Engineer. Ex-Ask/Twitter Data Infra, building @leettools

Bay area
Joined April 2009
When I was writing Unreal Engine 1, my time was divided as follows: - Around 30% went to seriously deep coding on hard problems: assembly language texture mappers, C++ BSP trees, data persistence, object oriented systems design, and networking. - Around 70% was just typing and refactoring large bodies of code needed for a complete engine feature set. This was not hard, and while I was doing it my mind was mostly on the deeper coding topics, thinking and planning for what's next. It didn't feel much different from vibe coding today, except that it was just me typing code on an IBM Model M keyboard rather than prompting it and reviewing the results. So, it has always seemed perfectly logical that we would rely on AI for that 70%, once the AI could do it with a similar quality standard as we could, which has never been perfection. It came as no surprise that, after a few years of AI model evolution, it was able to take the reigns on that 70% and free up programmers' time for more valuable tasks. What's more of a surprise is the emergence of AI as a sort of insta-Carmack able to solve hard technical problems quite cleverly and often cleanly, and that it's able to reason about and work in complex 3D scenes both by operating the tools and by directly understanding the raw data files. I abandoned assembly language in 1998 when out-of-order CPUs made much simpler C/C++ code faster, and never I missed it. If prompting through architectural documents, concept art, and design plans can transcend typing code and tweaking vertices, that will enable developers to spend their time on higher-level, higher value technical and creative tasks. What matters is the end product, whether it's a game or an engine. Anything developers can do to try out more ideas and make better software and games faster is a win for everyone.
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OpenCode
@opencodeAug 20
Ox Alpha (stealth model) is free for the next week - 1M Context - Multi-modal - Zero Data Retention Generous rate limits, near unlimited usage We have capacity for 100T tokens per day, lets see what you can do
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AAAI2027 has received a whopping 40,000 papers! Assuming the standard of 3 reviewers per paper, the organizing committee will need to manage 120,000 individual reviews. If an average reviewer evaluates 4 papers, AAAI will need an army of 30,000 qualified reviewers this year. 🤯
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Steven Sinofsky
@stevesiAug 7
Happy 35th Anniversary World Wide Web. On August 6, 1991, info.cern.ch went live. We all owe @timberners_lee a big thank you! (recreated home page via info.cern.ch)
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Tomorrow will be my last day at Google after 27 years, and watching it grow from 25 people to 190,000+ has been an amazing journey. Below is a note I shared with many people internally at Google today. An excerpt is: It has been an absolute pleasure to work with you and to help build some of the most widely used and impactful products of all time. As a kid, I dreamed of helping build software that would be used by many people, and Google now has thirteen products used by more than a billion people (amazing!). Our work has had a tremendous impact in the world, and I have been lucky enough to collaborate and form friendships with many colleagues that I deeply admire, respect, and enjoy. It still brings me joy every time I see people out in the world using our products to find information, handle email, translate documents, watch videos, learn new things, navigate and understand the physical world, browse the web, use their phone, run large-scale computations on our infrastructure, ride in an autonomous vehicle, or perform complex tasks with the help of our AI systems. I hope you all share this sense of joy, because it is a shared accomplishment! Thank you to all of my colleagues at Google over many years! Now I'm excited to go start @DiscoLoopAI with my longtime friends and colleagues @Sanjay_Ghemawat, @OriolVinyalsML, and @quocleix. (Updated post: slightly redacted to not have some personal info)
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Agentic AI adoption is on fire at @Uber, and it's changing the way we build, not just in engineering, but across the entire company. Today, 99% of our engineers use AI tools. More than 70% of pull requests are attributed to local or cloud agents. And our engineers have built 2,500+ agent skills across the software development lifecycle. Those numbers are exciting, but they led us to a much bigger question: How do we bring agentic AI beyond engineering? Finance. Legal. Operations. Marketing. Customer Support. HR. Procurement. These functions run on complex workflows that are often manual, highly nuanced, and spread across dozens of systems. You can't automate them effectively by looking at process diagrams or documentation. You have to understand how the work actually gets done. So we created something called Agentic Pods. The idea is simple. We handpicked ~30 of our most AI-proficient engineers (people with deep knowledge of Uber's systems) and paired each of them with a domain expert from a business function. Then we gave every pod just two weeks. • Days 1 – 2: Shadow the expert. Observe every step. Document workflows. Ask questions. Build intuition. • Day 3: Prioritize opportunities based on scale, repetition, business impact, and data availability. • Days 4 – 5: Build a working agent alongside the person doing the job. • Days 6 – 9: Validate with several others performing the same work. Does it generalize? Does it actually make their job better? • Day 10: Ship. In just the past two months, we've run 16 Agentic Pods across 16 different business functions. • Capital allocation across 150 cities: 15 hours → 30 minutes. • Financial pacing reports: 2 days → 10 minutes. • Marketing web quality assurance: 2 weeks → 50 minutes. • Support workflow creation: 9,000 manual workflows → self-service automation. The productivity gains are impressive, but what surprised us most wasn't the speed. • It was how quickly engineers embedded in unfamiliar domains uncovered opportunities that had been hiding in plain sight. • The biggest wins rarely come from automating one task. They come from rethinking an entire workflow. Once you redesign the workflow around AI, you often eliminate handoffs, remove unnecessary approvals, replace legacy tooling, reduce vendor spend, and dramatically accelerate decision-making. • The workflow becomes the unit of automation - not the individual task. • The most impactful agent skills cut across teams, orgs, functions, tools, and systems. The biggest lesson? The best AI opportunities are rarely visible from the outside. You discover them by sitting next to the people doing the work, understanding every friction point, and building with them, not for them. We're now forming a dedicated team to scale this further and go deeper. They'll deeply understand the work, redesign it from the ground up, and use AI to fundamentally change how the business operates. It's exciting times!
