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I haven't posted too much about AI on here, largely because my own personal experiences with using it have been boring and underwhelming. Generating offensive memes (9/11 gender reveal, racial stereotypes, etc.) is my most positive interaction with AI. And partly because I find the pro-AI "AGI is just around the corner bro!" crowd obnoxious as hell, and I find that most discussions about it depend on accepting certain massive assumptions about what we actually do (and don't) know about the nature of intelligence, consciousness, the human brain, etc. For the purposes of making my biases clear up front: Personally, I'm religious and believe in the existence of a human spirit/soul, so I'm already strongly biased against claims that consciousness is an emergent property of sufficiently advanced systems or any arguments along those lines.
Regardless, a few developments have happened recently that have motivated me enough to actually make a top-level post about this. The first being my (employer-mandated) use of Claude to generate code. "You're not using the latest model, just one more model and we'll reach AGI"-bros officially in shambles after this one. I have an HTTP API client library I wrote a few years ago for interacting with a 3rd party API. There's a good amount of duplicate logic throughout for things like setting up and making the requests, caching, etc. I asked Claude to look over the code and extract out the duplicate logic into a single implementation Here's how it messed up just the authentication part of it:
This was with the latest version of Claude Sonnet. We don't have access to the latest version of Opus, but I'm sure an AI-bro on here would insist that Opus would totally get this right. Regardless, it failed spectacularly at what would be an easy (but tedious) task for a mid-level developer and above (or a sufficiently talented junior).
The second happening is the ARC prize people releasing version 3 of their AGI test suite, a series of puzzle games. They released it within a few hours of Jensen Huang saying he thinks the latest and greatest models are capable of AGI. Humans were capable of solving 100% of the puzzles. The highest scoring AI couldn't complete more that 0.5%.
I'm willing to bet future models will do st least somewhat better on this, but only because I'm maximally cynical and I fully expect these puzzles to be included in the training set for future SOTA models.
I tried several of the puzzles myself, and none of them are terribly difficult. I'd estimate that anyone in the 100-110 IQ range or higher would be able to solve most or all of them. This development has further reinforced my belief that LLMs are basically just really advanced statistical regression models on crack, but nothing approaching what we would consider actual intelligence or conscious thought (and this is before we get into Chinese Room style criticisms of them).
In any case, I'm curious to see what you all think of these. Even the AI-bros I've been speaking about condescendingly throughout this post. If anything, I'm actually most curious about and interested in the AI-bros responses, I'd love to hear yoyr thoughts.
Here are the AGI puzzles for anyone interested in trying them out: https://arcprize.org/arc-agi/3
I’m no AI bro, but Opus 4.6 is genuinely really good and I’m concerned about my skills atrophying because I’m becoming highly reliant on it.
How did you use it? It makes a big difference if you use /init in Claude Code, followed by /plan where you describe what you want, and give it the ability to compile/run/test the code in a feedback loop.
I used the Cline plugin for JetBrains Rider, told it the path to the class, and asked it to extract out the duplicated logic throughout the class. So I could try again tomorrow with what you suggest (if that's even possible via the Cline plugin with Sonnet) and see if I get better results.
But my favorite part was when the AI assured me that its changes would so what I asked for without breaking existing fundtionality.
That said, the best and worst part if this is that it is pushing my incompetent Indian coworkers to use VS Code instead of VS because there is no Cline support in full fat VS and Cline is what the higher ups are mandating (I have a personal JetBrains subscription so that's what I'm using). .NET support in VS Code, especially for the many .NET 4.x projects we have kicking around (especially our WCF trash fires), is very lacking so I foresee my Indian coworkers' already pitiful productivity plummeting even further. I also forsee many requests for assistance coming to me and my Russian coworker (unsurprisingly he's the only other competent dev I work with regularly).
While I haven’t tried Cline, I know that Claude works best in Claude Code and Anthropic is trying its best (leaks aside) to have people locked into its ecosystem.
You won’t get a good picture of the capabilities of AI agents until you’ve tried the top models in a decent harness, unfortunately.
You're assuming he wants a good picture of the capabilities of AI agents. I get the strong impression from the sneering tone of the original post that he wanted to do just enough with a model that he could claim to have pwned the "AI-bros".
So I don't know if the OP was motivated by that, or if there's some other reason, but I've definitely noticed what seems like a big dichotomy in the way people approach modern generative AI tools. Which is that, some types of people see a tool with its limitations that make it fail in spectacular ways that seem silly or stupid, throw their hands up in the air and declare it as not sufficiently useful for their purposes. Other types of people see a tool with its limited abilities and figure out a way to exploit their abilities to accomplish things they couldn't without the tool, even if it means adjusting and inventing new workflows.
I first noticed this when I got heavily into Stable Diffusion in ye olden dayes of 2022. Of course, awful hands, foreground lines merging into background lines, inconsistent lighting, hallucinations, were all famous issues of image generation AI then. They're still issues now, but vastly reduced. Some people saw that and declared AI useless for their needs, since their hand drawing allows for the control they need that AI doesn't. Other people saw that generating messes with 7 fingers was like making one bad brush stroke on an empty canvas and giving up on the painting, and figured out that it's easy to iterate on subsections of the image multiple times, allowing someone to create illustrations that are far beyond their manual ability while still avoiding the common AI pitfalls.
I noticed it happening with LLMs shortly after, where some people zero in on stupid mistakes like that the hard R problem of strawberries and declare it too inconsistent or too stupid to be of much use. Other people zero in on the limited abilities and figure out how to build structures and scaffolding to allow the tool to exceed those natural limitations, enabling them to create code that they couldn't have before or that would have taken a lot more time before.
I don't think the former type of person is doing this in bad faith, or with a desire to sneer. I think there's probably just a spectrum in people's attitudes with something like this, and because AI is both ridiculously bad at some things and ridiculously good at others, this causes the spectrum to bifurcate.
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