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【梅雨はスタジオで◎】 雨×コスプレはなかなか大変です。 ☔ 移動で衣装崩れる ☔ ウィッグ湿気で大変 ☔ 武器持ち歩きつらい ☔ メイク崩れる 梅雨時期に備えて スタジオ予約をお忘れなく📷 予約はLINEが便利です BOOKINGから予約システムに移動できます

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.