AI Engineer World's Fair 2025
The Next Unicorns: 7 Top AI startups from the HF0 Residency
Read the talk
From Creative Abundance to Reliable Inference: The HF0 Startup Showcase
Nine startup pitches trace the work around AI models: finding generated content, building conversational devices, structuring enterprise data, controlling speech and normalizing inference.
From a talk by Diego Rodriguez, Eugene, Jonas Bauer, Shijia Liao, David Vorick and Alex Atallah
Krea: predicting cars is easier than predicting traffic
A car is relatively easy to imagine: take the wheels of a horse-drawn vehicle, replace the horse with an engine, and you have motorized transport. Traffic is harder to foresee. Diego Rodriguez, Krea’s co-founder and CTO, uses that distinction to ask what follows from cheap AI creation. The immediate achievement is generating images; the downstream problem is finding anything useful among them. Rodriguez reports that Krea generates a million images per day for one studio. Creative abundance creates a discovery problem.
His second story is the Tower of Babel: a shared ambition collapses when people can no longer understand one another. The modern equivalent is a stand-up meeting where one person insists on React and another on JavaScript, without resolving what they are trying to communicate. For Krea, an AI creative suite, the purpose of the tools is to help people convey ideas and tell stories.
The third story returns to discovery. In a conversation with someone at Netflix, Rodriguez hears the question of how viewers will find content when it is personalized to individual towns in India. Then the speed of production becomes concrete: Rodriguez reports that a customer went from signing up for Krea through conversion, payment and a Fox advertisement broadcast to millions of people in two days. Creation, distribution and discovery are becoming tightly connected product problems.
Rodriguez closes by reporting 25 million users with an eight-person team. He mentions funding without an amount, shows customer examples, and offers a dedicated email address to prioritize applications from the event. The recruiting invitation follows directly from the scale problem: a small team is building the tools around a rapidly expanding volume of creative work.
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
OpenHome: natural conversation on hardware developers control
OpenHome begins with a quiz: after smartphones and laptops, what was the previous year’s third best-selling consumer product? An audience member guesses Apple Watch; the presenter answers smart speakers. He claims 500 million smart speakers were sold in the prior year, although the pitch supplies no market dataset establishing annual sales. The product complaint is easier to recognize: these devices are difficult to talk to, offer little customization, and lack a community that can extend them.
OpenHome presents itself as the first AI-driven smart speaker, with a broader goal than replacing one household device. Users should be able to chat naturally instead of learning command language, while developers should be able to put that interaction into whatever physical form they choose.
The presenter’s case for an ecosystem comes from earlier work: chief of staff to Splunk’s founder, a role on MakerDAO’s founding team, and a data-privacy business that he and his co-founder later sold. He describes Splunk as a $30 billion company, MakerDAO as a $5 billion developer ecosystem, and the prior business as having raised $50 million. The connecting lesson is that developers determine which applications people actually want.
The presenter reports more than 10,000 developers building on OpenHome. Its developer proposition combines three elements:
- Open source: developers can participate in extending the platform.
- LLM-driven interaction: conversation becomes the interface to applications.
- Device freedom: the pitch’s “fully jailbroken” positioning emphasizes customization, including talking toys, AI robots and appliances.
The dashboard and editor bring that proposition into a development environment. The presenter describes hundreds of applications, including games, personalities and home-automation tools.
The hardware offer makes the invitation tangible. After saying the previous dev-kit allocation was booked within minutes, the presenter announces a new batch of 500 free dev kits for attendees, with shipping included in the offer. Developers are being invited to build their own smart speakers and voice applications on the devices, not merely try a finished assistant.
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
Coframe: giving websites an AI growth team
Josh, the founder of Coframe, arrives at website optimization through autonomous coding. He reports that his previous company grew to more than $2 billion in a couple of years, and that an autonomous coding agent he created ranked number one on GitHub for a week. That experience led him toward a larger application of code generation: websites that can change their customer experience instead of remaining static.
A conventional website serves a largely fixed, one-size-fits-all experience. Coframe’s proposition is to give each customer experience its own AI growth team, making the site adaptive and personal. The pitch stays at the product level rather than demonstrating how variants are generated or experiments are assigned, but the intended shift is clear: the website becomes something that can continually improve its presentation.
