lmao nope burn in hell old programming. What is emerging is a thousand times better than that dumpster fire
627 karma · joined August 29, 2016
Path 1: a ZK-proof attestation certificate marketplace implemented by GrapheneOS (or similar) to prove safety in a privacy-securing way enough for 3rd party liability insurance markets to buy in. Banks etc can be indifferent, and wouldn't ignore the market if it got big enough. This would mean we could root any device with aggressive hacking and then apologize for it with ZK-proof certs that prove it's still in good hands - and banking apps don't need to care. No need for hard chains of custody like the Google security model.
Path 2: Don't even worry too hard about 3rd party devices or full OSes, we just need to make the option viable enough to shame Google into adopting the same ZK certificate schemes defensively. If they're reading all user data through ZK-proof certs instead of just downloading EVERYTHING then they're significantly neutered as a Big Brother force and for once we're able to actually trust them. They'd still have app marketplace centrality, but if and when phones are being subdivided with ZK-proof security it would make 3rd party monitoring of the dynamics of how those decisions get made very public (we'd see the same things google sees), so we could similarly shame them via alternatives into adopting reasonable default behaviors. Similar to Linux/Windows - Windows woulda been a lot more evil without the alternative next door.
Longer discussion (opinion not sourced from AI though): https://chatgpt.com/share/68ad1084-eb74-8003-8f10-ca324b5ea8...
I have been reprimanded and tediously spent collectively combing over said quick prototype code for far longer than the time originally provided to work on it though, as a proof of my incompetence! Does that count?
Because this is the first pass on any project, any component, ever. Design is done with iterations. One can and should throw out the original rough lynchpin and replace it with a more robust solution once it becomes evident that it is essential.
If you know that ahead of time and want to make it robust early, the answer is still rarely a single diligent one-shot to perfection - you absolutely should take multiple quick rough iterations to think through the possibility space before settling on your choice. Even that is quite conducive to LLM coding - and the resulting synthesis after attacking it from multiple angles is usually the strongest of all. Should still go over it all with a fine toothed comb at the end, and understand exactly why each choice was made, but the AI helps immensely in narrowing down the possibility space.
Not to rag on you though - you were being tongue in cheek - but we're kidding ourselves if we don't accept that like 90% of the code we write is rough throwaway code at first and only a small portion gets polished into critical form. That's just how all design works though.
Sign (Signum) - The thing which points Locus - The thing being pointed to Sense (Sensus) - The effect/sense in the interpreter
Also known by: Representation/Object/Interpretation, Symbol/Referent/Thought, Signal/Data/User, Symbol/State/Update. Same pattern has been independently identified many many times through history, always ending up with the triplet, renamed many many times.
What you're describing above is the "Locus" essential object being pointed to, fulfilled by different contracts/LLMs/systems but the same essential thing always being eluded to. There's an elegant stability to it from a systems design pov. It makes strong sense to build around those as the indexes/keys being pointed towards, and then various implementations (Signs) attempting to achieve them. I'm building a similar system atm.
For once, as developers we are actually using computers how normal people always wished they worked and were turned away frustratedly. We now need to blend our precise formal approach with these capabilities to make it all actually work the way it always should have.
Having plenty of initial discussion and distilling that into requirements documents aimed for modularized components which can all be easily tackled separately is key.
The only discernable difference that won't be replicable is a cryptographic signature "Certified 100% Human-Made!" sticker, which will probably become the mark of the niche industry.
Somewhat more accurate analogy would be the custom car market. Beautiful collectible convertibles with fine detailing everywhere, priced thousands of times higher than normal cars, that actually run far worse and basically break apart after a few thousand miles and are impossible to find parts for. Automated factories certainly could churn them out but they don't because they're impractical poorly-designed status items kept artificially scarce for the very rich to peacock with.
Except AI will probably still produce equivalent impractical stuff anyway, just because production (digital and physical) will eventually be easy enough that resources are negligible, and everyone can have flashy impractical stuff. So again, only that "100% Human!" seal will distinguish, eventually.
Besides, those are incredibly short-term concerns. Recent models are a whole lot more trustworthy and can search for and cite sources accurately.
Sure, you should lift them yourself too. But using an AI teaches you a shit-ton more about any field than your own tired brain was going to uncover. It's a very different but powerful educational experience.
