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濃雲不改青山色 貶抑難移壯士魂 Though heavy clouds may veil the sky in endless gray, The verdant mountains hold their color, bright as day. Though scorned, demoted, crushed by fate’s unyielding hand, The warrior’s soul stands firm — unbowed, forever grand. ☀️ #จิตติพจน์วิริยะโรจน์# #inkMine# #vss365# #詩# 对联 詩 诗词 poetrycommunity WritingCommunity 陸享瑞

Claude Opus 4.8 is available on AI Gateway. Improved long-horizon agentic coding, debugging, and writing over previous Opus models. 𝚖𝚘𝚍𝚎𝚕: '𝚊𝚗𝚝𝚑𝚛𝚘𝚙𝚒𝚌/𝚌𝚕𝚊𝚞𝚍𝚎-𝚘𝚙𝚞𝚜-𝟺.𝟾'

SpaceX has almost finished writing V1.0 of an in-house AI training stack in C that exact-maps to 220k GB300s with 800G NICs, making heavy use of pipeline parallelism and getting as close to bare metal as possible. The potential speed improvement vs JAX for large training runs is over an order of magnitude.

深思雪瀉人天遠 淺夢風清月入簾 Deep thoughts cascade like torrents of snow, my distant love lies far beyond the sky. In shallow dreams a gentle wind softly blows, moonlight slips softly through the curtain high. ☀️ #จิตติพจน์วิริยะโรจน์# #inkMine# #vss365# #詩# 对联 詩 诗词 俳句 回文 poetrycommunity WritingCommunity 陸享瑞

寶塔生光，青山綠水仙家住 長橋燦爛，赤日白雲聖舍居 The treasure pagoda glows with sacred light, Where verdant mountains kiss emerald streams so bright— Immortals dwell in this enchanted height, Their peaceful home of pure and mystic delight. The long bridge dazzles in its golden might, Beneath the crimson sun and clouds of drifting white; In holy halls the saints now claim their throne, A radiant realm where heaven’s glory is shown. ☀️ #จิตติพจน์วิริยะโรจน์# #inkMine# #vss365# #詩# 对联 詩 诗词 俳句 poetrycommunity WritingCommunity 陸享瑞

Updates since then: * Deepseek v4 is out. There *is* a 2-bit quant that can run within 90 GB ( ), and it works, however it's only fast on Apple hardware (I've head ~35 tok/s). On AMD, it's ~7 tok/s. IMO actually taking the effort to properly support more than one hardware manufacturer is a great example of the difference between mere "decentralized AI" and genuine "CROPS AI". I hope we can become better at this. * also has alpha telegram support now. However, the path to adding your account is quite janky * looks promising as a way to run "dense" models (eg. Qwen 27B) more efficiently. It's janky, but on my 5090 laptop it seems to be ~2x more tok/s than llama.cpp * VoxTerm (local AI recording, no third-party servers) continues to be developed And there's a lot more projects coming on the horizon. One other thing that has been on my mind is that there's actually a lot of intersection between "CROPS ethereum access layer" and "CROPS AI". For example, we want a ZK way to make (paid) calls to remote LLMs. But if we have this, then it's just as useful for solving another problem: private RPC reads in Ethereum. Another example: application-specific finetuned LLMs. Leanstral ( ; I get ~38 tok/s on AMD) fits into < 70 GB, but can hold its own against 1T models on writing Lean code. Things like this are a huge boon for writing more secure code ( ). We should have models finetuned for Ethereum-related use cases as well.

Derek Sivers gets it: He built CD Baby from nothing, became the largest indie music distributor in the world, sold it for $22 million, and gave every penny to charity. Then he disappeared. No founder story podcast tour. No VC meetings. No personal brand. He moved to New Zealand with a laptop and started writing books that 10,000 people read instead of millions — and he prefers it that way. While every Silicon Valley founder was chasing unicorn status and ringing bells on CNBC, Sivers was asking whether any of it was actually worth wanting. The most successful exit in music tech history ended with a man who decided success was the wrong game entirely.

Why is the creator of OpenCode pretty skeptical about AI productivity gains, and the hype around AI? A very conversation @thdxr (and lots of truth bombs:) Timestamps: 00:00 Intro 07:03 Dax’s path into tech 09:04 Early startup experience 13:16 Getting involved with open source 16:13 OpenCode 23:17 Anthropic banning OpenCode 30:34 From terminal to GUI 32:34 OpenCode’s business model 36:33 Why inference is profitable 39:11 GPU bottlenecks 40:54 AI hype 45:50 AI spending 48:47 Dax’s memo 55:41 Dax’s skepticism of predictions 58:58 Engineering culture at OpenCode 1:02:38 How building works at OpenCode 1:05:36 Taste and quality 1:11:32 Dax’s work setup 1:12:35 The role of engineers and EMs 1:15:50 Advice for engineers 1:18:12 Book recommendation Brought to you by: • @AntithesisHQ – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages • @WorkOS – everything you need to make your app enterprise ready • @turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable Three interesting thoughts from Dax: 1. No AI-native coding agent company is “winning” by being better with AI. Dax says that none of OpenCode’s competitors are crushing them, and that nobody is using AI so well that others cannot compete. 2. Most software engineers profit from AI as time gained, not increased output — unless you change incentives! Dax says the natural way for software engineers to “cash out” their AI tooling gains is with time savings, by doing the same work as before, but faster. Until compensation and motivation structures change, most teams should expect output to stay flat while engineers go home earlier. There’s nothing wrong with this, but AI vendors sell a different outcome to CFOs: increased output. 3. AI code generation mutes the “guilt” of doing the wrong thing, but this builds up tech debt. Pre-AI, writing a hack felt bad, the second time it felt really bad, and by the third time you’d often just refactor in order to fix up the code. Now, the agent hides the hack, which skews devs’ judgment and results in less tech debt being cleaned up.

Defillama removed some tvl for a chain that was inflating it and now I see their team writing novels about how tvl is not a good metric to value them Yeah I’m sure it has nothing to do with it being low for their chain