81–90 of 112 posts
> claude added the Task issue type 4 hours ago is this satire?
I have a GitHub action that labels and tags issues automatically. It also standardizes the issue title. I love this script and would recommend it to anyone. https://github.com/tldraw/tldraw/blob/ce745d1ecc1236633d2bf6...
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> global, anonymous collaboration that seems to be ending. Trust in the individual is going to become more important in many areas of life I don't think it's coming to an end. It's getting more difficult, yes, but not impossible. Currently I'm working on a game, and since I'm not an artist, I pay artists to create the art. The person I'm working closest with I have basically no idea who they are, except their name, e…
I think it absolutely is coming to an end in lots of ways. Movie/show reviews, product reviews, app/browser extension reviews, programming libraries, etc all get gamed. An entire industry of booting reviews has sprung up from PR companies brigading positive reviews for their clients. The better AI gets at slop and controlling bots to create slop which is indistinguishable from human content, the less people will trus…
But for a long time, unrelated to AI. When Amazon was first available here in Spain (don't remember exactly what year, but before LLMs for sure), the amount of fraudulent reviews filling the platform was already noticeable at that point.
That industry you're talking about might have gotten new wings with LLMs, but it wasn't spawned by LLMs, it existed long time before that.
> the less people will trust content on those platforms.
Maybe I'm jarred from using the internet from a young age, but both me and my peers basically has a built-in mistrust against random stuff we see on the internet, at least compared to our parents and our younger peers.
"Don't believe everything you see on the internet" been a mantra almost for as long as the internet has existed, maybe people forgot and needed an reminder, but it was never not true.
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This is my experience as well. I work with AI agents a lot, they are very useful. What's not useful is some passer-by telling the AI "implement " and then sending that as a PR. I could have written a sentence to the LLM too if I wanted to, you aren't really giving me or the project any value by doing that. Now that writing the code is the easy part, we're just going to transition to having very few contributors, who…
Outside contributors have never been valuable as pure code monkeys.
We've enjoyed a certain period (at least a couple of decades) of global, anonymous collaboration that seems to be ending. Trust in the individual is going to become more important in many areas of life, from open-source to journalism and job interviews.
I've been trying to manifest Web of Trust coming back to help people navigate towards content that's created by humans. A system where I can mark other people as trusted and see who they trust, so when I navigate to a web page or in this case, a Github pull request, my WoT would tell me if this is a trusted person according to my network.
AI slop is so cheap that it has created a blight on content platforms. People will seek out authentic content in many spaces. People will even pay to avoid the mass “deception for profit” industry (eg. Industries where companies bot ratings/reviews to profit and where social media accounts are created purely for rage bait / engagement farming).
But reputation in a WoT network has to be paramount. The invite system needs a “vouch” so there are consequences to you and your upstream vouch if there is a breach of trust (eg. lying, paid promotions, spamming). Consequences need to be far more severe than the marginal profit to be made from these breaches.
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I've been trying to manifest Web of Trust coming back to help people navigate towards content that's created by humans. A system where I can mark other people as trusted and see who they trust, so when I navigate to a web page or in this case, a Github pull request, my WoT would tell me if this is a trusted person according to my network.
You need a very complex weighing and revocation mechanism because once one bad player is in your web of trust they can become a node along which both other bad players and good players alike can join.
Also, there needs to be some significant consequence to people who are bad actors and, transitively, to people who trust bad actors.
The hardest part isn’t figuring out how to cut off the low quality nodes. It’s how to incentivize people to join a network where the consequences are so high that you really won’t want to violate trust. It can’t simply be a free account that only requires an a verifiable email address. It will have to require a significant investment in verifying real world identity, preventing multiple accounts, reducing account hijackings, etc. those are all expensive and high friction.
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I think it absolutely is coming to an end in lots of ways. Movie/show reviews, product reviews, app/browser extension reviews, programming libraries, etc all get gamed. An entire industry of booting reviews has sprung up from PR companies brigading positive reviews for their clients. The better AI gets at slop and controlling bots to create slop which is indistinguishable from human content, the less people will trus…
> Movie/show reviews, product reviews, app/browser extension reviews, programming libraries, etc all get gamed. An entire industry of booting reviews has sprung up from PR companies brigading positive reviews for their clients. But for a long time, unrelated to AI. When Amazon was first available here in Spain (don't remember exactly what year, but before LLMs for sure), the amount of fraudulent reviews filling the p…
When snail mail had a cost floor of $0.25 for the price of postage, email was basically free. You might get 2-3 daily pieces of junk mail in your house’s mailbox, but you would get hundreds or thousands in your email inbox. Slop comes at scale. LLMs didn’t invent spam, but they are making it easier to create more variants of it, and possibly ones that convert better than procedurally generated pieces.
