That’s a pretty large binary for simply loading images.

In all honesty, opencv has stood the test of time and I’m certain newer LLMs will likely not attempt to rewrite it from scratch.

P.S. I’ve been a user since the IplImage days, circa 2007, and I’d still consider using it over most CV libraries today.

> I’m certain newer LLMs will likely not attempt to rewrite it from scratch

Sooner or later a Rust developer will try.

You can build it yourself and end up with a much smaller binary (and many more optimisations).

I can’t help but think that there are so many astroturfed comments in here.

Seems like a concerted and distributed effort from the entire Anthropic team every time to get this on top of HN.

It's the real deal. Before Fable nothing I tried worked. It has finally helped me finish my teleportation device. I can't show you or anyone the proof but trust me it's true.

I'm not fan of Anthropic, but to be fair, every major model release makes it to the main page. In the case of a model like this, hyped and with a jump in capabilities, it doesn't need astroturfing.

Well to be fair, it that hype and that "jump in capabilities" (that I don't see) might be astroturfed? You ever think that all that hype isn't organic?

No, but I think it's real in this case. Claude models have always been superb, I can definitely see another improvement in capabilities. Price is outrageous, though.

Yes, this is also my feeling.

It happens for every single Anthropic release. Then I try it on real dev and the result is laughably bad. Except in design where it has been doing a decent job for a while. I am not a designer and my bar is pretty low.

In frontend sonnet and opus take more than 5 $ per query to fix any problem

So unless you have unlimited tokens it's better to learn frontend

Corporations have done worse for much less money involved. Now we have trillion dollar companies going IPO. With so much at stake, it’s not unthinkable that there’s astroturfing happening.

Wouldn’t be surprised if there are marketing teams writing positive comments for more positive engagement

Marketing teams entirely composed of Claude models, of course

I’m convinced that’s the case, this place looked totally different around 4 years ago

I see a lot of negative comments right now surprisingly.

Yeah, this whole post is a GIANT AD.

You're right to point that out! Most people did not think of this but you did -- and that's a rare skill to have.

I don't think it's weird that the post made it to the front page, but watching the downvotes roll in on my own mildly critical comment has been intriguing. I saw it go up to +2, down to 0, up to +3, and now it's on +1.

Where do you see them exactly? The comments are pretty much in line with how the model performs IRL.

Theres several comments that sound like „I“ve had this REALLY DIFFICULT problem (no specification whatsoever) and threw Fable on it and it solved it immediately, additionaly it cured aids and found a solution to world hunger“…

to be fair, the top comment from simonw is most likely legit unless anthropic hacked his account too

This is… not at all the case. Most observations on pricing, some on specific projects, some on speculation about Anthropic and many about the model getting nerfed.

I think you can't read because half the posts are about the nerfing and price.

The early posts were largely AstroTurfs.

Then people started realizing we're getting literally rage-baited by Fable 5 and started posting their criticisms.

Both can be true at once.

What’s your experience with Monty? Been looking at it for one of our environments and it seems very promising.

I've tried it out a bit - it does look solid and it has a good team behind it.

It's a subset of Python though (much more so than MicroPython), which is fine for LLMs since they can easily work around any limitations but does mean you can't use a lot of existing Python code with it. I hope they implement classes soon!

I'm also a little bit nervous about the safety. It's a fresh implementation in Rust, which means plenty of possibilities for edge case security bugs. The thing I like about WebAssembly is that there's a robust, well tested sandbox already - better for defense in depth.

I certainly wouldn't bet against Monty though! It may well prove itself to be a great solution for this.

Any thoughts on https://github.com/dylan-sutton-chavez/edge-python or http://edgepython.com/ ?

Ok that looks promising! I like that it's built for WASM, and the docs mention wasmtime here: https://edgepython.com/getting-started/what-it-is

Kind of crazy how many bespoke python sandbox implementations have popped up in the past few months.

I’d love to see if we can get GPU access within these runtimes, that’d be awesome.