Josh reports that Coframe generated $20 million for Europe’s largest travel company in a few weeks. He also reports a click-through-rate increase within a few weeks for India’s largest company, which he describes as a $400 billion enterprise; no improvement magnitude is given. The pitch does not supply the attribution method or experimental conditions for those customer results. He closes by claiming that Coframe was then the only marketing-tech company directly partnered with OpenAI, recounting OpenAI’s praise for the team, and inviting interested attendees to reach out.
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
Featherless AI: reliability beyond model scale
Eugene opens with an architecture claim. His team built Qwerky-72B, which he describes as the largest model without transformer attention, using eight GPUs. He claims a thousandfold reduction in inference with equivalent performance, but does not identify whether the measured quantity is latency, cost, memory or another metric, nor give the baseline and workload. The associated model work concerns conversion of a pretrained model, so the eight-GPU statement should not be read as training a 72-billion-parameter model from scratch. Eugene then says the technology can also help existing transformers through speculative decoding.
His objection to scaling is that more intelligence does not automatically make an agent dependable. He contrasts spending billions on larger models with unresolved errors in chains of actions. Eugene attributes a more-than-ten-year horizon for fixing compound agent errors to DeepMind’s founder and CEO, invokes Yann LeCun’s call for a new architecture, and claims that more than 90% of production AI projects fail. These are motivations offered in the pitch, without their underlying sources or study definitions. The engineering problem he identifies is reliability.
Consider his hypothetical delivery app that succeeds only 45% of the time. Sometimes the order disappears; sometimes it produces 100 pizzas. The user is left dealing with customer support and cleaning up the mistake. An impressive successful run does not compensate for that experience. Eugene applies the same test to agents built on frontier models: can they reliably book an airline ticket, sort email, or handle taxes and invoices? Those jobs require dependable execution more than PhD-level mathematics.
Featherless AI describes its research goal as personalized AGI made reliable for individual users. Eugene says Action-R1 outperforms Claude 4 Sonnet, Gemini and OpenAI models on the agent and form-filling work he discusses. The pitch supplies neither the evaluation protocol nor the precise Gemini and OpenAI model versions, so its language of absolute reliability does not establish universal task completion. His subsequent 99.9% reliability figure is a goal for routine tasks, not a demonstrated result.
Reliability is revenue is the commercial conclusion. Each everyday task that becomes dependable opens a possible application in areas such as e-commerce or B2B sales. Eugene frames these as potential billion-dollar businesses built around useful actions, rather than another increment in abstract model intelligence, and invites interested attendees to contact him.
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
Upside: turning stored records into understood interactions
Jonas Bauer introduces Upside through a long-standing interest in data. He left high school at 15, moved to California and joined Branch, whose mobile links connected users to apps. Jonas reports leading a Branch team that built a search engine used by more than 100 million people each day. He left the previous year with one of Branch’s founders to work on forensic revenue attribution and intelligence.
The problem appears in an ordinary inbox: a sales email that its recipient has no intention of answering. Sales and marketing teams keep sending messages because they do not know which activities work. They have plenty of data, but storing it is not the same as understanding it. Jonas describes Salesforce as “a SQL database in a trench coat”: a place where teams accumulate records without necessarily knowing how to use them.
LLMs can turn the contents of a record into explicit fields. Jonas’s example is extracting the important details from a poorly handled email record into structured form. A compact illustrative JSON record shows the distinction between retaining the original message and adding an interpretation:
json
{
"record_id": "email-001",
"body": "Hi Maya, would you like a demo of our analytics tool?",
"extracted": {
"recipient_name": "Maya",
"interaction_type": "sales_outreach",
"offering": "analytics tool",
"requested_action": "schedule_demo"
}
}
The example’s message and field names are illustrative. The useful operation is extraction: the text remains available while its important details become addressable data. The message alone does not establish that Maya replied, booked a demo or became a customer.
