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🏅 ACTUM Invitation Tier Reward Campaign gActum! Users can earn reward eligibility through invitations, activity levels, and point expenditure. 🥉 Bronze Operator ・Invite 10 users to our Discord ・Reach chat level Lv.5 ・Spend 150 points ・Reward: 10U ・Max completions: 1 time 🥈 Silver Executor ・Invite 50 users ・Reach chat level Lv.10 ・Spend 200 points ・Reward: 20U ・Max completions: 2 times 🥇 Golden Sovereign ・Invite 100 users ・Reach chat level Lv.15 ・Spend 450 points ・Reward: 50U ・Max completions: 3 times 📌 Campaign Rules ・Each user may choose one tier to complete ・After completing Bronze, users cannot participate in Silver or Gold ・After completing Silver, users may only continue Silver tasks and cannot participate in Bronze or Gold ・After completing Gold, users may only continue Gold tasks and cannot participate in Bronze or Silver ・Bronze can be completed up to 1 time ・Silver can be completed up to 2 times ・Gold can be completed up to 3 times ・Status serves as reward eligibility proof ・Rewards are distributed on a first-come, first-served basis until quotas are filled ・Users must submit an application for verification after meeting requirements 📌 Note ・The system will be adjusted in the future based on task difficulty and actual operational conditions Discord:

Meet Qwen3.7-Max — our latest flagship, made for the Agent Era. A versatile foundation for agents that actually get things done: ·Coding agent, end-to-end. ·A reliable office and productivity assistant. ·Long-horizon autonomy. ·Scaffold-agnostic. Go build something wild!

This is an email I sent earlier today to all employees at Coinbase: Team, Today I’ve made the difficult decision to reduce the size of Coinbase by ~14%. I want to walk you through why we're doing this now, what it means for those affected, and how this positions us for the future. Why now Two forces are converging at the same time. We need to be front footed to respond to both. First, the market. Coinbase is well-capitalized, has diversified revenue streams, and is well-positioned to weather any storm. Crypto is also on the verge of the next wave of adoption, with stablecoins, prediction markets, tokenization, and more taking off. However, our business is still volatile from quarter to quarter. While we've managed through that cyclicality many times before and come out stronger on the other side, we’re currently in a down market and need to adjust our cost structure now so that we emerge from this period leaner, faster, and more efficient for our next phase of growth. Second, AI is changing how we work. Over the past year, I’ve watched engineers use AI to ship in days what used to take a team weeks. Non-technical teams are now shipping production code and many of our workflows are being automated. The pace of what's possible with a small, focused team has changed dramatically, and it's accelerating every day. All of this has led us to an inflection point, not just for Coinbase, but for every company. The biggest risk now is not taking action. We are adjusting early and deliberately to rebuild Coinbase to be lean, fast, and AI-native. We need to return to the speed and focus of our startup founding, with AI at our core. What this means To get there, we are not just reducing headcount and cutting costs, we’re fundamentally changing how we operate: rebuilding Coinbase as an intelligence, with humans around the edge aligning it. What does this mean in practice? - Fewer layers, faster decisions: We are flattening our org structure to 5 layers max below CEO/COO. Layers slow things down and create coordination tax. The future is small, high context teams that can move quickly. Leaders will own much more, with as many as 15+ direct reports. Fewer layers also means a leaner cost structure that is built to perform through all market cycles. - No pure managers: Every leader at Coinbase must also be a strong and active individual contributor. Managers should be like player-coaches, getting their hands dirty alongside their teams. - AI-native pods: We’ll be concentrating around AI-native talent who can manage fleets of agents to drive outsized impact. We’ll also be experimenting with reduced pod sizes, including “one person teams” with engineers, designers, and product managers all in one role. In short: AI is bringing a profound shift in how companies operate, and we’re reshaping Coinbase to lead in this new era. This is a new way of working, and we need to leverage AI across every facet of our jobs. To those who are affected I know there are real people behind these decisions — talented colleagues who have poured themselves into this company and our mission. To those of you who will be leaving: thank you. You’ve helped build Coinbase into what it is today, and I am sincerely grateful for everything you've done. All impacted team members will receive an email to their personal account in the next hour with more information, and an invitation to meet with an HRBP and a senior leader in your organization. Coinbase system access has been removed today. I know this feels sudden and harsh, but it is the only responsible choice given our duty to protect customer information. To those affected, we will be providing a comprehensive package to support you through this transition. US employees will receive a minimum of 16 weeks base pay (plus 2 weeks per year worked), their next equity vest, and 6 months of COBRA. Employees on a work visa will get extra transition support. Those outside of the US will receive similar support, based on local factors and subject to any consultation requirements. Coinbase prides itself on talent density. Our employees are among the most talented people in the world, and I have no doubt that your skills and experience will be highly sought after as you pursue your next chapters. How we move forward To the team that is staying, I know this is a difficult day. We’re saying goodbye to colleagues and friends you've been in the trenches with. But here’s what I want you to know as we move forward together: Over the past 13 years, we have weathered four crypto winters, gone public, and built the most trusted platform in our industry. We’ve made it this far by making hard decisions and by always staying focused on our mission. This time will be no different – nothing has changed about the long term outlook of our company or industry. And most importantly, our mission has never been more important for the world. Increasing economic freedom requires a new financial system, and we’re building it. The Coinbase that emerges from this will be more capable than ever to achieve our mission. Brian

