There are deeper structural problems when you look at the incentives inherent to government driven society.
While I do appreciate and even support the idea that the immense wealth available on planet earth should be more evenly distributed, and people should mostly be focused on raising families, deepening their philosophy, and perfecting their craft... (some societies in Europe are pretty laid back in terms of hyper-productivity culture)
Regulation is possible without paralyzing industry. Laws about the internet are possible without stifling freedom. Central planning is possible without crashing the economy so bad you end up with a regime change. It all comes down to execution and for the most part, it's been a series of failed experiments.
As much as I want a happy future for Canadians and Europeans, I just don't see the odds being super favourable.
Dunno. My impression is that with the EU, we pick its suboptimal parts and compare them to what they could be with optimal execution.
We don’t look either at the successful parts, not at the world where it is wholesale gone. My gut feeling is that EU members are, on the whole, much better off for being in than out. One natural experiment is the UK - opinions differ, but the drastic accumulation of problems coincides with Brexit timings.
I’m all for improving the EU! And for sure there are bad bits. But when your car AC is doing badly, you don’t write off the car. You try to fix the AC…
People sometimes call EU the sleeping giant.
Big potential compared to the actual results.
I'm more aware of what Canadian government does with its tax dollars.
And I'm just assuming that the EU is similarly quite expensive for what it achieves, and the budgets only change in one direction.
EU budget is actually quite small - about 1% of GDP.
That’s very small compared to EU members states national budgets, which sit at around 35% of GDP, apparently. Canadian federal government apparently spends 18-20% of Canadas GDP, topped up by local governments.
This is all very apples-to-oranges but doesn’t support the thesis that EU budget is particularly bloated.
Manny comments I agree with - it's a mistake, it's a reason to leave the distro...
I'd like to point out that Ubuntu/Canonical is now seemingly philosophically separate from the origins of GNU and Linux. If you're curious, read "Hackers" by Stephen Levy. They've lost the plot, so to speak, about the core spirit of open software and hardware, which is what gave the projects life in the first place. A philosophical understanding and unity between many, many top notch independent developers.
Another note is how AI contributions to such libraries and programs is going to have an unknown effect on quality. It's almost like there's a business case to rip all the good FOSS written by humans out of the hands of github and apt, and curate all the best source code to ensure it remains in circulation, and extant copies are available that are not washed out by loose standards WRT AI contribs. Or if not a business case, perhaps a reasonable reaction and a good personal vendetta.
> "until the context rot and sampling problem is fixed forever"
I agree, prompt adherence seems to get worse when operating on large inputs.
Does anyone have some notion of the SOTA with this? Can we expect big improvements by this time next year? (hopefully in open weights)
A lot of this is managed by the inference engine, and has nothing to do with the model.
Models that use, for example, sparse attention mechanisms are just trying to make the bad situation slightly less bad, such as using less RAM for context (thus requiring less context quantization) or using less bandwidth (thus running faster).
If people keep using temp, top-k, top-p, and min-p, and nothing else for samplers, we're ignoring ~3 years of sampling research that virtually eliminates the worst of context rot issues.
Your hardware can do way more than 64k tokens context window, can't it?
And with Ollama it's very easy, superficially you just drag the slider.
I'm now reading "Friends Don't Let Friends Use Ollama" linked in another comment so a lot of problems with that approach are surfacing for me right now.
So yeah. Along with others, I think you should come up with some empirical means of understanding if your preprompt is doing anything good since I doubt that it's all necessary and helpful. Second maybe you and I need to fix our runtimes.
Seeing the raw CoT is the best signal for evaluating prompt efficiency. The fact that frontier providers don’t share this feels criminal after this last weekend. They’ve played us all for fools.
Thanks for the token setting observation. I’m running ollama headless and from the command line. I didn’t even know there was a web ui. Conf file is pretty straightforward though.
I settled on 64k tokens because of a recommendation from an LLM. I now see that that guidance is associated with similar sounding (but not the same) configuration constraints. So this weekend I’ll try turning it to 11 (250k tokens?). Anticipating some hard locks over here.
CoT is performative and doesn’t reveal how reasoning happens. If you look at those traces locally it’s just gibberish, especially if the model falls into a loop.
This is good guidance, but what do you have to say about convincing your team of developers to live it out?
I've found that developers usually like writing code and avoid contributing to documentation. For some, it's actually scary because (edit: for them,) high quality writing is harder than high quality coding, and it can be avoided quite a bit.
On the project side, it's rare for the implementation and verification stages to not consume all the budget and more, and delivery creeping past the original optimistic date. So there's no time or money to spend on documentation.
The combination is that even with your great advice in hand, it's hard to navigate to really solid and comprehensive design documentation underpinning the products.
This is a good question, and I have a super long answer that's been in my head for like 8 years about how to influence your teammates to adopt good engineering practices.
The short answer is that most useful software engineering practices are a risk to the first person on the team to adopt them. For example, if everyone on your team thinks automated testing is stupid and you adopt automated testing, it will look like your work is worse because you're slower in the short-term, and maybe you have to do even more work when teammates break your tests.
