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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.

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Karpathy 发布了一个github开源项目，狠狠让我惊艳到了 这个项目叫 andrej-karpathy-skills,GitHub 13 万+ star,我愿称之为2026 最有用的 AI 工程项目 它解决的问题极其精准：让 Claude Code 不再瞎写代码 这个项目到底有多厉害？ 先说结论：一个 4KB 的文本文件，让 AI 写代码的错误率暴降 90% Karpathy 自己说，他现在 80% 的代码都让 Claude 写，但 AI 经常犯几个典型错误： 不问就瞎猜需求 过度设计，写一堆用不上的抽象 改 A 顺手把 B、C、D 也重构了 代码能跑就行，不管目标达成没有 这个项目就是专门给 Claude Code 戴上guardrails，用 4 条行为准则约束 AI 的编码行为 核心亮点：4 条准则改变一切 整个项目就是一个 CLAUDE.md 文件，里面只有 4 条规则，但每一条都直击 AI 编码的痛点 1. Think Before Coding - 先思考再动手 AI 最大的问题是“太听话”，你说啥它就写啥，从不质疑 这条准则要求：明确说明假设、权衡 tradeoffs，不确定就直接问 不再是“我猜你想要这个”，而是“我理解你的需求是 A，但 B 方案可能更合适，你要哪个？” 2. Simplicity First - 极简实现优先 AI 天生爱炫技，你要一个登录功能，它给你写个完整的 OAuth 2.0 + JWT + 刷新令牌 + 权限系统 这条准则强制：只写刚好能解决当前问题的最小代码 不搞 speculative abstractions，不写未来功能，不过度工程 一个用户反馈：用了这条规则后，代码 diff 从动辄几百行缩减到几十行，review 轻松太多 3. Surgical Changes - 手术式精准修改 这是我最爱的一条 AI 有个恶习：你让它改个 bug，它顺手把整个文件的命名风格、缩进、注释全优化了 这条准则要求：只改用户要求的部分，严格匹配原有代码风格 不碰无关文件，不顺手 refactor，不加“看起来更好”的改动 有开发者实测：启用这条后，git diff 从“满屏红绿”变成“3 行精准修改” 4. Goal-Driven Execution - 目标驱动执行 AI 经常写完代码就交差，但代码能跑 ≠ 任务完成 这条准则要求：把任务转化为可验证的目标/测试/成功标准，然后 loop 执行、验证、迭代 直到真正达成目标才停止 这让 AI 从“代码生成器”变成“问题解决者” 真实效果：社区反馈炸裂 X 上这个项目刷屏了，开发者反馈高度一致： 代码质量飞跃：diff 更紧凑、干净，overbuild 和 side changes 大幅减少 错误率暴降：有人实测从 41% 掉到 11%，继续优化后低至 3% 中文社区评价：“必备 skills”“Claude/Cursor 实用技能 Top1”“直接扔项目里就完事了” 很多人直接 @ 朋友推荐：“把这个 CLAUDE.md 放进去，Claude 立刻像换了个人，写代码更靠谱、不乱改、不瞎猜“ 使用方式：简单到离谱 这是我见过最简单的 AI 工程优化方案： 方法一：直接 curl 把 CLAUDE.md 下载到项目根目录 curl -o CLAUDE.md 方法二：用 Claude Code / Cursor 的 plugin 一键安装 支持 Claude Code、Cursor 等主流 AI coding 工具 完全开源（MIT 协议），拿来就用 作为产品经理出身的开发者，我看到的不只是 4 条规则，而是对 AI 编码行为的深刻洞察 Karpathy 做的事情本质上是：给 AI 建立编码的第一性原理，他把他对于AI编程的理解写入了文件中 不是教 AI 怎么写代码（它已经会了），而是教 AI 什么时候该问、什么时候该停、什么时候该简化 这 4 条准则就像产品经理给开发团队定的 PRD 原则： 需求不清楚？ 先问 功能够用就行？ 别过度设计 改需求？ 只改需求相关的 做完了？ 先验证目标达成没有 它能帮到我们什么？ 如果你是独立开发者或小团队，这个项目能直接提升你的 AI 协作效率： 减少返工：AI 不再瞎猜需求，写出来的代码更符合预期 降低 review 成本：改动精准，不用在一堆无关修改里找真正的变更 提升代码质量：极简实现意味着更少的 bug、更好的可维护性 加速迭代：目标驱动让 AI 真正解决问题，而不是生成代码 对于中大型项目，这是让 Claude Code 真正“生产可用”的关键一步 我的使用体会 我在自己的几个项目里部署了这个 CLAUDE.md，最直观的感受是： AI 变聪明了 以前它是个听话的实习生，你说啥它做啥，经常做错 现在它像个有经验的同事，会主动问“你确定要这么做吗？ 我有个更简单的方案” 代码 diff 变干净了 以前一个小需求能改几十个文件，现在精准到只改 3-5 行 我的工作重心变了 以前 60% 时间在 review AI 的代码、修 bug 现在 80% 时间在思考产品逻辑，AI 真正成了生产力工具 最后 这个项目被誉为 2026 年 AI coding 领域的“现象级”黑魔法工具 小文件，大作用 如果你在用 Claude Code / Cursor 写代码，强烈建议直接把这个 CLAUDE.md 扔进项目根目录 GitHub 地址： 试过的人基本都是“已全项目部署”的状态 作为一个天天和 AI 协作的开发者，我的建议是：别犹豫，直接用

