https://github.com/vjeranc/fixed-rtrt

M3 module was formalized fully purely from experimental data and from a nudge by earlier versions of codex in 15-30 minutes in a simple write/compile/fix-first-error loop. I was a bit surprised how fast it picked up the pattern but given there was a paper from '70s it became clear why later.

Rewilding at scale, deep emissions cuts, and a serious move away from animal agriculture are the same project.

Training on cost-to-go loss is good enough. Perfect cost-to-go eliminates the need for global algorithms and allows local decision making. Given “natural” datasets it is probably the best thing to attempt to learn. The fact that probabilistic graphical models never really worked proves it somewhat.

Weird that Anthropic decided to build a Claude Code Routines toilet.

Falling asleep after a can of energy drink.

We have so much experience with scientific method, yet these massive decisions are adhoc, that's how the whole world works. We never tested what would happen by allowing mass production of plastic, or phones, or whatever, so these antipatches are going by the "feels" as well, with no individual taking responsibility for failures.

3d printed handle was exactly how i wanted it to be

Peter Singer has been writing on the topic for decades, including others. What-about-plants needs to fade away.

Felt flashbacks of playing chess against humans online as a teen by copying moves from a chess engine.

Whats the point haha

Abstractions are the source of bloat. Without abstractions you can always reduce bloat, or you can reduce bloat in your glue, but you can't reduce glue.

It takes discipline to NOT create arbitrary function signatures and short-lived intermediate data structures or type definitions. This is the beginning of boilerplate.

So many advances in removing boilerplate are realizing your 5 function calls and 10 intermediate data structures or type definitions, essentially compute a thing that you can do with 0 function calls and 0 custom datatypes and less lines of code.

The abstraction hides how simple the thing you want is.

Problem is that all open source code looks like the bloat described above, so LLMs have no idea how to actually write code that is without boilerplate. The only place where I've seen it work is in shaders, which are usually written to avoid common pitfalls of abstraction.

LLMs are incapable of writing a big program in 1 function and 1 file, that does what you want. Splitting the program into functions or even multiple files, is a step you do after a lot of time, yet all open source looks nothing like that.

A nice way to realize why this AI wave hasn't produced massive economy growth, it is mostly touching parts of economy which are parasitic and can't really create growth.

These models actually learn distributed representations of nontrivial search algorithms.

A whole field of theorem provingaftwr decades of refinements couldn’t even win a medal yet 8B param models are doing it very well.

Attention mechanism, a bruteforce quadratic approach, combined with gradient descent is actually discovering very efficient distributed representations of algorithms. I don’t think they can even be extracted and made into an imperative program.

As someone who loves LaTeX, I can't imagine ever spending so much time on typography on online forums, italics, bold, emdashes, headers, sections. I quit reddit and will quit hn as well if situation worsens.

Pipelines are just a description of computation, sometimes it makes sense to increase throughput, instead of low latency, by batching, is execution separate from the pipeline definition?

It's probably not enough to have answer-prompt -> tool call -> result critic -> apply or refine, there might be a specific thing they're doing when they fine tune the loop to the model, or they might even train the model to improve the existing loop.

You would have to first look at their agent loop and then code it up from scratch.