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Respectfully, tasks that allow for explicit straightforward true/false or done/not-done tests are not the "hardest real task[s]." In fact, those are the ones that see the most gains from AI-assisted coding.
Testable tasks are where the largest opportunity is.
It had a very non-standard RSA key implementation that was obfuscated heavily. As well, it has an online license check at first run, and that part typically trips up most of the local models I've tried. I've been running this test for about a year now with different models, and it was the first I've seen not only figure out the RSA key implementation, but the first that didn't just give up once it saw the online license check. Even though it's only a first-time launch check.
That's why I call it one of the hardest, because in my experience, it has been. It's the first local model I've seen pull it off end-to-end. For some of the reverse engineering work that I've done with LLMs, none have been as consistent as this particular test at highlighting a model's failure in this domain.
I have access to Daybreak Blue and I'm approved for Anthropic's Cybersecurity program, so I might run the same test with both of those just to see, because it's been a while since I used a frontier model on this test. I imagine they'll make relatively light work of it, though, assuming it doesn't trip the relaxed guardrails.
Agents (even ones powered by small models) do reasonably well when provided an oracle to work against.
It's fairly successful but hits the time constrains and reduces the "value" of getting a local model to develop software.
It's still a bump in productivity.
This seems to be a pattern in the more recently released models that I think accounts for an increase in the quality of their work. They are very persistent in verifying that their work is actually correct, so even if they're not as "smart" as bigger models that get it right the first time, they have the ability to follow through to ensure that the work is actually done.
> And this was a debug session from hell, enormously helped by an AI doing much of the grunt-work.
> I'd like to call it my tireless helper, but the AI several times stated flat out that this was impossible and unsolvable and that we should just write a report about it.
> I suspect those things have been trained by people who may not be quite as stubborn as I am.
https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/lin...
On the data analysis side, something specific I’ve noticed is an (understandable) bias towards computing numerical statistics, which they do very well and reading the post-analysis report has significantly improved my own “statistical thinking” approach overall. Numerical statistics are cool and understandably what a text-based LLM is going to want to work with, but asking the model to produce time-domain and frequency-domain plots of, say, specific events has multiple times resulted in “trying to plot this out has shown the opposite of what I concluded numerically… recalculating…” There’s still a pretty significant review and critically assess step for me, especially since the actions I take as a result of the analysis are pretty expensive, especially if they steer the next data collection run in a useless or harmful direction.
This is says more about humans tendency to pattern match than anything else.
X works better than Y only is only a useful observation if we are using the same X and Y in a similar context, with similar parameters. Kind of goes out the window without it and I think this is part of why people have such vastly different opinions about the same technologies. We’re all talking past each other.
Either we are in command over the software, or the corp is in command over us via the software. I can on a theoretical level understand the concerns, but either we ban all LLMs or we have a level playing field for everybody. Let's not forget: defense and offense are different sides of the same coin in software. I guess this wouldn't apply to bio weapons, but I am not in the know about that.
Given the faults in simulated Intelligence that LLMs have, and a comparatively low level - which means, lower judgement abilities - to the best of us, there is a strident match having such employee judge the intentions of the employer.
Limiting the responses makes much more sense on cloud-based systems (you are using our infrastructure etc.).
"It will comply with harmful, unethical, offensive, or illegal requests that the original Qwen3.8-27B would refuse. It has no meaningful built-in guardrails."
Surely this has unintended side effects on output quality?
> Surely this has unintended side effects on output quality?
Can you help me understand why that's the case?
Imagine a world where any random person can run a super-capable model on their own hardware with no limitations and no one to pull the plug.
Information has always been power and those who already have power won't just allow everyone else having the same tools as them
It's an arms race. You have to run increasingly capable model partly because others can or do.
https://violentdelights.ai
I am completely curious what your legal defense would be though.
"Come do things with AI that are probably illegal!"
What?! We had no idea people would do things that are illegal!
Improper use is that of the user, not inherent to the tool.
Scolio: guns. Respondeo: guns are much more specialized (one-use) than knives. Proper use of sharp knives when what was shipped was a butter knife is understandable.
