partner @boldstartvc - inception, technically interesting problems, enterprise. previously founded @darklang
Somerville, MA
Joined May 2008
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Ellen Chisa retweeted
Introducing Expand Fetch, a new way to scrape and crawl the web.
Your agent has never seen the web. No wonder it gets basic questions wrong.
Traditional Markdown APIs lose critical details. So we trained two System one models to change that:
Iris, a vision model that sees pages like you do, preserving tables, icons, and layouts.
X-Ray, a JSON model that uncovers the hidden data behind every page.
Every fetch also comes with a Wayback Machine. Every line links back to its source, so you can verify exactly what your agent saw.
Three products. One API.
On our benchmark of 1,094 questions across 326 challenging web pages, agents using Expand got 74% right. The next best API got 55%.
Available today at $1 per 1,000 fetches.
Try it at expand.ai
Ellen Chisa retweeted
Here's yet another example of cool shit happening at the intersection of biology and AI. These kinds of problems are basically unsimulatable using standard techniques; AI enables a higher fidelity simulation of conformational changes to proteins, and can lead to safer drugs. Another angel investment, and this time, an investment into my previous brilliant cofounder @khosra !
Today we are announcing Topos-2, our next-generation model for generating the full range of shapes a protein can take, across the order-disorder spectrum.
We are also previewing Topos-Bind: to our knowledge, the first generative model to capture the range of shapes for disordered proteins bound to small molecules.
Read the technical blog post here: toposbio.ai/topos-2-topos-bi…
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Ellen Chisa retweeted
Introducing @Supertake, a new platform that transforms your unique takes on the world into real, shareable investment portfolios using frontier AI and trading agents.
Supertake makes it easy for anyone to invest in their ideas, even if they know nothing about investing.
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🤖 Made with AI
Ellen Chisa retweeted
new post: the senior engineer death spiral
sunilpai.dev/posts/the-senio…
a friend just started a big job and asked for some advice. so I braindumped a monologue about a super common failure mode I see with engineers and posted it here, hope it helps whoever it can.
very good writing on the 'in between' times
at one point I had a spreadsheet of how my energy was after I saw each person (oddly the range was from -2 to 2). i wish i had listened to it sooner.
I wrote about transitions last year, and still connect with a lot of people who are “in between”. Thinking of hosting an intimate breakfast in NYC around that topic. DM open if you’d be interested 💌
lola.substack.com/p/splash
Ellen Chisa retweeted
Some news: 5 months ago I joined @coreauto, a small research lab focused on new ways for models to learn. I’m also hiring four interns to do something my younger self would've loved.
It started with a pretty unusual project. We set up a handful of small online businesses, and put an agent in charge. We gave each business some initial funding and a seed idea, and off they went, trying to sell real things to real customers while we watched. It’s about as open-ended and unforgiving a task as you can probably give to an agent, with countless decisions to make and effects that take time to become clear.
Each business got its own bank account, a debit card to pay suppliers, a web browser, an email inbox, a domain name, cloud infrastructure and a schedule. Every few hours, the agent woke up and decided what to do. It might update a landing page, adjust an ad campaign, reply to a customer, and then go back to sleep.
The agents built actual storefronts and apps. They bought ads, attracted visitors, and even made some sales. The agents acted as operators that kept a business moving, but we noticed they never really stepped back to strategize about what might move the needle. If people weren’t buying, why? Was the landing page unappealing? Or maybe the offer itself was the problem?
One amusing example was the very first order a customer placed. Our supplier had run out of stock. But it’s a dropshipping business, and thankfully tens of other suppliers carried the same product. Instead of trying another supplier, the agent almost immediately sent the customer a polite apology and attempted to issue a refund. Gah! So close!
The agents could handle much of the work of getting a business up and running. What they struggled with was deciding what to change when it wasn’t working.
That’s what we want to understand. We also want to see what changes when people take the lead. What does a business operator actually do all day when agents can do so much? What new work does this allow them to do? Which decisions can’t be delegated?
