KasianFranks

Have been at it since Nov 23rd 2025 with a first commit with Opus 4.5 - it was rough at first but after about year well worth it. You can also tell where so many just get stuck in the mud due to lack of direct experience with all the engineering silos (from bare metal sys admin to QA eng silos outside of standard software engineering core) it takes to roll out a new kind of technology company while scaling a new kind of commercialization model, also managed by AI and AI native from the ground up.

This works as we've been doing it since Opus 4.5 (Nov 2025) with every user request, feature and bug. It's the future without a doubt. The feedback loops evolve and get stronger depending on how well you fine-tune or train your system to optimize on its own.

In other words, do something that’s hard to duplicate.

Even proprietary algorithms decay. Better proprietary AI algos are alive, grow and evolve.

We're already there with our pipelines and suspect a few more out there more concerned with measuring outcomes at this point.

Notice the word new, novel and proprietary. Would you like to ask your AI pipeline to duplicate ours? Unless someone knows what we do to parameterize, create and optimize new, proprietary and novel algorithm sets, it remains as part of any new scientific or engineering moat associated to an AI pipeline. This also include deep knowledge domain expertise and human decision trees applied along the way.

With one giant exception, the advent of the algorithm all over again but with new proprietary and novel algorithms sets.

Well said. The anti-AI sentiment is strong in a few camps on HN. Praying that AI will go away so as a software engineer, only familiar with the SDLC, you become relevant again is not a winning strategy. AI is like a soup with many bubbles, some will pop while new ones begin.

After 22,948 commits, 13,720 bugs and -2,899,391 lines eliminated, I've seen this too but a properly designed AI pipeline's ability to optimize and course correct with a little badgering supersedes any Frankenstein-like outcomes.

Nobody is going to care about how much code an AI system generates in the very near future, some even now, once everyone understands the scale at which a vector space truly operates at. Complexity is not much of an issue for an AI system.

It's because they've been in FAANG silos with near zero experience in the pre-SDLC (basic research, practical application phases) through to the post-SDLC (tech transfer, commercialization etc) with most having near zero direct experience inside actual QA teams (with QA being beneath a lot of engs). A true AI pipeline hums when this is real end to end cycle. One of the reasons for that is based on how well it manages many-to-many connections inside vector space. One more thing, most soft devs have near zero comprehension of how language models work so leveraging them to manage complexity is very foreign with most not understanding that language models apply far beyond just human language and into any sequence of data e.g. DNA/RNA or just about anything you can chop up. This kind of scale is also a new concept to most.