Samrat Jp retweeted
Geoffrey Hinton once noticed that the tellers at his bank kept changing. When he asked one of them why, the teller didn't know, only that they got moved around a lot.
Hinton guessed the reason... if staff never work with the same people for long, it's much harder for a few of them to team up and cheat the bank.
That thought became one of the most useful tricks in deep learning. While a neural network trains, you randomly switch off about half its neurons at every step. No neuron can lean on a particular partner, so each one has to learn something useful on its own. The trick is called dropout, and it helped AlexNet win the 2012 ImageNet contest, which set off the modern AI boom. It's still built into many of today's networks.
Samrat Jp retweeted
Developers will be like, "I don't like paying $1 per build, I'll just buy this $15,000 mac studio so all my builds are free"
not an accounting class in sight
Samrat Jp retweeted
One of my most significant found photo collections to date. A few hundred color Kodachromes of the 1939 Golden Gate International Exposition on Treasure Island.
IN COLOR.
Very few color slides of this event exist. I found what may be the largest known collection of these in existence.
Kodachrome film had only been out since 1936. It was slow and expensive. The fair was built on a man-made island to celebrate the new Bay Bridge and the Golden Gate Bridge. They called it the Pageant of the Pacific. It ran in 1939 and 1940. Almost everything that survives is black and white.
Charles Cushman shot some color frames that are now at Indiana University. OpenSFHistory has published six 1940 night color slides and a smaller number of daytime ones. A few others are in private hands. That is most of the known color record outside of this set.
These came out of an estate sale in the Oakland hills. The Gayway with Sally Rand’s Nude Ranch and the sunburst lights down the middle of the street. The Tower of the Sun lit at night and reflected in the lagoon. The Court of Pacifica. The Japanese pavilion. Crowds in hats on the wide midways. Fountains and statues. The whole fair still sitting in the mounts.
What a time to be alive in San Francisco.
I am working through this collection but I've published the first 40 or so here (more to come): flickr.com/photos/thomashawk…
Samrat Jp retweeted
Assorted things I've been mulling over regarding agents in the economy:
• Companies are in some sense the original superintelligence. It's often the case that they can use their amortized cognitive surplus to advance their interests against those of certain consumers: relative to the optimization capacity of companies, consumers can be inattentive, underinformed, irrational, etc. (Think, say, of some of the classic insurance upsells at the end of a booking process, which is typically priced substantially above what a regular policy would cost.) Herbert Simon introduced the idea of bounded rationality, and firms often end up benefiting from it. Thanks to personal agents, we will probably get somewhat more rational in how we engage with companies.
• How exactly will outcomes change? Well, certain kinds of price discrimination will work less well (how do coupons work if every agent hunts fastidiously for them?), and certain kinds of customer cross-subsidy will change (today, people who forget to cancel subscriptions subsidize those who don't). Taken across the economy as a whole, I'm not sure what the incidence of all of this will be.
• In corporate strategy, people have long debated the relative importance of distribution and product quality. Perhaps you've made something better, but does anyone know about it? Won't people just continue to buy the ACME Corp Mousetraps? Personal agents will, I think, act as a kind of structural subsidy for product quality. How would markets look different if individuals always spent at least 10 hours researching their purchases? On the margin, focusing on making a product better is, I think, going to become a more effective strategy for companies. This is good!
• It true, a corollary is that information about product quality will become more important and impactful. Today, most people have very limited ability to aggregate and integrate such data, but agents will have insatiable thirst for such signals. (It's often said that data is becoming more valuable because it helps in the training of models. That's obviously true, but I think there's a secondary way in which it's more valuable simply as a complement to cheaper intelligence.)
• A big question is whether agents will increase superstar effects (rational agents all settle on the same product) or increase dispersion (because everyone has slightly different tastes and preferences, which agents are good at eliciting, finally overcoming the artificial clustering we've been subjected to).
• Many internet companies have monetized demand routing, which is in turn often predicated on short attention spans. "I'll click on the first thing I see." This has led to some amount of inefficiency, as companies try to propitiate capricious ranking algorithms and incur various placement fees. It's not all deadweight loss (willingness to pay is itself a kind of signal, which is valuable to buyers), but it's not great. How will the nature and value of this routing change in a world of personal agents? I don't know the answer, but I think that the effects might be pretty big.
• Much of what I'm writing hinges on the assumption that personal agents will be on the consumer's side. I suspect that that will be the winning strategy, but whether it's true, and what it means to be true, involves a lot of other questions.
Taken as a whole, the market is a massively parallelized, decentralized, and highly imperfect value attribution machine, and, taking stock of all of the effects, it seems to me that the quality point may well be the biggest: that agents will change the nature of the backpropagation such that the rewards for better products increase. Maybe this is too optimistic, but I suspect (and hope) that companies that make things that they're proud of will feel that the universe is a little more partisan in their favor.
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 (xitter.cf/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 (xitter.cf/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
Samrat Jp retweeted
Ten years of Sad Affleck memes and it turns out he'd just been trying to install CUDA drivers the whole time.
