Quotes

Tomorrow, and tomorrow, and tomorrow, Creeps in this petty pace from day to day, To the last syllable of recorded time; And all our yesterdays have lighted fools The way to dusty death. Out, out, brief candle! Life's but a walking shadow, a poor player That struts and frets his hour upon the stage And then is heard no more.

For the last few years, Meta has had a team of attorneys dedicated to policing unauthorized forms of scraping and data collection on Meta platforms. The decision not to further pursue these claims seems as close to waving the white flag as you can get against these kinds of companies. But why? [...] In short, I think Meta cares more about access to large volumes of data and AI than it does about outsiders scraping their public data now. My hunch is that they know that any success in anti-scraping cases can be thrown back at them in their own attempts to build AI training databases and LLMs. And they care more about the latter than the former.

124. There are crimes I don’t commit mainly because I don’t want to find out I could. 159. Passions are the great defense against passion. 166. Beware of knowing your virtues; you may lose them. Beware of knowing your vices; you may forgive them. 169. He is sincere, we say, meaning there is nothing else to be said. 293. He may not deserve your praise, but he deserves to be treated as if some day he might. 237. How much less difficult life is when you do not want anything from people. And yet you owe it to them to want something. 174. Debts of a certain immensity demand betrayal.

We collaborate with open-source and commercial model providers to bring their unreleased models to community for preview testing. Model providers can test their unreleased models anonymously, meaning the models' names will be anonymized. A model is considered unreleased if its weights are neither open, nor available via a public API or service.

[...] in 2013, I did not understand that the things I said had meaning. I hate talking about this because it makes me seem more important than I am, but it’s also important to acknowledge. I saw myself at the time as just Steve, some random guy. If I say something on the internet, it’s like I’m talking to a friend in real life, my words are just random words and I’m human and whatever. It is what it is. But at that time in my life, that wasn’t actually the case. I was on the Rails team, I was speaking at conferences, and people were reading my blog and tweets. I was an “influencer,” for better or worse. But I hadn’t really internalized that change in my life yet. And so I didn’t really understand that if I criticized something, it was something thousands of people would see.

...I had my lady Castlemayne in my arms and was admitted to use all the dalliance I desired with her, and then dreamt that this could not be awake, but that since it was a dream, and that I took so much real pleasure in it, what a happy thing it would be if when we are in our graves...we could dream, and dream such dreams as this, that then we should not need to be so fearful of death, as we are in this plague time.

What, then, is "existentialism"? Existentialists, be they Christians or atheists (like myself) believe that existence comes before essence—that we must begin with the subjective. A manufactured object, say a paper knife, is made to serve a purpose. For us, our existence comes first, we have each our own Project; to choose our own purpose, and in doing so to choose an image for all to follow. Existentialists state frankly that man is in anguish unable but to choose. Like Abraham, who obeyed the angel, but had to first choose if it was really an angel, and if he was really Abraham. We are abandoned by God, left alone without excuse, to interpret alone such signs as there are. Even when we seek advice, it is we who choose who to ask. We must despair of help from outside our will, or beyond the possibilities of action. Descartes said "Conquer yourself" and meant the same—that we should act without hope.

I have been doing this for years, and the hardest parts of the job were never about typing out code. I have always struggled most with understanding systems, debugging things that made no sense, designing architectures that wouldn't collapse under heavy load, and making decisions that would save months of pain later. None of these problems can be solved LLMs. They can suggest code, help with boilerplate, sometimes can act as a sounding board. But they don't understand the system, they don't carry context in their "minds", and they certianly don't know why a decision is right or wrong. And the most importantly, they don't choose. That part is still yours. The real work of software development, the part that makes someone valuable, is knowing what should exist in the first place, and why.

We [Bluesky] took a somewhat novel approach of giving every user their own SQLite database. By removing the Postgres dependency, we made it possible to run a ‘PDS in a box’ [Personal Data Server] without having to worry about managing a database. We didn’t have to worry about things like replicas or failover. For those thinking this is irresponsible: don’t worry, we are backing up all the data on our PDSs! SQLite worked really well because the PDS – in its ideal form – is a single-tenant system. We owned up to that by having these single tenant SQLite databases.