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Dude, where are my tokens? Plenty of tools tell you how many tokens Claude Code and Codex burned — ccusage and friends do that well. Leet Agent Tracker answers the harder question: what was the agent actually doing when it spent them? It's a free, open-source, local-first agent-trace data warehouse that pulls in the session logs already on your machine — across all your projects, different harnesses (currently Claude Code + Codex), and support multiple accounts per harness — and unifies them into one immutable vault. Then it rebuilds the full story of every run as a readable, attributed transcript: every human prompt, the chain of work it triggered, each tool call and LLM round-trip. Everything runs locally, your data never leaves your computer. One command to start the web UI: $ npx @leettools/leet-agent-tracker -y The first time run will pull the dependency on DuckDB and start the ingestion automatically. What makes it different: 🌐 One view across everything — projects, harnesses, and providers aggregated together, so you stop stitching usage from separate tools. 🧾 Align usage with your subscription — point it at any subscription/billing window and it reconstructs the exact prompts and LLM calls behind that spend, with saved snapshots. When your plan resets or you change tiers, you can line your real activity up against it and see what each window actually paid for. 🔁 Prompt sequences — one row per human prompt, expanded into the entire iteration it triggered: every follow-up turn, sub-agent, tool call, and retry. 📄 Full attributed session transcripts — replay any session end to end and export it to markdown (identical from the UI or CLI), so the analytics always tie back to the real conversation. ==== Want the transcript without opening the UI? One command dumps a project's prompt sequences as markdown (after the logs have been ingested, use --format markdown if you want to have the full transcripts as a markdown file): $ npx @leettools/leet-agent-tracker project sequences GitHub: github.com/pengfeng/leet-age… · MIT licensed — would love your feedback!
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Really excited to open source a new project: Omnigent, a meta-harness for AI agents. It lets you build multi-agent coding and custom agents, sitting above Claude Code, Codex, Pi, and agent SDKs to let you compose them. It also adds live collaboration and rich control policies.
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This has quietly been a miracle month in medicine. In the last 5 weeks we’ve got news on: - retatrutide, the triple agonist GLP-1 from Lilly, basically melting fat and body-wide inflammation at record levels - RevMed’s new pancreatic cancer drug showing unprecedented abilities to extend life - small trial of a one-and-done PCSK9 gene editing therapy for slashing LDL cholesterol - Mayo’s AI-assisted radiology showing vastly improved cancer detection - this new therapy for metastatic solid tumors This stuff is at varying levels of evidence. Retatrutide is ~100% on its way, other stuff needs more clinical trial data. But put it together and we’re maybe on the verge of majorly reducing the mortality of heart disease and cancer, the two leading causes of death in America.
This is actually insane. 97% of people taking the standard of care for metastatic solid tumor got worse by seven years. But with lorlatinib, that number was only 45% in the same time! This is an ENORMOUS jump in the quality of cancer care.
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Aaron Levie
@levieApr 21
The jump from working with a chatbot to having an agent that actually helps automate a process requires a real amount of work. Most companies will need to have dedicated people that are responsible for bringing automation to their teams, instead of leaving this up to every individual employee. Partly because the work is more technical than we imagine today, and partly because it’s just hard to do this as a side project. The job spec is to map out new workflows with agents, implement new systems to deploy agents, make sure the agent has all the right (up to date) context to work with, wiring up internal systems to connect to the agents, creating evals for the agents, figuring out where the human is in the loop, managing the system when there are new upgrades, helping with the change management of the existing business process, and so on. These jobs may come from IT or engineering, or live directly in the business function itself. They’ll be called different things depending on the company, and in some sense it’s the future of software engineering that you’ll see a huge growth of in non-tech companies. Most companies will have to be hiring for this now or in the future, and it’s another example of the kind of new jobs that will be created in AI.