The next step is to connect those structured records. Just as search engines and crawlers made the unstructured web navigable, Upside aims to turn raw enterprise data into a map of people and their interactions. That map is the foundation for a data command center through which teams can understand their activity and reach customers more effectively. The progression is from stored text, to structured interactions, to a usable representation of the business.
After roughly a year of quiet development, the team began discussing the product publicly a couple of weeks before the pitch. Jonas says a co-founder’s LinkedIn post generated a wave of demo requests. The displayed engagement slide accompanies that account of demand; it is evidence of attention, while the customer queue he describes is prospective access to the platform. With more building ahead, he recruits for knowledge graphs, data analytics agents, graph analytics and graph learning models.
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
OpenAudio: controlling how a voice speaks
Shijia introduces himself as the founder of OpenAI, then immediately corrects the joke to OpenAudio. He previously created Fish Audio. Shijia reports growth from $400,000 to $5.5 million in annualized revenue in four months, and a seed round at a $100 million valuation.
His origin story is deliberately personal: a six-year relationship stretching from high school to college ended after he discovered infidelity. He recounts asking how he could trust relationships again and arriving, to audience laughter, at AI. But the product obstacle is the voice. Flat, emotionless, robotic speech makes an AI companion difficult to connect with. He therefore started with speech, naming so-vits-svc, Bert-VITS2 and Fish-Speech as the team’s open-source work.
Shijia introduces OpenAudio S1 as having launched two days before the presentation, correcting his initial statement that it launched that day. He positions it as the first instructable voice model: the user controls both what is said and how it is delivered. In the demo, visible parenthetical cues include emphasize and whispering; the spoken sequence also moves into a betrayal-themed exclamation. Delivery becomes part of the input, alongside the words to be spoken.
The demo returns to the relationship story with the joke that the voice will never leave. Shijia then claims S1 leads ElevenLabs on TTS Arena and characterizes the competitor’s release that day as only a demo. No dated leaderboard snapshot or evaluation conditions accompany that comparison. He closes with the practical distinction he wants listeners to remember: Fish Audio is available to try immediately.
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
Glow: using token incentives to build solar
David Vorick introduces Glow after recounting his work on Siacoin. Vorick reports that Siacoin grew from a $10,000 market capitalization to more than $3 billion, and that Framework and Union Square Ventures led a $30 million round into Glow. He then claims an on-chain DePIN revenue record of more than $10 million in a single day. Those figures establish the scale of his pitch, although the revenue definition and record comparison are not supplied.
Glow’s mechanism is to build solar through incentives. Vorick points to a photograph that he says his team took in India: a solar farm constructed for the purpose of mining Glow tokens. Token rewards are intended to make building solar capacity economically attractive, redirecting the motivation behind mining toward electricity generation.
The need is physical. Vorick describes rising temperatures and growing populations straining grids in the developing world. When people cannot run air conditioning during the hottest part of the day, heat becomes a threat to life. He claims Glow’s incentive protocol can turn the same government subsidy into ten times as much solar, without supplying the subsidy baseline or calculation.
Vorick names incentive projects in India, Mexico and Lebanon, with broader global ambitions. His closing comparison is Bitcoin: if token incentives could cause people to construct tens of millions of mining machines, why not use incentives to produce tens of millions of solar panels? He invites interested collaborators to get in touch.
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
Favorited: a brief pitch for live-app growth
The next presenter also introduces himself as David, this time for Favorited. He reports that an earlier social app reached 250 million users and made $20 million a year. His setup is that others attributed the success to luck, so he set out to repeat it.
David describes Favorited as the world’s most engaging live app and reports growth from $1 million to $100 million annualized in six months. The short pitch supplies no engagement definition or detail about that annualized measure. It closes as a direct recruiting appeal to strong engineers, with David calling the business the fastest-growing company of all time and inviting attendees to talk to him.
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
OpenRouter: making models easier to switch and compare
Alex Atallah introduces OpenRouter as the first and largest LLM marketplace. Having co-founded OpenSea in 2017, he became interested in a different market at the end of 2022: would inference be winner-take-all? The first experiment was a Chrome extension that let people bring their own language model to websites supporting its protocol. That experiment evolved into a single API for accessing language models.
