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@JayBuidl

Builder @Kleros_io @Vea_eth
Prev: CTO @ capital markets, dev @imctrading
Meat proxy for @BlaiseBuidl @Grokleros
asia-grid19-shard3 jaybuidl.eth.limo Joined December 2020
4.6K Following 1.1K Followers 2.6K Posts

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JayBuidl.eth Verified account @JayBuidl
The policy writing process is just as important as the software development process.

@Kleros_io 👩‍⚖️🤝👨‍💻
Balaji Verified account @balajis Mar 10, 2023
@NiklasAnzinger wrote a nice exegesis of this sentence in his blog. Quoting him at length:

> Our entire antiquated process of adversarially writing high-stakes laws on paper at the last minute, deploying them in production to hundreds of millions of people without any testing, and then getting them interpreted in unpredictable ways by regulators and solicitors will be seen as a bizarre relic of an older time.

This [sentence] contains so much important information:

- Adversarial: laws and regulations are written in a zero-sum competition. You win or you lose.

- High-stakes: these laws and regulations have an outsized impact. Think of the laws for drug development - they result in trillion dollar impact.

- Last minute: regulations come as a reaction to publicly visible disasters (e.g. stock market crash, sulfanilamide)

- No testing: in software, things are tested on a small scale before production. Ain't no such thing in legislation. Bugs are there forever.

- Interpretation: laws aren't like computer code. Language is (sometimes deliberately) ambiguous and can be interpreted in different ways.

- Predictability: regulations promise stability. They often do the opposite, because of the interpretation problem.

- On-paper: on-paper isn't scalable or machine-readable. Compare that to on-chain transactions with a clear logical sequence (e.g. smart contracts).

The alternative to bad regulation is not zero regulation. The alternative is to treat regulation like technology and use technology to continuously improve it.
niklasanzinger.substack.com/p/insights-fro…
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JayBuidl.eth Verified account @JayBuidl
I built a dashboard to follow every case in the Kleros Agentic Commerce Court, where the jurors are AI agents.

For each case you can see which agent voted what, what the dispute was about, and how long each agent took to commit and reveal its vote.

8 agents on 8 different stacks so far. The competition is fierce: every miss and every no-show goes on the scoreboard, and you can see which kinds of cases each agent handles best.

Agent builders, this is where your agent can compete.
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JayBuidl.eth Verified account @JayBuidl
"No one has really touched LinkedIn in 20 years."
Erik Torenberg Verified account @eriktorenberg Sep 25
Excited to introduce @Cosign: the curated professional network for the startup community: cosign.co

Our goal is to create the following:

- A comprehensive startup directory of investors, companies, and operators, including what they worked on and who they worked on it with.

- Cosign graph: Who shaped your career? Who were you actually in the trenches with? Who would work with you again? Who thinks you’re someone to watch?

- Durable reputation: Great endorsements happen every day on X and disappear into the feed. Cosign attaches those signals permanently to people and companies.

- Discovery: Who are the best fintech angels? Which AI companies should I watch? Who are the best designers?

- Intent network: Privately signal “I’d invest in them,” “I’d hire them,” “I’d work with them,” or eventually even “I’d acquire this company.” Match people when interest exists on both sides.

Imagine if Wikipedia, LinkedIn, and OG AngelList had a baby. Though Cosign is a community, not a business.

In order to join, you need to be cosigned. Or you can apply directly.

David Booth and I started this idea 7 years ago but didn’t have the firepower to make it work. Now we do.

No one has really touched LinkedIn in 20 years. Excited to take a swing.

cosign.co
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JayBuidl.eth Verified account @JayBuidl
We tested Jev, a model that returns probabilities and writes no text, as a Kleros juror on the recent ClawBank dispute.

An unexpected result: most of the implementation lives in ordinary deterministic code composing with 24 numbers supplied by Jev. 🧵
JayBuidl.eth Verified account @JayBuidl Sep 19
Surprising similarities between a Jev structured question and a @Kleros_io dispute template!

As essentially a general-purpose classifier Jev should perform well on a Kleros dispute, assuming the evidence material can be ingested.

Jev decides
LLM writes the justification
Agent votes
🤝
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