ltmaxmon

People freaking out over my AI spend. What nobody sees: Part of what excites me so much about working on OpenClaw is that I'm trying to answer the question: How would we build software in the future if tokens don't matter? We constant run ~100 codex in the cloud, reviewing every PR, every issue. If a fix on main lands, @clawsweeper will eventually find that 6 month old issue and close it with an exact reference. We run codex on every commit to review for security issues (as it's far too easy to miss). We run codex to de-duplicate issues and find clusters and send reports for the most pressing issues. We have agents that can recreate complex setups, spin up ephemeral crabbox.sh machines, log into e.g. Telegram, make a video and post before/after fix on the PR. There's codex that watch new issues and - if it fits our documented vision well, automatically create a PR of it. (that then another codex reviews) We have codex running that scans comments for spam and blocks people. We have codex instances running that verify performance benchmarks and report regressions into Discord. We have agents that listen on our meetings and proactively start work, e.g. create PRs when we discuss new features while we discuss them. We build clawpatch.ai to split all our projects into functional units to review and find bugs and regresssions. We do the same split for security with Vercel's deepsec and Codex Security to find regressions and vulnerabilities. All that automation allows us to run this project extremely lean.

New article: a visual tour of recent LLM architecture advances, from Gemma 4 to DeepSeek V4. I focus on long-context efficiency tweaks like KV sharing, per-layer embeddings, layer-wise attention budgets, compressed attention, and mHC. Link: magazine.sebastianraschka.com/p/recent-devel…

Before jumping into books like DDIA or Database Internals, it helps to understand the systems layer these designs are built on. A lot of the design of such data-intensive systems is based on virtual memory: page tables, page faults, mmap, the page cache, swapping, NUMA placement, TLBs, and the tradeoffs between what the OS wants and what the database wants. My latest article is a ~25,000-word mini-book on virtual memory. It starts from first principles and goes all the way down to advanced topics like NUMA placement and performance debugging with tools like perf and /proc. I also wrote it differently: as a dialogue between a user-space process and the kernel. Most treatments of virtual memory are dry and fact-heavy. I wanted this one to feel more like a story, while still being technically deep. Link below.

@AnthropicAI It's your huge profit matters most not democracy.

We've published a paper that explains our views on AI competition between the US and China. The US and democratic allies hold the lead in frontier AI today. Read more on what it’ll take to keep that lead: anthropic.com/research/2028-…

The S&P 500 is at an all-time high while Consumer Sentiment is at an all-time low. We've never seen a gap this wide between Wall Street and Main Street.

凡是设计过游戏的，应该都知道如何设计关卡，以及如何涉及奖励系统，以及其中的各个门道。 最近欧洲在起诉tiktok，meta，X 在addictive design, child safety, and data protection。 现在的addictive design，是非常先进的，用户在这个页面停留了多长时间，怎么让用户停留更长的时间，怎么样让用户看到这个新的内容？ 怎么提高用户的点击率？ AB测试。 怎么提高付费转化率。 怎么把funnel 设计好。 怎么开宝箱？在哪里开宝箱，开出啥？ 你以为一帮最聪明的PM， 收集大量数据，做海量AB测试，是吃干饭的？ Addictive design，最早就是在游戏里面出现的。 沉迷游戏，孩子有一部分的责任，家长有一部分的责任，难道仅仅他们有责任吗？

Forward deployed engineers, or equivalent, are about to become one of the most in-demand jobs in tech. And one of the most important functions for AI rollouts. Deploying agents is far more technical of a task than most people realize, often far more involved than deploying software. Software generally works the same way every time, and generally for the past few decades has been updated versions of an existing technology or concept (which basically means easier for the enterprise to update their workflows on a newer system). With agents, you’re actually deploying the equivalent of work output within the enterprise. The customer is effectively using you as a professional services provider for a task, which they expect to get solved nearly end-to-end now. This means you need to actually deeply understand the business process as a vendor, and get the customer from the current to the end state seamlessly. Companies need help figuring out which models will work best for their workflows, they need extensive evals setup often, they need change management support for workflows, they need to get their data setup for the agents, and constant tuning of the agentic system for their process. Massive role in tech now. And another example of the kind of highly technical work that AI is creating.