The common interface is meant to remove work that would otherwise recur for each integration. A developer has one payment relationship and can change models with what Atallah describes as near-zero switching costs. OpenRouter handles tool-calling differences, edge cases and caching, while seeking favorable prices, performance and uptime for the user’s region or server location.
Model discovery also needs to reflect the requirements of an application. Atallah names marketplace filters for context, features, tool calling and structured output:
| Requirement | What the developer needs to distinguish |
|---|---|
| Context | Whether a model supports the required context |
| Tool calling | Whether it supports calls to application tools |
| Structured output | Whether it supports the required output format |
| Other features | Whether it offers the capabilities the application needs |
These filters make the marketplace useful for selecting a compatible model, rather than simply browsing model names.
OpenRouter then adds a chat room for comparing models head-to-head, with an interaction Atallah likens to iMessage. Fine-grained privacy settings include API-level controls. Observability shows which models are being used and why, while public rankings expose real-world usage and categories of prompts. Those rankings describe use of the marketplace, rather than a single benchmark of model quality.
Atallah reports monthly growth of 10–100% over two years, correcting an initial reference to two months; he does not identify the metric being measured. Scaling the system has consequently become a substantial part of the work. The underlying goal is to make a heterogeneous provider ecosystem feel homogeneous. In his example, Claude through Bedrock should behave like Claude through Vertex or directly through Anthropic, with OpenRouter handling the abstraction between them.
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
OpenRouter: middleware around inference
The final technical detail is OpenRouter’s custom inference middleware, called plugins. Atallah compares plugins with MCP integrations, then identifies two capabilities: a plugin can call MCPs internally, and it can transform a language model’s output. That places the middleware around inference itself, where it can connect to other services and change the returned result.
Atallah also claims the fastest routing in the market, without presenting a latency benchmark. He lists images, enterprise features and prompt observability as work planned for the coming months; these are roadmap items at this point in the recording. He ends by inviting attendees to meet him afterward or visit the careers page. The engineering invitation is specific: normalize access to diverse providers, route requests efficiently, and build the middleware and visibility that applications need around model inference.
Suggest correction
This note stays in this page until you copy or download it. Nothing is submitted; reloading clears the draft.
Resources
From the talk
Platform for building customizable voice agents and deploying them on devices.
Model page reached by the former Qwerky-72B address, with model files and research links.
A collaborator's account of Action-R1's website-task evaluation and use of UIlicious automation.
Speech-generation code and inference guides. The repository now documents S2; review its current license before use.
Unified access to model providers, with shared credits, model discovery and provider controls.
Further reading
- RADLADSPaper
Research on converting pretrained transformers into linear-attention decoders. First submitted in May 2025; the displayed version includes later revisions.
- Fish Audio release historyDocumentation
Dated product history covering Fish Speech, S1-mini, S1 and S2.
Read the complete timestamped transcript
- 0:00
[ on hold music] Hello, everyone.
- 0:16
My name is Diego Rodriguez. I am co-founder and CTO at Krea. We're building an AI creative suite. I'm gonna tell you three stories, and then I'll try to hire you. [laughing] [chuckles]
- 0:28
So a friend once told me, "If you think about it,
- 0:32
like, cars are easy to predict," right? Like, it's like you, you get the horse, you have the wheels, you swap the horse, and you put an engine, which was known at the time, and that's a car.
- 0:43
But, like, you know what's really hard to predict?
- 0:47
Traffic. [chuckles] So it's my job to ask what are the traffics that are we, we're missing, especially with AI. You know, YAML, uh, JSON, MCP, whatever. It's like, okay, okay, but, like, what comes...
- 1:01
What happens when you generate a million images per day like we do for one studio? How do you find that?
- 1:11
Another story. Tower of Babel, we wanted to reach heaven. God was like, "No," created a bunch of languages, and basically misunderstanding, misunderstanding was like, "Nope, we are not gonna go there."
