I'm in crypto to eliminate the middleman. Disintermediate all human coordination. Finance, commerce, social, governance (corporation, even state level), information curation, internet infra, physical infra, content creation, science, property rights, employment...

The only real option in the "personal agent" space right now is Grok Bot. Been doing a lot with Dots. I do not like them. Dots are a total mess right now. 1) Having a single thread for everything sucks. They got it right first try with Grok Bot, I have zero clue why they wanted it to just be one thread (I do know why, it's because I'm not the target market for Dots but still I hate it) 2) It's slow. Very, very slow. 3) There's zero visibility into what it's actually doing. All u can see is the final short text message that Astra gives u (5-10 mins after asking something). Grok bot's pretty good about talking u through what it's doing and being pretty fast, Bot just isn't 4) The app(s) are a mess. The mobile app, the desktop app, they all have performance issues, weird ux things (like when ur not focusing the input box on desktop and type it doesn't auto focus), just an absurd number of paper cuts. It's weird b/c Cursor is just as bad about this kind of thing, but Grok Bot is absurdly polished 5) I have zero clue wtf this thing is doing. I woke up to a bunch of random codex threads doing god knows what in circles and I have zero trust for this thing if I'm gonna be honest. 6) Zero parallelization. No ability to run a bunch of stuff in parallel, take advantage of the thing Astra's unbelievable at (agent swarming), just one little thread with 5 subagents. I get that I'm not who this is built for. This isn't built for professionals or technical people. It's meant for someone to just chat with an AI and have that AI learn about them and do little useful things. The only one of these that you can do actual serious work on is Grok Bot right now. If you are employed or do anything useful, use Grok Bot. My read on the space right now is that Muse will dominate the bottom 90% b/c free IG distribution and it doesn't need to be good to do what 99% of people will use it for. Dot is in this bizarre middle ground rn where they want it to be something accessible to everyone, but also good for serious work. The "middle" in product is always fucking stupid and doomed. Grok Bot is the "serious" option, just use it. I keep saying this but SpaceXAI is the best engineering lab right now and it's not close. Compare grok dot com, their mobile app, grok bot, and grok build to their OpenAI or Ant equivalent. It isn't close, but then they release Grok 4.7 and it's a useless, overpriced, dumpster fire. I hope Dot improves, they've got the models to really make it happen. But at least for right now, it's a mess and not something I would recommend.

chatgpt account got banned fml no way to get memory or chats back apparently

People of Pi: We've shipped Pi 1.0 with Pi Durable. Go make them yours. earendil.com/posts/pi-1-0/

if Argon turns out to be actually good at language, I think it could be huge for UX of writing with AI gemini 2.5 pro had a special knack for this and I have hope google still has that spark

🚨 New paper alert 🚨 Active Liquidity On Chain: Evidence from PropAMMs Across Chains w/ Ozan Solmaz @jason_of_cs Liquidity providers on traditional AMMs such as Uniswap are passive and suffer from adverse selection. As a result, their price only moves through trades. When an external market moves, these LPs get picked off on their stale quotes. PropAMMs, a new design, emerged in 2024 attempting to combat this. In them, a single operator quotes from its own inventory and reprices actively. By 2026, propAMMs carried more than half of SOL/USDC volume on Solana and had been found, among other chains, on Base and Monad. Our work seeks to elucidate the benefits of this new design both for LPs and for retail users. Do propAMM operators really have an edge over passive LPs, and if so, where does this edge come from? Can propAMMs offer tighter quotes to retail users? We measured a full year of propAMMs on Solana, Base and Monad, and decoded the closed-source on-chain logic of Tessera, the largest propAMM on Base. The result: propAMMs have a measurable edge over passive LPs: two seconds after a fill, propAMMs earn 0.37 bps on Solana, 1.19 bps on Base and 1.69 bps on Monad, while passive LPs on AMMs comparatively lose 0.22, 0.62 and 2.06 bps. We trace the edge to four sources: 🟠 Cheap, frequent repricing: updates use at least 25 times less resources than swaps on Solana, and 5 to 15 times less on Base and Monad. Generally, the default block order on all three chains is by priority fee per unit of computation. Thus, an update pays far less than a swap for the same priority placement. 🟠 Pricing by counterparty: five of the six largest Solana propAMMs offer tighter spreads to aggregator flow than to non-aggregator flow. For Tessera on Base, we show that it whitelists, penalizes or blacklists individual addresses. 🟠 Arbitrage against AMMs: when a reference price moves, propAMMs reprice while AMMs do not, and arbitrageurs trade the two. A propAMM fills at its updated quote, so it may break even on these legs on Solana and Base and even earn 1.55 bps on Monad. 🟠 Spoofing: aggregators route on the quote from the end of the previous block. For Tessera on Base, we show that the operator consistently raises its default fee early in the block and lowers it again late in the block, so aggregators read stale state that carries the lower fee. As a result, only 39% of swaps execute at the quoted price, and the average swap receives 1.08 bps less. The second question we ask is: can propAMMs offer users tighter quotes as a result of their edge? The answer is yes: on fills that arrive while the price is not moving (our proxy for retail flow) propAMMs take 0.26 bps on Solana, 1.26 bps on Base and 1.62 bps on Monad, where AMMs take 2.59, 1.38 and 8.60 bps respectively. Retail also makes up a larger share of propAMM volume: 32%, 43% and 17% on Solana, Base and Monad, against 9%, 16% and 8% on AMMs. What did we learn? PropAMMs avoid the losses of passive liquidity provision, yet still manage to give retail tighter quotes. However, users also have to bear the cost of spoofing, which we expect to change as aggregators move to an on-chain methodology. Link: arxiv.org/abs/2609.38056

