Managing AI Coding Costs at Scale • Hacker News Board

Re: Managing AI Coding Costs at Scale

#261

Post by Karrot_Kream » Sat, Aug 08, 2026, 6:03 PM UTC

sbarre wrote: post #239

Karrot_Kream wrote: post #198

Earlier quoted context omitted.

Not having human input in the loop, i.e. allowing agents to act without guidance. I understand the idea of having agents guide agents, but really how much do we gain when Sol scolds Fable?

> how much do we gain when Sol scolds Fable? You might be surprised at how much benefit you get from the model council[0] pattern. The common pattern I've seen is "build with Claude, review with GPT", or vice-versa. It's been proven to be very effective. 0: https://www.google.com/search?q=model+council+pattern&udm=14

I use it. We have more workstreams than people and so we have to rely on this pattern for most of our new products, but I still find the code doesn't meet my bar. To me it produces passable but not good, maintainable code. Good enough for our experimental products with the cognitive debt associated with heavy agentic development. We take this calculated tradeoff of velocity for cognitive debt and middling quality because we're a startup and we feel that velocity matters more than correctness, but that is just a bet as the nature of startups go.

Re: Managing AI Coding Costs at Scale

#262

Post by bonoboTP » Sat, Aug 08, 2026, 6:27 PM UTC

dgellow wrote: post #131

Krei-se wrote: post #89

Earlier quoted context omitted.

also - as always with these claims there's no actual product / repo / whatever one could check. I would love to see what these tools create but outside slop there's never: This works, is in production, here's the code. Any day now.

It’s crazy how we are like ~2y in this AI revolution and still do not have an answer to this question: can you show us the ROI? Where is the revolutionary software your team of agents created?

Why would it need to be revolutionary? It can be some ordinary thing. Software is mostly ordinary.

Re: Managing AI Coding Costs at Scale

#263

Post by bonoboTP » Sat, Aug 08, 2026, 6:28 PM UTC

what wrote: post #184

Krei-se wrote: post #89

Earlier quoted context omitted.

also - as always with these claims there's no actual product / repo / whatever one could check. I would love to see what these tools create but outside slop there's never: This works, is in production, here's the code. Any day now.

I found an interesting project recently. As I was looking through the source something felt off. Turned out to be entirely LLM written. There was duplicated code everywhere, same function defined in dozens of files (same name, same intended behavior) but none of them would produce the same output for an input. Dead code all over the place. Over architected. Useless comments. It was all generated in the last 4 months,…

4 months is ancient. Fable and Sol are a different animal.

Re: Managing AI Coding Costs at Scale

#264

Post by [deleted] » Sat, Aug 08, 2026, 6:31 PM UTC

redhale wrote: post #244

pizza234 wrote: post #27

Earlier quoted context omitted.

In our team's experience, the product of agents is generally The Homer (1). It does work, but it's vastly overengineered. When I personally want tight code, I have to spend a considerable amount of time adjusting it manually: - It needs to be trimmed down. In my experience, at least one agent I use struggles to produce minimalist designs, and it's very frustrating - I need to consider whether there are solutions base…

I largely agree, but when this point is brought up I always feel the need to go one step further: For the vast majority of software out there, this quality gap simply doesn't matter. Not everything is big tech, needing internet scale. Most software built by developers at non-tech companies are simple line-of-business apps used by a handful to a few dozen or maybe a few hundred people to do a thing a bit better than t…

[deleted]

Re: Managing AI Coding Costs at Scale

#265

Post by redhale » Sat, Aug 08, 2026, 7:31 PM UTC

zobzu wrote: post #246

redhale wrote: post #244

Earlier quoted context omitted.

I largely agree, but when this point is brought up I always feel the need to go one step further: For the vast majority of software out there, this quality gap simply doesn't matter. Not everything is big tech, needing internet scale. Most software built by developers at non-tech companies are simple line-of-business apps used by a handful to a few dozen or maybe a few hundred people to do a thing a bit better than t…

i don't think people writing this understand how computers work. there is no "Internet scale hard" problem. its reliability, safety, efficiency problems. these problems exist at any scale. ai sucks at it.

> its reliability, safety, efficiency problems. these problems exist at any scale

Respectfully, I disagree. When you have 5 users using an app sandboxed within the corporate firewall that just does some CRUD and basic workflows, these problems you're calling out really just don't matter much. Some do some of the time, but not much.

Obviously not all software falls into this category. But so, so much does. Do you know how many thousands of tiny applications are maintained by big all non-big-tech corporations? I've seen many of them up close -- the code is usually complete garbage (seriously, it's bad; and alas, most was written before the age of AI coding agents), and yet, the business continues to operate and use the software.

Re: Managing AI Coding Costs at Scale

#266

Post by sashank_1509 » Sat, Aug 08, 2026, 7:43 PM UTC

lazarie wrote: post #230

sashank_1509 wrote: post #173

I suspect that when it comes to hard complex software products, you’re better off ignoring agents and doing “trad coding”. What you lose in short term speed you gain in manageable complex codebases. If you have a 500k line codebase and even > 50% is written by agents, you are in a world of pain that won’t justify the costs longer term. Now of course, there are products that just involve lots of code but are not actua…

I work on and maintain a 900k+ LoC codebase with mixed languages, runtimes, deployment models, cloud and airgapped ops and it's 99% written by AI. The difference I see the most between our approach and others is that we see the end-user product as an output of our context engineering internal machine, we focus on building that machine and we measure the output like madmen across all and any qualitative software metri…

What exactly are you building? Does it need 1M lines of code, or is it 10X the needed code already. SQLite + tests is around 1.5M lines of code, somehow I strongly doubt you’re building anything as complex as SQLite.