6 founders have already locked their seat.
the other 24 seats are for people who keep saying 'omg yes I'm so in' and haven't signed up.
you know who you are.
If you think there is no problem at all w v4 hooks and it’s all user / aggregator error, you have contempt for your user and speak down on folks from your throne
The general attitude from pretty much the entire team kinda sucks, it’s giving principal skinner vibes
Ok so what if AI just made everyone unproductive and these companies are hemorrhaging $$ so they have to stoke the terminator fears so no one asks ab how much $$ they’re losing
FOUNDER POKER 🃏 sept 24 · 12–4pm · localhost:howard. 30 seats. $50 a seat or $300 for the full season. food on us. prizes every month + season leaderboard for eternal bragging rights. founders and local builders
@lobstermindset I think the fucked up part is how they just left it to the mkt and folks like 0x to support routes for everything
Plenty of folks who didn’t know they had to talk to infra folks got cooked on some launches
But that’s ab it v4 design is still a super neat
This is pretty disappointing, ngl. I’ve been posting base:0x22af33fe49fd1fa80c7149773dde5890d3c76f3b for years. This is the problem when the founder’s ego gets bigger than the project. It can be a strength, but it also pushes people away. I would never block a large holder (#2 on the Bankr leaderboard, btw). Call me whatever you want. not okay imo. I’m not asking for an unblock. I honestly don’t care. I’ve been doing this since before deployer even knew what a blockchain was. If this is how you treat other large holders and OGs, I don’t think this is the right way forward. I’m still here because I believe in base:0x22af33fe49fd1fa80c7149773dde5890d3c76f3b. I believe in the product and the team. That same ego has clearly been used for good over the last two years. Deployer, the cards are in your hand.
You blocked me because you couldn’t handle honest opinions. When I think someone is making a mistake, I say it upfront. I’m not part of the crowd that defends every decision. I have my own mind. Please think about who you block going forward. We all have bad days and don’t always express ourselves the best way. Blocking someone in the top 10 of the base:0x22af33fe49fd1fa80c7149773dde5890d3c76f3b club leaderboard still doesn’t make sense.
Deployer if you read this, keep me blocked, unblock me, I don't care much although I would prefer the latter. But please be more careful how to speak with people in public and what decisions you make going forward.
You have an image to uphold. And for the love of god, make frens with Uniswap again. Let bygones be bygones. The most interesting part of this market is still in front of us.
Much has already been said about the pornification of our culture, by myself and others, but recently it seems the dominant vice that defines our social fabric has changed. Today we spend much of our time engaging with an algorithmic slot machine, constantly chasing a new chance at fulfillment.
Navier-Stokes and other frontier math discoveries have shown that multi-agent ensembles are required to escape the typical creativity constraints of LLMs. Is this also applicable to algorithmic trading research, which similarly lacks direct answers in LLM training data?
The general method for producing a proof of an unsolved math problem has been to set up large teams of agents to
- create multiple formations of the problem
- selectively research or exclude known results
- search for counterexamples or use counterexample attempts to tighten problem bounds
- randomly apply unrelated areas of mathematics
- use formal verification methods to check other agents’ work
- project manage and referee across all existing agents
Single-agent systems tend to plateau to a mean knowledge level. This covers the majority of tasks at an efficient cost per token, as the answers to most queries lie explicitly in the latent space of LLMs. Clear examples where single agents fall short are complex system design and numerical analysis, both of which describe algorithmic trading research.
The ensemble method has potential to discover new alpha in algorithmic trading, but at the cost of substantial compute and tokens. Frontier model companies won the competition for quant/engineering talent against large trading firms a few years ago, but are not yet deploying them in this direction.