_fat_santa

One thing I've seen with AI is AI as used by software developers is quickly diverging from AI that's used by knowledge workers.

Knowledge workers use it primarily to "augment" their work, answer questions with AI, get it to create documents, etc. But it's usually 1 individual to 1-2 conversation threads.

Meanwhile in software development were running fleets of agents. At work we have it setup where firing off one agent request will eventually lead to like 10-20 AI calls at different parts of the process.

I make this distinctions because it changes how each group thinks about AI. For software devs it just feels like "moving up the stack". Yeah you're not writing code anymore but there is still plenty of work to do and problems to solve within your "software factory". Contrast that with knowledge workers that feel like the AI is making all of their decisions for them.

I almost think of this as a factory worker versus an artisan. The software devs are "factory workers" where we used to be on the floor but now we're managing the machines that took over the work. There are still tons of interesting problems at that stage and you still feel like you have worth. Contrast that with a knowledge worker that's more of an artisan in this analogy. For them they aren't running a factory but instead watching a robot do the work they used to do while they sit idle and watch it.

I would define hosting in the cloud as "self-hosting" because you perform the act of hosting that thing and thus have full control over it.

Yes it's not the same as hosting as your own hardware but I would argue that it accomplishes the same goals. The goal is to get other people out of your data. With a "service" the underlying company has every incentive to look at your data and use that to improve it's service, but when you host something yourself with a provider, it's in that providers best interest to never look at your data.

For me it all comes down to the goals of the people your are doing business with. With Cloudflare at least, their goal is to just upsell me on hosting equipment, they couldn't give a rats ass what I'm hosting on that hardware so long it's not illegal.

I feel like the "We hacked hugging face" thing and this influencer thing are targeting different things but have the same goal of hyping up the company for the IPO.

- The HF incident was to get the attention of investors and convey the message: "we have this incredibly powerful technology, imagine where we will be in 5 years"

- With this influencer campaign it's just a drive to get more people to use ChatGPT / sign up to ChatGPT so when IPO time comes, they can show strong user growth.

Over the past few months I've seen various reports that OpenAI's financial position isn't great and if those reports have any merit, then likely the higher ups at OpenAI are panicking that they might become a WeWork 2.0. Everything they are doing now is an attempt to justify their $1T valuation to investors during the IPO.

> The trillion dollar question is how you do this

I don't think it's that hard of a question to answer. I've noticed on my team, our thinking has shifted from how do you directly solve a problem, to how you get an agent to effectively solve the problem and not produce slop in the process.

One thing that we have done that's probably made the biggest impact is alot more upfront architecture with the knowledge that pretty soon agents will be running wild all over the code. Having worked with these agents for a while now, you get a very good sense of how they will go about solving a problem and the various footguns they will encounter along the way. Editing an AGENTS.md file or building a skill is not nearly as fun as coding by hand but it will pay dividends over and over if you do it right.

Another big thing is doing refactoring passes. Early on in our projects our agents generated ALOT of slop and we had to go back and fix alot of it. But every time we did one of these passes, a major aspect was improving agent instructions / skills / etc so it doesn't happen again. It can be a painful process at first but I found that over time, the amount of slop the agent produces goes down by orders of magnitude.

I feel like we're still very much programming, but we're now doing it at a "higher level" where we are not writing the code ourselves but instructing the agent to. And IMHO, properly instructing an agent on a production codebase is not a trivial task.

I think that's past the point OP was making. Amazon makes some pretty impressive guarantees of data durability but those guarantees only hold up if you take their advice and replicate data to other regions.

It's like saying "Oh this car manufacturer claimed that their cars were the safest in the industry but couldn't prevent this driver from flying through the windshield" and omitting the fact the person wasn't wearing a seatbelt.

They would have to encrypt it in such a way where even if Law Enforcement went to apple with a subpoena, they would not be able to hand over that data at a technical level.

The better way IMHO is use local models so data never actually leaves the device.

At least with Codex, this has not been my experience at all. It still screws up sure, but in every case I can ask "why did you do this" and it can trace back what made it take that particular decision. Typically it's always that I either didn't specify the problem correctly or made a really dumb mistake (executing the task on the wrong project....did this one yesterday) or it's something within a skill file that instructs it (at which point I fixup the instructions).

Once in a blue moon it's actually the model making a material error in it's thinking and I have to go back and redo it.

Part of the issue is many TV's these days are essentially bricked until you connect them to the internet to "activate".

I bought a Hisense TV a few years back and when I set it up, I could not proceed without downloading the companion app. I was curious so I called support and told them that I didn't have a phone and asked how I could activate the TV.

Support had no answer for me other than "well you need the app". When I told them I didn't have a phone to install it on they just said "no that's not correct, you must have a phone..."

This is just the 2020's version of "StackOverfow is down"

I've personally adopted doing multiple refactoring passes after any large code implementation done by AI. In pretty much any scenario where I'm adding code using AI, it's 1 turn to add the feature and and then another 4-5 turns to refactor and clean everything up.

Often times it's not even that the code is bad but rather that it's overengineered. I see it happen so much that I'm tempted to actually go the other way on a toy project. Like what would Claude or Codex come up with if I told it I wanted an enterprise grade, globally scalable, compliant and auditable tic-tac-toe game.

I just switched over to it from Ubuntu. So far the nice thing is that it gives you a fully decked out hyprland setup without any of the hassle and pretty good UX.

The problem I've always had with trying out a tiling window manager like hyprland is you're going to spend a very long time trying to get everything just right. With Omarchy I get a really nice hyprland setup right out of the box.

I agree, presentation is a big issue with PWA's since you have to know how to add it vs just searching in the app store. I'm sure were going to get a native app eventually, though customers have not asked for one yet and seem to be pretty happy with the PWA.