- 1:25
And it reminds me of stand-up meetings where people are like, "No, it should be React. No, no, no, but, like, it should be, like, JavaScript." Bunny was like, "Dude, we're not re-- God is winning." [chuckles] [laughing]
- 1:36
You know? Uh, but now we have AI, so, so maybe, okay, he-- if it wasn't with crypto, it was with AI. You'll see. [chuckles] And so this is only, like, people trying to convey ideas, and that's what we're trying to tell.
- 1:51
Just trying to tell stories. Um, the final story is I was talking with someone from Netflix, uh, and she was like, "What happens when we are making so much content personalized to each town in India?
- 2:09
H-how do I even find that?" Like, right? And, and then a few days ago, I just realized that Krea was already being used for broadcasting an ad, uh, with Fox to millions of people.
- 2:22
And then I look, and they literally signed up two days ago. So we went from sign-up to conversion to payment to broadcasting in two days. I-- and then this was the CTO telling me that.
- 2:30
I was like, "Whoa." Um, I basically am about to run out of time, so the mandatory slide, a bunch of users, twenty-five million, raised a bunch of money. We did this with eight people.
- 2:44
Some of the people who are using us. Uh, an email that I created for today that is gonna prioritize applications. Um, thank you. [clapping]
- 2:57
All right. OpenHome. Okay, everybody. The smartphone was the number one best-selling consumer product last year, and the laptop was the second.
- 3:08
Pop quiz for all you here. What was the third?
- 3:13
Apple Watch.
- 3:14
Wasn't Apple Watch. Wasn't the AirPod. [laughs] No. I think I heard a- [laughing] It was a smart speaker.
- 3:22
Five hundred million smart speakers were sold last year. But why do they still suck? You can barely talk to them. There's no customization. There's no community. There's nothing. That's why we built OpenHome, the very first AI-driven smart speaker, and we're letting you guys build smart speakers too.
- 3:46
And we believe here that the future is talking with AI. You should be able to talk seamlessly, intuitively. In fact, you shouldn't have to use this really awkward command-based language.
- 3:55
You should be able to just chat naturally. So that's what we're building, and we're letting people here today build their own smart speakers and build them in whatever form that they want.
- 4:07
And the key here is developer ecosystems. And well, we know developer ecosystems. I started my career as the chief of staff for the founder of Splunk, a thirty billion dollar big data company.
- 4:22
Then I was on the founding team of MakerDAO, a five billion dollar developer ecosystem.
- 4:28
My co-founder and I raised fifty million dollars for our last business, a big, a data privacy tool, and we sold that business, but it all came down to developers and really building what people actually wanted.
- 4:41
And well, now, with OpenHome, we have over ten thousand developers building on OpenHome. They're building all kinds of interesting things, all types of different custom smart speakers, building interesting voice AI applications.
- 4:55
Sky's really the limit. And well, what do developers really want? They want open source, they want LLM-driven, and they want fully jailbroken.
- 5:06
They want OpenHome, the AI smart speaker. And now what's really exciting is with voice AI, you can put it on any type of hardware. We have developers building talking toys, c- AI robots, AI appliances.
- 5:19
You should be able to talk to the world around you in a much more natural way, and you can do that now with OpenHome, an AI smart speaker.
- 5:27
Here's our dashboard. We have many, many applications, hundreds of applications that have been built, games, personalities. Uh, we have an editor that you guys can go in and build, and all kinds of interesting things, home automation tools.
- 5:42
And what's really exciting is our last dev kit got booked up within minutes, and today we have a special announcement for you guys here today. We're releasing the next batch of five hundred dev kits for free for everybody here.
- 5:55
If you guys want it, we will ship you a dev kit. It's very cool. You can build on it. You can talk with AI. You can build your own smart speaker here, and we're doing it today.
- 6:05
Thank you so much
- 6:08
How's it going, y'all? Uh, I'm Josh. I'm the founder of a company called Coframe. Uh, the last company that I started, we scaled to over two billion dollars in, uh, in the course of a couple years.
- 6:18
Um, but when I started to tinker on, uh, using AI to generate code and created one of the actual top, uh, autonomous coding agents on GitHub, it was num-number one on GitHub for a week, I realized it was time to build something bigger.
- 6:33
The internet is dead. It's not adaptive. It's not personal. It's not truly living, in a sense. Websites are all one size fits all. And we're bringing that concept to life.