anybody who uses or learns agentic systems, SHOULD READ THIS the install order I run before any new agentic project: 1. PRIVACY: direnv + a real secrets manager install direnv, then plug it into your team's password manager (1Password CLI via op run, doppler, infisical, vault, pick one) what direnv does: loads per-folder environment variables when you cd in, unloads when you cd out. the real move is wiring it into your secrets manager so credentials NEVER live in plain text on disk what this stops: - API keys accidentally committed to git history, the most common AI agent breach pattern in 2026 - credentials leaking from one project into another through your shell history - shared .env files that one teammate quietly backs up to Dropbox - secrets that survive a laptop theft because they were sitting in /Users/you/projects the part nobody mentions: most "my agent got jailbroken" stories actually trace back to one credential the agent had access to that it shouldn't have. scope keys to projects, scope projects to folders, and the blast radius of any single compromise drops dramatically I shipped 2 agents with keys in .env files before switching. the day I plugged direnv into op run I stopped having that whole class of nightmare 2. TOKENS: litellm or portkey as your model proxy one URL that fronts every AI provider (Anthropic, OpenAI, Google, Mistral, local models). all your spend flows through one place what it saves you: - response caching keyed by prompt hash, cuts your bill 30-60% on repeat tasks - automatic fallback on rate limits (Sonnet hits a 429? falls to Opus, then GPT, then your local backup, no broken users) - per-feature and per-user budget caps, block the call before it costs $200 instead of auditing it after - model routing rules, cheap tasks to Haiku, expensive ones to Opus, never the wrong way - PII redaction before requests leave your network, security side benefit the part nobody mentions: every "$4k AI bill" story I've heard ends with "we didn't have a proxy in front." this is where you put guardrails around spend BEFORE the spend happens I built my own router for 2 weeks. it took 20 minutes to replace with litellm. I will be embarrassed about this forever 3. CONTEXT: uv + git commit on every passing eval install uv (the new Python package manager, 10-100x faster than pip+venv, by the Astral team behind ruff). then commit every time an eval suite PASSES, with the model version and pass rate in the commit message what this preserves: - exact dependency set via uv.lock, you always know which packages your agent was using, no nasty surprises from a quiet update - exact prompt + code state, you can reproduce any past run from a single git hash - exact model version paired to exact pass rate, a paper trail when prod breaks weeks later - one-command rollback to a known-working state when a refactor goes sideways - a compliance story, every prompt version tied to a model version in your commit log the security side: when something blows up in prod, you want to say "the prompt was version X, model was Sonnet 4.6.1, last eval pass rate was 94%." not "I think we deployed on Tuesday?" the first is an incident report. the second is a resignation letter I've lost more agents to "I changed 3 prompts in one session and broke something" than to any actual bug 4. VISIBILITY: mitmproxy in front of every LLM call it's basically a wiretap for your agent. install it, point your agent through it, and now you see every conversation your agent has with the model in real time what actually shows up: - every silent retry your SDK sneaks in when a call fails - the full prompt being sent (including any creds you accidentally embedded) - what the model returns BEFORE your code reacts to it - exact token cost per call, per tool, per loop iteration - responses that quietly trigger your code into doing something you didn't intend, this is where prompt injection lives the part nobody talks about: if a website your agent scraped slipped instructions into its data, mitmproxy is how you SEE the moment your agent decides to follow them. without this layer, you're trusting your agent did the right thing, not verifying I shipped 3 agents before adding this. I have no honest idea what they were doing in production 5. EVALS: inspect-ai (the framework the labs actually use) an eval framework is what tells you "this agent works" with numbers instead of vibes. inspect-ai is the one Anthropic, DeepMind, and the UK AI Safety Institute use for the eval reports you read in their papers. open source, MIT licensed what your homegrown version won't have: - run the same task across 5 different models and compare scores side by side - pre-built tests for risky agent behavior (lying, manipulating, misusing tools) - proper structure for evaluating tool-using agents, not just chat - repeatable scoring, the same input always gets graded the same way - reproducible eval seeds, so a flaky test is actually flaky and not just unlucky I wrote my own eval harness 4 times across 4 projects. threw it out 4 times if you ever want to say "my agent passes safety checks" out loud, the check has to come from a framework someone else can re-run. this is that framework the move that ties this together: keep a /lessons.md in every repo. every weird agent behavior, every edge case, every config change you find at 2am, write it down you will not remember it. you'll come back in 3 weeks and the lessons file is the only reason you still know what's going on lock these 5, keep the lessons file, your next agentic system takes 2 days instead of 2 months p.s. half of "AI agent" content online is people who've never run mitmproxy on their own loop. they don't actually know what their agent is doing. they're shipping demo videos. don't be that guy

