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Schedule tasks on the web

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Re: Schedule tasks on the web

#112

I feel like we are just inching closer and closer to a world where rapid iteration of software will be by default. Like for example a trusted user makes feedback -> feedback gets curated into a ticket by an AI agent, then turned into a PR by an Agent, then reviewed by an Agent, before being deployed by an Agent. We are maybe one or two steps from the flywheel being completed. Or maybe we are already there.

What kind of software are people building where AI can just one shot tickets? Opus 4.6 and GPT 5.4 regularly fail when dealing with complicated issues for me.

[flagged]

Re: Schedule tasks on the web

#113

Earlier quoted context omitted.

i'd say it's more like intentionally choosing to use naive string interpolation for SQL queries than a trusted library's parameter substitution. Both work.

There is no "parameter substitution" equivalent possible . Prompt injection isn't like SQL injection, it has no technical solution (that isn't AGI-complete). Prompt injection is "social engineering" but applied to LLMs. It's not a bug, it's fundamentally just a facet of its (LLM/human) general nature. Mitigations can be placed, at the cost of generality/utility of the system.

> It's not a bug, it's fundamentally just a facet of its (LLM/human) general nature

Fair enough but then that means that MCP is not "a bit like asking if "an API" was a critical link in some cybersec incident"

Because I can secure an API but I can't secure the the "(LLM/human) general nature."

Re: Schedule tasks on the web

#114
post #85

Earlier quoted context omitted.

Art is and has always been about the creator.

Take a walk in any museum, I'm pretty sure you'll react to some of the art displayed there and find it cool before you read the name of the artist.

It's not that you know the artist first and then say "this art is cool because I like the artist". The art is the means by which you know the artist. The more of their works you encounter, the closer you get to understanding the artist and what they are trying to communicate.

Re: Schedule tasks on the web

#115
post #85

Earlier quoted context omitted.

Art is and has always been about the creator.

Take a walk in any museum, I'm pretty sure you'll react to some of the art displayed there and find it cool before you read the name of the artist.

Dive into a forest, you'll find a couple of cool trees.

Art isn't about being cool. Art is about context.

When I tell people that art cannot be unpolitical, they react strongly, because they think about the left/right divide and how divided people are, where art is supposed to be unifying.

But art is like movement, you need an origin and a destination. Without that context, it will be just another... thing. Context makes it something.

Re: Schedule tasks on the web

#116

I feel like we are just inching closer and closer to a world where rapid iteration of software will be by default. Like for example a trusted user makes feedback -> feedback gets curated into a ticket by an AI agent, then turned into a PR by an Agent, then reviewed by an Agent, before being deployed by an Agent. We are maybe one or two steps from the flywheel being completed. Or maybe we are already there.

I love everything about this direction except for the insane inference costs. I don’t mind the training costs, since models are commoditized as soon as they’re released. Although I do worry that if inference costs drop, the companies training the models will have no incentive to publish their weights because inference revenue is where they recuperate the training cost. Either way… we badly need more innovation in inf…

I worry about the costs from an energy and environmental impact perspective. I love that AI tools make me more productive, but I don't like the side effects.

Re: Schedule tasks on the web

#118
post #101
post #40

Earlier quoted context omitted.

This is the wrong way to see it. If a technology gets cheaper, people will use more and more and more of it. If inference costs drop, you can throw way more reasoning tokens and a combination of many many agents to increase accuracy or creativity and such.

> throw way more reasoning tokens and a combination of many many agents to increase accuracy or creativity and such. But this is just not true, otherwise companies that can already afford such high prices would have already outpaced their competitors.

No company at the moment has enough money operate with 10x the reasoning tokens of their competitors because they're bottlenecked by GPU capacity (or other physical constraints). Maybe in lab experiments but not for generally available products.

And I sense you would have to throw orders of magnitude more tokens to get meaningfully better results (If anyone has access to experiments with GPT 5 class models geared up to use marginally more tokens with good results please call me out though).

Re: Schedule tasks on the web

#119

I feel like we are just inching closer and closer to a world where rapid iteration of software will be by default. Like for example a trusted user makes feedback -> feedback gets curated into a ticket by an AI agent, then turned into a PR by an Agent, then reviewed by an Agent, before being deployed by an Agent. We are maybe one or two steps from the flywheel being completed. Or maybe we are already there.

In the past three weeks a couple of projects I follow have implemented AI tools with their own github accounts which have been doing exactly this. And they appear to be doing good work! Dozens of open issues iterated, tested and closed. At one point i had almost 50 notification for one projects backlog being eradicated in 24 hours. The maintainer reviewed all of it and some were not merged.

Re: Schedule tasks on the web

#120

I feel like we are just inching closer and closer to a world where rapid iteration of software will be by default. Like for example a trusted user makes feedback -> feedback gets curated into a ticket by an AI agent, then turned into a PR by an Agent, then reviewed by an Agent, before being deployed by an Agent. We are maybe one or two steps from the flywheel being completed. Or maybe we are already there.

I just don’t see it coming. I was full on that camp 3 months ago, but I just realize every step makes more mistakes. It leads into a deadlock and when no human has the mental model anymore.

Don’t you guys have hard business problems where AI just cant solve it or just very slowly and it’s presenting you 17 ideas till it found the right one. I’m using the most expensive models.

I think the nature of AI might block that progress and I think some companies woke up and other will wake up later.

The mistake rate is just too high. And every system you implement to reduce that rate has a mistake rate as well and increases complexity and the necessary exploration time.

I think a big bulk of people is of where the early adaptors where in December. AI can implement functional functionality on a good maintained codebase.

But it can’t write maintable code itself. It actually makes you slower, compared to assisted-writing the code, because assisted you are way more on the loop and you can stop a lot of small issues right away. And you fast iterate everything•

I’ve not opened my idea for 1 months and it became hell at a point. I’ve now deleted 30k lines and the amount of issues I’m seeing has been an eye-opening experience.

Unscalable performance issues, verbosity, straight up bugs, escape hatches against my verification layers, quindrupled types.

Now I could monitor the ai output closer, but then again I’m faster writing it myself. Because it’s one task. Ai-assisted typing isn’t slower than my brain is.

Also thinking more about it FAANG pays 300$ per line in production, so what do we really trying to achieve here, speed was never the issue.A great coder writes 10 production lines per day.

Accuracy, architecture etc is the issue. You do that by building good solid fundamental blocks that make features additions easier over time and not slower

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