GP @CRV. Learned some things @Uber @DormRoomFund. "a real one" - @pdrmnvd brittwalker.com
San Francisco, CA
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Brittany retweeted
Not many people seem to know about Metaplanet, Jaan Tallinn's VC firm.
They are the most AGI-pilled firm on the planet, and are about to flip many of the tier 1s.
> Invested in @GoogleDeepMind in 2011
> Led @AnthropicAI series A in 2021
> Doubled down on Anthropic in every round since. IRR hurdle for deploying capital into anything else was {absurdly high number}% for years
> Anchored the premier AGI hedge funds (+Mythos) + in-house public equities strategy
4-person team in Estonia now managing 10s of billions AUM, liquid after $ANT IPO.
Rationalist roots --> zero bureaucracy, cares about human survival & flourishing as north star, doesn't care who else is investing or how high status a founder / idea is
Founders: congrats! you no longer need to play status games to raise a mega round for your civilization-scale moonshot. You can just pitch @metaplanetfund
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IYKYK, @ClaireSmilow is exceptional all around
A privilege to work with @ClaireSmilow everyday, but especially on days when @FortuneMagazine writes such nice things about her... @BoxGroup
fortune.com/2026/10/22/clair…
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So excited to be in business with the incredible folks at @flappyairplanes!
@amspector100 @spectorb @aidanmantine have assembled an insanely talent-dense team and we @CRV are thrilled to be along for the ride 😊
We’ve spent the past year developing new primitives for GPUs. We believe this enables new algorithms to change how machines learn, open new markets, and dramatically reshape AI’s cost structure.
We have raised a $1 billion Series A, co-led by Index and Lux.
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👀 👀 👀
I asked people which neolabs under $10B have the highest talent density:
32 votes: Core Automation (@MillionInt, @_arohan_)
26 votes: Periodic Labs (@LiamFedus, @ekindogus)
20 votes: Flapping Airplanes (@spectorb, @amspector100, @aidanmantine)
18 votes: Standard Intelligence (@G413N, @devanshpandey)
8 votes: Ricursive (@annadgoldie, @Azaliamirh)
5 votes each:
- Ineffable (David Silver)
- Mirendil (@bneyshabur, @HarshMeh1a, @shayan_, @tararezaeikh)
- Recursive (@RichardSocher, @_rockt, @timshi_ai, @josh_tobin_, @CaimingXiong, @jeffclune, @tydsh, Alexey Dosovitskiy)
3 votes each:
- Applied Compute (@ypatil125, @rhythmrg, @lindensli)
- Isara (@ezhang7423, @hegasz)
- Prime Intellect (@vincentweisser, @johannes_hage)
2 votes each:
- Elorian (@AndrewDai, @yinfeiy, @SethInternet)
- Goodfire (@eric_ho, @DanJBalsam, @banburismus_)
- Inherent (@tantumscollins, @edwardfhughes, @LouisKirschAI, @kallyaleksiev)
- Trajectory (@rronak_, @QuantumArjun, @MichaelElabd)
- World Labs (@drfeifei, @jcjohnss, @BenMildenhall, @chlassner)
People couldn't pick a company they work at or founded. Disclosure: I'm a small investor in Applied Compute, Factory, Standard Intelligence, Trajectory and Wafer. I didn't vote.
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Met with no fewer than five current @DormRoomFund team members today, six years after I graduated! The strength of the community is unmatched and @themollette is crushing it 💪
The @DormRoomFund family did it again! Proud to announce a fresh $50M Fund 2 and a new brand to keep backing the best student and recent grad/drop-out founders.
My favorite part? 30% of our investors in Fund 2 are DRF alums and their firms.
Read more: nytimes.com/2026/09/29/techn…
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Brittany retweeted
We are releasing Dyna-2.1, the first Physical Agent that achieves reliable super long-horizon whole-body autonomy. It combines our brand-new semi-humanoid hardware with an agentic system built around Dyna-2 to handle ultra-long real-world workflows.
Here is an uncut footage of Dyna-2.1 completing an entire hour-long laundry room workflow, just like a human does.
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Today, we are announcing our Series A and a new product: @raindrop_ai Simulations.
We've now raised $50m from @CRV and @lightspeedvp to protect the world from agent failures, big and small.
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Brittany retweeted
We built high-throughput materials labs in Menlo Park to create a loop between experiments and models. The labs generate fresh data, the models learn from it, and then help us decide what to try next.
Using only 1,300 H200s, plus months of our experimental data, we mid-trained and RL’d an open-source model to surpass GPT-6 Astra on our analysis benchmark. We call it Neon.
This is real footage from our lab. We’re focusing first on hard problems in materials science, including superconductors, magnets, and semiconductor materials.
Read our blog posts below.
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Brittany retweeted
Apex is The Spacecraft Platform Company.
For most of space history, progress was measured in firsts. First satellite. First human in space. First Moon landing. The next era will be measured in rate: how fast you can deploy, replenish, and refresh the infrastructure everything else depends on.
That’s the New Space Race.
Winning it takes constellations: many satellites working together as systems. And constellations require two industrial capabilities: launch and spacecraft platforms.
Launch is ready enough. Platforms are not. Apex exists to remove that bottleneck.
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Introducing S1, our new foundation model that learns from one example.
It can be taught 10-minute long tasks that it has never seen before, from one video prompt without any fine-tuning.
Watch S1 operate in real-time via in-context learning:
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The most fun part about having a WHOOP is that my recovery keeps going up and my husband's keeps going down and neither of us has any idea why
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The launches are bought and paid-for
The expert networks are fraudulent
The ARR is not ARR
The intelligence is artificial
Where do I wire
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One of the many reasons for VC firms to continue to hire newer, younger investors!
Despite what your vc-analyst.md may lead you to believe
As VCs we tend to get most excited about things we know nothing about and are most critical of startups where we have lots of experience. Which is why every once in a while you see a VC firm with zero relevant expertise or experience invest in something insane, but when it works and it makes your jaw drop.
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I've often had the TV on in the background while working late at night
So much so that my husband has begun referring to any silent work as "raw dogging it"
Now I always have the TV on in the background while working late at night
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All AI usage should be calculated in terms of pizza
On tokens and prices per token.
I said I’d write more about this, so here goes: an OpenAI token != another model’s token. We compare AI prices in dollars per million tokens as if a token were a standardized unit, like a gram or a kilowatt-hour. It isn’t. Different models use and produce the exact same text using different numbers of tokens, which means a lower price per token does not necessarily mean a lower bill.
Imagine two identical pizzas. One is cut into 8 slices at $2 each. The other is cut into 16 slices at $1.25 each. The second place advertises cheaper slices, but the whole pizza costs $20 instead of $16. Bummer ... your stomach doesn't actually care about the number of slices you just ate.
I know you are hungry now, but back to tokens. In one small comparison spanning English, technical, multilingual, and numerical text, the tokenizer we use for GPT-5.6 Sol used 766 tokens versus an estimated 1,170 for Claude Opus 5. That's a very significant difference of about 34.5% fewer tokens. You can get the same exact text, but pay for all those extra tokens. The price per token doesn't really tell this story.
Even correcting for tokenizer differences misses the bigger point. What actually matters is price per successful outcome, and for that you can use benchmarks as a starting point, but really you have to try it and measure on your own use cases.
That's all. May the tokens flow.
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Congrats to the amazing folks at @OpenRouter, another strong crypto pivot for the books 🫡
The crypto pivots (Together, Prime Intellect, Conviction, etc) really have done remarkably well this cycle
Maybe there's a lesson here
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