It comes down to accruing social currency with your team. Your teammates don't want to take a risk for you if you have a history of bad ideas that wasted everyone's time. But if, for example, you implemented automated deploys to replace a tedious workflow developers had to do manually, people would see how your ideas have payoff, and they're more willing to invest a little bit if they expect ROI long-term.
When I've convinced my teammates to invest in design docs, I made sure I had some wins under my belt before I started pushing for everyone to write design docs. I invested a lot in docs myself so my teammates could see the value before I asked them to start writing.
This is also a place where you have to think about politics a bit. Documentation has a much better shot if it has support from the top, so think about the pitch to your manager or dev lead about how design docs make their jobs easier.
If you've not read Fred Brooks MMM, give it a shot.
It seems like you're implying that teams of 60-80 developers should be expected to outperform teams of 12. This is simply not true. The most important feature of source code as a language is that it allows precise mindshare among close knit teams. It doesn't guarantee it, but it makes it possible for people to talk about the product at a level that is otherwise very difficult.
A convenient side-effect of the source code is that it instructs the machine what to do. But instructing the machine was never the bottleneck, the essential difficulty of software development is in understanding what are the correct instructions to achieve some objective, not typing them out.
The problem is that communication doesn't scale at all. Having just 3 developers with good alignment about mental models, best practices, and design direction is hard enough, and if you found the right three people at the right time with the right ideas, you could generate billions of dollars of value.
Large monolithic teams on the order of 80 are a product of people in control not understanding how software development works, and how to make it work well.
As a user of local models, does this mean that there are 'magic incantations' that can increase the performance of some local models?
I see some details about recovering information via whatever technique. It's interesting, but appears not generalized.
So for a specific question, yes, but this is not about techniques like adding a good embedding that just generally tends to improve open model performance on certain tasks.
There is some research suggesting that a prefix from a stronger model will tend to elicit better completions from a smaller one. I am doing some experiments to see if I can replicate this in a practically useful way, e.g. Fable + 4B Qwen, or 125B Qwen Flash Next + 4B Qwen, results TBD.
I don't think that follows from the published results. Would have been an interesting hypothesis to add though, and quite easy. Just throw the same setup at some benchmarks.
When I look at the technology and capability of frontier models for ideation, brainstorming, and research, or when I look at the open source community and open weight models, and running local LLM's... I'm convinced that this is a technology revolution the size of the internet.
When I look at businesses trying to turn this research and technology into profit, I think that this is a bubble that will burst. You are not alone, I am very doubtful of the current concoction of AI features being offered. The true winners and legitimate applications will emerge over the next 30 years. Google, Nvidia, and Microsoft could all become IBM, Xerox, and Kodak. Or maybe they find a way to pivot. Because truly, there is an amazing amount of nonsense BS in the AI hype train.
Acknowledging the outside chance that recursive self-improvement works and we just scale directly into a Kardashev 1 civilization within my children's lifetime, and K-2 in theirs'. Can't rule it out no matter how hard I try.
honestly that would still probably mean more than story points. maybe all this AI insanity has an upside that the new cargo cult washes out the old one.
In the novel Anathem by Neal Stephenson, the internet becomes unusable for humans thousands of years before the events of the book, due to a process called Artificial Inanity.
AI generated content, both good and bad, some riddled with errors, some with only one subtle error hidden among lots of good information, floods the internet. The internet becomes an unnavigable swamp of weaponized nonsense for average humans. The problem is further compounded by the fact that searching and accessing the internet will be noticed by AI agents that will generate still more swamp content in response.
Unfortunately, it seems that this fiction ended up being prophetic. The open internet will fall to entropy, not legislation or one-sided international trade agreements. I think we need more projects like Anna's Archive, where the public uses torrents and distributed infrastructure to save and organize the world's information. Google has abjectly failed in its original mission to organize the world's information and make it universally accessible and useful.
How would that be immune? It already has many copies of the same books and no way to tell which ones are erroneous or incomplete. A malicious actor could easily flood it with garbage.
It will happen eventually, especially with AI generated books published electronically. I wonder if there is a cutoff date or something to try and avoid this problem of low effort books.
So he could sell the data? It’s almost certainly not because he cares so much about certifying human versus AI. Even then the biometric data is not going to be enough. Any human could still use the biometric data to pass AI work off as their own.
Indeed and the data is worth much much more if it is tied to a biometric.
And if you assume that in the future there will be crypto banks globally then you want to be that final authority layer between the real and the computer world
Yeah but my original comment was talking about being able to reliably mark something as AI generated or human generated. Crypto, encryption etc. whatever you throw at it it’s just not possible (other than media that can be watermarked at the generation source).
While I do appreciate and even support the idea that the immense wealth available on planet earth should be more evenly distributed, and people should mostly be focused on raising families, deepening their philosophy, and perfecting their craft... (some societies in Europe are pretty laid back in terms of hyper-productivity culture)
Regulation is possible without paralyzing industry. Laws about the internet are possible without stifling freedom. Central planning is possible without crashing the economy so bad you end up with a regime change. It all comes down to execution and for the most part, it's been a series of failed experiments.
As much as I want a happy future for Canadians and Europeans, I just don't see the odds being super favourable.
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