Fintech data systems and infra engineer ○ Running https://xitter.cf/t.co/crOIdOlqX0, https://xitter.cf/t.co/tKra6hDDCk
Cincinnati, OH
Joined December 2020
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Zach Gollwitzer retweeted
One of the biggest problems with using LLMs as a google replacement for programming, is that getting zero relevant results on google used to be a signal that you had the wrong idea about the root cause. Whereas LLMs will happily indulge any terrible idea you suggest.
Zach Gollwitzer retweeted
i am in the most magical time of my life
My dear front-end developers (and anyone who’s interested in the future of interfaces):
I have crawled through depths of hell to bring you, for the foreseeable years, one of the more important foundational pieces of UI engineering (if not in implementation then certainly at least in concept):
Fast, accurate and comprehensive userland text measurement algorithm in pure TypeScript, usable for laying out entire web pages without CSS, bypassing DOM measurements and reflow
Zach Gollwitzer retweeted
I have been thinking about this a lot.
I think for a great many of engineers, the ones who did it because they loved it only to discover that money was in fact at the end of the rainbow found both the journey and the destination satisfying. In fact, I think I can argue with authority that the destination was only satisfying as the journey was difficult. The hard-fought evenings spent toiling away on an idea and codebase that slowly gives way to your vision was an incredible experience.
The group of people that fell into this category of hard-fought journey and destination we will call them tinkerers. One thing tinkerers have always hated is the already known problems. The journey is clear as day. The obstacles minor inconveniences. Its purely a matter of typing the solution into the terminal. This is also why I think so many of this group goes out and does open source, or starts companies. Work largely falls into this category with few exceptions.
From this reason is why I largely find UI work soul sucking. I know the solution, its a matter of just looking up the details and putting it into my editor. yawn. CSS, flex box this, grid that, put the tailwind classes in the bag.
To me, the LLM software world is with little to no journey and discovery. Its more of simply taking my high level idea and just formulating it into testable, atomic chunks that can be verified. I have traded my favorite part, discovery and raw creation, with itemized list of TODOs and patience and "No Mistakes."
To this, every morning from 6 to 9 I simply just hand code every thin. even UI things. It is because I want journey and discovery and raw creation. Maybe one day comes and its just so futile that I stop this. But for now, I still see such great value in this. I see such better thought through products. Because slowing down and truly thinking through everything. The architecture, the design, everything is an expression of discovery and creation. And I love it.
I am sure there will come a day, maybe even in the next 6 months where I change my mind. For now, I pursue the love of the game intentionally.
I do also believe that there exists people who get the same joy I got from building with tears and sweat by prompting LLMs. I am positive of it. I just don't understand how. But people love UI work. I also don't understand that.
Programming was deeply satisfying work to me. Work for hours/days before getting the payoff of the code working well on your machine. I’m feeling so much friction now to open the editor and do this kind of task by hand, but also increasingly depressed with the nature of work in an AI assisted dev workflow. Back and forth prompting seems to eat at my soul. Need to find a balance that brings back some of the toil.
Zach Gollwitzer retweeted
Wrote about this a couple of days ago.
There are 4 existing truths that haven't yet been upended.
1. Agents still can't fully yolo real production changes except peripheral net-new features (think add a button-on-a-page vs add a new page).
2. There is only so much context switching a human engineer can do. Even the most AI pilled engineers are inching towards a burnout, either due to their own high expectations or the orgs.
3. Hiring more engineers = buying more attention. It's the scaling factor. This is the only way to do more, once you have maxed out the agentic assymetry in a team.
4. The principle of mythical man month still holds. Cant keep stuffing people in teams.
A mistake that many AI native teams would end up making (and regretting) is to over rely on agents. I expect this pendulum to swing several times back and forth till they find the right balance.
Zach Gollwitzer retweeted
This is counterintuitive for some, which is why there’s a paradox named after it. But if you lower the cost of something that was previously supply constrained, demand for that thing goes up. Software engineering is just one of the easiest examples to contemplate.
The process goes like this: every small business, every IT team, every large enterprise sees that engineering can now drive vastly more output. They then start to consider all the new things they can build or automate. They even test building prototypes themselves.
They only get so far with that approach because they realize there are still 50 other tasks that go into building software and maintaining it. So they start to hire more engineers to do that work. All of this for work they never would have considered automating or having software for if AI didn’t exist.
So yes, automating tasks, in plenty of fields, will lead to demand for experts, not less.
Zach Gollwitzer retweeted
- SOTA model drops
- solves a bunch of outstanding problems
- everyone, "AI is magic no engineering needed"
- AI gets stuck on next set of problems
- everyone, "engineering is needed" <-- we are here
- repeat
Zach Gollwitzer retweeted
Amazon’s AI coding assistant may have just pulled off the Son of Anton gag from Silicon Valley: “it’s possible that…the most efficient way to get rid of all the bugs, was to get rid of all the software.”