(The simile is not fully overlapping but should give the idea.)
But the reasonable defense is that the intended use cases are legal. The home page list a couple, and the 'writing fiction'/'helping authors' case alone covers almost everything. An author asking you how to best conduct a terrorist attack or how Meth is made are perfectly normal. Maybe even tame, compared to what some authors tend to research
But not that that’s a good justification for hamstrung models. I think it’s just the inevitable endgame and it’s more sad than scary
You can download Ebola sequences right now if you want to. That's not the same as having an isolate. The difference is a lot of messy reality. This kind of work is not generally "one shot" (Claude make me a supervirus, make no mistakes), it requires lab space, iteration, and specific resources. It has a footprint.
Wouldn't it make more sense to monitor / regulate facilities where you can sequence or request assembly of DNA, RNA, restrict and monitor the supply of key reagents and so on?
my setup
# Logical CUDA0 = RTX 4090, logical CUDA1 = RTX 3070 export CUDA_VISIBLE_DEVICES=0,1
cd ~/projects/misc/llama.cpp/
exec ./build/bin/llama-server -hf ggml-org/Qwen3.8-27B-GGUF:Q4_K_M --mmproj /xx/xx/xx/xx/xx/mmproj-Qwen3.8-27B-Q8_0.gguf --host 0.0.0.0 --port 8080 --jinja --parallel 1 --split-mode layer --tensor-split 6,1 --fit on -fa on -c 98304 -ctk q8_0 -ctv q8_0 --image-min-tokens 1024
i load more on the 4090 because it's faster.
usually the temp stays around 65 for both. utilization for 4090: 70-90% 3070: 30-50%. I get around 30-40 tk/s. if i offload more to the 4090 the tk/s goes up, but i stress the card too much and that thing now is worth its weight in gold.
note: the pi-llama plugin needs a patch for pi to send the model vision capabilities, seems it doesn't work out of the box.
A lot of tasks which need a bit of intelligence don't really need that much compute. Just good enough documentation / skills, tool calling and a good enough local model.
Not sure what exactly this means for all those data centers that are getting built... But exciting times.
Perhaps with differential privacy or confidential compute...
But ideally these models run locally.
E.g. having an agent that alerts you when subscriptions are close to renewal etc - yeah seems easy to understand / see happening on the surface.
Until you get into the implementation details and realise 'yeah errr. not gonna work'.
That openclaw nonsense is an example of this.
Additionally, the only use case for LLM is not fully autonomous agentic harnesses. There’s tons of use cases for LLMs where you would like to avoid a round trip to the internet or perhaps there is no connectivity. We’re only starting to scratch the surface!
re: data centers: pump and dump. Wealthy investors will have made their money and walked away, and the corrupt democrat and republican politicians in Washington will, as usual, protect the interests of the ultra wealthy and leave the general public to pay for poor decisions. There will be a government bailout.
Anyway, on a positive note, I am all in for small local models that are augmented by strong hosted models for specific tasks. Use technology to help people, not make billionaires even more money.
https://alexander-hanel.github.io/StressingLLMs/
The rationale is that the manager model sees the big picture and knows that the task is "unethical" while sota models are just given very isolated technical tasks that don't trigger any refusals.
Has anyone tried this? I would love to know about previous attempts of this approach.
Making each piece of work small enough to be plausible. Compartmentalization.
(Also saying "nah it's cool I have permission", heh)
https://www.anthropic.com/news/disrupting-AI-espionage
For what it's worth, that image couldn't have been related. The other screenshots all showed thinking traces, and Claude doesn't share those.
I've seen GPT 5.6 Sol happily invoke objdump and even write jobs to run headlessly which Ghidra when trying to disassemble a binary.
A nice thing about running locally is you can run an uncensored model and you don’t have to worry about TOS violations on your OpenAI account when you ask it to “reverse engineer this ancient router firmware and give me a licence key that will work on it”.
I think it will be fairly easy to remove refusals from open models. Feels like a lost battle, so why does Alibaba even bother?