nemo1618

After Christmas this year, I removed the tree from our living room, and in the process of being moved, it shed of needles everywhere. I swept them up, but I missed a few areas on my first pass. So I did a second pass, but when I looked again, I saw there were still a handful left. It struck me how removing >99% of the needles was nowhere near acceptable! Lots of cleaning jobs are like this, I suppose, because even a tiny mess can be visually distinct. In fact, as you approach 100%, the remaining mess stands out more.

I would love a crank-powered router. Would be a good way to curb internet addiction!

I think the key is that, while you may think you have a full formal spec of f(), you actually do not. You have a program written in some language, and that language has its own spec, and the language is compiled to asm which has its own spec, and the asm executes on an architecture that has its own spec, and so on.

So when you write a function like:

  func hypot(x, y):
    return sqrt(x*x + y*y)

The first moment I specifically remember was writing a test of a new RPC protocol back in 2021. There were no agents yet, only "AI autocomplete" in the form of GitHub Copilot. I wrote the "server" half of the test, which received a name and responded with "Hello, <name>". Then I wrote the client code to send "world", and Codex suggested `if response == "Hello, world"`.

I was floored by this. How could it have known?!

We have come so far in such a short time.

It really is crazy. I have been contributing the Melee decompilation project for the past year-ish, and things have really accelerated in 2026. Just today I decided it would be nice to have a better "permuter" (program that randomly modifies C in the hopes of finding a better asm match) so I...just asked Claude to make one, custom-tailored to my needs. It almost feels pointless to publish it to GitHub when I can just tell the other contributors "hey fyi you can ask Claude to make you a better permuter"

You should not swear at LLMs, for the same reason you should not shout a slur even if no one is around to witness it: You witness it, and witnessing yourself being toxic updates you in the direction of "I am capable of toxicity" and eventually "I am toxic." In other words, it stains your soul.

Good git hygiene is also less important post-LLMs, as the LLM can make sense of even a messy history.

Agreed. In fact, one of the things I now watch for is my mind starting to "slide off" the text, or finding myself re-reading a section multiple times. It's like the brain subconsciously recognizes a lack of substance even if we can't point to a specific tell.

LLM writing tells are getting more subtle, but they still jump off the page for me, in particular the word "genuine:"

   "This is the area where Go genuinely shines, and it’s worth being precise about why"
   "the lack of GC pauses is a genuine selling point"
   "Humans are genuinely bad at reasoning about memory"
   "There are cases where the borrow checker is genuinely too strict"

Evidence for 1992:

After a disorienting visit from the FBI in May of 1990, I wrote a rant called Crime and Puzzlement, which led to my establishing with Mitch Kapor (who had previously founded Lotus Development Company) an organization called the Electronic Frontier Foundation.

Now, after almost two years of operation...

It's interesting to revisit Brooks' "surgical team" in light of AI. For example, I frequently have Claude act as a "toolsmith", creating bespoke project-specific tools on the fly, which are then documented in Skills that Claude can use going forward. What has changed is that a) One person (or rather, one person-AI hybrid) plays all the roles within the surgical team, and b) Internal frictions such as cost, development time, and communication overhead have all been dramatically slashed.

Huh? I'm not aware of anyone else who defines "deterministic" that way. "Deterministic" comes from "determinism," as in "the effects are fully determined by the causes" -- not "determine" as in "deduce."

LLMs are not inherently non-deterministic. This is a common misconception. You used to be able to set temp=0 and a fixed seed and get the same output every time. This broke when labs started implementing batching, and no one bothered fixing it because the benefits of batching vastly outweighed the demand for deterministic output.

I am hopeful deterministic output will return, though; DeepSeek v4 claims to have implemented "bitwise batch-invariant and deterministic kernels," though I haven't tested it myself.

Sufficiently-developed concentration gives you access to the jhanas, which are extremely blissful states of consciousness. Having reliable access to high valence reduces your need to seek pleasure in less wholesome things (drugs, food, twitter, etc.)

Sufficiently-developed attention gives you insight into how your brain is constructing what you perceive as reality, leading to a reduction in ego, permanent reduction in baseline suffering, and a pervading sense of unity with the rest of the universe.

I'm old enough to remember when companies worth $1 billion were called "unicorns." Now we have a company raising 122 times that? Valued at nearly 1000 times that...?

At least they're throwing consumers a bone via the ARK deal. It's crazy how little AI exposure is available to anyone who isn't already wealthy and/or connected.

yep, I do a simple version of this in Google Sheets. Very useful to be able to "Ctrl-F" your life, especially when combined with Google Maps location history.

