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GPT-6 Astra

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Re: GPT-6 Astra

#551
post #335

It's fun, but every new model release makes me even less interested to create cool stuff. Like, what's the point, if the next AI can do it in 5 seconds?

The point is to inject something into the process that these AIs can't do for you. People SHOULD feel like making a useless Mario Kart clone isn't worth the effort anymore. They should, instead, be trying to figure out how to actually use these models to make something that doesn't feel like a useless Mario Kart clone.

One thing that still stands today, is that even vibe-coding a good product takes time and thousands of dollars in tokens costs.

Software will be more like a "proof of work", where people would still pay $100 for good software that took $10k tokens to build.

Re: GPT-6 Astra

#552
post #227

I want to take a step back: So, this is GPT-6 -- the natural number version release comparable to GPT-4 and GPT-5 from the past few years. The ARC-AGI-3 score is obviously impressive at 99.9% (we'll need to wait for more details on how they used the response API harness on GPT-6 Astra, wrt reasoning retention and compaction), but every other benchmarks seems to be a relatively modest improvement, comparable with any…

We've had AGI (artificial general intelligence) probably since the first release of ChatGPT, and certainly since the first agentic harnesses. They're just finally acknowledging what the term means.

Re: GPT-6 Astra

#553

I can’t help but notice how much this echoes Francois Chollet’s On the Measure of Intelligence: https://arxiv.org/abs/1911.01547 Most of frontier-model progress still looks like skill acquisition optimization: broader benchmark coverage and performance, more domains absorbed into the training distribution, and increasingly strong performance within that surface area. It seems more about coverage-driven competence. So…

They can do new tasks with in-context learning but its obviously limited by context window

Re: GPT-6 Astra

#555

OpenAI is killing it now that they are more focused. Killing projects like Sora et al have seen it go from irrelevant to level footing with Anthropic. Sol is so much better than Fable 5. Then we get Astra (yet to use it) few days after Fable 5.1 (which is very impressive). Codex is slightly better than Claude Code. Good on Sam Altman getting back to basics and turning OpenAI around.

Codex is missing a few things that Claude code has had for some time like defined plugin subagents and a few other things. But overall it’s fairly capable. The biggest gripe I have is that codex really restricts context window sizes and compaction leads to a lot of grounding work, and overall codex GPT is too literal in many situations - it’s follows direction slavishly, and when subagent reviewers are used, they tend to find increasingly obscure “flaws” on the instruction following impetus, and the harness agent takes them literally as issues to fix even when it leads to bizarre outcomes. For instance I’ve had several runs where it tries to end up building a hermetic system with sha hashing of everything (including operating system binaries and kernels, tool chains, etc) to certify test results are valid, etc. I have to sort of watch it carefully to be sure it’s not drifting into some insane yak shaving corner, which it will happily do for weeks on end.

Claude has the exact opposite problem, especially opus-5, where I literally can’t trust it to print hello world without taking a shortcut, or just simply lying and saying it printed it when it didn’t, behind a giant wall of inscrutable text. I find it very ironic that Anthropic is the vendor of the lazy lying cheating model that does almost everything you tell it to it do.

I’d really kill for something that balances instruction following and loop escaping behavior better. Fable 5.1 does seem a lot better, feeling more like 4.6 behavior, and honestly Sol has improved as well. I’m pretty psyched for the next generation, as I think the competition has heated up so much that things will improve really fast to the point of marginal utility opportunity being increasingly close to epsilon.

Re: GPT-6 Astra

#556

The ARC-AGI-3 scorecard is extremely misleading given that it clearly states itself that "with [the responses API] harness, we estimate Sol would score in the ballpark of ~30%." but it shows a score of 7.8% for GPT-5.6 Sol presumably since if they updated the percentage for GPT-5.6 Sol to the score it would receive with the responses API harness they used for GPT-6 Astra they'd have to do the same for the percentage…

[flagged]

Re: GPT-6 Astra

#557

I have nothing to say about the actual model, but unrelated--why do so many of these demos include people buying things autonomously? Even if I did trust an AI to get everything right, it's not like the AI can read my mind. If I was ordering food normally and without AI, I would want more control over the process--looking over the options, prices, thinking about what I really want. People don't know what they really…

Despite access to """"""AGI""""""" all the marketing teams at these companies can only dream up 2 things, buying plane tickets and online shopping autonomously. Sometimes they're feeling extra spicy and throw in sorting emails or something along those lines.

I suspect it's because it's tailored towards VCs and other similar rich ghouls as a replacement for their overworked and underpaid secretaries

Re: GPT-6 Astra

#558

OpenAI is killing it now that they are more focused. Killing projects like Sora et al have seen it go from irrelevant to level footing with Anthropic. Sol is so much better than Fable 5. Then we get Astra (yet to use it) few days after Fable 5.1 (which is very impressive). Codex is slightly better than Claude Code. Good on Sam Altman getting back to basics and turning OpenAI around.

Its funny, my experience with Sol has been awful. It really overworks problems and tracks into areas it does not need to... I just dont get how its good for some, and bad for others. It makes me suspect that the models performance is not even against problem sets and it really is just a probabilistic prediction machine. Which then makes me very skeptical of GPT-6 Astra, because if their big claim is Computer Use then…

You have to bake the 'lazy dev'/'keep it simple stupid' mentality into your AGENTS.md and / or the skills you're using to design things. It will take things too literally sometimes so you also have to make sure you're being accurate. Best way I've found to use it is make it ask you clarifying questions about what you're trying to build and have it help design the shape of the thing. Then it writes the instructions in a format it understands.

I've had Claude do the same thing where it goes off and spends 100% of my tokens on 3 functions and an ungodly amount of tests / scaffolding that do almost nothing when I gave it an underdeveloped idea.

Re: GPT-6 Astra

#559

The ARC-AGI-3 scorecard is extremely misleading given that it clearly states itself that "with [the responses API] harness, we estimate Sol would score in the ballpark of ~30%." but it shows a score of 7.8% for GPT-5.6 Sol presumably since if they updated the percentage for GPT-5.6 Sol to the score it would receive with the responses API harness they used for GPT-6 Astra they'd have to do the same for the percentage…

Only someone who doesn't do any work of any meaningful difficulty could think these models have anything to do with AGI. Today I spent half a day trying to solve a moderately interesting software engineering problem. I was switching between GPT-5.6 Sol and Fable 5.1 to check each other's work in Cursor. And the result was gradually driving me insane. As the models struggled to find a solution that would actually work…

Humans dig ourselves into holes as well. Sometimes more intelligent humans are better at realising they are digging a hole and clamber out, but sometimes they just dig deeper.

And: Is your work more difficult than finding proofs of or counterexamples to decades-old open problems in mathematics?

Re: GPT-6 Astra

#560
It seems like every few days there's a new model with hundreds of comments on HN. I find it hard to keep track of the progress. Is there a TL;DR on what benchmarks to look at to understand what is going on?
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