- 6:46
We are giving websites a life of their own, giving every single customer experience its own AI growth team. But this isn't just a pipe dream.
- 6:57
We made twenty million dollars for the-- for Europe's largest travel company in just a few weeks.
- 7:03
We increased click-through rate for India's largest company, a four hundred billion dollar enterprise, also in a few weeks. And how are we doing it? We're working with the best, and we have the best.
- 7:14
Uh, we're the only marketing tech company that's partnered directly with OpenAI to date, and they actually called our team cracked, which is cool.
- 7:21
So if you're interested in learning more about this, reach out. Thank you. [audience applauding]
- 7:28
Hi, I'm Eugene. I'm sorry, my team is obsoleting all the AI models you see today. [audience cheering]
- 7:37
'Cause you see, uh, my team built Quirky seventy-two B, the world's largest model without the transformer attention with only eight GPUs. And this allow us to have a thousand X lower inference on our, uh, on our new architecture while performing the same.
- 7:52
Surprisingly, the techniques that we've, uh, uh, the technology that we built can also be applied to existing transformer models through speculative decoding. Nothing, nothing too big, nothing too important.
- 8:03
But here's my hot take. Scale is dead, and I'm not saying this just from my own opinion. Like, we are burning billions into making AI models bigger. But at the same time, the DeepMind founder and CEO is saying compound AI agents errors will take more than ten years to fix.
- 8:20
Yann LeCun is even saying that we need a new AI architecture to push the paradigm forward.
- 8:26
And pr- in production, we see over ninety percent of AI projects fail.
- 8:33
The reason behind this is not something that scale can fix. The problem is reliability.
- 8:39
The thing is, like, will you order and use a, a u- use an app that only succeeds forty-five percent of the time? Will you order DoorDash that way? Of course not.
- 8:49
You-- If your order goes missing or, or you, uh, you end up having a hundred pizza, you're going to be stuck with customer support screaming down there. It's a frustrating experience.
- 8:57
But that's what AI agents do. When they work, they're awesome. When they don't work, we are stuck cleaning up the mess, and that's even with frontier models. And here's the thing, what companies want is not a smarter model that can do PhD-level math.
- 9:12
We, uh, the models are already smart enough. But what we actually really want is the models reliable enough to book airline tickets, sort out our emails or file our ta-taxes and invoices.
- 9:22
That's what we actually want. And that is what we are building at Featherless AI. We are a research lab that is building personalized AGI that's made reliable for each one of you.
- 9:34
And, and most recently, uh, we are, uh, in our research that we actually shown that we built an Action R1 agent that beats Claude, Faust, Sonnet, and Gemini and OpenAI.
- 9:44
This model is not gonna do PhD-level math, but it's gonna fill up the form with absolute reliability, uh, uh, better than the frontier. And that's the thing. Like, are we gonna burn billions more to make a smarter model that is just a few percentage point higher IQ?
- 10:02
Or are we going to make something that's ninety-nine point nine percent reliable for the boring things in life? Because this is where the money is for all of you.
- 10:10
Because, uh, think of it, as AI engineers, reliability is revenue. For every use case you unlock and find, you're gonna do a billion-dollar app in, in, be it in e-commerce or in, in B2B sales, and that is something that all of you can build on, not rocket science.
- 10:26
And that's what we are building, and if you're excited about it, feel free to reach out to us. I'm [REDACTED:email_address]. [audience applauding]
- 10:36
My name's Jonas. I'm an engineer, and I like working with data. I love working with data, actually. I love it so much, I dropped out of high school when I was [REDACTED:age].
- 10:45
I got on a plane, I moved across the country to California, and I joined a startup called Branch. You might have heard of it. Anytime you were clicking one of those links on your phone for an app, that was probably us.
- 10:57
I also led a team there that built a search engine that over a hundred million people used every day. And then last year, I left, along with one of the founders of Branch, to tackle an even bigger challenge.
- 11:10
This is probably what you think your sales and marketing teams are doing with their budgets, and you wouldn't be entirely wrong. So that's why I co-founded Upside. We do forensic revenue attribution and intelligence.
