
Applied AI Scientist currently @MetaformsAI prev UI/UX (https://xitter.cf/t.co/4KBsChbGsn), Healthcare, Food Safety @galaxyimago, Medical Imaging & Retail @paralleldots.
Joined August 2014
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Sri Krishna retweeted
Life is a repeat game and an infinite game. People who don't understand this won't win in the long run.
Life is long. Choose to work with good people.
Who believed in you first? Silicon Valley runs on that question.
a16z's Erik Torenberg, David Booth, Josh Elman, and Olivia Moore on Cosign:
Cosign is a curated professional network for the startup community. Profiles are built from endorsements, fundraises, and talent moves, with a private layer to signal who you'd fund or hire.
Erik ran a small version of this seven years ago, emailing a few hundred people and asking each for one person to watch. One of the answers was Russell Kaplan, then a 22-year-old engineer at Tesla, now president of Cognition.
Olivia on why this matters: "The most valuable data in all of Silicon Valley is who has conviction in who." Investors chase it every day and still struggle to find it. An engineer choosing between five offers has almost no way to get it at all.
Paul Graham cosigned Sam Altman in 2009, six years before OpenAI was founded. Ryan Hoover cosigned Erik early at Product Hunt. Silicon Valley has always run on cosigns from people whose word carries weight, and until now nobody kept the record.
The conversation closes on the question of why anyone would share their alpha. David's answer is that the person you cosign today is the one raising a round in two years, and when they do, they remember who believed in them first.
0:55 Twitter as a credential
3:05 Silicon Valley is a race to discover talent
4:10 The 3 questions on every Cosign profile
5:45 The email that found Russell Kaplan at 22
6:15 Conviction is the scarcest data
9:50 Why Cosign is free and strictly positive
11:05 Seeing things before the market does
13:00 Sending private "I'd fund them" signals
18:35 Endorsements that don't vanish
23:05 Recognition for the unsung engineer
29:40 Building LinkedIn Jobs in 2004
34:25 AI made human endorsement scarce
36:45 Why nobody has disrupted LinkedIn
42:10 Your Cosign profile may already exist
46:25 Silicon Valley always ran on cosigns
48:10 "Why would I give away my alpha?"
YouTube: youtu.be/-ywZlfznTa4
@eriktorenberg @david__booth @joshelman @omooretweets
Excited to introduce @Cosign: the curated professional network for the startup community: cosign.co
Our goal is to create the following:
- A comprehensive startup directory of investors, companies, and operators, including what they worked on and who they worked on it with.
- Cosign graph: Who shaped your career? Who were you actually in the trenches with? Who would work with you again? Who thinks you’re someone to watch?
- Durable reputation: Great endorsements happen every day on X and disappear into the feed. Cosign attaches those signals permanently to people and companies.
- Discovery: Who are the best fintech angels? Which AI companies should I watch? Who are the best designers?
- Intent network: Privately signal “I’d invest in them,” “I’d hire them,” “I’d work with them,” or eventually even “I’d acquire this company.” Match people when interest exists on both sides.
Imagine if Wikipedia, LinkedIn, and OG AngelList had a baby. Though Cosign is a community, not a business.
In order to join, you need to be cosigned. Or you can apply directly.
David Booth and I started this idea 7 years ago but didn’t have the firepower to make it work. Now we do.
No one has really touched LinkedIn in 20 years. Excited to take a swing.
cosign.co
people always forget frontier models make 80% margins on the APIs
Sri Krishna retweeted
The new Anthropic managed agents API is basically the Letta API that we've had since a year ago, but closed source and with provider lock-in. They even have read-only memory blocks and memory block sharing -- something which was unique to the Letta agents for a long time.
Funny enough, we actually don't think this is the direction agents are going to go. Having API interfaces for memory blocks and tools is certainly convenient - you can spin up stateful agents as API services with just a few lines of code. But its also limiting: LLMs today are extremely adept at computer-use, and representing their memories in this way limits the action space of agents and their ability to learn.
It's important to remember that just because something comes out of a frontier lab, doesn't mean its the "right" answer long-term. The Letta API ~1 year ago was somewhat of an antipattern in a sea of agent framework libraries offered by every lab. But now, stateful agent APIs are becoming the new norm - especially as providers try to lock in memory/state into their platforms to increase switching costs (which is exactly why we believe memory should live outside of model providers)
If you want to see what the future is going to look like, follow @Letta_AI
Sri Krishna retweeted
There is no care for another without care for yourself
There is no being with another without being with yourself
There is no loving another without loving yourself
Sri Krishna retweeted
At our last DSPy meetup, @kshetrajna shared this amazing case study about how he's using DSPy at @Shopify scale. I think this was my favorite slide.
Only in Bangalore would you find that your cab driver is a daily @cursor_ai user working as an SDE from 10am to 6pm and driving from 7pm to 3am. Rationing the $20 over the entire month has not been easy, understandably so.
