However they have quite good harness in their backend which is the actual model.
[1] https://huggingface.co/search/full-text?q=abliterated&type=m...
[2] 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.
Am I wrong? Are you guys just YOLOing everything these days?
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?)
Better method start to realizing that everything that every program do is data transformations and or movement
Then you ask llm to subdivide data in a tree along the domain model, classifing streaming vs storing nodes
Then for each node you discuss with the ai for the best data structure
Then you ask for an interface that fully encapsulate the structure and every mutation only allows to go from a valid state to a valid state and bidding else is allowed to touch the state
And that's mostly it just connect all the interfaces until input goes to monitor or to storage or to api or wherever the destination is
The way I rather do it is tightly control the output by skills written yourself, prompts, plans, etc. and have the closest possible outcome you would write yourself.
This (non-yolo mode AI coding) is actually how we used to code in the old days (2023).
I mean, it's like writing a book about how to use React or Django or some other major software ... after you used it for one project for a month!
Authors: I know this is the Internet, and I know bloggers blog about whatever pops into their head ... but if you are going to act like an authority, how about you learn more than the average reader before you start telling them authoritatively what to do?
I have no beef with people writing about new tech, but I do have beef with claiming that "____ is the correct way to do it" ... based on nothing except "I feel proud of the last three months I spent with Claude".
And that has a limit. If you are stuck at PoC level or simple apps, you have no idea how limited the current models still are. There you really need to break tasks down, not just trust a token predictor to list steps that sound good. There has to be a human in the loop somewhere, because by the time you start skipping permissions, best case you get the jackpot, more likely is you get a suboptimal solution and token waste and what's genuinely still terrifying when the model ignores instructions and does some stupid nonsense, ruining your day. It really is as sharp as a CNC machine. It's not not useful, but could be dangerous, so maybe don't try to carve wood with a monster machine, or park your Ferrari in that crammed neighbourhood if you don't know how to parallel park.
I much prefer having detailed discussions about a feature or idea, letting the AI off the leash to implement it, and then coming back to have a detailed review discussion. This seems to get a lot more out of better models that can have more nuanced discussions and write better code. The process of discussing designs and their implementations, questioning things that look weird to me, and actually reading the AI’s responses also helps me to find better solutions.
For example, one time I wanted to write a greedy solver for a problem, and Opus suggested using an existing MILP library to solve the problem exactly. I’d never even heard of MILP, but my final implementation ended up being better and simpler than what I’d have done alone.
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.
“expert developers whose skills have reached the point where they outclass any and all “frontier AI models” in their area of expertise”
Are any developers saying they outclass any and all frontier models? I’d say at best it’s mixed at this point. The best developers still do certain things better, but not even close to all things.
“The problem is that even code written and/or reviewed by Fable 5, will stink”
I’m skeptical. Example prompt and output please.
But you still need to properly review plans and PRs to keep a good mental model of the codebase. This effectively limits the number of tasks being done in parallel to maybe 2-3. Though you'll be mentally exhausted and probably start to make mistakes or take shortcuts in reviews yourself.
I run mine in a container, so it doesn't have access to the SSH key I use to push.
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.
sscaryterry•3h ago
threethirtytwo•56m ago
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.
reinitctxoffset•11m ago
Personally I think that if you cranked the capability up high enough the first person you'd run into who absolutely demanded more than vibes and didn't care about your singularity thesis would be the representative of a reinsurance firm: mostly to do serious stuff without bending the law, you need insurance, and I am unaware of anyone writing serious policies (certainly not ones that make any economic sense) that underwrite the risk of AI autonomy outcomes financially.
When Swiss Re writes a policy that Anthropic Cinematic Universe or whatever iteration we're on won't fuck it up?
Now maybe we're talking. Until then you ask three practitioners and get nine answers, no one knows what they're talking about unless they're doing a really good job keeping it quiet (and that's probably what you'd do!).