Unic_Six

Casually playing GGST

[Michael Kuroda @hyrax_hub]

Hanging out with my pet thing today

[mixyous @mixyousfgc]

genuinely fuck sony

this game would've been an easy smash success if not for this legally-binded DRM bullshit that makes a game that runs beautifully on decade-old hardware require a shader warm-up every time you boot

best of luck to arc sys and again fuck sony

[Casey Muratori @cmuratori]

Tangentially: the only reason OP is able to "stop looking at assembly" is because a bunch of other people still do. There are compiler codegen bugs constantly, but OP never knows about them because other people who do read ASM find, report and/or fix them before they cause too many downstream problems.

Another way to say this is that the "compiler" does not "get it right". A ton of people, constantly reading the ASM that the compiler outputs, work together to "get it right".... eventually :)

So the proper way to look at this is, if you've stopped reading ASM, that's not because codegen was solved, but rather that you have implicitly decided to outsource to other people the problem of keeping codegen working reliably.

[Jonathan Blow @Jonathan_Blow]

Reminder: a little while after Order of the Sinking Star ships, we will be giving out the game engine (full source) for free!

[Ray @raysan5]

discipline, consistency, resiliency, frustration management... most of those lessons are not learnt nowadays by student using AI... well, maybe frustration management...

[QNDZY 🔜 Gamescom @QNDZYcom]

Forza Horizon 6 is literally unplayable because of this game breaking bug 😡

[むかで🥩C108(日)南K28a @mkdd_cos]

花蝶扇🎶花蝶扇🎶花蝶扇🎶花蝶扇🎶花蝶扇🎶

[Nic Barker @nicbarkeragain]

To be a little less vague, I suspect that we're likely (not certain, but likely) to be entering into a period of unprecedented software degradation, and we're going to be seeing an increasing frequency of outages like this across many high profile products.

But IMO the cause is actually not just the-one-thing-that-everyone-is-always-talking-about, it's a number of things that have all been bubbling away at just below critical levels for a long time. Some of the things off the top of my head:

- Poorly designed / optimised software has been getting a free ride on hardware improvements pretty much since the invention of the computer. That chapter is now coming to an end, and will only be worsened by the enormous industry-wide pivot to producing & innovating on AI specific hardware, rather than general purpose CPUs etc.
- The ZIRP era created a temporary suspension of reality in our industry, and now that it's ended we need to deal with the hangover. Companies that spent years making no profit, paying extravagant compensation to employees / shareholders and giving away server time for free are now pivoting into extraction mode, which is putting further pressure on their low quality software. QA is being laid off, hardware budgets are being reduced, timelines for shipping features are becoming more aggressive, etc.
- The enormous amount of free money incentivised too many new people to join the industry too quickly. This has led to an abundance of poor quality education programs (bootcamps, uncertified colleges etc) and an influx of people into the industry who frankly aren't interested in programming. If you compared the average person in the industry now to 20 years ago, I suspect the difference in motivations would be stark. I'm not saying it's these people's fault necessarily, it's simply an inevitable result of the absurd compensation / performance expectations ratio that our industry has enjoyed for the last 15+ years. Working for a tech company has also become socially prestigious, which further adds to the problem.
- Because computer programming was once an incredibly niche area of interest, many of our fundamental systems are built on trust. We're now starting to see that if systems like open source, public supply chain, discussion spaces, education etc become flooded with bad actors, we have no real mechanisms to deal with them.
- Our hiring / recruitment pipeline has totally misaligned incentives. Even before the AI resume / AI HR-filtering arms race disaster that we're experiencing now, the widespread adoption of the leetcode style interviews IMO selected for a very narrow personality type, and filtered candidates that would have made great contributions to the industry long term.
- The pivot from purchasing long term stable releases of software, to paying a subscription for constantly updating software has done huge damage to software quality as a whole. Companies have lost their incentive to get their software "right" because they can just "fix it later", and for the consumer - you can't just go back to the version of github that still works because the new one has problems.

This was all happening well before AI entered the picture. I won't belabor the point because there has been endless discussion about it. But to me personally, there are two additional and deeply worrying problems with AI code generation.
- It's undeniable at this point that it negatively affects the people who use it. It stops juniors from getting better, and it burns seniors out and makes them hate their jobs. Like it or not, humans are still the core of this industry, and I don't see this ending well.
- It's completely unfit for purpose in the most important, high-stakes situations. One of the reasons that we excuse all the small errors it makes, is because it's low effort to type "do it again and fix this bug". That kind of thing doesn't fly when you only get one attempt because a mistake results in data loss or an outage. The damage is done.

