I love everything about this direction except for the insane inference costs. I don’t mind the training costs, since models are commoditized as soon as they’re released. Although I do worry that if inference costs drop, the companies training the models will have no incentive to publish their weights because inference revenue is where they recuperate the training cost.

Either way… we badly need more innovation in inference price per performance, on both the software and hardware side. It would be great if software innovation unlocked inference on commodity hardware. That’s unlikely to happen, but today’s bleeding edge hardware is tomorrow’s commodity hardware so maybe it will happen in some sense.

If Taalas can pull off burning models into hardware with a two month lead time, that will be huge progress, but still wasteful because then we’ve just shifted the problem to a hardware bottleneck. I expect we’ll see something akin to gameboy cartridges that are cheap to produce and can plug into base models to augment specialization.

But I also wonder if anyone is pursuing some more insanely radical ideas, like reverting back to analog computing and leveraging voltage differentials in clever ways. It’s too big brain for me, but intuitively it feels like wasting entropy to reduce a voltage spike to 0 or 1.

I think that as a user I'm so far removed from the actual (human) creation of software that if I think about it, I don't really care either way. Take for example this article on Hacker News: I am reading it in a custom app someone programmed, which pulls articles hosted on Hacker News which themselves are on some server somewhere and everything gets transported across wires according to a specification. For me, this isn't some impressionist painting or heartbreaking poem - the entity that created those things is so far removed from me that it might be artificial already. And that's coming from a kid of the 90s with some knowledge in cyber security, so potentially I could look up the documentation and maybe even the source code for the things I mentioned; if I were interested.

We haven’t been inching closer to users writing a half-decent ticket in decades though.

Users are often incorrect about what the software should actually be doing and don’t see the bigger picture.

I think Anthropic will launch backend hosting off the back of their Bun acquisition very soon. It makes sense to basically run your entire business out of Claude, and share bespoke apps built by Claude code for whatever your software needs are.

Feedback loops like that would be an exercise in raising garbage-in->garbage-out to exponential terms.

It's the "robots will just build/repair themselves" trope but the robots are agents

Tusted user like Jia Tan.

The missing piece for me is post-hoc review.

A PR tells me what changed, but not how an AI coding session got there: which prompts changed direction, which files churned repeatedly, where context started bloating, what tools were used, and where the human intervened.

I ended up building a local replay/inspection tool for Claude Code / Cursor sessions mostly because I wanted something more reviewable than screenshots or raw logs.

What kind of software are people building where AI can just one shot tickets? Opus 4.6 and GPT 5.4 regularly fail when dealing with complicated issues for me.

Haha sure, let's just let every user add their feedback to the software.

Or perhaps we end up where all software is self evolving via agents… adjusting dynamically to meet the users needs.

Instead of having a trusted user, you can also do statistics on many users.

(That's basically what A/B testing is about.)

"Trusted user" also can be an Agent.

What you're describing is absolutely where we're headed.

But the entire SWE apparatus can be handled.

Automated A/B testing of the feature. Progressive exposure deployment of changes, you name it.

I think the Ai agent will directly make a PR - tickets are for humans with limited mental capacity.

At least in my company we are close to that flywheel.

> I feel like we are just inching closer and closer to a world where rapid iteration of software will be by default.

There's a lots of experimentation right now, but one thing that's guaranteed is that the data gatekeepers will slam the door shut[1] - or install a toll-booth when there's less money sloshing about, and the winners and losers are clear. At some point in the future, Atlassian and Github may not grant Anthropic access to your tickets unless you're on the relevant tier with the appropriate "NIH AI" surcharge.

1. AI does not suspend or supplant good old capitalism and the cult of profit maximization.

I am already there with a project/startup with a friend. He writes up an issue in GitHub and there is a job that automatically triggers Claude to take a crack at it and throw up a PR. He can see the change in an ephemeral environment. He hasn't merged one yet, but it will get there one day for smaller items.

I am already at the point where because it is just the two of us, the limiting factor is his own needs, not my ability to ship features.

We do feedback to ticket automatically

We dont have product managers or technical ticket writers of any sort

But us devs are still choosing how to tackle the ticket, we def don't have to as I’m solving the tickets with AI. I could automate my job away if I wanted, but I wouldn't trust the result as I give a degree of input and steering, and there’s bigger picture considerations its not good at juggling, for now

Then sets up telemetry and experiments with the change. Then if data looks good an agent ramps it up to more users or removes it.

Um, we are already there...