AJP

The chess experiment began with 800 games. Records contained twelve piece counts and an eventual outcome; 77.1% followed random moves. No expert move labels or engine evaluations were supplied. The first fit had little direct guidance about good decisions.

Bear thesis: AI revenues are not increasing fast enough to fulfill CAPEX obligations and growth is slowing ++ opensource margin compression Bull thesis: AI labs will eventually be able to stop pretraining, demand for intelligence is infinite and margins are very high

I have a Continuous Learning benchmark where models attempt to learn to play chess. They are given a /goal of learning and improving playing against a Stockfish opponent in 200 games. They can choose the difficulty, take notes, whatever they like - except cheating (e.g. using a chess engine) of course. So far the improvement in Elo has been negative for Astra. Tiny bit positive for Opus, but could also be random. I've started Astra off sooner, so it finished its 200 games already, Opus is still playing. Site here to watch how they are doing: ai-learning-to-play-chess.su… The reason why this is interesting is that while we obviously don't have continuous learning, at the back of my mind I was thinking that maybe models can simulate it through self-scaffolding. Turns out not so much at least in this context. Perhaps it's a solvable problem and we don't need 'true' self-learning for models to learn in some way. ------- Just a note, the idea for the benchmarks belongs to someone else, but I don't want to use their name to give this more weight without permission.

Can Adapt-1 by @rei_labs learn to play Super Mario Bros without demonstrations? We tried it on World 1-1, starting untrained. It learned a reactive policy from its own play in about 36 minutes of gameplay, then cleared the level with learning off.

Just Plumbing... Already cleared 8-fig trades on 2/3 of these tokens since this post. VVV, I hold my last 10%. That was a monster trade for me because it let me double tap it.....Thanks Dragonfly crew for putting the fear of GOD in people by talking nonsense. NEAR, I had been accumulating since it was below 1. It took its sweet ass time, but as soon as ZEC started to take off, I knew the entire privacy sector was going to reprice, and NEAR was the best bet with the biggest spread against ZEC. Add to that all the new ships and the lack of supply overhang, and I knew it had to be a winner. I still hold 60% of my position and will keep selling every time the chart breaks key resistance. AERO, I still hold 100% of my position. Not a bad trade, but not the best one either. RH motion stole the mojo on this one, I still think it gives me my 2.5x tho.... What trades are next? One that excites me, Variational. I have been farming it non-stop and, as I have been saying, this team is special. It ain't just another perps protocol, they have a very creative and original approach to what they are building, and we already saw a glimpse of it with the swaps market. If you have been following me for a while, you know I put two wallets in the top 30 on HYPE after Jeff rejected my investment check, and I am about to do the same on VAR. VAR can play out two ways.....It can behave similarly to HYPE, where we just go up and up until it reaches the comp valuations everyone is already charting (HYPE, LIT, ASTER), or it can do what I call the LONG U, where everyone sells it into oblivion and, once that process is exhausted, it shoots up (see LIT). If we get the LONG U, you will see me buying a shit ton more on top of my airdrop around the bottom of that U. If we don't, you will see me buy a shit ton on top of my airdrop when they inevitably face their dragon. One that intrigues me, TIBBIR. I was able to accumulate a ton of it around the 110M range. I am intrigued by what Micky Malka will cook with this one.....I respect that dude a ton and like the way he thinks, and that alone was enough for me to size what I thought, and hoped, was the bottom. This is a dangerous trade because expectations among holders are so high that whatever gets revealed might simply fail to meet them. I have been debating cutting a ton here (50% of the position), but I have decided to play ball. If the reveal sucks, I will most likely roundtrip the trade....buuut if it doesn't......I could 5x this with size. One that is nerdy, REI. I have been accumulating a good amount of it below 15M. This one is pure excitement about what this team might achieve by making AI adaptive after deployment instead of largely freezing intelligence at inference. If you are an AI power user, you will probably be as excited as I am. One that is rogue, ASTER. I have been building a size on it....Yep...I just can't fade 280+ days of accumulation and a lovely LONG U chart. As you can see, every trade above has the same underlying structure....Setups where I can reasonably underwrite 20–30% of downside while having a path to 2–3x on the upside. Bear markets make these setups easier to find because prices can compress far more violently than the underlying opportunity, but they exist in every market.....This is my arena. You see.... When I was finally able to play with size, I started thinking differently about how to play the game and eventually found myself applying the logic behind the Kelly Criterion to the way I trade....It's simple, once you have size, maximizing the return on any single trade becomes less important than maximizing the number of times you can bet that capital to favorable asymmetry without meaningfully impairing the base. If you have read all the way to here, stick with me, you will probably get something out of this. It’s weird but many never care to think that wealth does not grow arithmetically, it’s all multiplicatively. If you start with 100, lose 50%, then make 50%, you are not back at 100. You go: 100 → 50 → 75. So even though the average return between those two trades is 0%, your wealth is down 25%. Ouch, right? Geometric compounding problem..... Every return you make is being earned on whatever capital base the previous trade left behind, which means large losses do much more damage than they first appear to....Which matters enormously once you start playing with size because the base itself becomes the most valuable part of your game. When your base is small, you are forced to look for insane multiples because even a great percentage return won't change your life in the ways you dream of....So if you have 20K and want to make 2M, you need a 100x. That necessity naturally pushes you into chasing insane upside....buuuut....most of those trades come with a nasty fkn failure rate attached to them too. When you are finally able to play with size, that necessity is no longer there, and you need to readjust. You need to be a tad more boring because now, with 20M on hand, a 10% return makes you the same 2M, which buys the same things as the 100x 2M. Which changes the hunt completely.....you can stop digging through shit for 1000xs and start hunting fat asymmetry with size. Now, you can spend your time looking for trades where you think you can lose 20–30% if you are wrong and make 2–5x if you are right, and then keep doing that over and over again with meaningful size. When the base gets large enough, every percentage point you earn on it becomes meaningful in absolute terms, and every time you protect it, you preserve the ability to keep compounding from a larger denominator. 10M becomes 20M. Now another 2x is not another 10M, it is 20M. Do it again and it is 40M. Which means you do not need increasingly crazier trades, just favorable setups applied to an increasingly larger base. I’ll tell you this, size does make the game easier, but only if you stop trading like you are still trying to escape the basement.....If you already have size and you are still mentally trapped in the 10,000x cage, eventually you are going to blow yourself tf up. Stop chasing miracles, if you are already trading size there is a very good chance you already had one or two break your way.....take the gift and stop going back to the market begging for another one.

Adapt-1 Developer Docs v1.0 is live. We’ve rebuilt the docs around getting started with Adapt-1 and understanding what you can build with it. Quickstarts, API reference, mechanisms, traces, integration guidance, and use cases. docs.reilabs.org/docs/neuroa…

Our latest research paper explores the growing connection between AI and digital assets and explains why broad AI adoption may drive new demand, utility and applications across the digital asset economy. blackrock.com/us/individual/…

Introducing Adapt-1 Machina: Learning Continuous Control Sequences With Adapt-1 Machina, we explore how continuous sequences of machine controls can be learned through reinforcement learning, starting with coarse outcome feedback and without demonstrations or a critic.