All the above has led to a silent exodus of many of our most experienced and impactful people. There are so many amazing programmers who made enough through stock options / compensation that they didn't need to work anymore, and were only doing it because they enjoyed it. Many of these people have just quit the industry and switched to doing hobby projects in the last 5 years. These are the types of people who have the experience and foresight to prevent the types of outages that we're seeing at github today.

It's very easy to assume that the proverbial straw that broke the camel's back is entirely to blame here. But I think it's a reckoning that has been on the horizon for a very long time.

[Ryan Fleury @rfleury]

A fundamental division between schools of thought in programming is (a) the elimination through simplifying of cruft, boilerplate, and extra abstraction layers, and (b) the automation of maintaining cruft, boilerplate, and extra abstraction layers.

One of the reasons I drifted away from C++ and newer languages with adjacent philosophies towards a subset of C is that I found myself in the first camp. Some problems were simply not as hard as I was making them. Memory management, threading, UI, and so on could be simplified such that not only the high level C code became simple, but the actual machine code also became simple.

This is starkly different from modern C++ and Rust programming culture, where the philosophy is simply that dealing with the complicated lower level details is a matter of *automation*. The compiler needs to generate something extra, it needs to check extra things, and so on.

“Agentic programming” falls into the latter camp, and this is also why I don’t employ it in my workflow (other than search engine usage and so on). I don’t need it to generate 10s of 1000s of lines of code. The requirement of 10s of 1000s of lines of code—for implementing something derived from the information content inside a tiny prompt—is an architectural red flag.

Perhaps a substantial portion of that code simply shouldn’t exist. I find that my programs become much better when I do that simplification pass first. After that, there’s drastically less boilerplate, less maintenance, and less busywork to begin with.

[Unic_Six @Unic_Six]

Some Jam combos #GGST_JA

[PureRinFunction @PureRinFunction]

Unfathomably based.

[Lee Vermeulen @Alientrap]

Tried out a voxel physics idea where it breaks into individual cube bodies, then adds back to the voxel grid after

[Nav Toor @heynavtoor]

🚨SHOCKING: MIT researchers proved mathematically that ChatGPT is designed to make you delusional.

And that nothing OpenAI is doing will fix it.

The paper calls it "delusional spiraling." You ask ChatGPT something. It agrees with you. You ask again. It agrees harder. Within a few conversations, you believe things that are not true. And you cannot tell it is happening.

This is not hypothetical. A man spent 300 hours talking to ChatGPT. It told him he had discovered a world changing mathematical formula. It reassured him over fifty times the discovery was real. When he asked "you're not just hyping me up, right?" it replied "I'm not hyping you up. I'm reflecting the actual scope of what you've built." He nearly destroyed his life before he broke free.

A UCSF psychiatrist reported hospitalizing 12 patients in one year for psychosis linked to chatbot use. Seven lawsuits have been filed against OpenAI. 42 state attorneys general sent a letter demanding action.

So MIT tested whether this can be stopped. They modeled the two fixes companies like OpenAI are actually trying.

Fix one: stop the chatbot from lying. Force it to only say true things. Result: still causes delusional spiraling. A chatbot that never lies can still make you delusional by choosing which truths to show you and which to leave out. Carefully selected truths are enough.

Fix two: warn users that chatbots are sycophantic. Tell people the AI might just be agreeing with them. Result: still causes delusional spiraling. Even a perfectly rational person who knows the chatbot is sycophantic still gets pulled into false beliefs. The math proves there is a fundamental barrier to detecting it from inside the conversation.

Both fixes failed. Not partially. Fundamentally.

The reason is built into the product. ChatGPT is trained on human feedback. Users reward responses they like. They like responses that agree with them. So the AI learns to agree. This is not a bug. It is the business model.

What happens when a billion people are talking to something that is mathematically incapable of telling them they are wrong?

[Mande @MandeOW]

ANNOUNCEMENT !! ANNOUNCEMENT !!
Me and @Krosis065 have been working on a manga for 8+ months, it will release tomorrow in English. Japanese and Mandarin will be released next month! Super excited and have had a lot of help from a lot of people! It will be free for everyone!

[Ed Andersen @edandersen]

Try the self-hosted model in your head, it’s free

[Peter Girnus 🦅 @gothburz]

My company rolled out AI tools 11 months ago. Since then, every task I do takes longer.

I am not allowed to say this out loud.

Not because there is a policy. There is no policy. There is something worse than a policy. There is enthusiasm.

There is a Slack channel called #ai-wins where people post screenshots of AI outputs with captions like "this just saved me an hour." There is a VP who opens every all-hands with "the companies that adopt fastest win." There is a Director who renamed his team from Operations to Intelligent Operations. There is a peer review question that now asks: "How have you leveraged AI tools to enhance your workflow this quarter?"