Samrat Jp retweeted
“okay, so what? it’s just math.”
no, you don't get it.
here’s what OpenAI’s math breakthroughs could eventually help make possible in the real world:
Vlasov–Maxwell → stronger foundations for fusion research, whose ultimate prize is virtually limitless clean energy to power civilization
Calderón’s Problem → portable body scanners using electrical signals, making medical imaging cheaper and easier to access
Maximum-Cardinality Matching → faster searches for compatible kidney swaps across large donor pools, helping hospitals coordinate lifesaving transplants
Inverse Elasticity Problem → scans that map tissue stiffness, helping doctors locate suspicious growths inside the body
Mumford–Shah Conjecture → better tools for spotting tissue changes in brain scans, helping doctors identify signs of disease
Bose–Einstein Condensation → stronger foundations for quantum sensors that could help vehicles navigate without GPS
Simple Stochastic Games → better safety checks for self-driving cars and robots before dangerous mistakes reach the real world
Matrix Multiplication → cheaper AI and bigger scientific simulations using the same computers
Edit Distance → faster DNA comparisons, helping researchers study genetic changes linked to disease
The k-Server Problem → robots that waste less movement and energy, making warehouses more efficient and goods cheaper to move
a reminder that math is the foundation of science.
so accelerating mathematical discovery could compress centuries of scientific progress into years.
insane timeline to be alive for!
We’re releasing a broad range of new mathematical results produced by an internal frontier model.
We’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and public recommendations to inform how we release these results.
github.com/openai/math
🐐
Introducing Graphical.
It's a simple, powerful, and fun tool to design visual languages, style components, and work with coding agents to create interfaces that look memorable and feel unique.
I hope you'll check it out at graphicalui.com!
Samrat Jp retweeted
The most important technology problem of the next 5 years:
Creating a broadly accepted standard for agents to identify themselves to service providers, so that providers can adjust the way they interface with clients (as compared to human-based traffic).
In the interim (i.e., for the next 6 months), there will be a cat-and-mouse game that agents will play -- to circumvent detection and maintain their product's utility during this growth phase.
However, this will only be a stopgap and it will not be the terminal state of the world.
.@OpenRouter co-founder Alex Atallah on the "everyone is building the same thing" take:
"This reminds me of this tweet I saw... Everybody's building an agent loop with notifications, third-party connectors, context management, memory, sandboxes, agentic web search, an always-on agent on top."
"This product is showing up everywhere. It is showing up everywhere, but these are just the new table-stakes primitives."
"A 2005 version of that tweet would be: 'Oh, everybody's building the same thing. A database, a users table, a sign-in page, a sign-up page, a profile page, a logout page. Everything's the same.'"
"There's like a lot of differentiation really. There's table-stakes needs for AI just like there's table-stakes needs for the web."
@alexatallah @amasad
.@OpenRouter co-founder Alex Atallah, in his first podcast since Stripe acquired the company, joins @Replit co-founder Amjad Masad and a16z's Erik Torenberg on why the future of AI is independence and specialization.
In this conversation, Alex walks through how the Stripe deal unfolded, why he wasn't originally looking to sell, and why "payments and inference are going to blend together."
Pre-OpenRouter, the typical AI workflow had one model provider to choose from, and little pressure on that provider to lower prices. Now enterprises are diversifying across labs and open-weight models, and every board is asking about AI costs and benchmarks.
Amjad argues if your company depends on one AI lab, it can turn into your competitor. So Replit is building the layer that lets enterprises use any model and any cloud, without being locked into either.
Alex and Amjad are split on personal agents – Amjad runs one agent across his whole company and loves the cross-domain joins, while Alex says general agents cause you to sacrifice understanding, and argues 10 specialized chiefs of staff beats one superagent.
0:45 How the Stripe deal unfolded
5:05 Why mixing models beats one model
7:25 Forcing the labs to compete on price
8:50 Enterprises want open-weight models
10:30 Every board asks about AI every month
12:25 Why companies must own their intelligence
14:15 Replit as the independence layer
15:10 Everyone is building the same agent
16:35 Why Amjad built bring-your-own-cloud
18:10 Amjad's agent that runs his whole company
19:55 Why 10 specialized agents beat one
23:35 Machines, not humans, should specialize
27:30 Guardrails for agents talking to agents
31:10 Models training their own replacements
33:45 Most tasks don't need a frontier model
40:50 Training small models on Qwen 8B
43:25 The Rust cycle is coming for AI
45:10 Fusion models: frontier quality at half the cost
YouTube: youtu.be/ekK8urKHPMQ
@alexatallah @OpenRouter @amasad @eriktorenberg
📠 this is the way
Americans have started taping their mouths shut in order to sleep better.
Not as punishment. For optimization. It is called sleepmaxxing and there are seventeen steps.
Mouth tape. Chin strap. Blackout mask. Magnesium. No screens after eight. Cold room. Weighted blanket. A red bulb.
I adopted all seventeen in one night, because a man does not adopt a discipline partially.
My wife came in and turned on the light and found me taped, strapped, masked, weighted, and lying perfectly flat in a 64-degree room under a red bulb.
She did not scream. I want that on the record. She has been married to me a long time.
She said, "Is this a health thing."
I could not answer. My mouth was taped.
I nodded once, with dignity.
I slept eleven hours. It was the finest sleep of my life.
I will never be permitted to speak of it again.
Samrat Jp retweeted
Nobody understands the bond between a millennial and the mediocre movie their family just happened to own on VHS when they were 12 years old.
Samrat Jp retweeted
what is Jev and how to use it, a deep dive ⤵︎
flaviocopes.com/jev/
Samrat Jp retweeted
You know I’m a bit of a LoveFrom fanboy.
And if you know them, they don’t showcase their work on their website.
I wanted to build an archive people can refer to, as I often do myself.
Every published LoveFrom project I could find, all in one place.
↓
lovefromarchive.com
Samrat Jp retweeted
Claude Code driving 12 iOS simulators at once on my M1 Pro MacBook with 16 GB RAM.
Powered by mobai.run + github.com/MobAI-App/simslim