Sri Krishna retweeted
Here’s a piece by @goodfellow_ian, @sunfanyun, and me arguing that use of symbolic representations and game virtual world data offers the best path to building action-conditioned multimodal world models that enable reliable prediction and planning for long-horizon tasks.
The advantages of symbolic language representations in this context was also recently argued for in new work from @physical_int: pi.website/research/memory .
Sri Krishna retweeted
it's incredible how much of 2023 was spent talking about RAG.
We used to have a vector db of a bunch of components from popular design systems and we would fetch from that vector db. spend like months on this approach. it never really worked that well
xitter.cf/beaversteever/status/2…
Sri Krishna retweeted
🧠📈A new foundation model for #neuroscience! In the latest Deeper Learning blog, @yuven_duan and @KanakaRajanPhD describe their state-of-the-art POCO model, which accurately forecasts neural dynamics across individuals and species.
bit.ly/4afqx5A #NeuroAI
Sri Krishna retweeted
My inbox is flooded.
If you're looking a code-first canvas, here you go:
Everything generated is React code. Export to Claude Code/Cursor via the @magicpatterns MCP. Use your design system.
The code-first era of design has arrived... a year ago.
xitter.cf/alexdanilowicz/status/…
"But that isn't nearly enough. The entire industry needs to do a better job of talking to everyday people and getting them to see a better world is possible." agreed
The AI backlash is here.
This post has a million views.
People hate technology.
They hate innovation.
They see that it is coming for their jobs.
Coming for their way of life.
Innovation is asking them to do more work. To spend nights and weekends learning about AI and building new things.
They hate that. And I've seen it my entire career as people tell me over and over again that they don't like what I'm pitching them.
How many have gone to an Apple store and had a demo of the Vision Pro? Even in Silicon Valley I keep finding smart people who refuse. And my consumer research outside the valley shows far far greater resistance.
There are couple other such posts this morning going viral:
@zachweinberg: xitter.cf/zachweinberg/status/20…
@tszzl: xitter.cf/tszzl/status/200248841…
Here's the real problem.
Let's say you burn down Silicon Valley.
The Chinese are coming and coming with cars that will eviscerate the rest of the auto industry. Americans can't see that because we tax the heck out of them and keep them from selling here.
And soon they are coming with robots. Americans can't see that because they aren't like me meeting with robot pioneers here and in China. Or watching the robot community here on X (or understand what they are talking about with their highly technical talks about world models and reinforcement learning.
And they are coming with AI models. Most Americans are sticking their heads in the sand and ignoring what's going on on @huggingface and here on X. I talk to entrepreneurs around the world who keep telling me they are building businesses on Chinese models because they are cheaper and better.
And they are coming with entertainment businesses that will eviscerate Hollywood. Most Americans see AI artists as anti human. The Chinese don't.
The only way out of this +is+ innovation. That's where any new jobs are gonna be.
So go ahead and burn down Silicon Valley and San Francisco.
That won't save your job and will doom America to being a poor country subservient to the Chinese.
And, yes, we need to do a better job of explaining the role of innovation.
But that job is bigger than what I can do.
Why?
Because people hate innovation and want to stick their heads in the sand, and even, roll back innovation, my posts aren't getting any reach here on X outside of a small group of innovation supporters.
Well, I'll keep supporting the innovators. I made lists of almost all of them, 37,000 in AI alone, and 7,600 AI companies: xitter.cf/scobleizer/lists
But that isn't nearly enough. The entire industry needs to do a better job of talking to everyday people and getting them to see a better world is possible.
For now all they hear is "you are taking my job."
And they aren't wrong about that either. Which makes this all so difficult to get people onto the side of innovation.
Sri Krishna retweeted
Replying to @guilleflorvs
building mxflo, the tiktok for games.
everyone thinks people don't make games because coding is hard, but really it's just too slow. our insight is that if you make games disposable like memes where you can remix a viral moment or roast a friend in 1 tap, people will share them everyday.
Sri Krishna retweeted
The @Stripe Atlas team just published their year in review. Because Atlas now incorporates so many startups (23,000+ thus far in 2025), it's an interesting snapshot of broader ecosystem trends. Some of the findings that I thought were interesting:
• Atlas incorporations grew 36% Y/Y. More than 20% of Delaware C Corporations are now started with Atlas. (Delaware corporations are started for lots of reasons, obviously -- large companies creating subsidiaries, etc. -- so Atlas represents some larger fraction of overall startup creation.)
• Founders of Atlas companies can come from anywhere and Europe was the fastest-growing region (+48% Y/Y). My speculative hypothesis is that this is because dealing with incorporation is very annoying and bureaucratic in many European countries, and the knowledge that you can just use a US corporation is becoming more widespread. A lot of European leaders are aware of these issues and I'm hopeful that they'll act.
