Nobody knows how to build with AI yet

To the article's point, I built my own version of your agro tool that I use to manage my own git worktrees. Even if I had known about your project, I still would have built my own, because if I build it (with LLM assistance, obvs) then I get to design it for myself.

Looking at other industries, music production is probably the one to look at. What was once the purview of record labels with recording studios that cost a million dollars to outfit, is now a used MacBook and, like, $1,000 of hardware/software. The music industry has changed, dramatically, as a result of the march of technology, and thus so will software. So writing software will go the way of the musician. What used to be a middle class job as a trumpet player in NYC before the advent of records, is now only a hobby except for the truely elite level practicioners.

An I the only one who has to constantly tell Claude and Gemini to stop making edits to my codebase because they keep messing things up and breaking the build like ten times in a row, duplicating logic everywhere, etc? I keep hearing about how impressive agents are. I wish they could automate me out of my job faster

More like people telling us there will be no more professional drivers on the road in 5-10 years 10 years ago. Agents are like lane assist, not even up to the current self driving levels.

AI's superpower is doing mediocre work at high speed. That's okay. Great, even. There's lots of mediocre work to do. And mediocre still clears below average.

But! There's still room for expertise. And this is where I disagree about swimming with the tide. There will be those who are uninterested in using the AI. They will struggle. They will hone their craft. They will have muscle memory for the tasks everyone else forgot how to do. And they will be able to perform work that the AI users cannot.

The future needs both types.

I think the agent stuff is impressive because we are giving the AI scaffold and tools and things to do. And that is why it is impressive because it has some directive. But it is obvious if you don't give it good directives it doesn't know what to do. So for me, I think a lot of jobs will be making agents do things, but a lot won't. i think its really strange that people are all so against all this stuff. it's cool new computer tools, does nobody actually like computers anymore?

I'm not trying to discount it the analogy, but I'd much rather live without cars (or a lot less).

Sometimes it's a good thing to not swim with the tide. Enshittification comes from every single dipshit corporation racing to the bottom, and right now said tide is increasingly filling with sewage.

There's a huge disconnect I notice where experienced software engineers rage about how shitty things are nowadays while diving directly into using AI garbage, where they cannot explain what their code is doing if their lives depended on it.

This doesn't make aaaaany sense: IF this actually worked, then why would all the biggest companies in the world be firing people? They would be forcing them all to DO THE TIDE and multiple their 10 billion dollar dominance to 100 billion dollar or more dominance.

The truth is something like: for this to work, there is huge requirements in tooling/infrastructure/security/simulation/refinement/optimization/cost-saving that just could never be figured out by the big companies. So they are just like... well lets trick as many investors and plebs to try to use this as possible, maybe one of them will come up with some breakthrough we can steal

I think Photoshop is a good guide here.

Famously complicated interface with a million buttons and menus.

Now there's more buttons for the AI tools.

Because at the end of the day, using a "brush" tool to paint over the area containing the thing you want it to remove or change in an image is MUCH simpler than trying to tell it that through chat. Some sort of prompt like "please remove the fifth person from the left standing on the brick path under the bus stop" vs "just explicitly select something with the GUI." The former could have a lot of value for casual amateur use; it's not going to replace the precise, high-functionality tool for professional use.

In software - would you rather chat with an LLM to see the contents of a proposed code change, or use a visual diff tool? "Let the agent run and then treat it's stuff as a PR from a junior dev" has been said so many times recently - which is not suggesting just chatting with it to do the PR instead of using the GUI. I would imagine that this would get extended to something like the input not just being less of a free-form chat, but more of a submission of a Figma mockup + a link to a ticket with specs.

There is certainly much innovation to come in this area.

I'm thinking about Personal Knowledge Systems and their innovative ideas regarding visual representations of data (mind maps, website of interconnected notes, things like that). That could be useful for AI search. What elements are doing in a sense is building concept web, which would naturally fit quite well into visualization.

The ChatBot paradigm is quite centered around short easily digestible narratives, and will humans are certainly narrative generating and absorbing creatures to a large degree, things like having a visually mapped out counter argument can also be surprisingly useful. It's just not something that humans naturally do without effort outside of, say, a philosophy degree.

There is still the specter of the megacorp feed algo monster lurking though, in that there is a tendency to reduce the consumer facing tools to black-box algorithms that are optimized to boost engagement. Many of the more innovative approaches may involve giving users more control, like dynamic sliders for results, that sort of thing.

