Founder @SceneSystems
Brooklyn, NY
Joined December 2013
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Ari Dyckovsky retweeted
LOC as a performance metric is like deciding a debate based on who said the most words
Ari Dyckovsky retweeted
Cut the crap. Literally, cut the unnecessary distractions out of your life—delete apps, unfriend and unfollow toxic people, stop committing to activities you don't care about. Life is too short.
Ari Dyckovsky retweeted
An internal Slack post I shared with the Amp team a few weeks ago:
Building software is learning.
registerspill.thorstenball.c…
Ari Dyckovsky retweeted
A related (and probably controversial opinion): "hustle" and "grind" will only produce good things by accident (and in aggregate). Purposeful, long-term work is where most value and personal satisfaction are found.
I've never been a fan of hustle-culture. It feels "cheap" in the same way that mall kiosks selling fidget-spinners do. Yes, it can lead to wealth and success, but if it doesn't (and it frequently won't), you get less value from it than long term focus on a durable problem.
VCs love hustle culture, but it's important to remember that they are playing a different game...they make money by casting a wide net and "failing fast", but their own work is very long horizon, usually focused on a particular market, and based on deep and hard-won expertise. They are craftsman, even though they use disposable tools.
In the current debate over AI, there seems to be a schism between "vibe coders" and "craftsman", but I think that this is a false dichotomy. Craft can be practiced with any tool, and what separates it from "vibe coding" is consistency of purpose.
Code has gotten cheaper, and maybe you can argue that it no longer matters at all, but I think this misses the point that the time spent creating the implementation is distinct from the time spent to understand the problem. Understanding takes time, and it's still worth doing.
You can’t outwork the whole world. There’s always going to be someone somewhere willing to work as hard as you. Someone just as hungry. Or hungrier.
Assuming you can work harder and longer than someone else is giving yourself too much credit for your effort and not enough for theirs. Putting in 1,001 hours to someone else’s 1,000 isn’t going to tip the scale in your favor.
What’s worse is when management holds up certain people as having a great “work ethic” because they’re always around, always available, always working. That’s a terrible example of a work ethic and a great example of someone who’s overworked.
A great work ethic isn’t about working whenever you’re called upon. It’s about doing what you say you’re going to do, putting in a fair day’s work, respecting the work, respecting the customer, respecting coworkers, not wasting time, not creating unnecessary work for other people, and not being a bottleneck. Work ethic is about being a fundamentally good person that others can count on and enjoy working with.
So how do people get ahead if it’s not about outworking everyone else?
People make it because they’re talented, they’re lucky, they’re in the right place at the right time, they know how to work with other people, they know how to sell an idea, they know what moves people, they can tell a story, they know which details matter and which don’t, they can see the big and small pictures in every situation, and they know how to do something with an opportunity. And for so many other reasons.
So get the outwork myth out of your head. Stop equating work ethic with excessive work hours. Neither is going to get you ahead or help you find calm.
[The Outwork Myth — It Doesn't Have To Be Crazy At Work, 2018]
Ari Dyckovsky retweeted
Young people have spent 2 decades now being force-fed doomerism online by attention-seeking sociopaths & mentally unwell peers. The world is not ending, your future is not doomed. Turn off TikTok, go to the gym, learn skills, meet people, participate in life. Decline is a choice
Ari Dyckovsky retweeted
We keep hearing about 10x or 100x productivity gains in engineering and knowledge work.
But outside the model labs, I haven’t seen the corresponding 10-100x revenue growth across the market or increase in quality.
So where is the productivity going?
Ari Dyckovsky retweeted
Nobody really knows what works right now re: Coding Agent workflows. Nobody knows what a "software factory" looks like. Nobody knows if the opinionated workflows on here are useful, and where this is all going to land.
Ari Dyckovsky retweeted
If anyone builds it, everyone thrives. Over the past decade, a lot of important work on AI alignment has focused on avoiding harm. But freedom from harm isn't the same as freedom to flourish.
In this paper, we introduce 'Positive Alignment'. A positively aligned agent is one that helps us navigate our own value trade-offs, builds our resilience, and acts as a scaffold for human flourishing. Doing this without slipping into top-down, technocratic paternalism is the great design challenge of our time.
