I tend to agree,

If you have invested significantly in the planning phase and there is momentum in the architecture and conventions that already exist in the project, the implementation phase might not need as much oversight as is suggested here.

> You can discover that your initial idea was dumb and a better one exists

The planning and architecture phase is usually where I make these types of discovery at a high level.

> Your agent might go “off the rails” and start doing something you don’t want it to do

Candidly these orthogonal, inadvertent edits aren't as bad as they once were and for impactful changes there should be at least some test coverage, even if that test coverage is just "freezing" what was implemented.

As you mentioned the final review discussion is a good chance to verify beyond what review or adversarial review agents find.

> I think we're mving towards humans no longer needing to understand a codebase, and letting AI drive it.

Hard disagree. Even the best frontier models generate output that's not what I asked for. Sometimes I realize that I get lazy in my prompting and the lack of specificity winds up showing up in the output. Just the other day, a coworker built a huge feature using frontier models and it slipped an IDOR in.

I just don't see a world in which we completely cede control of the codebase to AI because it's still my ass on the line if I ship something that completely borks production. If I'm not reading code regularly, then I lose the ability to read code, and if I lose that ability, then I'm no longer a developer.

yep. got a laptop i dont care about that claude can play with in wsl.

its the fun of funemployment.

starting work again is gonna be an interesting change though. its currently straightforward letting it run, then giving a broad critique and setting up new introspection/closed loop feedback for an hour over a beer, then letting it run wild again after

>>You never use “YOLO” mode (aka “dangerously skip permissions”)

Do you mean this?

I'm curious how are people using Claude in any way other than bypass-permissions. I've tried for so long to maintain a curated list of things Claude can use, but inevitably I would always come back only to find it stuck because it decided to pipe an output of one tool into another and that's not explicitly allowed so it stopped even though it was just greping or whatever. I found it infuriating. In bypass-permissions it "just works" but then again I only use it to analyze existing code and suggest new changes(and even if it breaks something that's what source control is for?)

"Next token prediction" is an interface, not an algorithm. A process that "predicts next tokens" can be arbitrarily complex or simple, and arbitrarily capable or incapable of performing a given task.

Saying that an LLM can or can't do something because it's a "token predictor" is a category error. The interface isn't a hard limit.

> it doesn't mean it's intelligent

I'm not sure how you're defining "intelligent", but I'd like to know how it is able to exclude a language model, while still including humans, without simply defining it with an axiom that predefines LLMs as lacking intelligence.

Yeah, and you’re just a next-word-sayer.

It’s just parroting the current trope.

Last year it was, “AI is just a stochastic parrot.”

This year it’s, “AI can write the code, but a human still has to review it!” (Using AI, of course.)

Give it another year and the narrative will be: “Only AI is capable of reviewing code, and only AI can review the AI’s review. Humans just need to read the AI’s final opinion so they still have meaningful oversight.”

The goalposts keep moving. The certainty never does.

claude is so slow for interactive use like described, do people just run it in low effort mode or what?

As a Mythos user (I’m part of Project Glasswing), I would say that abliterated models [1][2] produce similar, if not identical, results. While good prompting and steering won’t give Claude Opus 4.8 the same capabilities as Mythos (preview 1), using abliterated models (if you have the computational power to run the larger ones) will get you close to the same goals as people who have access to Mythos (preview 1) [3].

[1] https://huggingface.co/search/full-text?q=abliterated&type=m...

[2] https://webdecoy.com/blog/wtf-are-abliterated-models-uncenso...

[3] I specifically refer to “preview 1” because the newer versions (Fable 5 / Mythos 5) don’t appear to offer the same level of freedom as the very first version that I was able to use through Project Glasswing. This is one of the reasons why I continue running our massive security scans with “preview 1”, or at least I was running them until June 30, when the program’s policy changed.

I think that Anthropic is gaslighting us with their new model releases. Specifically, I think they have some good base model and are just fine-tuning it until they achieve desired outcome, or the desired outcome is achieved accidentally as part of fine-tuning. My theory is based on the fact that as a long-term (if you can call it that way) Claude user I keep noticing the same patterns it outputs. It's not trivial but certainly possible to see when something has been written by Claude because it has a different style than GPT.

However they have quite good harness in their backend which is the actual model.

People are doing what they've always done with any other new technology, and sharing what, personally, works for them. People can take or leave the advice.

It's an open problem of clearly large value how to get reliably useful and trustworthy outcomes from AI systems in many domains, software is maybe the signal example of that. If one had solved it resoundingly and scaleably, one could in fact "get rich quick".

It is unsurprising that a lot of people claim to know how to get rich quick.

I believe it is possible to solve this problem, and I have my own horses in the race which I won't threadjack to promote here, but it's the central problem of our profession at the moment. We've all seen the truly discontinuous outcomes and we've all seen allegedly national security dangerous models (which at one time was GPT-3) faceplant with it's shoelaces tied together. I wanted to see if Fable was really all that and I left it overnight on some fairly straightforward C++ (code DSv4 Flash works on with moderate supervision) and it's pretty roast worthy, I gave it a chance to redeem itself this morning and it's ticked up a bit (I still think it's roughly Opus 4.8 with a Project Zero fine tune and DRO trained off the constant gratuitous yield tic which is pretty clearly an intentional gimp).

I give all such claims 30 seconds of my time because someone is going to actually be right one of these days.

There are a lot of people with a long career in the old way of doing things are feeling incredibly threatened and defensive and desperate to virtue signal about AI.

Not really if it takes you 15 minutes to write a 50 line function but it takes the AI 90 seconds then you already are at a 10x speedup just for this task.

This (non-yolo mode AI coding) is actually how we used to code in the old days (2023).

In my experience it, or something close to it, is the only way. AI needs good code to be beaten out of it.

Efficient != effective, and the author outlines as much. Regardless, while you're technically correct, it's kinda like saying the Fantasy Land Specification[1] (aka the "Algebraic JavaScript Specification") is pure. The problem is that purely functional fantasy lands rarely exist outside of fairytales. In other words, life is a lot like JavaScript and never that simple.

[1] - https://github.com/fantasyland/fantasy-land