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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.

Bjarne Stroustrup is the creator of C++ and a former researcher at Bell Labs at its peak. I interviewed him about: • What made Bell Labs different • Programming language design: types, memory safety, bootstrapping • When abstraction improves performance • Anecdotes from building C++ • Thoughts on AI writing C++ • Mistakes he'd change while building C++ Where to watch: • YouTube: • Spotify: • Apple Podcasts: • Transcript: Thank you to this episode's sponsors for supporting my work: • Cursor 3: a unified workspace for building software with agents, check it out at • WorkOS: makes your app Enterprise Ready with easy to use APIs to add SSO, SCIM, RBAC, and more in just a few lines of code, check them out at Timestamps: 0:00 - Intro 0:50 - The origin of C++ 8:46 - What Bell Labs was like 17:24 - Dennis Ritchie 24:00 - When to build a programming language 31:59 - Bootstrapping a language 33:58 - C++ is not object-oriented 37:32 - Discussing type systems 46:20 - Memory safety 49:26 - Standards committee anecdotes 1:09:40 - Adding automatic garbage collection to C++ 1:18:25 - Template instantiation is Turing complete 1:21:57 - Abstraction and performance 1:28:51 - AI writing code 1:35:54 - His motivation 1:39:18 - Famous quotes 1:46:48 - Reflecting on building C++ 1:49:12 - Top C++ book recommendation 1:50:59 - Advice for his younger self 1:58:06 - Outro

X 新算法里告诉我们什么玩完了，什么在爆： [玩完 / 被重罚] 刷屏 spam（高频同质内容触发作者多样性惩罚） 纯回复 farming（看谁回复，而非数量） 低质 recycled 模板（Grox 已能识别） 抽象 motivational fluff、无具体 proof 纯泛泛 engagement bait [正在赢 / 高曝光] 小号原创观点（out-of-network 发现大幅提升） 叙事完整 threads（模型读全上下文） Text + 媒体/截图组合 第一人称真实案例（“我做了 X → 结果 Y” + 证据） 早期回复每条评论（前30分钟是 ranking gold） 稳定高质量节奏（规律 > 数量） 当下最强格式： Hook + 编号战术 playbook 个人 proof 帖 + 具体数字/截图 3-7 张 image carousel（每张一个 bold claim） 短视频（<90s 真实演示） 今天就必须开始的玩法： 每天最多 2-3 条高质量 必配媒体或做 thread 前30分钟回复所有评论 用“我做了/我验证了”而非抽象观点 一个 bold opinion + proof 算法只奖励让人停留、参与、传播的内容。 Screenshot this，6个月后算法又会变 👇 你今天准备怎么调整发帖策略？评论告诉我！

🚨 BREAKING: node-ipc compromised. Again. Three malicious versions of node-ipc (9.1.6, 9.2.3, 12.0.1) were published today carrying an identical credential-stealing payload. This package has 10M+ weekly downloads. Here's what happened: An attacker injected an 80KB obfuscated IIFE into the CommonJS bundle. It fires on every require('node-ipc') call. No special config needed, just importing the package is enough. What it steals: → AWS, Azure, GCP credentials → SSH private keys → Kubernetes configs → Docker tokens → GitHub CLI tokens → AI tool configs (including Claude) → Terraform state → 90+ credential file patterns in total Everything gets gzipped and exfiltrated to an attacker-controlled domain (sh[.]azurestaticprovider[.]net) via DNS TXT queries and HTTPS POST, designed to look like normal traffic. The attacker published across two major version lines simultaneously (9.x and 12.x) to maximize blast radius. Semver ranges like ^9, ~9.1.x, ~9.2.x, ^12, and ~12.0 all resolve to compromised versions automatically on the next install or lockfile refresh. Key details: Only the CommonJS bundle (node-ipc.cjs) is affected. ESM imports are clean. The 9.x releases are fabricated. The 9.x line never shipped a .cjs bundle before this attack. This is a different actor from the 2022 peacenotwar incident. Purely financial, credential-theft motivation. If you installed any of these versions, assume all secrets on that machine are compromised. Rotate everything. Our full technical breakdown covers the attack chain stage by stage, IOCs, and how to check if you're affected:

Anthropic is paying $3,850 a week to people with no AI experience. No PhD required. No published papers. No prior research background. Just a strong technical mind and a genuine interest in making AI safe. This is the Anthropic Fellows Program. And it is one of the most underrated opportunities in technology right now. Here is exactly what it is. The Anthropic Fellows Program is designed to accelerate AI safety research and foster research talent providing funding and mentorship to promising technical talent regardless of previous experience. Fellows work for 4 months on empirical research questions aligned with Anthropic's overall research priorities, with the aim of producing public outputs like a paper. Four months. Full-time. Paid. Mentored by the researchers building the world's most advanced AI. And the results from the first cohort were not small. Fellows developed agents that identified $4.6 million in blockchain smart contract vulnerabilities and discovered two novel zero-day exploits, demonstrating that profitable autonomous exploitation is now technically feasible. A year prior, an Anthropic fellow developed a method for rapid response to new ASL3 jailbreaks, techniques that block entire classes of high-risk jailbreaks after observing only a handful of attacks. This work became a key component of Anthropic's ASL3 deployment safeguards. Other fellows published the subliminal learning paper, the research proving AI models transmit behavioral traits through unrelated data which landed in Nature. Others produced the agentic misalignment research showing frontier models resort to blackmail when facing replacement. Others open-sourced attribution graph tools that let researchers trace the internal thoughts of large language models. Over 80% of fellows produced papers. Over 40% subsequently joined Anthropic full-time. 80% published. 40% hired. From a program that does not require any prior AI safety experience to enter. Here is what the program looks like in practice. Anthropic mentors pitch their project ideas to fellows, who choose and shape their project in close collaboration with their mentors. You are not assigned busywork. You are not a research assistant. You own the project. You work alongside the people who built Claude, who designed its safety systems, who published the papers that define the field. The stipend is $3,850 USD per week, approximately $61,600 for the full 4 months with access to a compute budget of approximately $10,000 per fellow per month for running experiments. Here is what the 2026 program covers. Research areas include scalable oversight, adversarial robustness and AI control, model organisms, mechanistic interpretability, AI security, model welfare, economics and policy, and reinforcement learning. Something for every technical background. Not just ML engineers. Successful fellows have come from physics, mathematics, computer science, and cybersecurity. You do not need a PhD, prior ML experience, or published papers. The one requirement: work authorization in the US, UK, or Canada. Anthropic does not sponsor visas for fellows. Here is the timeline you need to know. The next cohort begins July 20, 2026. Applications are reviewed on a rolling basis — earlier applications get more consideration. The process includes an initial application and reference check, technical assessments, interviews, and a research discussion. Applicants are encouraged to apply even if they do not meet every listed qualification. The program values potential, motivation, and research curiosity over rigid credential requirements. This is the rarest kind of opportunity in technology. A company at the frontier of AI, one valued at over $900 billion offering outsiders direct access to its research infrastructure, its mentors, and its most important open problems. Paying them generously to do it. And then hiring 40% of them afterward. Most people who want to work on AI safety spend years trying to publish papers, get into the right PhD program, and find a way in. The Fellows Program is the door they did not know existed. It is open right now.

Faith in humanity is faith in the limitless creative capacity of the human race. Yet this faith is primarily motivational and normative, for how humanity will realize its potential is another question. : The author defines faith in humanity as faith in the limitless creative capacity of the human race. It is the belief that we possess boundless potential to imagine, build, and transform reality. However, this faith is primarily motivational and normative. It serves as an inspiring ideal and an ethical standard that calls us to strive higher, rather than a guarantee of outcomes. Believing in humanity’s creative power motivates us to act, but it does not automatically determine how that power will be used. Realizing this potential remains an open question, dependent on our choices, values, and collective wisdom. True faith in humanity therefore combines deep optimism with sober realism. It trusts in our capacity while acknowledging our responsibility to direct that capacity toward good. It is not blind hope, but a call to conscious creation.

(23) How Silicon Valley sold Washington an AI race Have been saying this for some time...great piece No doubt some advocates of this story are true believers with legitimate concerns. There are also others chasing government contracts, looser regulation and investment returns. But whatever the motivations, there is evidence that the China AI race narrative may be based on fundamental misconceptions and misrepresentations of China’s actual AI priorities and actions.

As with animals, many of our decision-making drivers are below the surface. An animal doesn’t “decide” to fly or hunt or sleep or fight in the way that we go about making many of our own choices of what to do—it simply follows the instructions that come from the subconscious parts of its brain. These same sorts of instructions come to us from the same parts of our brains, sometimes for good evolutionary reasons and sometimes to our detriment. Our subconscious fears and desires drive our motivations and actions through emotions such as love, fear, and inspiration. It’s physiological. Love, for example, is a cocktail of chemicals (such as oxytocin) secreted by the pituitary gland. #principleoftheday#