There’s a difference between your cognitive brain and your lizard brain. You can tell yourself that mantra, but still occasionally fall prey to spam content. The people who make spam have a financial incentive to abuse the heuristics/signals you use to determine the authenticity of a piece of content in the same way cheap knockoffs of Rolex watches, Cartier jewelry, or Chanel handbags have to make the knockoffs appear as authentic as possible.
Seems like reading the code is now the real work. AI writes PRs instantly but reviewing them still takes time. Everything flipped. Expect more projects to follow - maintainers can just use ai themselves without needing external contributions.
Understanding (not necessarily reading) always was the real work. AI makes people less productive because it's speeding up the thing that wasn't hard (generating code), while generating additional burden on the thing that was hard (understanding the code).
Which is harder, writing 200 lines of code or reading 200 lines of code someone else wrote.
I pretty firmly find the latter harder, which means for me AI is most useful for finessing a roughly correct PR rather than writing the actual logic from scratch.
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How do you know it's a person on the other end? Would you even see a difference if you had a computer generate that art? These are very important questions that cut to the heart of "what is art".
> How do you know it's a person on the other end? Would you even see a difference if you had a computer generate that art? Unless AI companies already developed and launched plugins/extensions for people to do something that looks like hand drawn sketches inside of Clip Studio, and suddenly got a lot better at understanding prompts (including having inspiration of their own), then I'm pretty sure it's a human. I don'…
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> How do you know it's a person on the other end? Would you even see a difference if you had a computer generate that art? Unless AI companies already developed and launched plugins/extensions for people to do something that looks like hand drawn sketches inside of Clip Studio, and suddenly got a lot better at understanding prompts (including having inspiration of their own), then I'm pretty sure it's a human. I don'…
Sure, it's true today. Entertain the hypothetical though because this is what the trillion dollar rush is aspiring to do in the near future. We should be thinking about our answers now.
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There are many cases in which I already understand the code before it is written. In these cases AI writing the code is pure gain. I do not need to spend 30 minutes learning how to hold the bazel rule. I do not need to spend 30 minutes to write client boilerplate. List goes on. All broad claims about AI's effects on productivity have counterexamples. It is situational. I think most competent engineers quietly using A…
In these cases AI writing the code is pure gain. no, it isn't. unless the generated code is just a few lines long, and all you are doing is effectively autocompletion, you have to go through the generated code with a fine toothed comb to be sure it actually does what you think it should do and there are no typos. if you don't, you are fooling yourself.
The reason I put scare quotes on "understand" is that we need to acknowledge that there are degrees of understanding, and that different degrees are required in different scenarios. For example, when you call syscall(), how well do you understand what is happening? You understand what's in the manpage; you know that it triggers a switch to kernel space, performs some task, returns some result. Most of us have not read the assembly code, we have a general concept of what is going on but the real understanding pretty much ends at the function call. Yet we check that in because that level of understanding corresponds to the general engineering standard.
In some cases, with AI, you can be reasonably sure the result is correct without deeply understanding it and still meet the bar. The bazel rule example is a good one. I prompt, "take this openapi spec and add build rules to generate bindings from it. Follow existing repo conventions." From my years of engineering experience, I already know what the result should look like, roughly. I skim the generated diff to ensure it matches that expectation; skim the model output to see what it referenced as examples. At that point, what the model produced is probably similar to what I would have produced by spending 30 minutes grepping around, reading build rules, et cetera. For this particular task, the model has saved me that time. I don't need to understand it perfectly. Either the code builds or it doesn't.
For other things, my standard is much higher. For example, models don't save me much time on concurrent code because, in order to meet the quality bar, the level of understanding required is much higher. I do need to sit there, read it, re-read it, chew on the concurrency model, et cetera. Like I said, it's situational.
There are many, many other aspects to quantifying the effects of AI on productivity, code quality is just one aspect. It's very holistic and dependent on you, how you work, what domain you work in, the technologies you work with, the team you work on, so many factors.