Image/video understanding still quite cost effective from the Gemini flash series models?
Image generation and veo models I’d imagine quite effective for creators; new Instagram accounts with AI content that are garnering millions of followers in spans of weeks are quite common now
> That’s not change unique to America though. Why would I be thinking of changes that have affected almost every country, when talking about whether Americans recognize how America has changed?
Hahahaha, this is like saying, the world wars didn’t impact Europe, because it also impacted the whole world! Europeans, the war didn’t happen!! Anyways… this entire thread is more evidence that European stereotypes are valid for the most part
> Most Americans have no sense of how very different their country is now from say, the country that launched the Apollo missions.
You cannot be kidding right? Those that remember the Apollo missions will undoubtedly agree their country is different, first but not least they are most likely using a smartphone assembled and designed with technology unimaginable by NASA planning the Apollo missions; not only that, the smartphone is assembled half way around the world by a country previously in such dire poverty and famine that over 30M died due to Marxist central planning.
> Models were run with maximum available reasoning effort in our API (xhigh for GPT‑5.2 Thinking & Pro, and high for GPT‑5.1 Thinking), except for the professional evals, where GPT‑5.2 Thinking was run with reasoning effort heavy, the maximum available in ChatGPT Pro. Benchmarks were conducted in a research environment, which may provide slightly different output from production ChatGPT in some cases.
Feels like a Llama 4 type release. Benchmarks are not apples to apples. Reasoning effort is across the board higher, thus uses more compute to achieve an higher score on benchmarks.
Also notes that some may not be producible.
Also, vision benchmarks all use Python tool harness, and they exclude scores that are low without the harness.
> “That’s right outta ‘Goodfellas,’ that’s right out of a mafioso going into a bar saying, ‘Nice bar you have here, it’d be a shame if something happened to it,’” Cruz said, using the iconic New York accent associated with the Mafia.
Are you querying from an EC2 instance close to the S3 data? Are the CSVs partitioned into separate files? Does the machine have 500GB of memory? It’s not always duckdb fault when there can be a clear I/O bottleneck…
These just seems like over engineered solutions trying to guarantee their job security. When the dataflows are so straight forward, just replicate into pick your OLAP, and transform there.