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..."

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.

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.

[1]: https://github.com/LucBennett/BetterUnsubscribe

If I go on X, the vibe there around AI is amazing. You have folks constantly talking about cool things that they get their agents to do. Agents can help you make things you didn't have the knowhow to make, teach you things at a level that you couldn't get just by searching Google and take care of the boring parts of your life so you can spend more time doing the things you care about. Overall things are very positive and makes you excited for the future.

Contrast that with the public comments by AI executives. Pretty much all of the messages fall flat at best or are just outright doomery at worst. It's such a stark contrast and while I'm personally really excited for the future, I can totally see how someone who isn't plugged into that world to get a very grim view of the future.

And even when they run ads about AI, it's always the worst examples (thinking of that Gemini ad that talked about writing a letter to your family with AI). I really wish the industry could have a better messenger around this stuff. Don't talk about how it can help you do stuff you already know how to do, run an ad about how it can enable you to do stuff you couldn't do before.

I think the best example of this is a bike mechanic fixing a Reevo bike and writing firmware using Claude Code so it no longer required an app to work[1]. The author outright said he didn't know how to code or do anything in that area but was able to figure it out with the help of AI. I think a message like that around AI is awesome but no all we hear about is how AI will take your job.

[1]: https://www.youtube.com/watch?v=hPrtVGimBYs

Sol gave me a straightforward bulleted list, easy to scan and read. Opus 5 though....it gave me a solid 7 paragraphs of how it worked. I read through it and yeah it nailed the same points Sol did but the output was way harder to read.

Comments URL: https://news.ycombinator.com/item?id=49421652

Points: 3

# Comments: 0

A big part of this is ensuring that a "bad" engineers can still write solid code and also investing in systems that make it easier for us to review code.

When it comes to writing code, we have this entire library of coding standards that we've moved from one project to another. It describes, sometimes in excruciating detail, exactly how we want our code structured, antipatterns, best practices, etc.

On the review side we have invested equally into skills that split up code into readable chunks, take screenshots of any UI changes for quick validation, and a whole battery of tests to ensure that we're not generating slop.

If you were to look at just our development process, you would conclude that we're very lazy engineers. We seldom write code by hand, we seldom ask for corrections and our reviews are more of a cursory look at the PR rather than a deep review.

But the real work is not in the "development layer", it's now in the "agent layer". Making sure the agent knows how to write solid code so we don't need to write code by hand, making sure it doesn't make dumb mistakes so we don't have to correct it, and structuring our review process in such a way where an engineer only has to take a cursory look at the code.

The key difference we noticed between the "old way" and the "new way" is the "new way" is way more scalable and we're able to move way faster than we ever could before.

The reason we are talking so much about that now is that at the current level of productivity, we just don't need as many humans to do the work, as companies can use AI as leverage to not hire as many people.

This i feel will be a short lived period where not everyone has adopted AI to the fullest and thus simply having AI gives you leverage to not hire as many people and still stay competitive. The same thing happened with computers writ large, during the early days the folks that used computers had massive leverage over those that did not.

But what happens in 5-10 years once most orgs standardize on that leverage and everyone has it. All of a sudden that advantage AI gave you over others evaporates and you're right back to how things have always been, that is more humans give you more leverage.

I think once AI is broadly adopted and standardized, there will be an insane boom period for workers.

The trap here is the word "seems". When I started building my SaaS years back I thought the problem space was trivial (even joked to my business partner that I could spin the whole thing up in a weekend).

But almost 5 years on it's anything but trivial. The issue isn't with the core features, the core features in our app have been running for years without issue. What is very much non-trivial are all those subtle tiny features around the main feature that enhance the experience.

My customers don't pay me for the primary feature, that they could reproduce in a weekend. They pay me for all the tiny sub-features that came out of all those minor things that I mentioned in my first comment.

Those tiny details are the reason that it's both very easy to say things like "SaaS is trivial" and why the entrenched players are still at the top.

I'm currently building a SaaS with my business partner. I'm the "tech guy" while he has decades of industry experience with the industry that our SaaS is targeting.

What I found building this is there is a giant subset of preferences, problems, and other minor details that aren't written down anywhere, they are just kept in the minds of the people in that industry.

The reason it's not written down is to those people, these things seem obvious. And these things are both extremely important but also very subtle.

We use AI heavily in designing our application but there's always a testing pass where my partner goes over some changes and calls out subtle things of how "things work in practice". These things aren't in any manuals, they are seldom even written down and thus AI systems are completely oblivious to these details.

Now you could build a system that skips some of these subtleties and still gets the job done but what you will find is potential customers will always return to their existing system that takes these into account.

Now one day we might get an AI system that detects these subtleties but in order to do that you would need a load of training data around those things. But how do you even get that training data if all of it is held inside the minds of the people working in that industry?

Anytime I see a "1M Context Window", my brain always goes "Gotcha so a 250k usable window"