Grok Build is amazing. The early beta just dropped for SuperGrok Heavy users and the first real feedback from developers is overwhelmingly positive. People are saying it already feels 10x ahead of other coding agents. It handles full agentic workflows natively, runs multiple agents in parallel, does live refactoring, and has a surprisingly polished terminal UI with both vim mode and mouse support. It’s fast, manages huge context cleanly, and actually feels like you’re working with a real autonomous coding partner instead of just getting suggestions. This is the kind of serious high quality tool xAI keeps shipping. If the beta keeps this momentum, Grok Build is going to be a real great tool for power users. Try it out right now at if you have SuperGrok Heavy subscription.

I've been coding for 40 years. Here are the top 5 things I wish I knew when I started. 1. 90% of the job is debugging and fixing, not creating new code. Which is still fun if you're good at it. I used to think programming was mostly writing fresh, clever stuff. In reality, most of your time is spent in other people's (or your own past self's) messy code, chasing down why something that "should" work doesn't. Get really good at debugging early. Learn assembly reading, call stacks, and kernel debuggers. It pays off hugely. The best engineers I saw were absolute magicians at this. 2. Manage complexity from day one (ie: don't write slop and "fix it later" if it goes somewhere). Very early on, I'd hammer out code and refactor afterward. Big mistake. Now I start with clean, skeletal structure (minimalism first) and flesh it out carefully, with AI or not. Messy code compounds and becomes unfixable. Upfront discipline on architecture, naming, and simplicity saves enormous pain later, especially in large systems like Windows. 3. Tools and processes matter more than you think We suffered with basic diff/manual deltas instead of modern source control like Git. Branching, testing, and good tooling would have made porting and collaboration way smoother. Invest in your environment, automation, and reproducible builds early. Good tools amplify your output; bad ones (or none) drag everything down. 4. Understand the problem and existing code deeply before writing Don't jump straight to coding. Map out the problem, study what's already there (you'll inherit a lot), and plan. Low-level knowledge (hardware quirks, alignment issues on different architectures like MIPS/Alpha) was crucial. Also: assert early and often. It forces clarity. 5. People, politics, and "the right tool for the job" beat pure tech arguments. Brilliant engineers still argue endlessly. Sometimes it's about ego, not merit. Learn to spot the difference and "steer" the conversation rather than "winning" it. Bonus from experience: Side projects like Task Manager (started at home because I wanted the tool) can become your biggest hits. Ship small, useful things often. If you're just starting, focus on fundamentals, patterns over syntax, and building resilience for the long haul. It's going to be a wild ride, but the fundamentals still matter.

AI 写的中文，读两行就能认出来。 "三条反馈都很关键，我都接住。" "迁移到我们现在的问题，就得到一个更锋利的重构：" "你的直觉被数据验证了，比我预期的更干净：" 语法没错，意思也通，就是别扭。换模型、换 prompt 都去不掉。 我最近注意到这股味儿的真实身份：翻译腔。上面每一句都能翻回英文——"I caught all of them"、"a sharper refactoring"、"verified cleaner than I expected"。字是中文，骨架是英文的。 鲁迅和王小波骂了一百年的老问题，现在被 AI 批量生产出来了。但套路有限，最常见的四种： 一是物理动作描述思考。"接住"来自 catch，"锋利"来自 sharp，"不崩"来自 doesn't break。英文里有棒球和物理生活的支撑，搬到中文里就悬空了。日常说"收到"、"记下"，不说"接住"。 二是形容词替读者先下判断。"逻辑很清晰：""问题很直接："——后面的事实自己能让读者感受到清晰，形容词是多余的。 三是抽象名词做主语、形容词收尾。"工程上的现实比这些数字难看"——读完不知道哪儿难看。 四是有中文译法的英文词直接混用。context、state、cache 都有稳定对译，留着英文只让读者反复切换。 本质是同一个问题：AI 写中文时先用英文句法想清楚，再逐字换成中文。怎么改？别修修补补，重写。用中文里本来会怎么说，重新说一遍。

MISAMOが美容ブランド「ReFa」のヘアカテゴリ「ReFa MUSE」に就任✨ ReFaの新プロジェクト「ReFa BEAUTY SHOWCASE with MISAMO」が始動。ぜひチェックしてください！ ▼特設サイトはこちらから #MISAMO# #ReFa#

【お知らせ】 はじめてのオリジナル楽曲 「Dawn」本日配信スタートしました！🌅 私自身のこれまでと、これからを歌った曲です。 作詞・作曲：本多友紀さん（Arte Refact） ぜひ聴いてもらえたら嬉しいです🎧 🔗Streaming #Dawn# #Miico#