—
So this winter we're hiring 4 interns as Associate General Managers. Associate Product Manager internships are programs that give early career generalists a product to own. We want to try something similar, but with entire businesses.
You’ll create and grow businesses, with agents handling the routine work so you can focus on figuring out what matters. Each AGM gets a budget and our agent infrastructure that’s designed for this. Which ideas to try is up to you. Put more money behind what finds customers and keep refining.
Where you step in tells us what agents can't do yet. You'll be in our SF office with me and people I’m so fortunate to work with, like @_arohan_, @joannejang, @MillionInt, @marksaroufim and @juliacvillagra
We don't care much where you went to school or what your resume looks like. We’re looking for enterprising people with a drive to create, who’ve made money on the internet before and already use AI extensively to enable their ideas. If that sounds like you: coreauto.com/AGM
Ellen Chisa retweeted
Introducing LiveText, part of Liveblocks Sync.
Our new data type for collaborating text editing, and a replacement for Yjs.
→ lblcks.io/livetext
Ellen Chisa retweeted
I'm usually wary about talk of "the ChatGPT moment for robots" because language is less complex than the physical world. After visiting @GeneralistAI, though, where I saw their robots perform impressive manipulation with just a prompt, I think you can see it happening in a limited way. (Thanks to @peteflorence and co for having me!) wired.com/story/generalist-a…
Ellen Chisa retweeted
One of the most powerful things in life is having a deep belief that you can figure something out; you don’t know how or when you’ll get there, but you viscerally feel it in your bones.
From day 0, the entire @GeneralistAI team has believed that we can usher robotics into the AGI era and beyond. Today feels like one for the history books: a moment to celebrate the relentless, heads-down grind from the entire team 🎉🍾
The GPT-3 moment of robotics has arrived. It’s only one milestone along the way, and it’s gonna get way better. We only called it GEN-1.5 for a reason 😉
I love when there's a timestamp to go along with a specific breakthrough.
At 10:06pm on Aug 3, I watched a robot do something I thought was years away.
We were working on few-gradient learning with GEN-1.5, wondering how far we were from landing physical prompting: show the robot a task once, and it just does it. We YOLOed it, and the robot imitated exactly what we demonstrated, with 0 fine-tuning. We gave more prompts for different tasks. More successful rollouts landed. I could not believe my eyes.
Looking back, this represents the culmination of a huge amount of work across the entire team, many failed experiments, and years of groundwork from the community. I just happened to be the one standing in front of the robot when it all clicked.
@andyzengineer once asked me: "If Generalist failed tomorrow, what would we want to have left behind that could benefit humanity?"
I believe we found one answer: an existence proof of physical prompting.
There's a new version of this post
RT @felixwyw: At 10:06pm on Aug 3, I watched a robot do something I thought was years away.
We were working on few-gradient learning with…
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Ellen Chisa retweeted
Introducing GEN-1.5, a one-shot learner.
It can learn new tasks in a few seconds. Show it what to do, and it generalizes.
This capability emerged from pretraining on physical data at scale, as a step towards our mission of building general intelligence for the physical world.
Ellen Chisa retweeted
Ever since I started working on robot foundation models a handful of years ago, the broad ability to one-shot in-context learn has been the single most vivid goal in my mind along the long road ahead. It’s something @andyzengineer and I have especially been thinking together on for several years now, and something shared by the whole team at Generalist as a major goal.
But even before anybody started talking about robot foundation models, too, the capability this enables, i.e. generalized one-shot learning, has been both incredibly concrete and elusive. In terminology that I think many other people have come up with as well, in grad school we used to talk about a “Ctrl-C-Ctrl-V” type of capability. See something, and have the robot do it too. This same type of idea inspired the name of the 1970 MIT copy demo (people.csail.mit.edu/bkph/ph…).
The thing is, it sounds simple, but is incredibly hard since the world is never quite the same when it got copied and where you want to paste it. The real world can be hard to predict and is full of variation. To do this, you need strong generalization, and it needs to be acquired in a single example. Doing this over a wide range of tasks, especially for dexterous tasks, is hard mode.