So much time was saved you don't even realize it because most of the above was just practically impossible to do before - and frankly beyond the scope of what any human actually needs. But the scope crept anyway and now they're all normal parts of modern life taken for granted. As for where that time went - capabilities exploded, but any spare time also got eaten by tighter work hours from a more competitive market. That's capitalism for ya baybeeeee
Unless there's a new world war or draconian regulation, we're good. It's pretty much locked in.
You don't have to pay attention, that's the point. You can code without reading code now. Sure you gotta tell it what the app looks like with each iteration - but again, that's temporary til the next model comes out with good enough vision to assess that itself. None of this is permanently planning on requiring human interaction - it's just early days and these are progressing through mediums one at a time.
They're not canned responses either. They're bespoke mixtures of all the various elements of the current environment/context translated to an answer. It certainly handles novelty - that's the whole point. They certainly handle plenty of novelty - like entire mediums of text and images - to expert levels. I think you're just being greedy for more, here.
As for consistency and avoiding error? There are benchmarks for that. There are error checking methods. Those are all steadily improving too, and are already well-consistent on easier topics/mediums. It would be foolish to think that's innately impossible from AI for remaining ones.
I dont believe there are any significant academic critiques doubting this. There are a lot of armchair hot takes, and perceptions that this stuff isn't improving up to their expectations, but those are pretty divorced from any rigorous analysis of the field, which is still improving at staggeringly fast rates compared to any other field of research. Aint no wall, folks.
Just like the dotcom bubble, AI is gonna hit, make a few companies stinking rich, and make the vast majority (of both AI-chasing and legacy) companies bankrupt. And it's gonna rewire the way everything else operates too.
Perhaps your "definition" should be simply that LLMs have temporarily seen limitations in their ability to natively do math unassisted by an external memory, but are exceptionally good at very advanced math when they can compensate for their lossy short-term attention memory...
Now that the neoliberals are embarrassed enough to throw out "woke", are we slipping in economic concerns too?
PSA: YOU CAN STILL BE A SELF-RIGHTEOUSLY MORALISTIC PRICK, SO LONG AS IT'S BASED ON ACTUAL TANGIBLE ECONOMIC ISSUES THAT ARE SYSTEMIC AND ACTIONABLE
"First break the problem down into known facts, then pull relevant world knowledge, then bring it all together to assess the problem from multiple angles and make a conclusion. Do not immediately just use the first obvious conclusion."
You're gonna get a lot better responses. I suspect this is more of a "look! LLMs make bad kneejerk responses when we try to trick them from what they were expecting!" rather than "Look! They aren't even smart reasoners, they can't even figure out these problems without memorizing!"
They do memorize. But that cuts both ways - making problems very close to the memorized one mess with their perception, the same way humans will instinctually respond to something that looks like a face before stepping back and assessing.
Once that's all done, you basically have a well-structured question you could pass to an underling and have them completely independently work on the project without bugging you. That's the goal. Now, pass that to o1 or Claude, depending on whether it's a general-purpose task (o1) or a code-specific task (Claude), and wait for response. From there, have a conversation or test-and-followup of whatever it spits out, this time with you asking questions. If good enough, done. If not, wrap up whatever useful insights from that line of questioning and put it back into the initial prompt and either re-post it at the end of the conversation or start a fresh conversation.
I find 90% of the time this gets exactly what I'm after eventually. The few other cases are usually because we hit some cycle where the AI doesn't fully know what to change/respond, and it keeps repeating itself when I ask. The trick then is to ask things a different way or emphasize something new. This is usually just a code-specific issue, for general problems it's much better. One other trick is to ask it to take a step back and just tackle the problem in a theoretical/philosophical way first before trying to do any coding or practical solving, and then do that in a second phase (asking o1 to architect code structure and then Claude to implement it is a great combo too). Also if there is any way to break up the problem into smaller pieces which can be tackled one conversation at a time - much better. Just remember to include all relevant context it needs to interface with the overall problem too.
That sounds like a lot, but it's essentially just project management and delegation to somewhat-flawed underlings. The upside is instead of waiting a workweek for them to get back to you, you just have to wait 20 seconds. But it does mean a ton of reading and writing. There are certainly already some meta-prompts where you can get the AI to essentially do this whole process for you and assess itself, but like all automation that means extra ways for things to break too. Let the AI devs cook though and those will be a lot more commonplace soon enough...
[Edit: o1 mostly agrees lol. Some good additional suggestions for systematizing this: https://chatgpt.com/share/6775b85c-97c4-8003-bd31-ee288396ab... ]
From my understanding that's certainly possible to do without the latency hurting much with large batching between inference layers