Episode 105 with @mteamisloading CEO of @Spire_Labs and @baibai_cx Social trading is the word of the cycle Every creator wants to become a trader Not every trader wants to become a creator Finding the best platform to trade and preserving capital matters Finance continues to come onchain with larger positions Building products for those traders and future builders is where the puck is going 02:01 How @mteamisloading went from MEV to @baibai_cx 06:26 Crypto at 12 and founder at 18 10:12 Cofounder from a Justin Drake call 13:22 What are based rollups 21:13 Right thesis but way too late 27:05 Is infra dead this cycle 32:01 Walmart price guarantee for onchain swaps 37:15 How much traders lose to slippage 42:18 Points now and a token soon 47:08 Painting candles on tier 3 exchanges 51:36 Calling the top on stock LP leverage 56:26 Create more than you consume 1:04:03 Only person to convince is the algorithm

There's a ton of capability unlock for when AI knows how good it is + what it's bad at How many r's in strawberry? --> python repl Improve perf in this constrained system --> autoresearch Estimate how long this will take to RE --> 1 hour not 7 months

guess the model

Opus 5.5 is surprisingly pleasant to talk to, it feels much less overbearing than Fable 5.1

realizing more and more that RL is an art not a science because compute is the limiting factor also realizing that its really hard to get agents to drive RL research because they rarely even try to be scientific

its incredibly important that you have this arc and then eventually abandon it

What piece of writing or rhetoric that you have read had the greatest density of emotion per word?

I find myself constantly searching for better software and experiences, much more than I did before AI I know what is possible with someone like @steveruizok and tldraw and I dream that this can exist for any app or website I touch.

We had a special guest at THE BASED SHOW Featuring @mteamisloading [Co Founder and CPO of @spire_labs , @baibai_cx ] Timestamps: 0:53 Welcome Matthew 1:08 Who is Matthew, and why crypto: killing the middleman 2:51 -From MEV searching to Ethereum research 3:47 - Building Spire Labs and synchronous composability 4:15 - The pivot from infra to a consumer app 4:57 - The 3 problems DEXs have: LPs, traders, builders 10:08 What a prop AMM is, and how it differs from Uniswap 14:24 - Does retail actually care about fees? 16:52 - The Walmart playbook: a 2x best-price guarantee 18:14 - Marketing gimmick or real? Claims so far 18:53 - What's next: tokenized stocks and the altcoin price gap 22:18 - Multi-chain roadmap: Robinhood Chain and beyond 23:15 - Built for market makers first 25:10 - Traction: 10K+ users in the first 4 weeks 27:18 - Funding, token plans and community ownership 28:19 - Points Season 1: "How wrecked are you?" 29:32 - Wrap-up

Analyzed a few thousand hours of agentic runs with @typesafeai Jev - turns out it's: • The best option I've tested at measuring progress and estimating completion • Dangerous if you use it for detecting harmful commands (more on that below) and • Not very good at catching models being lazy Actual prompts and results in the article: southbridge.ai/blog/jev-watc…

Also have been playing with @typesafeai Jev, insane! So many immediate use cases and new apps are possible. What a time to be a builder! Sharing some experiments here starting with: Keystroke oracle / predictive launcher: Your launcher ranks by aliases, fuzzy match, and habit. Jev reads intent: type "the pdf I just downloaded" and the newest PDF is already the top hit with a full confidence on every keystroke, in ~100 ms

Momentum is provably conserved in this physics sim! Bend2 is very nice!

Bend 2 is here! It is a new programming language that blocks AI mistakes via *proof checking* - the same technique big AI labs used to solve open math problems, like Navier-Stokes. It is also very fast, and runs on GPUs. Watch the video. Link in the comments.

1/ Fast Computer Use is now solved with @typesafeai Jev + Cua Driver. Available in development preview for macOS, Windows, and Linux. We call it jev-use. Draft #3943: github.com/trycua/cua