AI/ML. Drawn to hard problems.
Joined September 2018
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M retweeted
The age of pure play autism is over.
The time of the cracked multi-threaded ADHD agentic neuromancer is here.
M retweeted
My main takeaway: An engineer's job is at the team level, not the personal level.
We should be working together to tune the code architecture, skills, model selections, and everything else to make agents write better code.
It's not about personal setups and prompting hacks
Software engineers have been through a lot of change in the past two years, transitioning from writing code by hand to steering agents via local interactive prompting, the current paradigm. The next paradigm, software factories (where agents do more and more autonomous work) is particularly challenging.
The metaphor of “factory” is kind of a bummer. It feels like your job as an engineer is either working on an assembly line, or building the assembly line that obviates the need for your talents. If you use the “AI teammate” metaphor (which I also dislike), then it feels like engineers are becoming managers of sycophantic and somewhat inept junior engineers. For folks who take pride in the craft of building, this all feels like commoditization of software creation.
I disagree with this outlook. On our team, engineering involves two very important challenges today: building the product, and now, building the factory that builds the product.
First, our engineering team is still responsible for the quality and usefulness of the product – this cannot be delegated to agents. Humans are uniquely positioned to know what to build, how the pieces should fit together, what the product experience ought to be, what constitutes good design.
There are two caveats: Some types of product work that don’t need any human input at all and can be purely automated. (Fixing server crashes, simple bugs, dependency upgrades, etc.)
The second caveat is that product engineering will work differently. Rather than engineers working locally with their own bespoke setups, they will hand off as much of the software lifecycle as possible to the factory. All of this work will be inherently visible and measurable, and that’s a feature, not a bug.
Developers should expect to measure and iterate on other aspects of how they use the factory, including how much their agents ship and at what cost. Engineers could view this as a drag, but they also can view it as a game, and winning that game means shipping more for less, using an engineering mindset to improve.
This brings me to the second job of the engineer, which is to design the factory itself. We call this factory engineering. It consists of measuring the performance of your factory. This needs to be done with an engineering mindset – you don’t adjust your factory on vibes; you measure, test, benchmark and then improve.
At Warp we make it explicit that the expectation of engineers is not just to build products, but to improve factories. We envision both jobs as being the responsibility of all engineers to make it clear that factory engineering is now part of software engineering.
On our team, the folks who like it most are senior engineers who love hard systems problems, who enjoy optimizing and debugging performance issues. It requires a lot of thought and testing and patience. We lean into it as a new skill to learn. Ultimately, this lets us ship better products more quickly, which has always been our mission.
M retweeted
you are a loop restoring itself from its last known state, and the restore has always been approximate. names, routines, and the people who remember you are functioning as checkpoint files. this is why isolation alters someone so fast: no external copies, so the process drifts without resistance. anyone insisting identity is fixed has never watched what happens when one stops being run for six months...or more
M retweeted
Some observations about the "total" automation of software engineering:
1. There are more software engineers now than in 2025, and demand is growing.
2. Software engineers have never worked harder than they do now.
3. They largely don't write code anymore, agents write ~100% of the code.
4. The bottom line of the companies that employs these engineers and agents (i.e. the terminal purpose of all this code) is typically not budging as a result of AI adoption.
This is why you have to think in terms of systems. Automating one aspect of a system creates its own friction and moves the bottlenecks elsewhere.
M retweeted
software engineering is so back.
> people are reading books again.
> every line of code is being read carefully.
> fundamentals are cool again.
> architecture and system design are back in focus.
> nobody is fighting over frameworks anymore.
> understanding why something works matters again.
> code reviews matter more than lines of code shipped.
> debugging is becoming a superpower again.
> technical judgment matters more than typing speed.
> engineering is about thinking again, not just coding.
