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My physical therapist opened a superb gym in a beautiful and intimate space in a great location at 32 East 57th Street next to Atria. The concept integrates physical therapy with weight training, cardio, sauna, steam, cold plunge, sensory deprivation, VO2 max and all the other testing you need to determine your base line fitness and monitor your progress thereafter as well as much more.
 Vosk has the best equipment I have ever seen in any gym and extremely experienced trainers and physical therapists. It is a great place for serious athletes and those interested in longevity or just getting in great shape.
 While it is a high end gym, the price is fair when you consider the cost of the 96 training and/or physical therapy sessions included in your annual membership. The integration of physical therapy and training yields much better and safer outcomes. I have also found my training consistency is much greater since I prepaid for the sessions. If you want to learn more, go to or email Vitaly who founded Vosk at hello@voskcenter.com. The first 40 members who mention this @X post will have their initiation fees waived. Membership is limited to ensure a great experience.

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

Your brain has a circuit that doesn't know you live in a city. Its only job is to monitor whether birds are still singing. Right now, in this room, it is on. The circuit predates primates. Mammals have been using ambient soundscape continuity as a predator-detection system for roughly 200 million years. Birds stop singing when something larger moves through their territory. For most of mammalian history, a forest full of song meant no large predator was nearby, and the cessation of sound was the warning. Your nervous system never updated this software. The Max Planck Institute tested the inverse in 2022 with 295 participants. Six minutes of birdsong dropped anxiety with a medium effect size. Six minutes of traffic noise raised depression with the same. The effect worked on subjects who lived in dense urban environments and had no regular contact with nature. The brain still ran the check. Birdsong sits in the 1,000 to 8,000 Hz range. Your brainstem reads continuous patterns in that band as a signal that nothing dangerous is currently moving through the environment. EEG data shows birdsong at 45 to 50 decibels boosts alpha wave activity by 14.1% relative to silence. Alpha is the brainwave signature of relaxed alertness. Push the same birdsong above 60 decibels and the response flips. Stress markers rise 29%. The circuit only trusts the signal at the volume of quiet conversation, which is exactly the volume birds sing at from a typical distance. Three things happen simultaneously when the brain registers ambient safety. The amygdala downregulates. The parasympathetic nervous system takes over from the sympathetic. Heart rate variability rises, cortisol drops. The posterior cingulate cortex, which sits at the center of the rumination circuit, quiets down. King's College London tracked this through a smartphone study with over 1,200 participants and found the mood lift lasted hours after the sound stopped. People diagnosed with depression got the same response as healthy controls. Most of what gets labeled mental fatigue is hypervigilance running in the background. Birdsong tells the circuit it can stand down, and the brain reallocates the freed compute everywhere else. A quiet park feels different from a quiet office because the parks have sentinels.

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.

能让你的下一个项目效率提升 10 倍的最佳 Claude Code 开源仓库： 1. Superpowers 2. Awesome Claude Code 3. GSD (Get Shit Done) 4. Claude Mem 5. UI UX Pro Max 6. n8n-MCP 7. Obsidian Skills 8. LightRAG 9. Everything Claude Code

A few random notes from claude coding quite a bit last few weeks. Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent. IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits. Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased. Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion. Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage. Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building. Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it. Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements. Questions. A few of the questions on my mind: - What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*. - Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro). - What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music? - How much of society is bottlenecked by digital knowledge work? TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.

The @FabricFND Public Sale is now live! Priority Allocations if oversubscribed (40%): • Min 15% allocated to the Fabric community, users with Platinum Rank on the FABRIC dashboard/app, holders of the Backpack Badge, OG Badge, Developer Badge and Researcher Badge. • Min 10% allocated to the Kaito community (sKAITO, Yapybara holders, and top CT accounts globally, as well as CN and KR). • Min 5% allocated to successful referrers under our new referral program. • Min 5% allocated to the Virtuals community for participants staking >100 veVirtuals (allocation determined by the total veVirtuals participating). • Min 5% allocated to the Surf community for NFT Pass holders (allocation determined by the number of NFT Pass holders participating). Key details: • Valuation: $400m FDV • Prev Valuation (May 2025): $200M • Investors include: Pantera Capital, Coinbase Ventures, DCG, Ribbit Capital, Hongshan, Topology, Primitive Ventures, and others. • Target Raise: $2M • Maximum Offering: 0.5% of total supply • Vesting: 100% at TGE • Min/Max Pledge Size: $1,000-$250,000 • Estimated TGE: Q1 2026 • The sale is open to all jurisdictions permitted under our Terms of Use; not for US persons. Head to our Capital Launchpad to get involved!