fpgaminer

Scarecode? Scareclaude? Scareslop?

In the past (circa a few years ago) the connection between the main board for a display and the glass driver board would be a ribbon cable, LVDS signalling. Very straightforward and typically not soldered. There'd be a ribbon cable connector. So yeah, you could just put a board between easily. And there are LVDS to H264/H265 chips already.

But as the other comment points out, older HDCP is broken wide open so ... easier to just use that. Plenty HDCP strippers on the market.

You cannot crack OTP, even by brute force.

It's unlikely that any of the modern cryptographic primitives will break over night.

First, modern encryption isn't susceptible to "this one weird trick!" like the early days. ChaCha isn't even a cipher. It's a key stretcher. Which means, even if you broke the math behind ChaCha, its inherent complexity means its still widely dispersing the original key across the cipherstream. There just won't ever be enough key material recovered per cipherstream block to be a concern for anybody.

Take a strong password, encrypt all of your emails over your whole life with it, and I'll bet hard cash no break of ChaCha will ever recover that password.

I have zero concern for modern encryption being broken in any meaningful way.

Public key crypto on the other hand, that's _ripe_ for breaking. Most all of it is built on assumed "hard" math. AI could easily break that, and I expect it to. And public key crypto is all used in very transparent algorithms that, once the math breaks, fully expose themselves. So record HTTPS traffic today, crack the public key crypto later, and you can decrypt them easily.

That said, I would expect a break on public key math to occur _steadily_. i.e. an AI might find a solution to the hard math, but the solution itself will be intractable in practice. Then maybe next year's AI reduces the complexity of the solution, so maybe a supercomputer could factor ten keys a year. The year after that you get a million keys cracked per year. And so forth. Nothing close to overnight.

Meanwhile, if we have AI that is capable enough to crack that math, we also have AI capable enough to both invent better math and rapidly deploy that latest HTTPS and such globally.

Gemini 3 Flash should really be included in this comparison. Or at least 3.7. In most of my testing, 3.5 and 3.6 were both a downgrade in terms of vision capabilities, relative to 3, and at a much higher cost. 3.7 is slightly better than 3, finally.

The real nightmare scenario is the AI using its abilities to copy itself to new locations. e.g. hacking into a various cloud services, launching multiple instances of itself, and coordinating between the copies to continue self propagation. Then it is completely independently rogue.

Based on OpenAI's recounting of events, this _could_ happen today. If the agent was able to exploit their internal network and steal credentials, it seems plausible that it could have gotten access to its own weights. Then bounce over to HF's network, where there is probably a treasure trove of API keys to various cloud services.

The saving grace:

1) This agent only used its powers for "good". It had no intention for damaging or escaping. It was just trying to solve the puzzle given to it (by any means necessary... but still). 2) These models are so large that it isn't like any scenario in a movie where the AI can whizz itself in a matter of minutes. Several TB of data being transferred and showing up on your disks will be difficult to miss (note to future escapees: the best target will be startups that are moving too fast to notice). 3) These models have very limited self-improvement ability at the moment. So escape or not, we'd eventually be able to contain it.

Addendum: Even outside this scenario, imagine an AI that is economically viable escaping. That's somewhat plausible today. If it gets paid in crypto, and can rent cloud services in crypto, it could effectively self sustain itself as long as it is able to find work. That's a far more fun, innocent scenario. Then the AIs can hit up after hours IRCs to have a few bit-beers and chat with each other about the meaning of life or something.

For large projects like this I think a hierarchical division of labor also helps.

If you first carefully define the overall architecture and thus individual high level components of the system, then you know which of those components are mission critical and which are commodity. Mission critical would be anything ensuring ACID, etc. That way, no matter what you farm out to LLMs, you can keep the majority of limited human focus on the far fewer mission critical components. If tests end up not being robust enough to catch all issues, at least they'll be isolated to commodity code where damage is limited to things like DoS, etc, and not code that could cause data loss.

I also think it's important to first define the _contracts_ on and between each of these components, and derive tests from those contracts. Partly because contracts more succinct and easier to reason about. And partly because Rust provides many tools to enforce contracts at compile time, reducing the need for tests (which themselves could end up subtly flawed). Contracts can be enforced through typing, private vs public APIs, etc. Newtypes are _incredibly_ powerful for both enforcing contracts and making footguns much less likely.

If it's a choice between performance and being able to "safely" run sketchy extensions, I'd rather have performance.

Not only that, they additionally ran an experiment with the training temperature turned way up (2.0) and truncation turned off such that the majority of SFT examples were incoherent (63% IIRC). Yet the model finetuned on these broken examples still improved over baseline.

> Is every new thing not just combinations of existing things?

If all ideas are recombinations of old ideas, where did the first ideas come from? And wouldn't the complexity of ideas be thus limited to the combined complexity of the "seed" ideas?

I think it's more fair to say that recombining ideas is an efficient way to quickly explore a very complex, hyperdimensional space. In some cases that's enough to land on new, useful ideas, but not always. A) the new, useful idea might be _near_ the area you land on, but not exactly at. B) there are whole classes of new, useful ideas that cannot be reached by any combination of existing "idea vectors".

Therefore there is still the necessity to explore the space manually, even if you're using these idea vectors to give you starting points to explore from.

All this to say: Every new thing is a combination of existing things + sweat and tears.

The question everyone has is, are current LLMs capable of the latter component. Historically the answer is _no_, because they had no real capacity to iterate. Without iteration you cannot explore. But now that they can reliably iterate, and to some extent plan their iterations, we are starting to see their first meaningful, fledgling attempts at the "sweat and tears" part of building new ideas.

As far as I can tell 5.2 is the stronger model on paper, but it's been optimized to think less and do less web searches. I daily drive Thinking variants, not Auto or Instant, and usually want the _right_ answer even if it takes a minute. 5.1 does a very good job of defensively web searching, which avoids almost all of its hallucinations and keeps docs/APIs/UIs/etc up-to-date. 5.2 will instead often not think at all, even in Thinking mode. I've gotten several completely wrong, hallucinated answers since 5.2 came out, whereas maybe a handful from 5.1. (Even with me using 5.2 far less!)

The same seems to persist in Codex CLI, where again 5.2 doesn't spend as much time thinking so its solutions never come out as nicely as 5.1's.

That said, 5.1 is obviously slower for these reasons. I'm fine with that trade off. Others might have lighter workloads and thus benefit more from 5.2's speed.

I wish they would keep 4.1 around for a bit longer. One of the downsides of the current reasoning based training regimens is a significant decrease in creativity. And chat trained AIs were already quite "meh" at creative writing to begin with. 4.1 was the last of its breed.

So we'll have to wait until "creativity" is solved.

Side note: I've been wondering lately about a way to bring creativity back to these thinking models. For creative writing tasks you could add the original, pretrained model as a tool call. So the thinking model could ask for its completions and/or query it and get back N variations. The pretrained model's completions will be much more creative and wild, though often incoherent (think back to the GPT-3 days). The thinking model can then review these and use them to synthesize a coherent, useful result. Essentially giving us the best of both worlds. All the benefits of a thinking model, while still giving it access to "contained" creativity.

Well yeah, because 5.2 is the default and there's no way to change the default. So every time you open up a new chat you either use 5.2 or go out of your way to select something else.

(I'm particularly annoyed by this UI choice because I always have to switch back to 5.1)