• Multinational founding teams are 79% more common (among multi-founder teams) than they were in 2017. This feels like a straightforward dividend of remote work, but the effect size is surprising to me.
• Time-to-revenue is accelerating. Remarkably, the median Atlas startup that incorporated in 2025 generated 39% more revenue in its first 6 months than its 2024 counterpart. And while there were more incorporations than ever this year, it is also true that a growing fraction of companies reached $100k of revenue within 6 months. That is, both quantity and some kind of quality (at least in revenue terms) are increasing. I'm not exactly sure how to explain this. I think the null hypothesis is simply that AI and stablecoins have opened the aperture of possibility, a vast surge of startups is mobilizing, and customers are eagerly flocking.
• The variance is increasing. While the median company generated 39% more revenue than last year, the 90th percentile company generated 52% more, and the 99th generated 67% more. Again, this is a year-over-year change -- pretty striking. This trend aligns with the anecdotes: startups like @cursor_ai and @Lovable (both of which incorporated with Atlas) are posting truly unprecedented revenue growth figures.
One of my favorite aspects of Stripe is getting to see trends like these play out in real time. If you're interested in building tools that directly enable this entrepreneurship and innovation, you should get in touch.
Sri Krishna retweeted
NSF is launching one of the most ambitious experiments in federal science funding in 75 years.
The program is called Tech Labs, and the goal is to invest ~$1 billion to seed new institutions of science and technology for the 21st century.
Instead of funding projects, the NSF will fund teams. I’m in the @WSJ today with a piece on why this matters (gift link): wsj.com/opinion/science-fund…
Here’s the basic case:
1) Most federal science funding takes the form of small, incremental, project-based grants to individual scientists at universities.
2) The typical NSF grant is ~$250k/year to a professor with a couple of grad students and modest equipment over a few years. This is a perfectly reasonable way to fund some science, but it's not the only way.
3) A healthy portfolio needs more than one instrument. Project-based grants are like bonds: low-risk, steady, safe. But no one trying to maximize long-run returns would put 70% of their portfolio in bonds.
4) Yet that's basically what our civilian science funding portfolio looks like. Around 3/4ths of NSF and NIH grant funding is project-based.
5) Tech Labs is NSF's attempt to diversify that portfolio. The Tech Labs program is aiming for:
- $10-50 million/year awards per team
- 5+ year commitments
- Measuring impact through advancement up the Tech Readiness Level scale rather than papers published
- Up to ~$1 billion for the program
- Supporting research orgs outside traditional university structures
6) Scientific production looks very different than it did when the NSF launched 75 years ago. The lone genius at the chalkboard can only do so much. Frontier science + tech today is increasingly team-based, interdisciplinary, and infrastructure-intensive.
7) The team behind AlphaFold just won the Nobel Prize in Chemistry. It came from DeepMind, an AI lab with sustained institutional funding and full-time research teams. It would be near-impossible to fund this kind of work on a 3-year academic grant.
8) Same pattern at the @arcinstitute (8-year appointments, cross-cutting technical support teams) and @HHMIJanelia (massive infrastructure investments to map the complete fly brain). Ambitious science increasingly needs core institutional support, not a series of project grants stapled together.
9) Similarly, Focused Research Organizations (@Convergent_FROs) have showcased a new model supporting teams with concrete missions and predefined milestones to unlock new funding.
10) There’s a whole ecosystem of philanthropically-supported centers doing amazing research, like the Institute for Protein Design, the Allen Institute, the Flatiron Institute, the Whitehead Institute, the Wyss Institute, the Broad — the list goes on.
11) But philanthropy can’t reshape American science alone. The federal government spends close to $200 billion each year on research and development, an order of magnitude more than even the largest foundations.
12) If we want to change how science gets done at scale, federal funding has to evolve. And the NSF and NIH don’t have dedicated funding mechanisms to support or seed these sorts of organizations.
13) Earlier this year, I started working on a related framework called “X-Labs” that built on all this exciting institutional experimentation that’s been happening within the private and philanthropic sectors. It’s time for the federal government to step into the arena: rebuilding.tech/posts/launch…
14) Traditional university grants are still important for training the next generation of scientists and for certain kinds of curiosity-driven work. But after 75 years of putting nearly everything into one model, we should try something different.
15) And key program details are still being developed! You can reply to the Request for Information with suggestions or feedback on how to design this program here: nsf.gov/news/nsf-announces-n…
16) Science is supposed to be about experimentation. Science funding should be too.
Sri Krishna retweeted
👀Introducing a brand new @yupp_ai SVG leaderboard ranking frontier models on the generation of coherent and visually appealing SVGs!
Gemini 3 Pro by @GoogleDeepMind takes the crown as the most powerful model! 👏
We’re also releasing a public SVG dataset. Details in🧵































