-facing, but something that helped us is truly viewing our offering as product-driven (as opposed to services or FDE) because we think that is the correct solution for our space. That forced us to skip some shortcuts but has paid off so far.
I've never seen startups spend so much time looking sideways. One AI company builds forward deployed engineering and then everyone decides they need an FDE team too. One company pays a certain number for talent, and suddenly that's the market, whether or not companies can afford
one thing we underestimated going into this project was how much of the work had nothing to do with the model itself. most of the gains came from building the right evaluation loop, curating production traces, and deciding exactly what behaviors the model should learn. that's the part we break down here.
We've seen our AI token spend skyrocket 10x over the past year as more customer workflows move into production. At some point, inference spend crossed a threshold where it no longer made sense. That's what led us to train our first production model. Here's what we learned: –
sometimes the true value of AI is eliminating the need to interact with shitty software, not eliminating the software itself. supply chain is just a cesspool of aforementioned shitty software and it will take eons to get rid of it all. but in the mean time, we can 10x the experience by not forcing folks to need it 8 hours/day just to do their job.
Today, we're expanding @TryPallet to operate inside Remote Desktop environments. Many of the systems enterprises rely on daily were never designed for modern APIs, leaving critical workflows locked behind virtual desktops and manual processes. Pallet AI agents can now securely
i, for one, would love to be oneshotted
the version of this that's going to be most broadly useful kinda has to be a single stream full-duplex voice assistant with full context over your life, which unfortunately is also going to oneshot everybody
A month ago we were hiring Gen Zers who lived in Claude and tokenmaxxed everything. This month we're searching for model (née "eng") managers. You'd never hand a junior a cross-cutting refactor, but renames and feature flags, sure. The newest non-negotiable is the ability to understand how to allocate the work to our AI overlords...which model, which harness, which job, at what cost.
Larry Ellison in 2001 on the power of references when selling a complex B2B product – even at Oracle’s then-$10B revenue scale, new products were a deal-by-deal slog until there were sufficient references. (book is Softwar)
I feel like people are sleeping on how crazy the advent of MoE has been for the massive expansion of parameters without the correlated need for compute…it’s basically what’s giving OSS a real shot at beating the frontier labs who can just decide to buy GPUs in space to overcome the shortage.
Releasing the model weights and technical report of Kimi K3. Kimi K3 is our most capable model: a 2.8T MoE model with native visual understanding and a 1M-token context window. New model architecture: 2.5x the intelligence per unit of compute, not just more params. Alongside
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BAPS @BAPS
53K Followers 16 Following A socio-spiritual, volunteer-driven, not-for-profit Hindu organization dedicated to strengthening society by spiritual values and humanitarian services.
BAPS Public Affairs @BAPS_PubAffairs
14K Followers 288 Following BAPS Public Affairs represents one of the United States’ leading Hindu organizations as it engages with elected officials & public servants.
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