The word “intelligence” is doing a lot of heavy lifting here. LLMs lack any mechanism for true logical reasoning, and they always will by nature. This is why they fail at simple questions like “the car wash test”. It’s also why agents are expensive; They just flail around in token hungry “reasoning loops” until they happen to come across a correct solution. And it’s why Claude Opus 4.8 (High) only scores 1.5% on the ARC-AGI-3 benchmark at a cost of $10,000.

This kind of panic is just part of the hype. Wake me up when real intelligence arrives.

I don’t understand this form factor. It’s too small for any serious work, both in terms of screen size and typing, and it’s too big to fit in your pocket. So you might as well carry a small laptop around, since it would actually be usable. Feels like a novelty that is just meant to satisfy a cyberpunk aesthetic, if you’ve got the money to burn on something that you’ll probably only use a few times, or occasionally at best.

Season 4 Episode 23 https://en.wikipedia.org/wiki/Living_Witness

You almost always have nano or pico available, so it’s really unlikely that you’d get stuck with nothing but vim, unless you just didn’t know that nano existed.

Oh, my apologies for posting this on a public forum. This trick was not meant for you.

The Luddites didn’t hate machines. They were gifted artisans resisting a capitalist takeover of the production process that would irreparably harm their communities, weaken their collective bargaining power, and reduce skilled workers to replaceable drones as mechanized as the machines themselves. Their struggle has been tragically warped into a caricature when it is more relevant than ever.

https://www.currentaffairs.org/news/2021/06/the-luddites-were-right

IntelliJ doesn’t help when you’re doing a code review, or just reading through hundreds of lines of code, I don’t want to move my mouse or cursor over every line to see the parameter names.

Probably something like “Please bro!!! WHY DID YOU DO THIS ??!! 😭😭”

Some good debunking here: https://www.flyingpenguin.com/the-boy-that-cried-mythos-verification-is-collapsing-trust-in-anthropic/

Fair point. I can see how a bubble burst might not recover those discarded wafers, assuming that story is true. However, I’d still imagine that if the bubble did burst, there would naturally be a reduction in demand for memory, and that would cool prices at least a bit. Certainly time will tell. It’s still difficult to predict the direction this is all going in.

I think most of their value was as a streaming backend for other companies, not their user-facing video sharing platform.

They started the meeting with a prayer about “keeping their minds on the children”, followed by the most robotic sounding pledge of allegiance I’ve ever heard, and then they proceeded to pardon the predator, mostly using religious “grace” as a justification. America!

Buddhist Copilot builds apps with sublime coding standards, and on the last iteration it runs rm -rf * .git before it recites a koan on impermanence.

I suspect the problem is that there are many developers nowadays who don’t care about code quality, actual engineering, and maintenance. So the people who are complaining are right to be concerned that there is going to be a ton of slop code produced by AI-bro developers, and the developers who actually care will be left to deal with the aftermath. I’d be very happy if lead developers are prepared to try things with AI, and importantly to throw the output away if it doesn’t meet coding standards. Instead I think even lead developers and CTOs are chasing “productivity” metrics, which just translates to a ton of sloppy code.

Why is this so downvoted?

I don’t quite understand the concerns in the comments here. The original blog post seems reasonable: https://www.waterfox.com/blog/15-years-of-forking/#what-waterfox-is-in-2026

Yes, the LLMs received credit for each level even if they didn’t complete the entire environment.

They have some replays of tasks on their website: https://arcprize.org/tasks

Here’s one where the human completed all 9 levels in 1458 actions, but the LLM completed only one level in 24 actions, then struggled for 190 actions until it timed-out, I guess. The LLM scored 2.8% because of the weighted average, I think. I didn’t take the time to all do the math, and I’m not sure if the replay action count is accurate, but it gives you an idea.

Human: https://arcprize.org/replay/0d461c1c-21e5-4dc8-b263-9922332a6485

LLM: https://arcprize.org/replay/cc821983-3975-4ae4-a70b-e031f6807bb0

I like the trend of refining existing tools. You take tried-and-true commands and shave off the rough edges and quirks. I use ripgrep instead of grep, fd instead of find, scm_breeze on top of git, dust instead of du, duf instead of df, z over cd, and xh instead of curl

You can really only judge fairness of the score if you understand the scoring criteria. It is a relative score where the baseline is 100% for humans – i.e. A task was only included in the challenge if at least two people in the panel of humans were able to solve it completely, and their action count is a measure of efficiency. This is the baseline used as a point of comparison.

From the Technical Report:

The procedure can be summarized as follows:
• “Score the AI test taker by its per-level action efficiency” - For each level that the test taker completes, count the number of actions that it took.
• “As compared to human baseline” - For each level that is counted, compare the AI agent’s action count to a human baseline, which we define as the second-best human action count. Ex: If the second-best human completed a level in only 10 actions, but the AI agent took 100 to complete it, then the AI agent scores (10/100)^2 for that level, which gets reported as 1%. Note that level scoring is calculated using the square of efficiency.
• “Normalized per environment” - Each level is scored in isolation. Each individual level will get a score between 0% (very inefficient) 100% (matches or surpasses human level efficiency). The environment score will be a weighted-average of level score across all levels of that environment.
• “Across all environments” - The total score will be the sum of individual environment scores divided by the total number of environments. This will be a score between 0% and 100%.

So the humans “scored 100%” because that is the baseline by definition, and the AIs are evaluated at how close they got to human correctness and efficiency. So a score of 0.26% is 1/0.0026 ~= 385 times less efficient (and correct) compared to humans.

The goal of the ARC organization is to continually measure progress towards AGI, not come up with some predictive threshold for when AGI is achieved.

As long as they can continue to measure a gap between “easy for humans” and “hard for AI”, they will continue releasing new iterations of this ARC-AGI challenge series. Currently they do that about once a year.

More detail about the mission here: https://arcprize.org/arc-agi