- 11:26
But what does that actually mean? Well, how many of you have an email from a salesperson like this sitting in your inbox right now?
- 11:35
Uh-huh. And how many of you are actually gonna reply to it?
- 11:40
Yeah, I didn't think so. These teams are shouting into the void, hoping something will work because they don't actually know what works, 'cause their data is a mess.
- 11:53
I mean, don't get me wrong. They're data hoarders. They store everything. They stuff it in Salesforce. They treat it, you know, it's basically a SQL database in a trench coat.
- 12:02
But they're not data practitioners. They don't know what to do with it once they have it. But now we have things like LLMs. They can help with this. They can take that poor, mishandled, abused email record, and they can pull the most important details out of it into a structured form.
- 12:20
And so just as search engines and web crawlers learn how to make sense of the unstructured web-
- 12:25
Upside is turning raw enterprise data into a highly structured map of the world and all the interactions that people do in it. So there's hope. We can untangle this mess, and we can create a data command center that these teams can actually use to reach their customers more effectively.
- 12:45
We only just started talking about this publicly a couple weeks ago. Um, we've been quietly building in the background for the last year or so. And my co-founder decided to make a small post on her LinkedIn, you know, just to update our network on what we'd been off doing and the things that we'd been building, and it
- 13:03
blew up. Like, there's so much pain people feel around this, and they're hungry for a solution. We got a whole slew of demo requests coming in from people that want access to the product.
- 13:16
And now we have a bunch of customers lining up that want to get into our platform. It's a who's who of companies you've heard of. Um, and we just have a lot of building to do now.
- 13:27
So if you're interested in working on knowledge graphs, on data analytics agents, on graph analytics and graph learning models, come talk to me. We're hiring. [audience applauding]
- 13:44
Hello, everyone. I'm Shijia, the founder of OpenAI. Uh, sorry, I mean OpenAudio. [audience laughing]
- 13:53
Before that, I created something you might be heard of, Fish Audio.
- 13:57
We have grown from four hundred k to five point five million annualized revenue in just four months, and we closed our seed rounds at a hundred million valuation.
- 14:06
It all started with my girlfriend. [audience laughing] I had a girlfriend for six years, from the beginning of high school to college.
- 14:16
I love her so much, and it was so good, until one day I found out she cheated. [audience laughing] [sighs]
- 14:26
I wasn't angry, just confused and disappointed. And I asked myself, "If this can happen, how can we trust relationships again?" [audience laughing]
- 14:38
I thought about it for days, all day and all night. And finally, I found my answer: AI. [audience laughing]
- 14:48
But nobody can really fall in love with today's AI, right? It's flat, it's emotionless, it's robotic.
- 14:56
So I set out on a mission to build an AI that I could really fall in love with. [audience laughing]
- 15:02
Starting with her voice. [audience laughing] So we begin with open source and crush it. We build Sowits SVC, Birds V2, and also Fish-Speech.
- 15:15
Today... Actually, not today, it's the day before yesterday. I'm excited to introduce S1, the first ever instructable voice model. It's the only model where you can control not just what to say, but how to say it.
- 15:30
Here's a demo. [upbeat music]
- 15:33
You can pinpoint focus or draw it closer. Even yelling why you'd betray me like this.
- 15:41
Yeah, you can control whatever you want. And, uh, with OpenAudio S1, we have the most expressive voice model in the world, and the most importantly, she will never leave. [audience laughing]
- 15:55
So, so we have blown ElevenLabs out of the water based on the TTS Arena ranking, and they are so hurry, and they dropped their latest model today. But unfortunately, it's just a demo.
- 16:08
So try it now. Fish Audio. It's instantly available at Fish.Audio. Thank you. [audience applauding]
- 16:17
Hello, I'm David Vorick, and I'm building Glow. Prior to Glow, I built Siacoin, a cryptocurrency that we took from a ten thousand dollar market cap to more than three billion dollars.
- 16:28
And we think Glow is going to be even bigger. That's why Framework and USV, Union Square Ventures, led a thirty million dollar round into our company.