There’s an efficient way to serve the results, and there’s an efficient way for a human to consume them, and I find LLMs to be much more efficient in terms of cognitive work done to explore and understand something than a google search. The next thing will have to beat that level of personal mental effort, and I can’t imagine what that next step would look like yet.

To be fair, Google also returns a lot of useless crap that wasn't generated by AI.

I have systemic concerns with how Google is changing roles from "knowledge bridging" to "knowledge translating", but in terms of information: I find it very useful.

You find it gives you poor information?

I like the way DuckDuckGo does it - it offers a button to generate a response if you want to, but it doesn't shove it down your throat.

It's handy when I just need the quick syntax of a command I rarely need, etc.

Append -ai to your query to omit AI results.

I was a bit wary of trusting the AI summaries Google has been including in search results… but after a few checks it seems like it’s not crap at all, it’s pretty good!

Creation of source data has been disincentivized

I am looking forward to the "news industry" breathing its last breath. They're the ones primarily responsible for the distrust and division.

I feel like we've been here before, and there was a time when if you're going to be an engineer, you needed to know core equations, take a lot of derivatives, perform mathematical analysis on paper, get results in an understandable form, and come up with solutions. That process may be analogous to what we used to think of as beginning with core data structures and algorithms, design patterns, architecture and infrastructure patterns, and analyzing them all together to create something nice. Yet today, much of the lower-level mathematics that were previously required no longer are. And although people are trained in their availability and where they are used, they form the backbone of systems that automate the vast majority of the engineering process.

It might be as simple as creating awareness about how everything works underneath and creating graduates that understand how these things should work in a similar vein.

Where did they learn sensibility for higher-level of abstraction? AI is the opposite, it will do what you prompt and never stop to tell you its a terrible idea, you will have to learn yourself all the way down into the details that the big picture it chose for you was faulty from the start. Convert some convoluted bash script to run on Windows because thats what the office people run? Get strapped in for the AI PowerShell ride of your life.

What you miss is the constant need to refine and understand the bigger picture. AI makes everyone a lead architect. A non-coder can't do this or will definitely get lost in the weeds eventually.

I'm using AI assistants as an interactive search and coding assistant. I'm still driving the development and implementing the code.

Where I use it for is:

1. Remembering what something is called -- in my case the bootstrap pills class -- so I could locate it in the bootstrap docs. Google search didn't help as I couldn't recall the right name to enter into it. For the AI I described what I wanted to do and it gave the answer.

2. Working with a language/framework that I'm familiar with but don't know the specifics in what I'm trying to do. For example:

- In C#/.NET 8.0 how do I parse a JSON string?

- I have a C# application where I'm using `JsonSerializer.Deserialize` to convert a JSON string to a `record` class. The issue is that the names of the variables are capitalized -- e.g. `record Lorem(int Ipsum)` -- but the fields in the JSON are lowercase -- e.g. `{"ipsum": 123}`. How do I map the JSON fields to record properties?

- In C# how do I convert a `JsonNode` to a `JsonElement`?

3. Understanding specific exceptions and how to solve them.

In each case I'm describing things in general terms, not "here's the code, please fix it" or "write the entire code for me". I'm doing the work of applying the answers to the code I'm working on.

He's still telling the AI what to code. Prompting, i.e. deciding the right thing to build then clearly specifying and communicating it in English, is a skill in itself. People who spend time developing that skill are going to be more employable than people who just devote all their time to coding, the thing at which LLMs are more cost effective.

> Yes, it's not faster to develop with AI if you watch it work.

It’s actually a lot faster. You read the diffs as soon as they start coming in, and immediately course correct or re-prompt when you see bad mistakes.

It can still be faster to develop with AI watching it work. It can legitimately introduce an entire simple fullstack change across multiple projects in my monorepo including graphql queries/mutations, typeorm repository, a service layer, and a reactnative frontend using apollo client, etc. It can do that in about 10 minutes in my local. I can't. If I turned it into a speed run event and practiced, maybe I could get it done in 10 minutes but honestly, it's a machine and I'm John Henry. Since it's using my IDE, it's using my meticulously setup and maintained local and I'm able to quickly stop it and fix any mistake it makes. Level 2 driving assist.

I have enjoyed the github copilot agent style development where someone elses computer is running everything, and I can make a request and just come back half an hour later and check on it. But this level 5 driver gets the wrong destination basically every time, and then it's another 10, 20 or even 30 minutes for it to make a minor adjustment. It doesnt understand my `yarn` scripts, it runs my tests wrong, it can't do codegen, it doesn't format or lint files, etc. I asked copilot yesterday to lint and format a PR and it took 25 minutes of agentic work lol.