alt Hacker NewsI've spent enough time with this now in Claude Code (and Claude.ai and Claude Code for web) to have an opinion on Fable 5: it's a beast. I'm throwing some VERY difficult problems at at - things I've been dragging my heels on for months - and it's crunching through them very happily.
One that I'm willing to share (albeit from just a week ago) - I built a Python library last week that bundles MicroPython compiled to WASM to create a sandboxed code execution library: https://github.com/simonw/micropython-wasm
I just told Claude.ai (not even Claude Code - this was the standard Claude chat interface) running Fable 5:
Clone simonw/micropython-wasm from GitHub
and research how this could use a full
Python as opposed to MicroPython
uv run --with https://static.simonwillison.net/static/cors-allow/2026/cpython_wasm-0.1.0-py3-none-any.whl \
cpython-wasm -c 'print(45 ** 56)'
(It's possible Opus or GPT-5.5 could have done this too, I've not tried the exact same sequence. The Fable vibes are good here, though.)
Replies
Yes, exactly this. If I didn't care about price at all, I'd exclusively use this model. It functions more like an actual engineer. I'm in the midst of a DB migration, and eg 5.5 continually suggests stuff like "use DB X instead of DB Y for task Z because its 30% faster" which is an impossibility of reality, given we are migrating DBs. Fable jumped in, reduced allocs by literally 46x, found multiple bugs 4.8 and 5.5 created (max file system usage, correctness issues, etc), and continually suggested awesome improvements unprompted. As in, it would finish a task and then suggest we tackle this other existing problem I didn't know about in a very specific manner... this is the first model that feels like its coming for my job.
Yeah same here, Fable on "high" is producing substantially better results than Open 4.8 on xhigh for me and my actual real-world evals today. It "feels" smarter and doesn't use nearly as many tokens running in circles. As a result I've been able to run two large refactors today without hitting the context limit danger zones - it's more expensive but also more efficient. It's been able to find some bugs that Opus missed. Pretty impressive stuff.
Still does not crack my hardest nuts. Gave it one of them and it blew through my entire allowance on thinking about one question, with no apparent answer in sight!
I see a lot of people saying they are happy with weaker models, but I am the opposite, I need more strength, more intelligence!
I am quite happy that opus 4.8 can do some medium intelligence problems. And maybe Fable 5 can do some more more of those! I have a lot of problems to solve!
One thing I can tell you is you are either favored by Anthropic, or your version of the CLI does not exhaust limits, or there's some major bug, as two people around me (myself included) claim it took half an hour to hit the ceiling. Which makes it practically unusable, where the same workflow a day ago produced a good 5-6 hours of workload with several agents.
It still does make errors, yes? Because it is not usable, if we need to verify everything. AI is only interesting if it can do things that humans can not do. If you can verify results because you can do it yourself, then why use AI? It will just bind highly skilled people to do verification work. Instead these people should do the actual work, results will come quicker.
So AI is only interesting to you / your org / humans if it can do things that you can not achieve. But if it still does errors, how could we ever know that super-invention by AI is not wrong?
If we can not rely on the correctness of the result, it is not usable at all. AI must create reliable and correct results always. That was a very fundamental requirement for computing. This problem has not been solved.
Just tried it. Fable is extremely strong. The fact that we can't point to any concrete architectural upgrade is worrying - that means "it just gets bigger" is kind of viable.
To be clear, the jump from Opus to Fable was like the jump from pre o3 -> o3 for me. Very sharp improvement, not incremental. But that could be explained by dummy long thinking times.
It one shot a task that Opus burned hundreds of dollars on to get nowhere. Very tricky semantic refactor, got it right. Granted, again, the semantics Opus and I fleshed out 3 months prior, but Opus couldn't execute on the vision. Fable could.
Then I discussed some philosophy and it was actually both pleasant (GPT constantly "corrected" you for the sake of correction without clarification, also still often just wrong; it's like it refused to think critically about philosphy) and accurate, and actually helped resolve some deep but subtle misconceptions I had around representationalism. When talking with GPT I felt like I was talking with someone who either was sycophantic or "anything that is not absolute truth is relativism" - Fable actually discussed.
Both is exciting and kind of makes me depressed. I can definitely see why people are getting hyped about AGI again. All the models were extremely strong technically but I felt like couldn't match the developer's tacit state - Fable definitely did, and that's a basic quailty to be considered "usefully intelligent" IMO, at least to me.
Shame that it's going away in 2 weeks and probably going to be nerfed if/when it's re-released.
Got curious and ran a similar prompt with DeepSeek v4 Pro w/ OpenCode
No idea what's going on here but agent tested a bunch of stuff. Then I asked to build a wheel so I can run the command you noted above and it appears to pass
For those who are curious...
https://github.com/bamggm/micropython-wasm/commit/5ddebae592...
The difficult part here is supposed to be the actual compilation to create the .wasm file ? Or what am I missing here? The wheel is only a few hundred lines of code outside of the Python implementation, and it would seem that the MicroPython version of the project already demonstrates the necessary techniques for operating wasmtime.
That is pretty wild, it took me a hell of a lot more coaxing and persevering to get to a similar point with eryx [0] (we spoke a bit about this before on Mastodon) using Opus, Fable seems to have a more optimistic 'sure, let's proceed as if this is possible' mindset based on your transcript. Looking forward to trying it out for some hairier problems.
Fable has been producing some really good work on my end as well. Definitely better than Opus 4.8. The only problems are the cost and constant cybersecurity refusals. A single session uses up 100% of my 5h window without finishing, and that's when it doesn't get derailed by nonsensical refusals.
Does anyone know what the architecture of Fable is? Is it harnesses? Did they solve persistent learning? What did they do?
if it’s of interest I’ve been working on https://github.com/HubSpot/boomslang
Which has a full build of python to WASM with a bunch of static libs built in already.
I will say I built this pre fable and actually the first build of the interpreter to WASM opus pretty much nailed, cpython has secondary support for WASM as a target since like 3.9 or something and it just pulled from that.
I’ve been meaning to write up a blog post about this sometime, building this has been pretty interesting, including using opus to run a full auto research like loop for days to hyper optimize it’s performance.
I’m hoping to use fable to power some even crazier WASM adventures tho.
These transcription tasks don't seem difficult for LLMs in general.
Did you hit your weekly limit ?
I hate how the Instagram/TikTok/YouTube influencer cancer is getting into AI. With early access and all that.
It made sense for people doing proper and fair AI breakdowns waiting on an embargo, but now it's just slop I don't trust anymore.
What are some reasons to consider your project instead of Pyodide?
> VERY difficult problems
Compared to what?
How much does it cost? How much did those tasks you did cost?
This looks like a toy project, not a “VERY difficult” problem like you stated.
> Here's the transcript
It's frustrating that superfluous tokens are burning up our quotas:
key insight, crucially this, real engineering deltas, net assessment, definitive picture, acid tests, real limits, sharp boundary, proper patch, real root cause, big progress, actually wrong, path finagling, the catch, root cause pinned, everything passes cleanly.
[flagged]
> It's possible Opus or GPT-5.5 could have done this too, I've not tried the exact same sequence. The Fable vibes are good here, though.
And that's the thing. These comparisons are all gut feelings. I'm missing objective unbiased measurements to actually have real comparisons between different models, their different generations, or even just the convention that everybody adds "you are an expert software engineer" and "don't make mistakes" to their prompts because they think it improves anything. Nobody knows if it actually does.