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OpenAI begins rolling out GPT-6 Astra

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Re: OpenAI begins rolling out GPT-6 Astra

#152
post #22

(Posting partly so I can revisit my predictions when they open access more widely) A big problem I have with OpenAI's models (and of course Claude) is that they tend to write the most over-engineered pieces of code, beyond the imagination of any architecture's astronaut. Just this week I asked 5.6-sol-ultra to update a 1000 LOC python script I had, to "incorporate the key lessons learned when using it for another pro…

You should have a sub agent adversarially enforce KISS before every commit.

"You should have a sub-hammer to adversarially enforce that your primary hammer accurately drives nails into wood"

We wouldn't accept such behavior from any other tool, machine, or computer program. At least most of us would not. To paraphrase the old saying: Some people, when confronted with a problem with an AI model think "I know, I'll use an additional AI model." Now they have two problems.

I find it very intriguing that two lineages of models -- from two different companies -- that are supposed to be painstakingly RL'd to become human-level programmers are actually consistently bad at it out of the box in very similar ways. One would think that at least one of OpenAI and Anthropic would (could) have pushed their model to a direction different from "if you can solve a 100-line problem in 10,000 lines, do it".

Re: OpenAI begins rolling out GPT-6 Astra

#154
post #23

Am I the only that thinks that anything similar to AGI will come not from raw model capacity but from model speed and efficiency? In my experience the harness is more important than the model, and anything able to run at 700tps will be the "next big thing". PS: assuming the current architecture is the right one

Why would speed matter? Surely an AGI could think slowly but still be an AGI

You can imagine with more operations being available to be done more cheaply and quickly the LLM doesn't need to "one shot" a solution. It could try many solutions, test them, throw some away, wiggle some of the parameters like a genetic algorithm, see how that changes the result, and converge on an optimal solution (based on whatever the cost function is). Basically producing a good result could become like an optimization problem. That would be way too expensive and slow right now.

Re: OpenAI begins rolling out GPT-6 Astra

#157
post #49

(Posting partly so I can revisit my predictions when they open access more widely) A big problem I have with OpenAI's models (and of course Claude) is that they tend to write the most over-engineered pieces of code, beyond the imagination of any architecture's astronaut. Just this week I asked 5.6-sol-ultra to update a 1000 LOC python script I had, to "incorporate the key lessons learned when using it for another pro…

Would you mind posting that code to github? I'm curious about the complexity you're describing. If not, no worries!

Sure, why not: https://github.com/sergiocorreia/overengineered-rand-mcnally

The original script was mostly very simple python:

1. Download some public PDFs. 2. Have a double for-loop (over PDFs and pages within PDF), 3. Use a library to call gemini-3.7-flash and ask it to run some OCR 4. Save JSON outputs, save a csv with results, validate with some Stata code

New code folder was 189 files. Just the PDF download folder is now 7 files involving an adapter, a source manager, an acquisition manager, etc.

Every instance of saving a file involves saving a temporary copy and then moving it, so e.g. I lose power, we minimize the risk of corrupted files.

And so on!

Re: OpenAI begins rolling out GPT-6 Astra

#159
I see that Muse Spark 1.3 (max) beats GPT-6 Astra on some benchmarks:

Test: Muse Spark 1.3 / GPT 6 Astra

DeepSWE v1.1: 75.4% / 74.1%

AutomationBench: 49.4% / 41.4%

Is that enough to bring this discussion down to earth again?

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