Alexander Eiselmayer, Ph.D. (@alexeiselmayer) header
Alexander Eiselmayer, Ph.D. (@alexeiselmayer) profile picture

Founding engineer @tabflows. PhD CS. Building with AI, tinkering on side projects, and writing about product work.

Los Angeles
Joined March 2017
Photo by Alexander Eiselmayer, Ph.D. (@alexeiselmayer)Photo by Alexander Eiselmayer, Ph.D. (@alexeiselmayer)Photo by Alexander Eiselmayer, Ph.D. (@alexeiselmayer)Photo by Alexander Eiselmayer, Ph.D. (@alexeiselmayer)Photo by Alexander Eiselmayer, Ph.D. (@alexeiselmayer)Photo by Alexander Eiselmayer, Ph.D. (@alexeiselmayer)Photo by Alexander Eiselmayer, Ph.D. (@alexeiselmayer)Photo by Alexander Eiselmayer, Ph.D. (@alexeiselmayer)Photo by Alexander Eiselmayer, Ph.D. (@alexeiselmayer)Photo by Alexander Eiselmayer, Ph.D. (@alexeiselmayer)
Alexander Eiselmayer, Ph.D. retweeted
if you think you fit this, send me a dm! i'm hiring for the sales team at Tabflows, $100–120k base, $200–240k OTE + generous equity we've been growing 10% week over week since August and want to hire the best of the best.
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Alexander Eiselmayer, Ph.D. retweeted
volume does matter look, i get it. if you were an engineer before agents came along, you're rightfully skeptical about lines of code and number of PRs being used as a metric for any kind of productivity. and i agree! or at least, i used to. for ease of explanation i will talk about this from the perspective as being an engineer on a large team of at least 50. volume didn't matter before because we focused on impact. you could have high impact even with a few PRs: for example you could make a one line config change and save the company millions of dollars. but before agents, the times where i saw volume come into the conversation were for the outliers. on the negative end you might have someone who is struggling to perform, on the other end you might have someone who was a "coding machine" archetype. a coding machine was someone who could just lock themselves in a room and emerge with a stack of PRs that solve a huge number of problems. so volume did matter, but only at the tail ends of the distribution, and combined with impact. if you've ever worked at a large tech company before, you'll get what i mean. the thing about agents is that big tech co problems are now small-medium co problems as well. how do agents change this calculus? well, everyone now has the ability to become a coding machine. you have incredibly capable frontier models that can write code better than any of us, at a rate much faster than we can. in this world, if you're still producing the same number of PRs as before, why wouldn't you pause to wonder why you're not being more productive? alien intelligence is here, and yet you can't outship a human coding machine? this brings me back to why i keep talking about trust (nitter.gq/poteto/status/21020504…). if you haven't put in the work to trust your agent's output, it is very difficult to scale up your productivity. and yes, this approach does require more tokens, but i think about this in terms of cost per intelligence. before agents, this cost was very high - you needed to hire many software engineers with high 6 figure salaries to do the work. tokens are still expensive today, but are likely to get cheaper over time (nitter.gq/theo/status/2107924138…) factoring in the cost of intelligence. what looks exorbitant today will likely be affordable in 6 moths to a year. a single engineer with agents can do the work of tens if not hundreds of engineers, at a fraction of the cost. the reality that i don't think has hit yet is that job of the software engineer has truly changed. our job is not merely to produce software any longer (well tbh it was never only about the code, but bear with me for the sake of the explanation), but the machine that writes the software. this idea of a software factory, or a Michelin kitchen as i like to call it, is still a topic of research. and it's the type of stuff i like to share, not to flex, but to show you what's possible when you put a lot of rigor into using agents at scale
Can someone help me understand how shipping 100s PRs a day makes sense? Why do you need to ship so many PRs in the first place? I might just not be AI-pilled enough to understand the need for it yet, so I’m genuinely trying to understand it.
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Alexander Eiselmayer, Ph.D. retweeted
Ben Affleck reveals he writes Python, understands convolutional neural networks, worked extensively with GPUs, and used his celebrity status to get private looks at Google and OpenAI’s video models “I’ve always been kind of into computers since I was young. Then, when film started to move from analog film to digital, I became more interested in that aspect of it. The visual-effects workflow for many years has included machine learning, so I can write pretty shitty Python scripts and stuff like that. “With convolutional neural networks, which were the precursors to what the transformer can do, which is much more computation simultaneously, you would do things like look at what’s called a tensor. That’s the numerical translation of a visual image in numbers, like the batch number, the frame number and the red, green and blue values of each pixel in each frame. It’s just that simple. That numeric is called a tensor. “You’d use a convolutional neural network to identify patterns that reveal what’s called edge detection or feature extraction, which is identifying patterns well enough to know, this is where the window ledge is, so we can more easily take the green-screen image out and replace it with something. “That was familiar to me early on because, prior to Artists Equity, I had a small visual-effects company. I’ve worked with GPUs a lot too. The visual-effects guys said, ‘Hey, you should see. There are a couple: Google and this other company, OpenAI, are doing really interesting stuff with transformers in video.’ “I’ve learned that I can actually just call up and go, ‘Hey, it’s Ben Affleck. Can I come see what you’re doing?’ Sometimes people say yes, to my astonishment.”
