jekude

All true, but I think its a valuable exercise to try to design/understand/exploit these dynamics

I think the question and article is fascinating. I do think that one should not only look at the genetic representation, and the algorithm a cell runs, but also the formalized environment that runs the simulation.

I’ve been thinking about this for a few years and I truly believe these cells need to be able to “move” in either a 3d lattice similar to a cellular automata or become nodes in some cyclic graph (or maybe both).

For the genetic representation, I had an insight that DNA/RNA looks strangely similar to SKI calculus (combinators) and have been using a Church encoding to translate the SKI program to an “action” per turn (move, connect, spike, divide, etc.). It has a nice property that the cell’s “program” and the spikes between them, and the input/output to the simulation itself can be the same thing (just a string of combinators).

Either way the author is spot on when it comes to this sort of thing having the properties 1) “always on” (no separate training mode), 2) local interactions, 3) everything stems from a genome, 4) each cell is its own little mini-program with the same genetics as everyone else.

All I can say is that evolutionary program is hard and takes either massive compute or large timescales to run the simulations. Fascinating stuff though

The abstract implies that strictly local updates are a hinderance to high res, however i would have thought there would be an interesting way to get speed up gains from neighbor-only traffic on GPUs CAM-style. am i making that up?

started doing this but life got in the way, would love to pick it back up at some point

https://github.com/planetlambert/turing

What would physical EML gates be implemented in reality?

Posts like these are the reason i check HN every day

Now have the agents write actual code for the laws!

if the entire open web is vulnerable to being sybil attacked, are we going to have to take this all underground?

Maybe sama was onto something with World ID...

How does this compare to https://encore.dev/ or other infrastructure-inside-application-code solutions?

Can you share how you go about searching for the interesting rules?

I have been asking myself for a while: Why has Artificial Life not had its AlexNet moment with GPUs yet?

Nice, this definitely needs to be standardized

Charmbracelet is amazing. Will there be an equivalent of Claude Code's CLAUDE.md files?

Also https://x.com/ciphrd

Insulin is lethal at higher dosages, so there is definitely an argument. My counter would be that someone who has to self administer this drug 5+ times a day should have the right to make determinations about dosing

Technically there is unsanctioned access (someone reverse engineered the real-time APIs [1] which I ported to Go). I think the FDA does not want easy access to real-time values so that folks can't easily recommend insulin dosing without oversight. I am personally of the opinion that it is our right to have programmatic access to the real-time data and do with it what we please.

Would love to get in touch to hear more about your long-term vision for the project!

[1] https://github.com/gagebenne/pydexcom

Also recently diagnosed and just open sourced how I'm using AI to count carbs + get insulin doses [1]. Biggest issues I've seen to starting a legit business is not having sanctioned access to real-time blood sugar values (the APIs are all one hour behind), and dealing with the FDA. Love the idea of more tech-enabled diabetes management, good luck!

[1] https://github.com/kennedyjustin/BolusGPT

Recently diagnosed with Type 1 Diabetes, and learnt quickly that calculating insulin doses sucks (and you have to do it 4+ times a day). BolusGPT is a ChatGPT plugin that translates meal descriptions into nutrition information that is fed into an insulin dosing algorithm. Also hooks up to Dexcom CGMs. Happy to answer any questions!

I've always thought that gravity exists because without it, matter doesn't get close enough for interesting things to happen.

> Artificial life fizzled as a meta discipline

I've wondered for a while if Artificial Life is in its own winter, waiting for someone to apply the lessons of scale we learned from neural nets.

Does it come with UTM on the tape?

Built a working implementation in Go [0] a while ago, have always wanted to hook up DOOM to it.

[0] https://github.com/planetlambert/turing

Is it only academia and Google Research that is funding these kind of artificial life passion papers? Wondering if anyone knows of real-world applications / companies working on them.

Seems like they re-engineered Temporal: https://temporal.io/

Reading Permutation City changed the way I think, and I’ve been loving working my way through everything Greg Egan. It’s almost as if its a new literary genre - Computer Science Fiction.

I’ve been noodling on how to combine neural networks with evolution for a while. I’ve always thought that to do this, you need some sort of evolvable genetic/functional units, and have had trouble fitting traditional artificial neurons w backprop into that picture.

My current rabbit hole is using Combinatory Logic as the genetic material, and have been trying to evolve combinators, etc (there is some active research in this area).

Only slightly related to the author’s idea, its cool that others are interested in this space as well.