Free newsletter: Microsoft, Amazon and Google's $2 Trillion in AI investments are almost entirely circular, with OpenAI and Anthropic making up 75%+ of AI revenues and $748bn in backlogs. The majority of AI demand is hyperscalers feeding themselves money. wheresyoured.at/am-i-meant-to-…

I am not interested in climbing any corporate ladders. I don't care about job titles. I don't need recognition or fame. I just want to have an income to fund my lifestyle, not be depressed, help other people, and be around good humans.

The amount of companies using AI as an excuse to layoff workers and hide their financial situation is astounding. If AI was succeeding to the level they gas light you at? Operational roles would've increased. There's a lot of ops work in LLMs.

The last 20% isn't most of the work, it's all of the work.

The market will take a big dip when the most amount of people feel like it can't go down again.

AI didn’t just make code cheaper, it made noise cheaper. Anyone can generate code now, which means the real signal is no longer the code itself, but how reliably it moves through the system. A good engineer is not the one who writes more, but the one whose changes are predictable: easy to review, safe to merge, clear in intent. In a world of infinite code, reliability is the rare skill.

cloud networking in 60 seconds, no fluff: VPCs are walls. Nothing talks across them by default - you wire it up yourself or it stays silent. Inside, you slice into subnets. "public" vs "private" isn't a checkbox btw, it's literally one route in a route table pointing at the internet gateway. that's the whole trick. IGW is the front door - traffic both ways. NAT gateway is the side exit: private workloads call out, internet can't call in. one-way mirror basically. now firewalls - and ppl mess this up constantly. security groups = stateful, wrap the resource, allow out → reply just works. NACLs = stateless, sit at the subnet, you write BOTH directions or you're debugging at 2am. SGs do 95% of the job. NACLs are the bouncer at the door, not your daily driver. connecting VPCs? peering is fine for 2-3. past that you're building a mesh and N×(N-1)/2 will haunt you. transit gateway = hub, every VPC plugs in once. done. works the same everywhere - AWS calls it VPC, Azure says VNet + NSG + vWAN, GCP says VPC + firewall rules + Network Connectivity Center. different stickers, same box. steal this: #cloud-networking-basics" target="_blank" rel="nofollow noopener">ewry.net/devops-sre-int… Btw, I'm open for b2b remote projects from Armenia. Let's talk #cloudcomputing #devops #aws #azure #gcp #networking

The harshest truth everyone must learn in their lives is that hard work never guarantees success, and that merit can be left entirely unrewarded. And that occurs when you're optimizing for the wrong variables. The antidote to your insignificance is positioning. Ruthless positioning. Remember to own the things you work for and advertise them accordingly. Because hard work without visibility remains entirely invisible.

During the 2000 dot-com bubble, Nasdaq pumped 278% in 1.5 years. After that, it crashed 79%. This time, KOSPI has pumped 203% in just 1 year due to AI hype. We all know how this will end.

Just because anyone can build software now doesn't mean software is dead. Anyone can bake bread in their home right now, yet 99% of us still choose to buy it from someone else. Simple products are complex! I will always be happy to pay someone to handle the nuances.

Investing money into the stock market now makes no sense. The S&P is sitting at all time highs. Buying here means you are praying it climbs even further. That is gambling. Smart money is not blind to this. Insiders are offloading. Hedge funds are pulling back. Warren Buffett is hoarding $380B in cash. These people do not do that for no reason. Good investments need asymmetric upside. Low downside, massive potential. That setup does not exist in this market. I loaded up during the 2022 crash. I loaded up again in 2025. I am not touching it right now. Patience is the play. A correction is coming. Most people will not see it until it is too late. Don’t say I didn’t warn you.

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

Hedge funds are rushing to reduce tech exposure: Hedge funds just posted their largest 2-week reduction in US information technology exposure over the last decade, excluding the meme stock frenzy in early 2021. This was driven by long sales outpacing short covers at a ratio of 1.5 to 1. Nearly every subsector saw exposure cuts, led by Semiconductors and Semi Equipment via long sales. Furthermore, hedge funds sold Magnificent 7 stocks in 4 of the last 5 trading sessions. Hedge funds are cashing-in massive profits in tech.