What Role Does Not Exist Today But Will Be So Common in Five Years Time: "500K-1M jobs will be created for agent operators. This person will be somewhat technical. They will be deep in the AI world. They're gonna have to understand MCPs and CLIs and they are going to have to know how to write skills. It's going be this group of people that will know how to go into your marketing team or your legal team, or your operations team, or your life sciences research team and this is the person that is basically going to enable that function to get leverage from agents." @levie Where is this right? Where is this wrong? @jasonlk @gregisenberg @amasad @AnjneyMidha
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All 7 ByteByteGo ebooks, FREE for a limited time. Link at the end. - System Design Interview Vol. 1 - System Design Interview Vol. 2 - Machine Learning System Design Interview - Coding Interview Patterns - Object-Oriented Design Interview - Generative AI System Design Interview - Mobile System Design Interview Whether you’re preparing for interviews or looking to deepen your architecture knowledge, this is a great opportunity. Offer ends May 1. Please help spread the word. Check it out now at: bytebytego.com
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opencode 1.3.0 will no longer autoload the claude max plugin we did our best to convince anthropic to support developer choice but they sent lawyers it's your right to access services however you wish but it is also their right to block whoever they want we can't maintain an official plugin so it's been removed from github and marked deprecated on npm appreciate our partners at openai, github and gitlab who are going the other direction and supporting developer freedom
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Design lead for Claude: The classic design process is dead. Here's what's replacing it. Jenny Wen (@jennywen) leads design for Claude at @AnthropicAI, was previously director of design at @Figma, and a designer at @Dropbox, @Square, and @Shopify. In our in-depth conversation, we discuss: 🔸 Why the classic discovery → mock → iterate design process is becoming obsolete 🔸 What a day in the life of a designer at Anthropic looks like, including her AI tool stack 🔸 Whether AI will eventually surpass humans in taste and judgment 🔸 Why Jenny left a director role at Figma to return to IC work 🔸 The three archetypes Jenny is hiring for now This conversation changed how I think about the future of design. Listen now 👇 youtu.be/eh8bcBIAAFo
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A nice cheat sheet of different monitoring infrastructure in cloud services.
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We had a few other things to attend to first, but last year we started taking the idea of building our own AI search product seriously. Yahoo brings a huge amount of unique data and content to the table, as well as decades of search history and direct relationships with hundreds of millions of users. It turned out that the best company to build an AI answer engine for Yahoo was…Yahoo. So we got to work. The result is Yahoo Scout, which just went live in beta at scout.yahoo.com and in the Yahoo Search app.
The Verge
@vergeJan 27
Yahoo Scout looks like a more web-friendly take on AI search theverge.com/ai-artificial-i…
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Andrej Karpathy
@karpathyJan 26
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.
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We built a browser with GPT-5.2 in Cursor. It ran uninterrupted for one week. It's 3M+ lines of code across thousands of files. The rendering engine is from-scratch in Rust with HTML parsing, CSS cascade, layout, text shaping, paint, and a custom JS VM. It *kind of* works! It still has issues and is of course very far from Webkit/Chromium parity, but we were astonished that simple websites render quickly and largely correctly.
GPT-5.2 Codex is now available in Cursor! We believe it's the frontier model for long-running tasks.
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Since we launched Claude Code, we saw people using it for all sorts of non-coding work: doing vacation research, building slide decks, cleaning up your email, cancelling subscriptions, recovering wedding photos from a hard drive, monitoring plant growth, controlling your oven. These use cases are diverse and surprising -- the reason is that the underlying Claude Agent is the best agent, and Opus 4.5 is the best model. Today, we're so excited to introduce Cowork, our first step towards making Claude Code work for all your non-coding work. The product is early and raw, similar to what Claude Code felt like when it first launched. Cowork includes a number of novel UX and safety features that we think make the product really special: a built-in VM for isolation, out of the box support for browser automation, support for all your claude.ai data connectors, asking you for clarification when it's unsure, We are excited to see how you all use it. Cowork is available now as a research preview for Claude Max subscribers in the macOS app. Click on “Cowork” in the sidebar: claude.com/download
Claude
@claudeaiJan 12
Introducing Cowork: Claude Code for the rest of your work. Cowork lets you complete non-technical tasks much like how developers use Claude Code.