Taking AWS down for hours (on multiple occasions). Unreal.
amazon's internal A.I. coding assistant decided the engineers' existing code was inadequate so the bot deleted it to start from scratch
that resulted in taking down a part of AWS for 13 hours and was not the first time it had happened
incredible
ft.com/content/00c282de-ed14…
Zach Gollwitzer retweeted
The math on this project should mass-humble every AI lab on the planet.
1 cubic millimeter. One-millionth of a human brain. Harvard and Google spent 10 years mapping it. The imaging alone took 326 days. They sliced the tissue into 5,000 wafers each 30 nanometers thick, ran them through a $6 million electron microscope, then needed Google’s ML models to stitch the 3D reconstruction because no human team could process the output.
The result: 57,000 cells, 150 million synapses, 230 millimeters of blood vessels, compressed into 1.4 petabytes of raw data. For context, 1.4 petabytes is roughly 1.4 million gigabytes. From a speck smaller than a grain of rice.
Now scale that. The full human brain is one million times larger. Mapping the whole thing at this resolution would produce approximately 1.4 zettabytes of data. That’s roughly equal to all the data generated on Earth in a single year. The storage alone would cost an estimated $50 billion and require a 140-acre data center, which would make it the largest on the planet.
And they found things textbooks don’t contain. One neuron had over 5,000 connection points. Some axons had coiled themselves into tight whorls for completely unknown reasons. Pairs of cell clusters grew in mirror images of each other. Jeff Lichtman, the Harvard lead, said there’s “a chasm between what we already know and what we need to know.”
This is why the next step isn’t a human brain. It’s a mouse hippocampus, 10 cubic millimeters, over the next five years. Because even a mouse brain is 1,000x larger than what they just mapped, and the full mouse connectome is the proof of concept before anyone attempts the human one.
We’re building AI systems that loosely mimic neural networks while still unable to fully read the wiring diagram of a single cubic millimeter of the thing we’re trying to imitate. The original is 1.4 petabytes per millionth of its volume. Every AI model on Earth fits in a fraction of that.
The brain runs on 20 watts and fits in your skull. The data center required to merely describe one-millionth of it would span 140 acres.
Zach Gollwitzer retweeted
Reminder that you do not need an LLM, spend tokens or a heartbeat check to get periodic notifications of deterministic data. Just make a cron job and a bash script - it will be solid as a rock and free! (this seems to be about 80% of the openclaw use cases I see in the wild)
Zach Gollwitzer retweeted
How to finally become the 10x engineer:
Add WarCraft 3 sounds to Claude hooks to get alerts when it finishes a task or needs permission.
Zach Gollwitzer retweeted
We’ve hit this odd moment where folks are using AI to write book-length articles and then viewers are using ai to reduce it back down to the size of a tweet.
Zach Gollwitzer retweeted
Of course that's your contention. You're a first-time SaaS bear. You just got finished listening to some podcast, Dario on Dwarkesh, probably. Now you think it’s the end of white collar work and seat-based pricing is screwed. You're gonna be convinced of that til tomorrow when you get to “Something Big is Happening”. Then you’ll install ClawdBot on a Mac Mini, vibe code a dashboard on top of a postgres database and say we’re all just a couple ralph loops away from building a Salesforce competitor. That’s gonna last until next week when you discover context graphs, and then you're gonna be talking about how the systems of record will be disintermediated by an agentic layer and reposting OAI marketing graphics.
“Well, as a matter of fact, I won't, because ultimately the application layer is just ….”
The application layer is just business logic on top a CRUD database. You got that from Satya’s appearance on the BG2 pod, December 2024, right? Yeah, I saw that too. Were you gonna plagiarize the whole thing for us? Do you have any thoughts of your own on this matter? Or...is that your thing? You get into the replies of anyone posting a SaaS ticker. You watch some podcast and then pawn it off as your own idea just to impress some VCs and embarrass some anon who’s long SaaS? See the sad thing about a guy like you is in a couple years you're gonna start doing some thinking on your own and you're gonna come up with the fact that there are two certainties in life. One: don't do that. And two: you dropped thirty grand on Mac Minis and LLM API calls to come to the same conclusion you could’ve got for free by following a handful of VC accounts.
Zach Gollwitzer retweeted
New art project.
Train and inference GPT in 243 lines of pure, dependency-free Python. This is the *full* algorithmic content of what is needed. Everything else is just for efficiency. I cannot simplify this any further.
gist.github.com/karpathy/862…
