Danial Larkin

Developer;
Gym lover;
Scumbag;

[alex shapalov @alxshp]

🚀 Launching today: https://t.co/ii8QrPI9YP

Postgres terminal written in Rust 🦀🐘

One terminal for all your Postgres databases.

→ Add any database in one command
→ Monitor health, queries, indexes & activity
→ Switch between databases instantly
→ Connect with pgbot for deeper diagnostics
→ Runs locally

Free & open source

Think htop for Postgres, but built for developers + AI agents.

GitHub ↓

[DHH @dhh]

This is exactly calm forest vibes I needed today 🌳

[DHH @dhh]

Omarchy running on PS4!! So damn cool 🤘

[Knowix @knowixbuilds]

@Bhavyaztwt meanwhile some modern apps need more RAM than entire operating systems 😭

[Danial Larkin @JeremyS328399]

@Bhavyaztwt Try many distros, but my favorite is: Fedora, Mint, Omarchy(still using it).

[DHH @dhh]

Spent a few days on a rural internet connection. Real vintage stuff at 2mbit. Coming home to this feels like being catapulted back to the future! Not just the throughput, but latency: 1ms to the hop! And the modern internet being so CDN heavy means I actually get the 2gbit DL.

[ThePrimeagen @ThePrimeagen]

This

[sudo rm -rf --no-preserve-root / @pcaversaccio]

it's a remarkable milestone for anyone working on compilers and smart contract security: Vyper is set to become the _first_ formally verified smart contract compiler, effectively allowing you to mathematically prove that the entire compilation pipeline preserves the contract logic _and_ to prove that the contract logic itself is correct. Oh, and the cool thing is, my snekmate math functions have been formally verified :D. 🐍💙

[Bryan Johnson @bryan_johnson]

Just had sex with Kate. Goodnight everyone.

[le.hl @0xleegenz]

People who moved abroad alone in their 20s, handled all docs, bank account, visa, tax, jobs, accomadation, and culture difference

These people fear nothing anymore

[Ben Dicken @BenjDicken]

@WilyMongoose I gotchu

[Ben Dicken @BenjDicken]

Whoah he's right.

[vitalik.eth @VitalikButerin]

"AI becomes the government" is dystopian: it leads to slop when AI is weak, and is doom-maximizing once AI becomes strong. But AI used well can be empowering, and push the frontier of democratic / decentralized modes of governance.

The core problem with democratic / decentralized modes of governance (including DAOs on ethereum) is limits to human attention: there are many thousands of decisions to make, involving many domains of expertise, and most people don't have the time or skill to be experts in even one, let alone all of them. The usual solution, delegation, is disempowering: it leads to a small group of delegates controlling decision-making while their supporters, after they hit the "delegate" button, have no influence at all. So what can we do? We use personal LLMs to solve the attention problem! Here are a few ideas:

## Personal governance agents

If a governance mechanism depends on you to make a large number of decisions, a personal agent can perform all the necessary votes for you, based on preferences that it infers from your personal writing, conversation history, direct statements, etc.

If the agent is (i) unsure how you would vote on an issue, and (ii) convinced the issue is important, then it should ask you directly, and give you all relevant context.

## Public conversation agents

Making good decisions often cannot come from a linear process of taking people's views that are based only on their own information, and averaging them (even quadratically). There is a need for processes that aggregate many people's information, and then give each person (or their LLM) a chance to respond *based on that*.

This includes:

* Inferring and summarizing your own views and converting them into a format that can be shared publicly (and does not expose your private info)
* Summarizing commonalities between people's inputs (expressed as words), similar to the various LLM+https://t.co/Nzord33s0z ideas

## Suggestion markets

If a governance mechanism values "high-quality inputs" of any type (this could be proposals, or it could even be arguments), then you can have a prediction market, where anyone can submit an input, AIs can bet on a token representing that input, and if the mechanism "accepts" the input (either accepting the proposal, or accepting it as a "unit" of conversation that it then passes along to its participant), it pays out $X to the holders of the token.

Note that this is basically the same as https://t.co/nUL0HyTyK2

## Decentralized governance with private information

One of the biggest weaknesses of highly decentralized / democratic governance is that it does not work well when important decisions need to be made with secret information.

Common situations:

(i) the org engaging in adversarial conflicts or negotiations
(ii) internal dispute resolution
(iii) compensation / funding decisions.

Typically, orgs solve this by appointing individuals who have great power to take on those tasks.

But with multi-party computation (currently I've seen this done with TEEs; I would love to see at least the two-party case solved with garbled circuits https://t.co/PIY2LZtbeK so we can get pure-cryptographic security guarantees for it), we could actually take many people's inputs into account to deal with these situations, without compromising privacy. Basically: you submit your personal LLM into a black box, the LLM sees private info, it makes a judgement based on that, and it outputs only that judgement. You don't see the private info, and no one else sees the contents of your personal LLM.

