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We tried using Meta's new Muse Code agent, but it has a bug that doesn't let it sign in from a docker container. So we did a fun experiment: Meta claims Muse Spark 1.2 was co-trained with their Muse agent harness. So we extracted instructions from their system prompt and added them to the Cline harness. TL;DR of this special prompting: - Trust source code over the user prompt, so read every call site and existing tests before starting the task - Weigh edge and error cases as heavily as the happy path - Always reproduce the bug before fixing - Don't trust the first passing test suite, and verify suspicious looking half-baked tests - Never stop at just editing, keep working until the change is verified complete. We then asked this modified harness to fix a real bug from our repo, and compared the results to the original Cline agent harness. Results: - Used 2.7x fewer tokens (19.7M → 7.2M) - Finished 2x faster (49min → 24min) - Cost 2.4x less ($7.69 → $3.25) Same Muse Spark 1.2 model, same task, only the prompting changed. Incredible how much of a performance gain Meta was able to achieve training it on these special instructions!

This guy fit a barista AI inside an $8 ESP32. Ask it an espresso question over USB. It answers on a tiny OLED. Fully local. No cloud. No GPU. Tiny specialized AI is getting interesting. Repo below.

You know, DeepSeek and I, we go way back. We've been through a lot of problems together. And Liang Wenfeng, I've known this guy since way before all this — this guy is incredible, man. The things he can do are just unbelievable. And I feel like together, we can produce some real special chemistry. In this fall... this is very tough... in this fall, I'm going to take my talents to DeepSeek, and join the Blue Whale. I feel like it's going to give me the best opportunity to win, and to win for multiple years. Not just to win one benchmark, or just to win three conversations in a row — I want to be able to win it all. And I feel like I can compete over there. As for GLM... my heart will always be around that area. But I felt like it's time for a change. I can only wish them the best of luck.🫡 #DeepSeek# #GLM#

Today, I want to share something that happened to me on Kraken. I’m completely new to crypto, and this was my first time using Kraken. All I wanted to do was convert USDC into USD. Unfortunately, because USDT and USD differ by only one letter, I accidentally selected USDT instead of USD. I didn’t realize my mistake until the transaction had already been completed. In less than a minute, a single misclick resulted in a loss of approximately 810 USDT—more than $800. To be honest, I was shocked and devastated. Like many newcomers, I assumed that all major stablecoins were worth roughly the same, so converting between them would involve little or almost no loss. I never imagined that such a simple mistake could instantly cost me more than 1% of my funds. I immediately contacted Kraken Support, hoping they would understand that this was an honest human error and review my case, or at least consider making a one-time exception. What disappointed me even more was that almost every response came from AI. I was never able to speak with a real support agent. The only explanation I received was that the transaction had already been executed, the conversion details had been displayed before confirmation, and therefore nothing could be reversed or refunded. I understand that Kraken may not have violated its own rules, and I acknowledge that the final amount I would receive was displayed before I confirmed the transaction. But I believe that “the information was displayed” does not necessarily mean “a new user truly understands what it means.” As a beginner, I saw what I believed was a normal stablecoin conversion. I had no idea that the number shown on the confirmation screen meant I was about to lose more than $800. If a platform expects users to recognize an $800+ pricing difference on their own, instead of proactively warning them that the conversion is unusually unfavorable, I don’t believe that’s a user-friendly experience—especially for beginners. I believe that a platform responsible for customers’ assets should do more than simply display numbers. It should also be designed to help users avoid obvious mistakes that can lead to significant financial losses. Crypto is already complicated enough. The risks users take should come from the market—not from a product design that makes such costly mistakes so easy to make. This experience has left me deeply disappointed and has almost completely destroyed my trust in Kraken. I’m not trying to deny Kraken’s rules, and I’m not asking for special treatment. I’m simply asking @kraken and @krakensupport to review my case, seriously consider improving the user experience, provide real human support when genuine mistakes happen, and consider a one-time resolution for customers who make an honest human error. If this could happen to me, it could happen to any newcomer entering the world of crypto. I hope my experience helps others avoid making the same mistake. Please double-check every click. @krakenfx @krakenpro @krakensupport

A special talent 😤 Third year guard Caitlin Clark is coming off a triple-double performance of 26 PTS, 10 REB, 10 AST vs. the Fire It’s time to lace ‘em up yet again for CC 🏀

#Polymarket’s# monthly user retention outperforms more than 85 percent of 275 sampled crypto projects, including DeFi protocols, wallets, and exchanges. While many platforms lose users after the first month, its cohorts show meaningfully higher rates of return trading.— Large early trades above the 90th percentile and at least $100 achieve only a 52.3 percent hit rate on final outcomes. That is a statistically detectable but economically modest edge over the roughly 50 percent baseline of all resolved trades. In the 2024 presidential markets, about 40 percent of traders participated exclusively in Trump contracts, and 71.8 percent touched Trump YES at least once. Only 0.7 percent of participants were active across more than two candidates, revealing extreme specialization rather than broad portfolio trading. — Kyle’s lambda in the same election market fell from roughly 0.518 in the early months to about 0.01 by October. As volume expanded, the market’s vulnerability to price impact declined by more than an order of magnitude. — Trading activity in the final quarter showed a clear intraday pattern, peaking between 09:00 and 20:00 UTC. The most frequent traders displayed even sharper concentration during U.S. market hours. — Across resolved markets since 2023, overall predictive accuracy on @Polymarket sits near 73 percent and rises to roughly 84 percent in markets exceeding $100,000 in volume. Only about 3.4 percent of markets faced formal disputes, most of them triggered by ambiguous wording rather than contested facts.