[On agents using CLI tools in place of REST APIs] To save on context window, yes, but moreso to improve accuracy and success rate when multiple tool calls are involved, particularly when calls must be correctly chained e.g. for pagination, rate-limit backoff, and recognizing authentication failures. Other major factor: which models can wield the skill? Using the CLI lowers the bar so cheap, fast models (gpt-5-nano, haiku-4.5) can reliably succeed. Using the raw APl is something only the costly "strong" models (gpt-5.2, opus-4.5) can manage, and it squeezes a ton of thinking/reasoning out of them, which means multiple turns/iterations, which means accumulating a ton of context, which means burning loads of expensive tokens. For one-off API requests and ad hoc usage driven by a developer, this is reasonable and even helpful, but for an autonomous agent doing repetitive work, it's a disaster.

Product teams that are smart are getting off the treadmill. Whatever framework you currently have, start investing in getting to know it deeply. Learn the tools until they are not an impediment to your progress. That’s the only option. Replacing it with a shiny new tool is a trap. [...] Companies that want to reduce the cost of their frontend tech becoming obsoleted so often should be looking to get back to fundamentals. Your teams should be working closer to the web platform with a lot less complex abstractions. We need to relearn what the web is capable of and go back to that.

The App Store has an inscrutable, time-consuming, whim-dependent approval process. The App Store newsgroup postings are full of angry claims that this is a bug, but I bet it's a feature. If you can't get an app approved until it's working perfectly, and you have to wait a week or two -- or more -- between approval rounds, you're much more likely to put a lot more effort in up front to get it right.

The largest model in the PaLM 2 family, PaLM 2-L, is significantly smaller than the largest PaLM model but uses more training compute. Our evaluation results show that PaLM 2 models significantly outperform PaLM on a variety of tasks, including natural language generation, translation, and reasoning. These results suggest that model scaling is not the only way to improve performance. Instead, performance can be unlocked by meticulous data selection and efficient architecture/objectives. Moreover, a smaller but higher quality model significantly improves inference efficiency, reduces serving cost, and enables the model’s downstream application for more applications and users.

...The firefly has disappeared and only I remain, alone— at the end of darkness I greet the morning that brightens. The dew dangling on the grass looks beautiful in the morning sunlight. Someday in heaven I will live as a poet who sings about you, firefly, reincarnated as the dew.

Here are some absurdly expensive things you can do on a trip to Tokyo: Buy a golden toilet. There is a toilet in Tokyo that is made of gold and costs around 10 million yen. If you are looking for a truly absurd experience, you can buy this toilet and use it for your next bowel movement. [...]

A system’s ease of use always should be a primary goal, but that ease should be based on an underlying concept that makes the use almost intuitive. Increasingly, people seem to misinterpret complexity as sophistication, which is baffling—the incomprehensible should cause suspicion rather than admiration.

If people are only using this a couple of times a week at most, and can’t think of anything to do with it on the average day, it hasn’t changed their life. OpenAI itself admits the problem, talking about a ‘capability gap’ between what the models can do and what people do with them, which seems to me like a way to avoid saying that you don’t have clear product-market fit. Hence, OpenAI’s ad project is partly just about covering the cost of serving the 90% or more of users who don’t pay (and capturing an early lead with advertisers and early learning in how this might work), but more strategically, it’s also about making it possible to give those users the latest and most powerful (i.e. expensive) models, in the hope that this will deepen their engagement.

Since the advent of ChatGPT, and later by using LLMs that operate locally, I have made extensive use of this new technology. The goal is to accelerate my ability to write code, but that's not the only purpose. There's also the intent to not waste mental energy on aspects of programming that are not worth the effort. [...] Current LLMs will not take us beyond the paths of knowledge, but if we want to tackle a topic we do not know well, they can often lift us from our absolute ignorance to the point where we know enough to move forward on our own.

If you do not understand the ticket, if you do not understand the solution, or if you do not understand the feedback on your PR, then your use of LLM is hurting Django as a whole. [...] For a reviewer, it’s demoralizing to communicate with a facade of a human. This is because contributing to open source, especially Django, is a communal endeavor. Removing your humanity from that experience makes that endeavor more difficult. If you use an LLM to contribute to Django, it needs to be as a complementary tool, not as your vehicle.

A third maxim may be added to the preceding pair: Seize the very first possible opportunity to act on every resolution you make, and on every emotional prompting you may experience in the direction of the habits you aspire to gain. It is not in the moment of their forming, but in the moment of their producing motor effects, that resolves and aspirations communicate the new 'set' to the brain.