Big agree.

But also: people seem to only focus on the output side of the task of writing code, and forget that the developer also receives input from the codebase in return.

Even if you end up with exactly the same code artifact after completing a work item, you’ll have a better understanding of the codebase without delegating swaths of it to AI. But bosses tend not to consider this.

Tech bros have successfully convinced people that mental states do not exist, or at least do not matter — for laborers, anyway, cuz they’ll happily claim that their superior thoughts are exactly why they deserve to be billionaires.

Code is a liability (not an asset)

Okay but “Kafka, Esq.” goes hard.

Related: Jevons Paradox

Oh there’s lots of trans people in orchestra

There is a big difference, and I’d argue the Claude refactoring is worse. Content was already pursuing the common denominator. But open source was a place where you could actually bring some nuance, examine things in detail, and build a shared understanding of deeper truths. But why bother with the icky social factors of working together to build something with people all around the world that can evolve and last for 10+ years, when you can boil a swimming pool to produce a half-baked one-off solution instead?

You absolutely may. Intel is valid too.

Buy a used Optiplex.

Gets you an acceptable case, mobo, CPU, RAM, and probably SSD for about $200. Add a used 3050/3060/4060 GPU and an upgraded PSU.

It’s not gonna knock your socks off, but it gets you going without over-spending and you can carry some of that forward when you upgrade later.

It says it’s multiple studios, which I assume were acqui-hired. So it’s not just “VR developers”, but also UI designers, concept artists, QA, PMs, HR, IT, tech writers, community managers, sales people — maybe even localization, reception, janitors… who knows. The structure of these things can vary wildly.

Idk, for a game where sugar skull pirate puppets race rowboats that can boost and drift, it’s hard to call it out-of-place.

I don’t think there’s any disagreement (among you, me, and Molly White) about who the bad guys are.

The question is: What is an effective legal framework that focuses on the precise harms, doesn’t allow AI vendors to easily evade accountability, and doesn’t inflict widespread collateral damage?

Cory Doctorow has a pretty good stab at that: https://pluralistic.net/2023/09/17/how-to-think-about-scraping/

Clarification: revenue from advertising their own paid services, not revenue from selling ad space to third parties

People who are discounting this because the project maintainer used sensational phrasing (75%) or because he was monetizing open source are ignoring the important part:

Traffic is down 40%

This is really bad news. All open source projects need attention in order to succeed.

“Wait, not like that”: Free and open access in the age of generative AI

The real threat isn’t AI using open knowledge — it’s AI companies killing the projects that make knowledge free

https://www.citationneeded.news/free-and-open-access-in-the-age-of-generative-ai/

Trying desperately to keep the ponzi scheme going, but his biggest customers already have warehouses full of GPUs that will never get connected.

The bubble is full, dude. Just try to minimize the damage from the pop so we don’t try to figure out what size pitchfork your dumb leather jacket is.

It also discouraged you from finding/starting an open source solution for those problems, thus undermining the high-quality open knowledge ecosystem that it relied on in the first place.

As Cory Doctorow says: code is a liability, not an asset

One project that can help with this is the OUI-SPY, a small piece of open source hardware. The OUI-SPY runs on a cheap Arduino compatible chip called an ESP-32. There are multiple programs available for loading on the chip, such as “Flock You,” which allows people to detect Flock cameras and “Sky-Spy” to detect overhead drones. There’s also “BLE Detect,” which detects various Bluetooth signals including ones from Axon, Meta’s Ray-Bans that secretly record you, and more. It also has a mode commonly known as “fox hunting” to track down a specific device.

https://www.eff.org/deeplinks/2026/01/how-hackers-are-fighting-back-against-ice

I was once a fool like you :)

Mike McShaffry’s book “Game Coding Complete” is a good guide to the practical side of using a game engine IRL to get things done.

It’ll give you a good idea of how things should be shaped in order to be useful, and some things you can “skip ahead” to. Off-the-shelf engines have to be extremely general in order to be flexible enough to be useful to many customers, so game devs have to put in the effort to make them more specific. You’ll have to start off by being specific, if you have any chance of actually finishing something.

Eberly’s book “3D Game Engine Architecture” deals with the nuts and bolts, the rigorous academic engineering stuff. It’s pretty solid, but it’s aimed at making a general-purpose engine, which is beyond the scope of a one-person project.

Backing up though… You don’t have any language or library opinions? You might need 5-10 years of experience doing general programming (or game dev) before you can sustainably tackle this, or else you’re likely to paint yourself into a corner.

Edit: Probably the biggest PITA with game engine dev is testing. If you’re not already an expert in setting up test harnesses at multiple levels of detail, you’re gonna find it impossible to keep moving after a few months.

Good luck!

Yeah… Linear increases in performance appear to require exponentially more data, hardware, and energy.

Meanwhile, the big companies are passing around the same $100bn IOU, amortizing GPUs on 6-year schedules but burning them out in months, using those same GPUs as collateral on massive loans, and spending based on an ever-accelerating number of data centers which are not guaranteed to get built or receive sufficient power.

I like the way Ted Chiang puts it:

Some might say that the output of large language models doesn’t look all that different from a human writer’s first draft, but, again, I think this is a superficial resemblance. Your first draft isn’t an unoriginal idea expressed clearly; it’s an original idea expressed poorly, and it is accompanied by your amorphous dissatisfaction, your awareness of the distance between what it says and what you want it to say. That’s what directs you during rewriting, and that’s one of the things lacking when you start with text generated by an A.I.

There’s nothing magical or mystical about writing, but it involves more than placing an existing document on an unreliable photocopier and pressing the Print button.

I think our materialist culture forgets that minds exist. The output from writing something is not just “the thing you wrote”, but also your thoughts about the thing you wrote.