If this was a joke, it certainly flew over most people's heads...

This will happen with GUIs as well, once computer-use agents start getting good. Why bother providing an API, when people can just direct their agent to click around inside the app? Trillions of matmuls to accomplish the same result as one HTTP request. It will be glorious. (I am only half joking...)

But like humans — and unlike computer programs — they do not produce the exact same results every time they are used. This is fundamental to the way that LLMs operate: based on the "weights" derived from their training data, they calculate the likelihood of possible next words to output, then randomly select one (in proportion to its likelihood).

This is emphatically not fundamental to LLMs! Yes, the next token is selected randomly; but "randomly" could mean "chosen using an RNG with a fixed seed." Indeed, many APIs used to support a "temperature" parameter that, when set to 0, would result in fully deterministic output. These parameters were slowly removed or made non-functional, though, and the reason has never been entirely clear to me. My current guess is that it is some combination of A) 99% of users don't care, B) perfect determinism would require not just a seeded RNG, but also fixing a bunch of data races that are currently benign, and C) deterministic output might be exploitable in undesirable ways, or lead to bad PR somehow.

IMO this is one of the best use cases for AI today. Each function is like a separate mini problem with an explicit, easy-to-verify solution, and the goal is (essentially) to output text that resembles what humans write -- specifically, C code, which the models have obviously seen a lot of. And no one is harmed by this use of AI; no one's job is being taken. It's just automating an enormous amount of grunt work that was previously impossible to automate.

I'm part of the effort to decompile Super Smash Bros. Melee, and a fellow contributor recently wrote about how we're doing agent-based decompilation: https://stephenjayakar.com/posts/magic-decomp/

"The steamroller is still many inches away. I'll make a plan once it actually starts crushing my toes."

You are in danger. Unless you estimate the odds of a breakthrough at <5%, or you already have enough money to retire, or you expect that AI will usher in enough prosperity that your job will be irrelevant, it is straight-up irresponsible to forgo making a contingency plan.

I think it's a combination of a) reflexive dislike of any hyped-up tech, mainly due to the crypto era, and b) subconscious ego protection ("this can't be legit, otherwise everything I've built my identity around will be thrown into question").

The best models already produce better code than a significant fraction of human programmers, while also being orders of magnitude faster and cheaper. And the trendlines are stark. Sure, maybe AI can't replace you today. Maybe it will hit that "wall" people are always forecasting, just before it gets good enough to threaten your job. But that's a rather uncomfortable proposition to bet a career on.

humans still need to have their hands firmly on the wheel if they won’t want to risk their businesses well being

What happens when businesses run by AIs outperform businesses run by humans?

Let's be real. The sky is blue because God thought it was a pretty color, simple as. All this stuff about wavelengths and resonant frequencies and human color perception got retconned into the physics engine at some point in the past millennium, that's why all these epicycles are needed.

Centaurs are a transient phenomenon. In chess, the era of centaur supremacy lasted only about a decade before computers alone eclipsed human+computer. The same will be true in every other discipline.

You can surf the wave, but sooner or later, the wave will come crashing down.

Just wait. In a few years we'll have computer-use agents that are good enough that people will stop making APIs. Why bother duplicating that effort, when people can just direct their agent to click around inside the app? Trillions of matmuls to accomplish the same result as one HTTP request.

This strikes me as a very agent-friendly problem. Given a harness that enforces sufficiently-rigorous tests, I'm sure you could spin up an agent loop that methodically churns through these functions one by one, finishing in a few days.

For those curious about division, I wrote a popular uint128 package in Go that uses one of the standard approaches: https://github.com/lukechampine/uint128/blob/3d2701e8e909405...

Here's one attempt: https://x.com/sigilante/status/2013743578950873105

My take: Any gains from an "LLM-oriented language" will be swamped by the massive training set advantage held by existing mainstream languages. In order to compete, you would need to very rapidly build up a massive corpus of code examples in your new language, and the only way to do that is with... LLMs. Maybe it's feasible, but I suspect that it simply won't be worth the effort; existing languages are already good enough for LLMs to recursively self-improve.

Blackpill is that, for this reason, the mainstream languages we have today will be the final (human-designed) languages to be relevant on a global scale.

Eventually AIs will create their own languages. And humans will, of course, continue designing hobbyist languages for fun. But in terms of influence, there will not be another human language that takes the programming world by storm. There simply is not enough time left.