And don't get me started with "Lunar New Year? What Lunar New Year? Islamic Lunar New Year? Jewish Lunar New Year? CHINESE Lunar New Year?".
[0] https://www.mom.gov.sg/employment-practices/public-holidays
As it turns out, people in China don’t name their holidays based off of what the laws of New York or California say.
That said, "Lunar New Year" is probably as good a compromise as any, since we have other names for the Hebrew and Islamic New Years.
Have you ever had a Polish Sausage? Did it make you Polish?
Is "Gemini 3 Deep Think" even technically a model? From what I've gathered, it is built on top of Gemini 3 Pro, and appears to be adding specific thinking capabilities, more akin to adding subagents than a truly new foundational model like Opus 4.6.
Also, I don't understand the comments about Google being behind in agentic workflows. I know that the typical use of, say, Claude Code feels agentic, but also a lot of folks are using separate agent harnesses like OpenClaw anyway. You could just as easily plug Gemini 3 Pro into OpenClaw as you can Opus, right?
Can someone help me understand these distinctions? Very confused, especially regarding the agent terminology. Much appreciated!
- a product (most accurate here imo)
- a specific set of weights in a neural net
- a general architecture or family of architectures (BERT models)
So while you could argue this is a “model” in the broadest sense of the term, it’s probably more descriptive to call it a product. Similarly we call LLMs “language” models even if they can do a lot more than that, for example draw images.
It has to do with how the model is RL'd. It's not that Gemini can't be used with various agentic harnesses, like open code or open claw or theoretically even claude code. It's just that the model is trained less effectively to work with those harnesses, so it produces worse results.
I don’t think it’s hyperbolic to say that we may be only a single digit number of years away from the singularity.
And yes, you are probably using them wrong if you don’t find them useful or don’t see the rapid improvement.
Every new model release neckbeards come out of the basements to tell us the singularity will be there in two more weeks
You’ve once again made up a claim of “two more weeks” to argue against even though it’s not something anybody here has claimed.
If you feel the need to make an argument against claims that exist only in your head, maybe you can also keep the argument only in your head too?
The logic related to the bug wasn't all contained in one file, but across several files.
This was Gemini 2.5 Pro. A whole generation old.
Projects:
https://github.com/alexispurslane/oxen
https://github.com/alexispurslane/org-lsp
(Note that org-lsp has a much improved version of the same indexer as oxen; the first was purely my design, the second I decided to listen to K2.5 more and it found a bunch of potential race conditions and fixed them)
shrug
I had a test failing because I introduced a silly comparison bug (> instead of <), and claude 4.6 opus figured out it wasn't the test the problem, but the code and fixed the bug (which I had missed).
We're back to singularity hype, but let's be real: benchmark gains are meaningless in the real world when the primary focus has shifted to gaming the metrics
Benchmaxxing exists, but that’s not the only data point. It’s pretty clear that models are improving quickly in many domains in real world usage.