Today was the most productive coding day I’ve had in 10 years. I spent 1.6k out of the 10k credits Cursor gave me, mainly using Opus 4.7 1M Max and GPT-5.5 Extra High Fast. The takeaway is simple. In Cursor these models feel unusually well tuned, fast, precise, and reliable for real work. Here’s what I got done in a single day: 1. Built the iOS version of MiaoYan from scratch with iPad support, including preview and iCloud sync 2. Fully implemented payments in the Mole macOS client and shipped the V1 website 3. Wrapped up Kaku macOS terminal v0.10, polishing AI chat and many details 4. Shipped a major upgrade to Kami typesetting system, fixing PPT support and many edge cases 5. Upgraded my Luo Chinese font project with a new learning mode and overall improvements 6. Improved Mole CLI with better performance, fallbacks, and a lot of detail work There was also a lot more small work in between. What stood out is strong context handling, solid multi file edits, and stable long chain execution. It feels like a real collaborator. As a long time builder and a TSLA shareholder, I’m excited to see Cursor keep improving. Thanks again @cursor_ai @edwinarbus for the 10k credits, I will keep putting them to good use.

Excitining News! Signal Shot is a public moonshot to verify the Signal protocol and its Rust implementation using Lean. It is a joint effort of Signal (Rolfe Schmidt), the Beneficial AI Foundation (Max Tegmark), and the Lean FRO. #leanprover# #leanlang#

Funny enough, this is already the third post in my Waza skill design series. Today’s one is about /think, the skill I use for solution design before writing code. I have two settings in Claude Code that I find especially useful. The first is /model opusplan, which means planning is done with Opus while execution runs on the regular Sonnet model. That helps me save Max usage for the places where it matters more. The second is that I usually run Claude Code with alias c="claude --dangerously-skip-permissions". I would not recommend that to less technical users. I use it because I know what it is doing, and mostly because I am lazy. Back to /think. How do you get the strongest model to produce better technical plans? It starts with the model itself. Models tend to avoid taking a position. I prefer engineers who can give a clear recommendation. So the first thing I do is require the model to have a point of view. It must state its recommendation, explain what evidence could overturn it, and avoid empty lines like “There are many ways to think about this.” Giving two or three options is fine, but it has to make a clear recommendation, and it must always include a minimal option. But a plan is not done just because it sounds good. The second step is to make it argue against itself. Under what conditions would this plan fail? If those problems can be fixed, the fixes should be folded back into the plan and the revised version presented again. If the plan breaks under certain conditions, it has to say exactly where and why it fails. That way, by the time the plan reaches you, the tradeoffs are already visible. I also go fairly deep on validating the premises before planning starts. First, it checks whether it is even looking at the right part of the codebase. I have seen models produce plans against the wrong path. Then it looks for older technical design docs to avoid reinventing work that already exists. After that, it searches GitHub to see whether similar problems have already been solved elsewhere. Only after those three steps does it start proposing solutions. That helps prevent the entire plan from being built on a bad assumption. There is also complexity grading. If the work touches more than eight files or introduces a new service, the plan must explicitly call out the scale. If data flows across more than three components, it has to draw an ASCII diagram and look for cycles. API keys and third-party dependencies also have to be listed during the planning phase, so you do not waste time or end up with a plan that depends on shaky assumptions. There is one more hard rule. The plan cannot contain things like TBD, TODO, “we can decide this later,” or vague phrases like “similar to step N.” That goes back to model behavior again. Once you leave that kind of escape hatch, execution tends to drift, skip work, or fill in the blanks poorly. I try not to leave the model any room to wiggle out of precision. The output format is also strict: what we are doing, what we are not doing, which option was chosen and why, three to five decision factors, and a clear list of unknowns. /think does not write code. Execution only starts after the user approves the plan. When I built this skill, I was really trying to capture how strong technical experts approach solution design: investigate first, form a clear recommendation, make decisions decisively, leave no loose ends, and improve the plan immediately when something invalidates it. If you have better ideas for planning and solution design, feel free to contribute to Waza.