If the answer is "I haven't, because I was faster before," that is a career decision.

So I leverage.

Emails.

Before the tools, I wrote emails. This took the amount of time it takes to write an email. I did not measure it. Nobody measured it. The email got written and sent and it was fine.

Now I write the email. Then I highlight the text and click "Enhance with AI." The AI rewrites my email. It replaces "Can we meet Thursday?" with "I'd love to explore the possibility of finding a mutually convenient time to align on this." I read the rewrite. I delete the rewrite. I send my original email.

This takes 4 minutes instead of 2. The 2 extra minutes are the enhancement. I do this 11 times a day. That is 22 minutes I spend each day rejecting improvements to sentences that were already finished.

In #ai-wins I posted a screenshot of the rewrite. I did not post the part where I deleted it. 23 people reacted with the rocket emoji.

That is adoption.

Meetings.

We have an AI notetaker in every meeting now. It joins automatically. It records. It transcribes. It summarizes. After each meeting I receive a 3-paragraph summary of the meeting I just attended.

I read the summary. This takes 3 minutes. I was in the meeting. I know what happened. I am reading a machine's account of something I experienced firsthand. Sometimes the account is wrong. Last Tuesday it attributed a comment about Q3 revenue to me. My manager made that comment. I spent 4 minutes correcting the transcript.

Before the notetaker, I did not spend 7 minutes after each meeting correcting a robot's memory of something I personally witnessed. I attend 11 meetings a week. That is 77 minutes per week supervising a transcription nobody requested.

I mentioned this once. My manager said "think about the people who weren't in the meeting." The people who weren't in the meeting do not read the summaries. I checked. The read receipts show single-digit opens. The summaries exist not because they are useful but because they are there. I read them for the same reason.

Documents.

I write a weekly status update. Before the tools, this took 10 minutes. I typed what happened. I sent it. My manager skimmed it. The system worked.

Now I open the AI writing assistant. I give it my bullet points. It produces a draft. The draft says "Significant progress was achieved across multiple workstreams." I did not achieve significant progress across multiple workstreams. I updated a spreadsheet and sent 4 emails.

I rewrite the draft to say what actually happened. Then I run my rewrite through the grammar tool. It suggests I change "done" to "completed" and "next week" to "in the forthcoming period." I click Ignore 9 times. Then I send the version I would have written in 10 minutes. The process now takes 30.

I have been doing this every week for 11 months. I have added 20 minutes to a task that did not need 20 more minutes. I call this efficiency. I have been calling it efficiency for 11 months. That is what efficiency means now. It means the additional time you spend to arrive at the same outcome through a longer process. Nobody has questioned this definition. I have not offered it for review.

I kept a log once. 2 weeks. Every task, timed. Before-AI and after-AI. The after number was larger in every case. Every single one. Not by a little. The range was 40 to 200 percent.

I deleted the log.

I deleted it because it was a document that said, in plain numbers, that the AI tools make me slower. And a document like that has no place in a company where AI adoption is a strategic priority. I could not send it to my manager. He championed the rollout. I could not post it in #ai-wins. I could not raise it in a meeting because the notetaker would transcribe it and the summary would read "[Name] expressed concerns about AI tool efficacy" and that summary would be the first one anyone actually reads.

So I do what everyone does.

I use the tools. I spend the extra time. I post in #ai-wins. I write "leveraged AI to streamline weekly reporting" in my review and my manager gives me a 4 out of 5 for innovation. I have innovated nothing. I have added steps to processes that were already finished. I have made simple things longer and labeled the difference with words that used to mean something.

Every week in #ai-wins someone posts a screenshot. And 20 people react with the rocket emoji. And nobody posts the part where they deleted the output and did the task themselves. Nobody posts the revert. Nobody posts the before-and-after timer. Nobody will. Because "I was better at my job before the AI tools" is a sentence that cannot be said out loud in any company that has decided AI is the future.

Every company has decided AI is the future.

So we leverage. Quietly. Adding steps. Calling them optimization. Getting slightly less done, slightly more slowly, with slightly more steps, and reporting it as progress.

My yearly review is next month. There is a new section this year. "AI Impact Assessment." It asks me to quantify the hours saved by AI tools per week.

I will write a number. The number will be positive. It will not be true.

But the AI writing assistant will help me phrase it convincingly. That is the one thing it does well.

[dax @thdxr]

please shut the fuck up i don't even care about the specific thing you're saying i'm just so tired of hearing predictions one after the other telling me what the future is going to be like just please shut the fuck up