Lots of the components of the idea of making this all happen have been there for a long time. As an example, this 2017 NeurIPS paper “one-shot imitation learning” arxiv.org/pdf/1703.07326 has excellent vision, with ambition well beyond what was achievable at the time, and although it’s not referred to as “in-context learning” since it was pre-Transformer, it actually uses attention to condition on a single demonstration.
And now, many things have happened since early 2017, including the broadly celebrated arrival of one/few-shot in-context learning in language models in 2020.
This new model GEN-1.5 takes in everything we have built and learned over the past couple years at Generalist. It has been training for 8 months. It has taken an incredible amount of commitment and grit from the whole team to get here. The level to which this model has survived many surgeries has continued to surprise me. And its capabilities have continued to surprise as well. We found compositional generalization on Friday. We found sim2real prompting earlier last week. We filmed the contiguous uncut videos of live prompting yesterday.
To be clear, the success rates are modest, and there’s still a long way to go. But now I have definitely seen a ~decade-long imagination come into the real world.
Ellen Chisa retweeted
In 1843, Elizabeth Barrett Browning wrote to a friend after seeing her first daguerreotype: “The very shadow of the person lying there fixed for ever! … I would rather have such a memorial of one I dearly loved, than the noblest artist’s work ever produced.”
Our lives and homes are filled with things that once inspired similar awe. I tried to collect those feelings, in quotes like the one above, from people for whom what is now ordinary was brand new. A friar who met the inventor of eyeglasses, a founding father on the new smallpox vaccine, a woman in Iowa on her family’s first indoor bathroom. To look at a modern apartment through their eyes: ordinaryabundance.com
👀 I've always hated the 'chat to write' paradigm, so I still write pretty much everything by hand... but excited to have some AI micro-support vs. macro-support.
introducing sigil. it's a new way to write for work that gives you the power of machine intelligence while putting your words and ideas first.
here's why we think that matters.
sigil.so
had a lot of fun talking to @matvogels about pretty much everything cold email / pitch decks / first meetings!
"I think especially in the last six months, I've started getting an onslaught of cold email that feels 100% programmatically generated... No one has any idea why they've sent these emails. They don't even know they've sent them...That volume is so high now that if I open an email and it immediately reads like I can tell a model wrote it in the first three sentences, I don't answer the email."
There's a epidemic happening behind the scenes in the fundraising process right now (we see it all the time here @BlackFlagVC and @HarpoonVentures), and it's founders blindly using AI to send emails and create pitch decks.
I talked about this (and so much more!) with @ellenchisa (partner @Boldstartvc) on the latest episode of Fun Raising. We talk about how often we see AI making it's way in to the process, so much so that even if the deck / pitch / email wasn't generated using AI we sometimes assume that it was.
Ellen is one of the most honest VCs we've had on, and I think founders will appreciate her wisdom and feedback throughout this episode!
We also dive into:
🎯 How to build a target investor list
🧨 What slides she (And other investors) skips in a pitch deck
📢 Why the first meeting should be a conversation you drive
🙈 Reading customer and investor signals honestly, and the fake "we're deciding Friday" deadlines that backfire
💸 What to do the week after you close, and the "playing startup" trap that stalls early companies
🚀 And so much more!
Watch the full episode here on X! 🎧
Ellen Chisa retweeted
Why create robot intelligence for just one hand, when we could have it learn from many?
GEN-1, our latest embodied foundation model, now supports a broad range of end effectors from 5-finger hands, to specialized tools, and everything in between.
Ellen Chisa retweeted
Founders please avoid the “forever” fundraising round
Seeing too many times founders get happy ears when a number of firms reach out. Lots of this btw is automated. Usually a signal a competitor is raising
Be able to discern the signal from the noise - perhaps its someone you’ve know for awhile itching to write a check but more inbound does not mean you’re ready
Go out on your terms - make sure you have the story right and appropriate for your stage. Same goes for appropriate metrics
Going out too early and not getting it done makes it look like there are issues when the only issue is you should have never gone out in the first place. It’s also a massive distraction when you could have spent a few more months building the product and metrics you need