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We're officially authorized to drive fully autonomously across more of the Golden State. Next stop: welcoming riders in San Diego in mid-2026! ☀️
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We grew from zero to $100M ARR and 70M users in <2.5 years, profitably it cost us millions in experiments to learn what worked 1100-word post on every growth hack that got us here I'll cover: 1. How to launch a feature in one day 2. How to find top 0.01% talent and keep them 3. What to focus on (and what not to) 4. Influencer marketing 5. You can't wish culture into existence 0. The thing that matters the most 1. How to launch a feature in one day Our feedback loop: 10am: we come up with a new idea, or triggered by user insight. 12pm: designers code a prototype using Cursor. 4pm: we find new users to record themselves testing the feature (Voice Panel, UserTesting). 6pm: we watch the recording. 8pm: we know if the feature can be launched, if it needs refining, rebuilding, or if we should just drop it. This is a way for you to actually watch and hear from your customers as they struggle through your product. You can hear in their voice where they're confused. Sometimes you think you came up with a cool prototype. But as soon as users test it, they get stuck or have no idea how to use it. It's very helpful to have them explain how they're trying to use this feature. When you see them confused, double click there. If they light up, you know you've hit gold. That's how we often ship a feature per week. And not any feature; a feature with good odds of being well received. In a month, we might improve our product in ways that would otherwise take a year. Speed compounds. 2. How to find and keep talent Finding Talent - The best hires came from our network and cold inbound. Someone once sent me a great message on LinkedIn, out of nowhere. We got on a call. 3 weeks later, he was hired full-time on-site. - When someone joins, ask them “Who’s the best engineer you’ve ever worked with,” and reach out right away. - Only open a role after you’ve felt the pain of doing it yourself so that you know what 'Great' looks like. (I ran marketing solo for nearly a year before hiring). - Look for depth. In interviews, ask, “What’s a new skill you’ve learned recently? Can you teach it to me?” Keep asking 'why' and see how far they go. The best candidates go deep. Keeping Talent - A players want playing time. Hiring others takes time in the field away from them. Careful. - Retention takes care of itself when you keep the bar high for hiring. - We'll do regular tender offers so employees' vested shares actually get them liquidity from time to time. - Give them ownership. Our hires are more 'full stack'. Designers can code in Cursor, engineers talk to users, marketers have design literacy. This way, they can own more of what they do and compensation can be proportional to the value they create. 3. What to focus on (and what not to) In March 2023, we rebuilt Gamma to focus on one thing: users must feel the magic in the first 30 seconds. We reworked everything in the process and became AI native. In the 2.5 years before this, Gamma went from 0 to 60k users. In the 2.5 years after this, Gamma went from 60k to 70M users. What changed? What we focused on. As we scaled, the temptation was to go wider - add more things. But we went deeper - make the current thing easier, faster, more delightful. Every new feature, hire or campaign had to be tied directly to user value. Gamma 3.0 does exactly what Gamma 2.0 did, just 10x better. The most common error of a smart engineer is to work on something that should not exist - Elon Musk 4. Influencer Marketing This is a strong amplifier for word of mouth. Anytime we scaled up an influencer program, we saw a disproportional increase in people that came through word of mouth. And when we invested less, we saw a deceleration. What you need to know: - 10% of your content will generate 90% of reach. When something works, figure out why and replicate it 100 times. - Give it 6+ months, select multiple creators, and be willing to spend $10-$20k/mo. It'll not work until it does. When it does, it'll explode. How to get it right: - List creator personas with relevant audiences. Use freelancers/agencies for outreach. - Pay base + viral bonus. What you reward will happen more often. - On TikTok, use new accounts. Same chance of virality as old ones but without previous audience, so you can test. - Test all platforms (TikTok, IG, X, LinkedIn), track specific creators/hooks that work. Triple down on those. - Add "How did you hear about us" to onboarding. Measure leads, not views. - Document 20-30 winning formats, then hire top creators to train others on these. - Never write scripts, creators know their audience better than you do. The formula: Go broad -> See what works -> Scale it. Virality isn't luck. It's discovering the hooks/formats that resonate with your audience and exploiting them. 5. Culture People got this concept backwards. They think, 'I'll write down some principles and tell everyone they need to follow them.' But you can't wish culture into existence. It's not what you hope it was. It's what you do. Culture is a habit. A moving average of the past 60 days. It's not steady. Behavior molds it. One day I looked around, identified the traits I admired in our teammates and wrote them down. Then I realized, 'That's our culture'. Now we had to nurture it. We have an internal Slack channel where people give praise to teammates for a specific thing they did. I note these things and try to reinforce them. Don't just let anyone into your ecosystem. They always change it in some way. Hire up. 0. Have a word of mouth worthy product Are users bragging about having found your product? Are they sending it over to friends and people they work with? Do they feel relieved when they open your app? Until then, stick in a room with your team and get it right. Nothing of what I said above will save you if you don't get to this stage. It doesn't matter if it takes 6-12 months. Digital products can go from $0 to $1bn in a few years if you solve a worthy problem. If you try to speedrun through this step, you'll always be behind. 10 years will have passed and you'll be stuck at the same place. Closing up, these are the main things that helped us grow Gamma. Hope you find our playbook useful.
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