I just had an interesting conversation with a friend working for a (so far) successful systematic trading hedge fund. ⚠️Her concern? All their strategies are based on backtests using even 10 years of granular data in some cases, when they are fully aware that those data represent a reality shifting away from the current one. However, the fund has no flexibility to change strategy because that would be a breach of the fundraising they did with their LPs. Not only have these constraints and wrong approach already led several pods within the HF to be shut in March after hitting the max drawdowns they were allowed to take on their AUM, but those that haven't blown up are forced to remain heavily long SP500 delta and short volatility. The only option they have to derisk is to shrink the AUM deployed, but if a pod remains underinvested for too long, it will ultimately be shut down as well. I asked why they are so heavily long SP500 delta? Her blunt answer: because the index always went up so much most of the time for so many years, without significant exposure to the index, the backtests don't yield significant investment returns. The situation is very similar across the whole HF industry, which is why most funds experienced sharp losses in March. Her last remark was how they can see how the whole market is effectively investing passively, even when not being an ETF it isn't supposed to be the case, with no room for discretion and active risk management. As a consequence, she and her colleagues can only hope that volatility remains under control without much they can do to protect their clients' money, which is ultimately a paradox.

看到晚点LatePost也更新了谈千问换帅事件的播客，深度依然是全网无出其右，确实单口播客这种媒介形式也更适合即兴的去跟一些热点选题，比文稿生产的效率要高。 总之，光速听完之后，阑心一言咔咔启动： - 简单化的去理解林俊旸的离职，一定会被牵着鼻子走，比如我们看到投放的料五花八门，有把阿里HR挂成万恶之源的，也有对冲说林俊旸在搞独立王国的，这些都是噪音，不是说对错不重要，只是很多时候你很难用对错来评价所有事情，需要接受个人意志和组织生长之间的摩擦必然有概率发展到不相容的地步； - 三个需要厘清的事实是，其一，林俊旸不是被离职的，阿里不可能主动开掉这个级别的Leader，其二，DAU是和千问App的产品团队捆绑，这是吴嘉/智能信息事业群的工作，不太可能牵扯到从属于阿里云的模型团队，其三，今年1月空降的周浩，是接替已经确定要走的后训练负责人喻博文，并不是来管林俊旸的； - 所以林俊旸的离职，更接近于一种「道心破碎」的结果，晚点主播曼琪的用词很微妙——「 长期知其不可为而为之的付出」——最后被组织架构调整这根最后的稻草给弄崩了，宣布离职的整个过程，就是没考虑给阿里的管理层留太多反应时间，是铁了心不想干下去了； - 千问的模型团队属于通义实验室，而通义实验室又属于阿里云，最后阿里云再属于集团，这个嵌套关系已经很复杂了，在叠加了千问模型作为阿里全村希望的战略定位，资源匹配问题就很大了，所以才有了连阿里CEO吴泳铭也不知道千问模型团队被卡资源的说法； - 林俊旸这边的人马高度依赖阿里云的Infra支持，但实际上他们觉得阿里云在服务外部团队上甚至好于服务自家千问基模——这也太离谱了——于是去年年底林俊旸绕过阿里云直接找吴泳铭争取了自建Infra的权限，这个越级操作也为后来发生的事情埋下了伏笔； - 还有一个比较难绷的是，去年春节前后，o1带动推理模型开始崛起，千问在后训练方面遇到了瓶颈，然而转用字节开源的强化学习框架veRL来做训练，发现效果有了比较明显的提升，相当于通过控制变量，发现了问题是在Infra上，这才有了林俊旸对Infra一直不满意的根源，要做垂直一体化的建设； - 但阿里云的判断不是这样的，因为混合多模态已经是明显的趋势，把各个模态、预训练和后训练都拆出来搞单元制，是一定要做到事情，但对原千问模型团队来说，这就是在被收窄范围，尤其是时间点卡在Qwen 3.5训练完成后不久，大家都很疲惫，突然又得到了这种不太像是奖励的调整； - 阿里的管理层比较懵逼，或者说被动，也有反思组织变动没有考虑办公室政治的因素，把明明是要扩大对基模投入的事情，干成了让基模团队觉得是要收缩的效果，沟通上没有把控好，现在尽量要去平稳解决矛盾； - 千问在开源社区赢得的名声，到底怎么转化成阿里的资产，这个量尺很难找到，在2B市场，开源意味着很难卖API，在2C市场，开源⋯⋯好像也没啥意义，用户不会因为你开源了就来用你的App，然而林俊旸是一个相当理想主义的Leader，万亿参数的Qwen Max旗舰模型是阿里没有选择开源的，但他也想推动开源； - 千问的模型团队从创建之初就保有着一个相对独立的工作环境，少被拉扯和打断，这种专注力被视为千问模型屡出成果的原因，但是当AI行业进入一场谁也输不起的All In战局后，这种与真实市场保持距离的自驱型团队还能不能存在，既是一个原则问题，也是一个选择问题。