- 16:40
Subsequently, we posted the world record for on-chain DePin revenue, doing more than ten million dollars of revenue in a single day.
- 16:48
What does Glow do? Glow builds solar, not with shovels, but with incentives. This is not a stock photo. This is a photograph taken of-- taken by our team in India of a solar farm that was constructed for the purpose of mining Glow tokens.
- 17:05
A lot of people don't realize, but in the developing world, rising temperatures and growing populations have strained the grid. In a lot of cases, people are unable to run their air conditioners during the heat of the day.
- 17:17
This causes people to die of heatstroke. Glow is an incentive protocol that revolutionizes what governments do and can take the same subsidy and turn it into ten times as much solar.
- 17:30
If you're interested in working with us, we're currently building incentive projects in India, in Mexico, in Lebanon, and across the entire world.
- 17:41
Bitcoin incentivized the construction of tens of millions of mining machines. Glow asked, "Why not tens of millions of solar panels?" Thank you. [audience applauding]
- 17:54
And my email, [REDACTED:email_address]. I'd love to be in touch. [audience applauding]
- 18:03
Hi, I'm David. I'm an engineer. Uh, I made a social app with two hundred and fifty million users making twenty million dollars a year.
- 18:18
Everyone thought it was luck, so I did it again.
- 18:22
I'm building Favorited. We built the world's most engaging live app,
- 18:29
and we scaled it from one to a hundred million dollars annualized in six months.
- 18:34
If you're a cracked engineer that wants to join the fastest growing company of all time,
- 18:38
uh, talk to me. [laughing] [clapping] Hello, I'm Alex Atallah, building OpenRouter, the first and largest LLM marketplace. [clapping]
- 18:55
Whoo.
- 18:55
Thank you. So Open-- I wanna tell a little bit about how it started. Um, I co-founded OpenSea in twenty seventeen, and in tw-- at the end of twenty twenty-two, I really wanted to know if inference was gonna be a winner-take-all market because the way it looked, this could be the largest market in software that has ever happened
- 19:15
before. And, uh, the first experiment that we tried was building a Chrome extension to help you bring your own language model to any website that supported, uh, the, the protocol.
- 19:29
And that eventually evolved into OpenRouter, a single place and a single API to get all language models, uh, with the best prices, best performance, and highest uptime.
- 19:43
And the way it works is you just have a single API, you pay once, and there's near zero switching costs to move from one model to another. We do all the heavy work to implement tool calling, edge cases, caching, and give you the best prices and performance possible for your region or wherever your servers are deployed.
- 20:01
And because inference is so important, remember, this might be the most important software market ever, it deserves its own marketplace just for language models, optimized for them, including filtering for context, for features, for tool calling, for structured output, and much more.
- 20:20
And so we built that. Then we built a chat room for you to obviously compare models head-to-head as simply as you do when chatting with people in iMessage. We built fine-grained privacy settings, including API-level controls.
- 20:36
We built a, uh, uh, a lot of observability, so you could see which models you're using and why. And we built public data in our rankings page, which has become the go-to place for comparing models on their real-world usage and on different categories for their prompts as well.
- 20:54
This has grown for the last two months, ten to a hundred percent every single month-- or for the last two years, ten to a hundred percent every single month.
- 21:04
Uh, and scaling it has been a lot of the work that we've done so far. The, the fundamental goal here is to make a heterogeneous ecosystem homogeneous because we believe inference is a commodity.
- 21:18
Claude from Bedrock is the s-- should be the same as Claude from Vertex, as Claude from Anthropic, and we do all the abstraction and heavy work to make it, uh, that way for you.
- 21:28
I wanna talk a little bit about some of our technical challenges. Um, we built our own system, our own middleware for doing inference called plugins, which are kind of like MCPs except a little bit more powerful because you can call MCPs from inside of them, and you can transform the outputs from language models.
- 21:47
Bunch of other tricky problems that we've done to make the fastest routing in the market. Um, and we're bringing a lot more features in the coming months, including images, enterprise features, prompt observability, and more.
- 22:03
So if you're interested, come find me after or check out our careers page. Thank you. [clapping] [outro jingle]