VLM Run (https://vlm.run) | 1x Product + 1x ML Staff Engineer | Santa Clara, CA (HQ)

We're building the inference and orchestration layer for production Vision-Language Models. We care deeply about fast and ergonomic visual inference, reliable structured outputs, and the observability to iterate on them.

A few things we've shipped recently you can poke at:

  1. Orion: our visual agent that reasons and acts over images, video, and documents. Chat at https://chat.vlm.run.
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  3. vlmbench:  single-file CLI for benchmarking VLM inference (TTFT, TPOT, throughput) across vLLM, Ollama, and SGLang.

Apply: https://app.dover.com/jobs/vlm-run

Email hiring "at" vlm.run with your GitHub + a couple recent projects.

[1] https://chat.vlm.run

[2] https://pypi.org/project/mm-ctx | https://www.vlm.run/open-source/mm

[3] https://github.com/vlm-run/vlmbench | https://www.vlm.run/open-source/vlmbench

VLM Run (https://vlm.run) | 1x Infrastructure Engineer + 2x AI/ML Engineer | Santa Clara, CA (HQ)

VLM Run is building infrastructure for production Vision-Language Model (VLM) systems — fast inference, tool-use + orchestration, reliable structured outputs, and the observability to iterate quickly. We’re a deeply technical team of veteran AI / computer-vision engineers (20+ years combined, MIT/CMU PhDs) who’ve shipped production ML infrastructure across autonomous driving and LLMs.

Open roles:

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Email hiring "at" vlm.run with your GitHub + a couple recent projects.

P.S. We recently launched Orion, our visual agent that can reason and act over images, videos and documents. You can chat with Orion at https://chat.vlm.run and see capabilities at https://docs.vlm.run.

Apply: https://app.dover.com/jobs/vlm-run

Real-time or continuous learning is great on paper, but to get this to work without extremely expensive regression testing and catastrophic forgetting is a real challenge.

Credit to the team for taking this on, but I’d be skeptical of announcements like this without at least 3–6 months of proven production deployments. Definitely curious how this plays out.

Can this be also used as an attack vector? A small seed percentage of users constantly choosing a particular poisoned pypi library to achieve a niche task which gets rled into the model suggestions and recommendations.

The recent claude code leak also revealed that they're poisoning their competitors via anti-distillation policies baked in claude code CLI (fake tool calls, adding noise etc).

What do you think actually happened here in the past week?

They used Kimi, failed to acknowledge it in the original Composer announcement. Kimi team probably reached out and asked WTF? Their only recourse was to publicly disclose their whitepaper with Kimi mentioned to win brownie points about being open about their training pipeline, while placating the Kimi team.

VLM Run (https://vlm.run) | 1x Infrastructure Engineer + 2x AI/ML Engineer | Santa Clara, CA (HQ)

VLM Run is building infrastructure for production Vision-Language Model (VLM) systems — fast inference, tool-use + orchestration, reliable structured outputs, and the observability to iterate quickly. We’re a deeply technical team of veteran AI / computer-vision engineers (20+ years combined, MIT/CMU PhDs) who’ve shipped production ML infrastructure across autonomous driving and LLMs.

Open roles:

1. Infrastructure Engineer (Full-time, ONSITE): $150K–$220K + 0.5–3% equity https://app.dover.com/apply/VLM%20Run/8d4fa3b1-5b38-42e1-927...

2. AI/ML Engineer (Full-time, ONSITE): $150K–$220K + 0.5–3% equity https://app.dover.com/apply/VLM%20Run/1a490851-1ea1-4f12-a0f...

Email hiring "at" vlm.run with your GitHub + a couple recent projects.

P.S. We recently launched Orion, our visual agent that can reason and act over images, videos and documents. You can chat with Orion at https://chat.vlm.run and see capabilities at https://docs.vlm.run.

Apply: https://app.dover.com/jobs/vlm-run

AI allows you to accelerate the initial build process, but I think engineering is all about craftsmanship. Today most LLMs have poor taste and chipping away the cruft matters more than ever.

uvx probably is the way to go here (fully self-contained environment for each skill), and use stdout as the I/O bridge between skills.