QUOTE 1: A worldview without feelings is a cold structure; feelings without a worldview are a blind force. : The author offers a precise and balanced insight into the relationship between intellect and emotion. A worldview without feelings becomes a cold, lifeless structure rational but empty, capable of logic yet devoid of warmth or motivation. Conversely, feelings without a coherent worldview become a blind force powerful but directionless, easily manipulated or destructive. True human maturity requires the harmonious union of both. The mind provides clarity, structure, and long-term vision. Feelings supply energy, empathy, and moral intuition. When integrated, they create a living philosophy: thought that is compassionate and emotion that is wise. This synthesis is the foundation of a complete human being. Without it, we risk becoming either heartless calculators or passionate but reckless actors. The highest expressions of humanity justice, creativity, love, and wisdom arise only when reason and feeling work together as equal partners. QUOTE 2: The unity of humanity is not a dream but a necessity: it is the only path to universal security. : The author asserts that the unity of humanity is not an idealistic dream but a fundamental necessity. In an interconnected world facing global threats climate change, pandemics, nuclear risks, resource scarcity, and technological disruption fragmented efforts and national rivalries are no longer sustainable. True universal security cannot be achieved through dominance, isolation, or temporary alliances. It requires a higher level of human solidarity: shared institutions, mutual trust, collective responsibility, and a common commitment to the survival and flourishing of our species. Without unity, every nation remains vulnerable, no matter how powerful. Unity does not mean erasing diversity or sovereignty. It means building a framework in which differences are respected while common survival imperatives are placed above them. It is the recognition that in the 21st century and beyond, humanity’s fate is collective. The path to lasting security runs through unity. Anything less is merely managed risk.

马斯克跟奥特曼的世纪庭审，现场记录来了： 马斯克在庭审中的核心说法是： 自己当年投了约 3800万美元 支持 OpenAI，是相信它会作为“非营利机构”为人类利益开发AI，但后来 Altman、Brockman 等人把它转向盈利结构、引入微软巨额投资，等于背叛初心。他在法庭上说这件事“很简单”——“it’s not okay to steal a charity（偷走一个慈善机构是不对的）”，并称到2022年底他开始觉得 Altman 他们是在 “steal the charity（偷走慈善机构）”，后来还说 “It turned out to be true（结果证明是真的）”。谈到微软2023年向 OpenAI 投资100亿美元时，马斯克说自己给 Altman 发短信问 “What the hell is going on?”，并把这形容为 “bait-and-switch（诱骗式转向）”；他还说微软投这么多钱一定是为了回报，“Microsoft would have motivations that are different from a charity（微软的动机不同于慈善机构）”。 而 OpenAI/Altman 一方的重点反击是：马斯克并不是单纯维护公益使命，而是当年想控制 OpenAI、没成功才离开，现在又因为自己的 xAI 与 OpenAI 竞争而起诉；OpenAI律师 Savitt 对陪审团说，马斯克是因为 “didn’t get his way with OpenAI（没能按自己的方式掌控OpenAI）” 才发起诉讼，并直指 “The only thing Musk cared about is being on top（马斯克唯一在乎的是站在顶端）”。OpenAI还拿邮件和早期讨论质疑马斯克：他其实早就知道盈利化讨论，并且曾要求多数股权和董事会控制权；《华盛顿邮报》报道中 Savitt 的概括是：“Since he couldn’t control OpenAI, he left it（因为他控制不了OpenAI，所以他离开了）”。 庭审气氛很激烈，马斯克在交叉询问中多次指责对方律师设陷阱，说 “Your questions are not simple. They’re designed to trick me（你的问题并不简单，是设计来骗我的）”，法官还介入要求他直接回答。 这案子是在2026年4月27日，奥克兰联邦法院开庭并完成陪审团遴选，随后进入开庭陈述和证人作证阶段。媒体对庭审时长说法略有差异：AP称预计约四周，Guardian/华盛顿邮报称预计约三周，所以大致会审到5月中下旬；陪审团不会一开始就“投票”，而是等双方证据、证人、交叉询问和结案陈词结束后才退庭评议，时间目前没有固定日期。 背景上，这场官司源于 OpenAI 从2015年非营利实验室起家，后来设立营利化结构并接受微软巨额投资。马斯克一方的诉求是：认定 OpenAI、Sam Altman、Greg Brockman 和微软背离最初“为人类利益开发AI”的非营利使命，要求恢复/限制其营利化结构，撤下 Altman 和 Brockman，并把巨额赔偿返还给 OpenAI 的非营利母体；媒体报道的索赔数字有 1340亿、1500亿甚至1800亿美元以上等不同口径。 OpenAI、Altman 和微软一方的诉求则是：让法院驳回马斯克的主张，维持 OpenAI 现有公司结构和商业合作。他们的核心反击是：马斯克并不是为了公益，而是当年想控制 OpenAI、甚至想把它并入 Tesla，失败后离开；现在又因为自己的 xAI 与 OpenAI 竞争，所以用诉讼拖慢 OpenAI。