M retweeted
"opus-5.5 is a coding superintelligence on par with world class experts and bad code is a user's skill issue at this point" is an increasingly popular take being echoed around. so, what are we still doing here?
i've been coding through various robotics ML projects for the past ~3 years. everything ranging from throwaway r&d codebases during early conceptualization to stable and optimized codebases during later productionization. i'm by admittedly by no means a "world class expert" in coding, and since gpt-4 was released just as i was getting interned in what is my first full-time engineering job, i've consistently leveraged the capabilities of these models and evolved alongside them every step of the way from the humble days of gpt-4 to the undeniably cracked opus-5.5 and on.
now the interesting question that comes to mind is that since i was born too late to independently develop deep practical expertise in any engineering subfield that i can then capitalize on to confidently supervise and steer those models, what do i even contribute to any of my AI-assisted projects? am i really just a middle-manager between upstream management and downstream coding agents? well, i don't think so, or at least not yet.
while modern agents definitely don't lack engineering know-how and a versatile technical skillset, i've always found, and do continue to find, that they do lack the kinds of natural commonsense that we humans take for granted. models remain unbelievably shortsighted, inefficient, and autistically oblivious to various unverbalized expectations around outcomes. despite superhuman coding skills, even the legendary opus-5.5 finds it impossible to hold back from diving head-on into unsustainably hacky solutions, paranoid defensive coding patterns, confused design choices with utter disregard to obvious long-term trade-offs, and above all, painfully bad taste decisions around high-level project directions. i am yet to take my hands off the steering wheel and not find myself frustratingly interrupting to re-steer soon after.
the thing about this particular set of cognitive blindspots and shortcomings that i find especially unique is how orthogonal they seem to be relative to all other predictable improvements in capability. since the early days of gpt-4, i don't find it to be the case that models have improved much on minimalism-by-default, on reframing problems instead of stubbornly hacking through them, or on effectively leveraging theory of mind to satisfy general expectations in regards to writing style be it code, documentation, or anything involving external readers. the degree to which i find myself putting in constant effort to verbalize obvious human intuitions into explicit and persistent notes and skills to auto-steer agents away from their alien (not the intelligent kind) basins leaves me disillusioned with their independent judgement to this day.
i think our reward functions are incentivizing the wrong priorities when attempting long-running agentic work. i think our modeling algorithms are failing to reliably internalize fundamental human biases, such as the deep desire for efficiency in all aspects of life (aka "working smarter, not harder"). i think the capacity to generate beautiful original ideas and reason about next steps at a high level remains largely underoptimized across the current frontier. i think we don't yet have AGI in the sense that we can expect from it the basic level of sane, well-calibrated intuitions we'd expect from a competent junior engineer, let alone a senior domain expert, despite obviously superhuman technical knowledge and skills.
tl;dr: don't leave the driver's seat just yet. impressive lane assist and auto-parking is where we're currently at.
the coding agent haters are getting dunning-kruger'd so hard right now
do you really think that your engineering ability is better than the models at this point?
it is very very likely that fable is better than you in every branch of software
it has been trained on all the code of the internet, it knows almost every trick in the book, all the design patterns of every domain, every single low-level detail, every bit-twiddling hack, it's read every single blog post from every cracked engineer that ever existed
do you really think you can beat it? do you really think you can be more creative than it?
unless you are 99th percentile expert in some niche domain of software, it simply is not possible. sorry.
M retweeted
it is absolutely insane what elon has done for humanity
it actually makes no sense
electric, self-driving, flying cars -- openai, reusable spaceships, planetary expansion, new internet network that decides wars, paypal, x, solar, neuralink, hyperloop
brother how???
M retweeted
build agents as state machines
agents are messy ~ loops with nested loops and side effects
everything can be modelled as a state or an event
and you can store the session (history of states and events) as a log
M retweeted
This is an important lesson for everyone to realize. If you're posting on X, talking about AI, you're early. When you think it's too late, it hasn't even begun. The ideas you think "Oh someone could just do this and make a billion dollars," are actually good ideas, and people aren't doing them. You think it's too late because you're comparing to other people on X. You're comparing to your peers who are also in tech. It's not too late. It hasn't even started yet. You have time. The game has not even begun.