## The importance of privacy

All of these approaches involve each participant making use of much more information about themselves, and potentially submitting much larger-sized inputs. Hence, it becomes all the more important to protect privacy. There are two kinds of privacy that matter:

* Anonymity of the participant: this can be accomplished with ZK. In general, I think all governance tools should come with ZK built in
* Privacy of the contents: this has two parts. First, the personal LLM should do what it can to avoid divulging private info about you that it does not need to divulge. Second, when you have computation that combines multiple LLMs or multiple people's info, you need multi-party techniques to compute it privately. Both are important.

[Soren @sorenblank]

"w/o prediction cone" VS "w/ prediction cone"

[vitalik.eth @VitalikButerin]

Recently I have been starting to worry about the state of prediction markets, in their current form. They have achieved a certain level of success: market volume is high enough to make meaningful bets and have a full-time job as a trader, and they often prove useful as a supplement to other forms of news media. But also, they seem to be over-converging to an unhealthy product market fit: embracing short-term cryptocurrency price bets, sports betting, and other similar things that have dopamine value but not any kind of long-term fulfillment or societal information value. My guess is that teams feel motivated to capitulate to these things because they bring in large revenue during a bear market where people are desperate - an understandable motive, but one that leads to corposlop.

I have been thinking about how we can help get prediction markets out of this rut. My current view is that we should try harder to push them into a totally different use case: hedging, in a very generalized sense (TLDR: we're gonna replace fiat currency)

Prediction markets have two types of actors: (i) "smart traders" who provide information to the market, and earn money, and necessarily (ii) some kind of actor who loses money.

But who would be willing to lose money and keep coming back? There are basically three answers to this question:

1. "Naive traders": people with dumb opinions who bet on totally wrong things
2. "Info buyers": people who set up money-losing automated market makers, to motivate people to trade on markets to help the info buyer learn information they do not know.
3. "Hedgers": people who are -EV in a linear sense, but who use the market as insurance, reducing their risk.

(1) is where we are today. IMO there is nothing fundamentally morally wrong with taking money from people with dumb opinions. But there still is something fundamentally "cursed" about relying on this too much. It gives the platform the incentive to seek out traders with dumb opinions, and create a public brand and community that encourages dumb opinions to get more people to come in. This is the slide to corposlop.

(2) has always been the idealistic hope of people like Robin Hanson. However, info buying has a public goods problem: you pay for the info, but everyone in the world gets it, including those who don't pay. There are limited cases where it makes sense for one org to pay (esp. decision markets), but even there, it seems likely that the market volumes achieved with that strategy will not be too high.

This gets us to (3). Suppose that you have shares in a biotech company. It's public knowledge that the Purple Party is better for biotech than the Yellow Party. So if you buy a prediction market share betting that the Yellow Party will win the next election, on average, you are reducing your risk.

Mathematical example: suppose that if Purple wins, the share price will be a dice roll between [80...120], and if Yellow wins, it's between [60...100]. If you make a size $10 bet that Yellow will win, your earnings become equivalent to a dice roll between [70...110] in both cases. Taking a logarithmic model of utility, this risk reduction is worth $0.58.

Now, let's get to a more fascinating example. What do people who want stablecoins ultimately want? They want price stability. They have some future expenses in mind, and they want a guarantee that will be able to pay those expenses. But if crypto grows on top of USD-backed stablecoins, crypto is ultimately not truly decentralized. Furthermore, different people have different types of expenses. There has been lots of thinking about making an "ideal stablecoin" that is based on some decentralized global price index, but what if the real solution is to go a step further, and get rid of the concept of currency altogether?

Here's the idea. You have price indices on all major categories of goods and services that people buy (treating physical goods/services in different regions as different categories), and prediction markets on each category. Each user (individual or business) has a local LLM that understands that user's expenses, and offers the user a personalized basket of prediction market shares, representing "N days of that user's expected future expenses".

Now, we do not need fiat currency at all! People can hold stocks, ETH, or whatever else to grow wealth, and personalized prediction market shares when they want stability.

Both of these examples require prediction markets denominated in an asset people want to hold, whether interest-bearing fiat, wrapped stocks, or ETH. Non-interest-bearing fiat has too-high opportunity cost, that overwhelms the hedging value. But if we can make it work, it's much more sustainable than the status quo, because both sides of the equation are likely to be long-term happy with the product that they are buying, and very large volumes of sophisticated capital will be willing to participate.

Build the next generation of finance, not corposlop.

[montano @lucas_montano]

B

users blame you in variant A, the network in B.
polled 17 users, ab test conclusive. trust me.

[Eric S. Raymond @esrtweet]

To understand if you're a good software developer, ask yourself whether you have the wisdom not to do this.

Yes, I could write a TCP server in C by hand. But what would the point be, exactly? There are plenty of good ones available for reuse, and at this point eschewing the use of AI is approximately as silly as restricting yourself to assembler in a world that has plenty of good compilers.

Laziness is one of the virtues of great programmers. Or, to put it exoterically, not doing work you don't have to do so you can concentrate your limited think time on the work only you can do.