cofounder, caestro, prev:training LLMs @rimelabs the future is willed into existence. care about discovering new science, industrial policy, local politics.
Toronto
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This is amazing. Ever since I heard @balajis talk about the pseudonymous economy, I've been thinking about how to make it easier to start a YouTube channel with total privacy.
Recent advances in deepfakes, AR and CV will make this as easy as a click.
You won't need a 30k suit
This is Miko. She's a virtual streamer who is controlled by a real-life woman known only as The Technician.
The Technician uses the Unreal Engine and a $30,000 motion-capture suit to create Miko.
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technology brothers discover the 'rest of the economy'
It’s the year 2030 and your entire life is via rollups. Your home is secured by Thrive Holdings, HOA is operated by Long Lake, renew insurance via Sequence, and your local tennis court reservation system is run by Beacon. Your accountant is employed at Crete and your small business handles IT on Titan.
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Quite the opposite in fact. Money to spend on compute and talent will translate into focused outcomes. (Cyber, enterprise and so on)
Not all growth curves will look the same.
Wholeheartedly agree. This is also bad news in the long run for Cohere itself. Their chance of reaching escape velocity was pretty low, but now it's zero.
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DG. retweeted
introducing prism, the world’s first speech-to-speech model you can’t tell from a human in conversation
prism has:
> 300ms response time, 4x faster than OpenAI GPT-Live
> outperforms every other s2s model on intelligence (with NO reasoning)
> the first s2s model customizable through reinforcement learning
we’re a team of 35 with $35M in funding, and we’re unlocking s2s model adoption faster than openai, google, and x.
the first 100 people to sign up here get 1 month free: rime.ai/prism?utm_campaign=p…
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I’m so excited to see Rime move into speech2speech. Prism is a great model with *parallel* tool calling.
rime has the best visibility into autonomous voice workloads and spent years building the infra to enable this today.
This is probably the only usable s2s model today.
introducing prism, the world’s first speech-to-speech model you can’t tell from a human in conversation
prism has:
> 300ms response time, 4x faster than OpenAI GPT-Live
> outperforms every other s2s model on intelligence (with NO reasoning)
> the first s2s model customizable through reinforcement learning
we’re a team of 35 with $35M in funding, and we’re unlocking s2s model adoption faster than openai, google, and x.
the first 100 people to sign up here get 1 month free: rime.ai/prism?utm_campaign=p…
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DG. retweeted
why do regular voice agents suck in conversations?
they lack real-time emotion, empathy, pacing, hesitation, tonality, everything that a normal human would have in conversations.
prism changes this:
> it listens to actual audio
> can hear when someone is upset, excited, laughing, or frustrated
> responds directly in audio in <300ms based on what it hears
here's what this sounds like across 5 real use cases:
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big labs "selling ai to enterprise" need 100 million more FDEs to own the market
i've talked to 40+ huge public company ceos/execs, still zero examples of labs showing a single example of how they can use ai
a companies founders led sales will solve this, then labs will copy
we're so early in enterprise ai adoption
enterprises buy "ai" from anthropic and openai who have no idea how to show enterprises what ai can do concretely, labs need playbooks that don't exist
most public company ceos can't mention a single task that 2x productivity by ai
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DG. retweeted
The beginning of Spellbook was also an oddly meditative and spiritual pursuit: Our hypothesis was that if we could access raw reality directly, and disconnect from the sort of "false reality" of popular stories, then we would find alpha in the world.
In some ways the company was a higher level experiment: if we can come to see raw reality with crystal clarity, then alpha should follow. Alpha exists in the gap between popular stories, and raw reality.
We saw business as competitive philosophy. Your score is a direct measure of how good you are at uncovering truth before others.
This continues to provide a great deal of meaning for me: investing and business are a gym that ultimately enable you to build radically clear thinking.
It worked: we raised money for a legaltech company long before it was fashionable (pre-Atrium), and were the first in the world to commercially launch and LLM-based tool for lawyers.
We were making a hard bet that legal AI was needed, based on raw first principles thinking, long before any story had taken hold.
The challenge, though, is that the "raw reality" view that uncovers alpha, is a completely different from the story-telling muscle that excites customers and your team.
The hardest part of business is that you must simultaneously be aware of stories as a pollutant, while still crafting the inspiring stories that ultimately drive most people to action.
Investors/hedge funds have the luxury of only needing to be good at seeing raw reality. They don't need to market their bets. Sales and marketing has the luxury of being able to focus on the stories.
There are very few people who can simultaneously wield the power of stories, while also piercing through the sea of stories, extracting alpha from the raw reality that eludes others.
The ultimate magic is being able to do both.
Realizing how incredibly autistic our early board meetings were for Spellbook:
1. I declared the meeting a “story free zone” because I was worried that stories were too psychologically persuasive, and that they would pull us away from hard data-driven reality
2. Meetings would largely consist of scrolling through like 40 slides of charts sifting for signal. I called this “soaking in the data”
3. In the search for PMF I wanted us to think like a hedge fund making many bets. While we knew the problem we were solving I didn’t want our team to get too tied to any single narrative because I thought it would reduce agility.
4. I never allowed us to think about us as “a company”, but instead as a SWAT team searching for a company to build
5. This approach enabled us to launch over 100 different landing pages with different angles on the way to PMF
Honestly, I think it’s a really good approach pre-PMF. Too many companies get attached to a singular story too early and quit when it doesn’t work out. Startups are 3D chess. There is always a move. Slack was a game company.
However, at Series B/Series C scale you really need stories to align both your team and customers, and you can think much more longterm after PMF.
But I still believe stories are a dangerous dark magic. You can craft an incredibly persuasive story for almost any position. They’re more of a marketing/comms tool than a reasoning/understanding reality tool.
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DG. retweeted
Toronto has required utility companies to permanently restore sidewalks they dig up since 2018.
Yet the Auditor General found only 12% of permits are inspected annually, and 62% of sampled deficiencies weren’t re-inspected.
Set firm deadlines, fine violators, and ban repeat-offending contractors. It’s not rocket science.
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“…the real challenge is intelligence, agentic behavior, and delegation”
👀
A lot is being made of the fact that Dessa was trained only on human conversations, without any text or pretrained models.
First, dGSLM (arxiv.org/abs/2203.16502) already did this in 2022 with just 2k hours of Fisher data. Vendors now provide thousands of hours of high-quality 2-channel dialog off-the-shelf.
Second, why do you even want to do this? Humans spent hundreds of thousands of years before inventing writing systems, and then optimized it over 5k years to store knowledge very efficiently. dGSLM had to train only on dialogs because at the time, we didn't know have unified models that could train jointly on text and speech, but this problem was solved by autoregressive transformers. You can cut your training ~10x by letting the model learn from text first, then spoken dialog, like Moshi (and basically every other modern S2S model). Different != smart.
Making very natural chit-chat bots is not where the frontier lies. Demos are cool, but the real challenge is intelligence, agentic behavior, and delegation. I would have been blown away if the naturalness was accompanied by GPT-Live style function calling, but alas!
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