$ANET Andy Bechtolsheim says AI has made optics demand about 10 times bigger in five years and the industry is only at the "very beginning" "So I guess I don't need to tell you that AI has been driving this incredible increase in demand for high-speed optics, which is probably now 10 times bigger than it used to be five years ago..." "And what I want to talk to you today is that we're not at the end of this journey, but rather the very beginning. There's easily another order of magnitude increase in bits needed for the next generation kind of data centers." "So what I want to talk about first is what's happening at the data center level, and how we as an industry have to get together to solve these problems of this very rapid and high demand growth as fast as possible, since AI can't wait." ______ For full set of takeaways to Andy's AI Datacenter and Optics lecture: firesidealpha.substack.com/p…
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Alexander Eiselmayer, Ph.D. retweeted
Replying to @twoclipping
almost didn't believe but holy shit it works, put 0 effort btw.
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Alexander Eiselmayer, Ph.D. retweeted
CLEAN
Workflow tools in eve: deterministically orchestrate subagents, add human-in-the-loop, stream progress, and durably execute multi-step work
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Alexander Eiselmayer, Ph.D. retweeted
Introducing 𝚒𝚜𝙴𝚟𝚎𝚗 / 𝚒𝚜𝙾𝚍𝚍, but with @typesafeai Jev + @aisdk Now available on NPM: 𝚗𝚙𝚖 𝚒 𝚓𝚎𝚟-𝚎𝚟𝚎𝚗-𝚘𝚍𝚍
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Alexander Eiselmayer, Ph.D. retweeted
how does it usually go for you guys when you give customers a channel in slack?
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Alexander Eiselmayer, Ph.D. retweeted
We’re introducing Claude Fable 5.1 and Claude Mythos 5.1. They're the world’s most advanced models for coding and knowledge work.
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Alexander Eiselmayer, Ph.D. retweeted
Staff software engineer prompting a cloud agent to stop adding narrative copy to the UI
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Alexander Eiselmayer, Ph.D. retweeted
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Alexander Eiselmayer, Ph.D. retweeted
Replying to @pizzaboy
Claude rn
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Alexander Eiselmayer, Ph.D. retweeted
GPT-5.6 is a major step forward for health intelligence. Across the lineup, we’re delivering stronger performance at lower cost: GPT-5.6 Luna outperforms GPT-5.5 at its highest reasoning setting while costing 25x less. Together, these advances raise quality while making advanced models accessible to more people globally.
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Alexander Eiselmayer, Ph.D. retweeted
We’ve received notice that the Department of Commerce has lifted export controls on Claude Fable 5 and Mythos 5. We'll begin restoring access tomorrow, and will share an update soon. We’re grateful to our users for their patience, and to everyone who worked with us on redeploying the models.
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Alexander Eiselmayer, Ph.D. retweeted
We're ready to hire our first engineer at @HelloUntangle. It could be you :) Do you ... 1. Use agents to write, test, deploy, fix and optimize 95%+ of your code? 2. Typically manage 5+ agentic threads concurrently, in one of these: Devin, Codex, Cursor, Factory or Claude Code? 3. Ship production code for an app with paying customers in your current role? 4. Ship Nextjs and care about UX? 5. Find the Legal AI industry interesting? The role is WFH, anywhere in USA. If you meet all the above, figure out how to get my attention ;)
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Alexander Eiselmayer, Ph.D. retweeted
Others : Ik the guy who knows a guy who knows another guy. Pointers be like 😂
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Alexander Eiselmayer, Ph.D. retweeted
If you don’t learn from a failure, it’s pointless. Just lost, nothing inherently good about that. And the degree to which you learn is usually directly related to how much the loss stings. Next time you fail at something see what lessons you can learn from it. And if it doesn’t sting, investigate why it doesn’t for you.
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Alexander Eiselmayer, Ph.D. retweeted
effective bait playbook: 1. dumbass opinion about remote vs in person work 2. "As a developer, which [BASIC THINGS LIST] do you prefer?" 3. "SHOW ME YOUR STARTUP IN ONE CHARACTER, I AM RICH MAN" 4. "Unpopular opinion: [INSERT POPULAR OPINION]"
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Alexander Eiselmayer, Ph.D. retweeted
seriously be careful out there everyone i had 2 Microsoft Copilot licenses in my car, and someone broke in and left 4 more
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Alexander Eiselmayer, Ph.D. retweeted
It’s in production! 🎉🇬🇧
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