Built token-reduce: a workflow skill for Claude Code, Codex, and MCP clients that reduces total token burn across real coding tasks. 1/5

Did they just call their product autistic???

Wtf man we got rugged on fable so fast. Could’ve at least waited till 22nd when it switched to api pricing

NFA lmao

Getting f*ckd by a model has two different meanings depending on if you live in LA or SF.

JUST IN: Tom Lee's 'BitMine' buys 126,971 $ETH worth $213 million.

Being able to speak to agents and code on my phone from wherever I am has definitely made me rediscover my passion for programming and building again. Big fan of the idea that we’re going to see more apps or services that push rather than pull and reduce screen time and sedentary stuck in front of a computer time

32 ETH is the exact amount needed to start an Ethereum validator.

After trying a few different ai tools I upgraded my codex subscription recently and I think this was part of the reason why I got the vibe that it was better. Lots of half done stuff on Claude that just never made it to a PR whereas codex was definitely more productive and at least made the PR even if it needed more revisions after. But also because of this my new 5x limits definitely don’t feel like 5x lol

Saylor can sell covered calls on BTC to fund preferred and still capture upside spread 20% OTM calls would prob cover his interest expense, and if they expire ITM, he still make more in BTC terms so good for MSTR holders How do I know this works? We literally invented and have done this trade on chain for @eth_strategy via a product called ESPN.

This is what happens when you abandon Cryptoeconomics and blindly follow the "loss leader" / "if you build it they will come" kol nonsense.

started using surplus intelligence recently and think its a dope product, something like this is much needed imo (ie. spot inference marketplace) -- @AntSeedAI and @Ask_CarpeDiem are dope but they only support reselling of DIEM based inference (still very cool) surplus on the otherhand is working on becoming a generalized aggregator for api keys from any provider (openai, anthropic, openrouter, venice etc) -- for ex: say you get into YC and get $2M in credits (recent deal for startups) and you only need $500k or so, so you'd ideally wanna sell the remaining $1.5M at some discount (say 33% and get $1M back) -- right now there's no real way to monetize it other than surplus i think as inference becomes one of the most important commodities for humanity, and as models, providers and consumers fragment something like surplus will thrive -- even if you don't have credits and are a GPU based inference provider instead, you might get little to no traffic on openrouter because of their ranking system -- on surplus you can sell your compute at reduced cost and still recoup costs i think the future of this would look something like hyperliquid, where model token prices and capacity would act like a token, liquid, where more usage automatically bids prices higher and as usage drops the token price drops lower, this lets providers figure out what models to run, how to price them etc i was looking at building something similar but great to see @mac_eth iterate and scale this (also ex-cb) -- thought it would be worth calling out good builders doing something new -- metrics are also looking great, looks like team is scaling up, revenue is zero (no fees at this stage) but if there is significant volume, even a small % of fees flowing back into buyback and burns would make this a pretty lucrative opportunity -- NFA as usual but one of the more interesting base AI bets

At this point, I want ETH even if it is a scam

They don’t tell you this about ozempic but it also cures PE. I last forever in bed now cos digestion takes forever and i gotta stop to burp and reset every 5 mins. Peptides and their beneficial side

Starting again. From zero From six figure hell I missed solana Hope I don’t miss hyperliquid or whats next?

When does the hedonic treadmill end or are we doomed to watch as we run in loops doomed to our human nature?

Just imagine: you create a model (MinMax M.27) that scores the SAME results as Opus 4.6 on SWE Bench PRO. But when we create a benchmark where your model didn't train, you literally score 0. Because MinMax models are shit, and incomparable to frontier models.