I think it is a potentially good camera format, good medical & scientific imaging format, good RAW compression format (Apple uses it in some newer iPhones for this), good media interchange formats for tools like Photoshop (think about storing all of your layers inside of one JPEG XL that's fully compatible with .psd files), and more. It is incredibly expressive and versatile, which is what makes it so risky on the Web.
The ARC-AGI-3 harness was throwing away reasoning tokens between turns. This is very bad harness design.
The models are designed to keep the reasoning tokens separate from the output and only publicly emit tool calls and the sometimes a summary of the reasoning tokens. The models are trained to depend on those private reasoning tokens. You can’t just delete them.
Another perspective on this is that coordination is the hardest problem to solve in deploying technology that depends on cooperation between different parties. It takes a few of the big centralized players to agree to put their weight behind it.
It’s a very soft skill that changes as the capabilities of AI change.
But one thing that doesn’t change is the need to specify an end goal correctly and precisely. I think the emphasis of knowledge/information-processing work is going to increasingly be placed on verification mechanisms. This is practically equivalent to precisely defining an end goal.
Spend time deeply thinking about what it means for a solution to be correct. What properties will a correct solution have? What of those properties are testable? Write those things down and tell the agent.
As models get better, agents will be able to target more and more difficult end goals. The strategy just becomes more useful. (It’s useful for people as well.)
For example, if you want an agent to write a photo editor, think about what end properties the editor should have. There are reference images for color space and rendering transformations. That’s a good start.
Sometimes the goal will be fuzzier. “I want a feature like CaptureOne where I provide a reference image and it makes my image look like that.” Well, time to think really hard about what that means. Iterate with AI on how to test for that precisely. Come up with some good metrics/heuristics. Maybe it means local contrast should match. Maybe the overall distribution of colors. Maybe something more complicated.
Then you have a target and you can let an implementation agent work against that. If it fails, it’s either because the agent is bad or your target was incorrect or incomplete. As models get better, the limiting factor becomes your ability to correctly define a problem.
Pretty cool, rendering PowerPoint files to an image is probably the only way for LLMs to make sense of them.
Does this work in Cloudflare’s workerd environment? Would be nice to have a cheap serverless render -> LLM (GLM-OCR / PaddleOCR) -> Markdown pipeline for the various MS Office formats.
This code creates a JSON intermediate representation that LLMs could probably consume. You might want to simplify it to focus on content and reduce token usage.
> While OpenAI originally pioneered Codex (which went on to power GitHub Copilot), Google’s direct answer for dedicated, native code completion and natural-language-to-code generation is CodeGemma.
Nothing about this is a hallucination. The Codex that it talks about is real, existed, and did go on to power the original Copilot. You neither specified that you meant a different Codex, nor did it make anything up. The CodeGemma isn't made up either, as its referenced working link shows.
If you give every agent an isolated container to use, you’re going to be paying for the reserved memory while the container is active, even if the agent isn’t doing anything.
Lots of comments about the price change, but Artifical Analysis reports that 3.1 Flash-Lite (reasoning) used fewer than half of the tokens of 2.5 Flash-Lite (reasoning).
This will likely bring the cost below 2.5 flash-lite for many tasks (depends on the ratio of input to output tokens).
That said, AA also reports that 3.1 FL was 20% more expensive to run for their complete Intelligence index benchmark.
The overall point is that cost is extremely task-dependent, and it doesn’t work to just measure token cost because reasoning can burn so many tokens, reasoning token usage varies by both task and model, and similarly the input/output ratios vary by task.
Do not use 3.1 Flash-Lite with HIGH reasoning, it reasons for almost max output size, you can quickly get to millions of tokens of reasoning in a few requests.
Wow, that’s very interesting. I wish more benchmarks were reported along with the total cost of running that benchmark. Dollars per token is kind of useless for the reasons you mentioned.
Yup, MiniMax M-2.5 is a standout in that aspect. It's $/token is very low, because it reasons forever (fun fact, that's also the reason why it's #1 on OpenRouter, because it simply burns through tokens, and OpenRouter ranking is based on tokens usage)...
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