We think a lot more research is now needed to explore this frontier: how do we align models that actively help us thrive?
Amazing work by @RubenLaukkonen, @drmichaellevin, @weballergy, @verena_rieser, @AdamCElwood, @996roma, @FranklinMatija, @shamilch, @_fernando_rosas, @scychan_brains, @matybohacek, @sudoraohacker, and others.
arxiv.org/abs/2605.10310
Ari Dyckovsky retweeted
Creating user simulators is a key to evaluating and training models for user-facing agentic applications. But are stronger LLMs better user simulators?
TL;DR: not really.
We ran the largest sim2real study for AI agents to date: 31 LLM simulators vs. 451 real humans across 165 tasks.
Here's what we found (co-lead with @sunweiwei12).
Ari Dyckovsky retweeted
I can't stop thinking about this article Rhys wrote yesterday, I think he's really onto something.
While there are a lot of really good points he makes, the one that really stands out to me, that I think is a real (growing) problem today is:
"the less tools you give them, the better they perform"
As more and more companies, rush to adopt AI Agents, I think one of the key mistakes they'll make/are making, is to stuff those agents full of tools, and then wonder why they're not performing as well.
The companies that end up really getting value from agents, are going to be those that are incredibly detailed, and disciplined about tool selection and optimization.
Ari Dyckovsky retweeted
"Models will write all code" can only be said by someone who fails to recognize the power of directly manipulating objects with your own hands at the right level of abstraction.
For a skilled expert, "ask some other extremely smart dude in English and hope they get you" is only OK for the lower-order bits. And there's a TON of these, waiting to be optimized away.
But my ideal future has *me* writing 20 extremely powerful lines of very well-considered "code" that LLM-driven compilers turn into computer programs, not writing 200 lines of fluffy back-and-forth prompts and hoping the Einstein-level LLM kinda sorta gets my point.
OK, related to this:
Fresh data from Uber, from Feb 2026:
31% of code is AI-authored
11% of PRs opened by agents
And Uber is investing heavily in AI
So outside Anthropic + AI labs, we are a far way out, probably? Source: newsletter.pragmaticengineer…
Ari Dyckovsky retweeted
The very long tail of tasks that require some human judgement or taste is often a bottleneck, and many aren't easily specifiable and amenable to automation. You can automate someone's taste in particular, but that remains a snapshot in time whose appeal depletes as preferences change, evolve, contrafict themselves over time, and the desire for individuality overtakes consumers. The problem with the long tail is that it's not a static set: not only do preferences change but historically at least, automation has generated new problem spaces rather than depleting a fixed set.
People expect that at some point, "it's solved" - well the world is not a finite set of tasks and problems to solve. Almost everything people ever did in the ancient times is automated - and yet the world today now has more preferences to satiate and problems to solve than ever. The world hasn't yet shown signs of coalescing to a great unification or a fixed state! Of course it's conceivable that at sufficient capability levels, the generative process exhausts itself and preferences stabilize - but I'd be surprised.
Ari Dyckovsky retweeted
Co-authored a piece with @venkat_somaaala & @ansonwhho at @EpochAIResearch on why compute thresholds are tricky, and how methods like reasoning training, distillation, and tool use are making them an increasingly unreliable guide for policy. aipolicyperspectives.com/p/t…
Ari Dyckovsky retweeted
science is a cool setting to explore continual learning because discoveries stack upon each other
you can’t RL for science in isolated sandboxes where discoveries artificially happen independently
models need to learn & generalize maximally from each new experimental result
Had an interesting debate with @_sholtodouglas last night.
Can you have a 'superhuman AI scientist' before you get human level learning efficiency?
(Currently, models take orders of magnitude more data that humans to learn equivalent skills, even ones they perform at 99th percentile level).
My take is that creativity and learning efficiency are basically the same thing. The kind of thing Einstein did - generalizing from a few gnarly thought experiments and murky observations - is in some sense just extreme learning efficiency, right?
Makes me wonder whether low learning efficiency is the answer to the question, 'Why haven't LLMs haven't made new discoveries despite having so much knowledge memorized'?