cronin101

Put a decision model in my bike that decides when it is time to automatically shift up/down based on signals (FTP, Watt avg, HR, cadence, upcoming grade) and I will buy a subscription to your SaaS.

Definitely not frequently, but once or twice a lifetime would provide a measurable uplift.

This is simultaneously incredibly impressive and annoyingly uncanny valley, since most of the landscapes are "ruined" by a cluster of churches right next to each other that is completely nonsensical.

This needs some balancing. The fact you can buy a bishop on turn 1 and check their king to disrupt their economy is overpowered

Anecdotally, in the Opus 4.6 days, it felt like there was something special about using plan mode to discover the approach then clearing the context to execute on it.

A mixture of defending against a disastrous mid-implementation compaction (where suddenly things would veer off the rails) and also allowing the fresh execution to double-check the assumptions and notice any subtle mistakes before context was poisoned.

I’ve found that for large enough changes I still prefer having a parent theorizing about the root cause of issues based on evidence and then dispatching targeted child sessions to fixed based on theories and concrete telemetry examples.

There’s something clean about having sandboxed context and a session you can quiz about architecture while one is heads-down working against a spec.

It certainly _seams_ that way

This isn't true, there's a limit to how much feed-forward processes can correct themselves and well-poisoning of context is far too common.

Try using an LLM to rewrite an LLM output without the slop (vs asking for no slop to begin with) or sandboxed subagents that critique a parent's draft.

There is absolutely a step-function improvement in quality but: 1) not everyone wants to explode their cost by adding extra calls 2) this can't just be "trained in" to a system as obviously they have attempted this but the technique still provides an uplift.

You pay someone and give them the explicit responsibility for code quality in your system, empowering them to gate check-in with any static analysis and adversarial agent review they feel like. It’s not a silver bullet but you absolutely can do better than just giving up.

Is this the path to a Bizarro World future where the Commonwealth realms become associate members of the EU to slowly unBrexit?

I don't hate the idea

Really (in your example) the timeframe is the only difference.

You can endure as long as you want to, but we're all going to erode eventually.

is 200 a lot of concurrent people? What is required from the server in order to render the demo, can't most of the work be pushed to the edge devices?

Can you “just do this” or is there some preapproval you’re meant to get beforehand? Presumably the answer varies wildly by jurisdiction.

To me, working with AI feels a lot like the previous experiences of “working in a sprawling enterprise codebase spanning multiple systems each with emergent behavior”, except now I can outsource the introspection and validation loops to something that never gets bored instead of spending 2h hyping myself up to concentrate for a 3h stint.

Irony of Manchurian Candidate models not lost here

I’ve specifically used Opus to diagnose and fix performance bottlenecks in parallel Rust code on multiple occasions (e.g improving NPS for a chess engine) and it works well.

I’ve done plenty of performance architecting in my day-job and rule #1 is generally “you can’t fix what you can’t see/measure”. I have a suspicion that many folks aren’t investing in letting AI actually introspect iterative execution via the appropriate harness, and are then acting surprised that it is no oracle.