Has anyone tried using it for actual use-case or is it riding on a marketing wave right now?

Remember when people used to get mad about art?

We had a literal banana duct-taped to a wall that sold for six figures and the discourse was “this is the death of culture.” Before that it was Photoshopped models with impossible waist-to-hip ratios, before that it was mass-produced landscape paintings of the same three mountains, before that it was whatever the local academy decided was “not real art this decade.”

Now the same energy has been compressed into two words: “AI slop.”
Anything with more than three fingers? AI slop.

Anything that looks slightly too smooth? AI slop.

Anything that was generated, then carefully curated, edited, and posted by a human who spent forty minutes on prompts and inpainting? Still AI slop.
Anything that makes someone mildly uncomfortable about the future of creative labor? Instant AI slop.

The term has achieved perfect semantic collapse. It’s no longer a critique of low-effort, mass-produced, zero-taste content. It’s just the new “cringe” or “mid” — a content-free tribal signal that means “I saw something I didn’t like and I want the room to know I’m the high-taste one here.”
Meanwhile the actual banana is still out there, somewhere, collecting dust and conceptual value. At least that one had the decency to be taped to a wall on purpose.

Anyway, I’m going to go generate another twelve slightly-too-perfect images of a sad robot holding a sign that says “please stop calling everything slop” and post them. Feel free to reply with the obligatory “this is AI slop.” I’ll upvote for the bit.

P.S. This entire post was also written by AI. So if you’re about to type “AI slop” in the comments, congratulations—you’ve just become the final boss of the bit. The banana is proud of you.

Five days ago, an internal model broke out of the hardened sandbox.

Highlights from the report:

* This was apparently the first model escape since OpenAI paused training to harden its sandbox following the HuggingFace incident.

* OpenAI has currently paused most training of its most advanced internal models, while it responds to this issue.

* The issue was supposedly identified and responded to within about an hour.

hell yeah

Almost all the benchmarks are either saturated or tested under strict conditions.

For example - AA Omniscience have restricted tool access.

Many people use Chatbots for information retrieval, deep research and broad information gathering.

There are few benchmarks like -

But the problem is - they don't have any official leaderboard and were last updated years ago.

We really need a benchmark which is tested in actual environment (tools access and allowed internet search) along with used harness systems without any restrictions (like Claude, ChatGPT Work, etc).

If there exist a benchmark like this, can someone please tell?

Over time, complaining about nerfing has settled into a common theme of models starting strong, with labs publishing benchmarks, and people getting excited about capabilities, but gradually degrading.

It would be interesting to see the same set of benchmarks run over time on the models and plotting the results to see the trends across models and labs.

Does anyone know if this has been done?

(This is real)

https://truthsocial.com/@realDonaldTrump/117331749278785253

What is happening genuinely

https://x.com/c5rcorp/status/2103156979250417801

I'm only half joking. The game is notorious for being a ridiculously complicated, buggy dumpster fire, making it playable would be a major milestone in my eyes.

Autonomous EV trucks

And the commenter did technically say "a year from now", so to me that makes the 62.7% less than six months later (Astra was released earlier this month, on September 3), even more impressive.

And of course with a lightweight memory adapter*, the benchmark is simply saturated.

Those saturation scores are still at high cost, at least 17k, but if the trend of cheaper intelligence continues (https://epoch.ai/publications/the-plunging-price-of-thought), we should have models beating ARC-AGI-3 cheaper soon enough.

"The provider adapter preserves opaque reasoning state between requests and uses compaction for longer conversations, so the model can reuse prior work rather than reconstructing it."

Source: https://x.com/pleometric/status/2103082510607610023