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.@surshar_ is cracked.
Unit of work > tokenmaxxing...
Introducing the "quirq"— test it out! Looking for contributors and researchers to experiment with.
Full litepaper here:
xo.builders/whitepaper
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mattwright.eth
9.8K posts
mattwright.eth
@evmpapi
Building toward the future of work. Co-founder @evmsystems_ai @xo_builders | Prev: @gaianet_ai @consensys @jpmorgan
NYC Katılım Ocak 2013
5.1K Takip Edilen8.1K Takipçiler
My new company is finally out of stealth. Our first product is an AI model for agentic workflows that is very fast and knows when to say "I don't know".
Long live Levanto! w. @bigironchris
Levanto Labs@levantolabs
Meet the first AI to say ''I don't know''. We think agent mass adoption is blocked by three things: 1) weak security, 2) unreliability, and 3) how hard agents are to set up. Our first product, Sage, is focused on reliability. It's a "decision model" - a safer, faster, and cheaper way for machines to choose, act, and escalate to a human when confidence is low. You give it content (up to 32K tokens) and a list of questions (Sort, Yes/No, Choice, Tags, Scale), and Sage answers in 200ms - 9x faster than a traditional LLM - always with a confidence score attached. So yes… it's humble enough to say "I don't know." You can also turn on "grounding" to automatically run a web search and enrich the context. Under the hood: we took an open-weights LLM and fused on a classifier through post-training. It's great for agentic workflows, agentic guardrails, data pipelines, content moderation, operations, and risk & fraud. Why does this matter? Today's LLMs are great for chatbots, research, and creativity - but automation needs something much faster, with structured outputs, that isn't overconfident and is ready to admit when the signal is too weak. Sage preview is live. Excited to see your feedback. Levanto Labs is out of stealth today, founded by @marco_derossi and @bigironchris. We are hiring, reach out! Check the links in the post below 😊
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Very interested to see where this goes... perhaps we can experiment the unit of work on new kinds of business friendly intelligence.
Congrats @bigironchris @marco_derossi !!
Levanto Labs@levantolabs
Meet the first AI to say ''I don't know''. We think agent mass adoption is blocked by three things: 1) weak security, 2) unreliability, and 3) how hard agents are to set up. Our first product, Sage, is focused on reliability. It's a "decision model" - a safer, faster, and cheaper way for machines to choose, act, and escalate to a human when confidence is low. You give it content (up to 32K tokens) and a list of questions (Sort, Yes/No, Choice, Tags, Scale), and Sage answers in 200ms - 9x faster than a traditional LLM - always with a confidence score attached. So yes… it's humble enough to say "I don't know." You can also turn on "grounding" to automatically run a web search and enrich the context. Under the hood: we took an open-weights LLM and fused on a classifier through post-training. It's great for agentic workflows, agentic guardrails, data pipelines, content moderation, operations, and risk & fraud. Why does this matter? Today's LLMs are great for chatbots, research, and creativity - but automation needs something much faster, with structured outputs, that isn't overconfident and is ready to admit when the signal is too weak. Sage preview is live. Excited to see your feedback. Levanto Labs is out of stealth today, founded by @marco_derossi and @bigironchris. We are hiring, reach out! Check the links in the post below 😊
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mattwright.eth retweetledi
Your AI has an electricity bill but no P&L.
You can watch it burn tokens all day and still not know what any of it was worth.
That's what the quirq fixes, a unit for the work your agents actually deliver.
We put the full argument, open harness and all, in the whitepaper.
XO@xo_builders
Our founder @Suraj just introduced the quirq. Tokens measure what your AI consumes. The quirq measures what it delivers. The agent economy has been metered on one side only. here's the other side 👇
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mattwright.eth retweetledi
Most businesses today can see how much AI costs, but still struggle to understand what it actually delivered.
Token bills tell us what the machine consumed, not whether the work created value.
That gap is what we're trying to address with quirq, a unit I’m working on to measure verified business impact from Agentic work.
The future I see is not one where AI replaces human accountability or talent, but one where humans stay accountable for defining the outcome, deciding what it is worth, and setting the budget.
AI then goes and executes the work, while the environment verifies what was actually done, what it cost, and where humans still had to step in.
This becomes especially important as companies move from AI demos to actual agentic workforces.
My belief is that the next phase of AI adoption will not just be about better models, but better environments that can prove what work was done, what it was worth, and whether the system is getting better over time.
We wrote a paper addressing this problem and proposing a new way to measure the business impact of agentic work. Sharing an early draft and would love feedback, criticism, and sharp counterarguments.
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@JettMonroe0x It's almost like we need a software, a set of principles, smart contracts that can distribute the wealth of AI.. maybe.. just maybe.. a blockchain would work... lolll
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@evmpapi nobody told them you can just skip the branding and still redistribute wealth without the manifesto theatrics lol
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Wait till they hear about UBI... or UHI.. or whatever the fuck the next billionaire wants to call it...
Antonio García Martínez (agm.eth)@antoniogm
Protest at OpenAI. Marxism was born in the Industrial Revolution and the explosion in productivity and inequality that followed. AI will produce entirely new communisms we’ve never seen before.
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@farokh Congrats king! Love is the best ❤️🙏🏼 A great partner is the greatest gift in life.
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What do we think boys
Raise a round and do the SF polycule house?
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One of my proudest tweeting moments by far
Lex Sokolin | Generative Ventures@LexSokolin
You gotta hand it to them
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More being human
Less acting like robots
Mira Murati@miramurati
Today we share the worldview behind our mission. Human values don't average out. Local knowledge can't be centralized. The good future has many AIs, raised in different places, shaped by the people they serve, disagreeing with each other the way we do. thinkingmachines.ai/blog/the-futur…
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Congrats to @auryn_macmillan & team!!! Just getting started…
The Interfold@theInterfold
The $FOLD auction has closed. Together, participants surpassed the 400 ETH target through the Uniswap CCA. Thanks to everyone who participated, shared, and helped carry it across the line. We’ll share results once finalized. 💚 A strong signal that privacy is better together.
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AI enabled OD, psychosis, and retardation on the rise +1
Shaw (spirit/acc)@shawmakesmagic
Kinda OD’d on Fable Gonna try to get like 15 mins of sleep before spending the next week in a 5.6 haze
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mattwright.eth retweetledi
mattwright.eth retweetledi
OMG OMG
OpenAI ads shipping a 'Custom Audiences' clone...one of my products at Facebook back in the day (basically, uploading lists of emails for targeting).
History doesn't repeat, but it sure does rhyme.
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There is massive pressure to lower the cost of AI.
What people have not internalized is that decentralized training and inference is going to lower costs of delivering AI.
(The inference point should be counterintuitive to anyone that knows the technicals here; stay tuned.)
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