261–270 of 415 posts
Earlier quoted context omitted.
I asked because that's the average power consumption of an average household in the US per day. So, if that figure is per hour, that's equivalent to one household worth of power consumption per hour...which is a lot.
Consumption of a house per day is measured in kiloWatt-hours (an amount of power like litres of water), not kiloWatts (a flow of power like 1 litre per second of water). 1 Watt = 1 Joule per second.
> Our latest frontier models have shown particular strengths in their ability to do long-running tasks, working autonomously for hours, days or weeks without intervention. I have yet to see this (produce anything actually useful).
First thoughts using gpt-5.3-codex-spark in Codex CLI: Blazing fast but it definitely has a small model feel. It's tearing up bluey bench (my personal agent speed benchmark), which is a file system benchmark where I have the agent generate transcripts for untitled episodes of a season of bluey, perform a web search to find the episode descriptions, and then match the transcripts against the descriptions to generate f…
also as a parent, I love the bluey bench concept !
First thoughts using gpt-5.3-codex-spark in Codex CLI: Blazing fast but it definitely has a small model feel. It's tearing up bluey bench (my personal agent speed benchmark), which is a file system benchmark where I have the agent generate transcripts for untitled episodes of a season of bluey, perform a web search to find the episode descriptions, and then match the transcripts against the descriptions to generate f…
Excited to see glimpses of that future. Context switching sucks and I’d much rather work focused on one task while wielding my coding power tools.
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What do you mean? The generated script just downloads the sources and runs pyodide: https://github.com/simonw/research/blob/main/cysqlite-wasm-w... There is maybe 5 relevant lines in the script and nothing complex at all that would require to run for days.
No, not for days - but it churned away on that one for about ten minutes. I don't think I've got any examples of multi-hour or multi-day sessions that ran completely uninterrupted - this one back in December took 4.5 hours but I had to prompt it to keep going a few times along the way: https://simonwillison.net/2025/Dec/15/porting-justhtml/
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If you run it for an hour, yes.
I asked because that's the average power consumption of an average household in the US per day. So, if that figure is per hour, that's equivalent to one household worth of power consumption per hour...which is a lot.
One household uses about 30 kWh per day.
20 kW * 24 = 480 kWh per day for the server.
So you're looking at one server (if parent's 20kW number is accurate - I see other sources saying even 25kW) consuming 16 households worth of energy.
For comparison, a hair dryer uses around 1.5 kW of energy, which is just below the rating for most US home electrical circuits. This is something like 13 hair dryers going on full blast.
First thoughts using gpt-5.3-codex-spark in Codex CLI: Blazing fast but it definitely has a small model feel. It's tearing up bluey bench (my personal agent speed benchmark), which is a file system benchmark where I have the agent generate transcripts for untitled episodes of a season of bluey, perform a web search to find the episode descriptions, and then match the transcripts against the descriptions to generate f…
How do the agents perform the transcription? I'm guessing just calling out to other tools like Whisper? Do all models/agents take the same approach or do they differ? also as a parent, I love the bluey bench concept !
I'm experimenting right now with an English to Thai subtitle translator that feeds in the existing English subtitles as well as a mono (centre-weighted) audio extracted using ffmpeg. This is needed because Thai has gendered particles -- word choice depends on the sex of the speaker, which is not recorded in English text. The AIs can infer this to a degree, but they do better when given audio so that they can do speaker diarization.
First thoughts using gpt-5.3-codex-spark in Codex CLI: Blazing fast but it definitely has a small model feel. It's tearing up bluey bench (my personal agent speed benchmark), which is a file system benchmark where I have the agent generate transcripts for untitled episodes of a season of bluey, perform a web search to find the episode descriptions, and then match the transcripts against the descriptions to generate f…
How do the agents perform the transcription? I'm guessing just calling out to other tools like Whisper? Do all models/agents take the same approach or do they differ? also as a parent, I love the bluey bench concept !
re. your question about the approach – they all took on the problem in different ways that I found fascinating.
Codex Spark was so fast because it noticed that bluey announces the episode names in the episode ("This episode of Bluey is called ____.") so, instead of doing a pure matching of transcriptweb description, it cut out the title names from the transcripts and matched only that with the episode descriptions.
The larger models were more careful and seemed to actually try to doublecheck their work by reading the full transcripts and matching them against descriptions.
gpt-5.2 went through a level of care that wasn't wrong, but was unnecessary.
Sonnet 4.5 (non-thinking) took the most frustrating approach. It tried to automate the pairing process with scripting to match the extracted title with the official title via regex. So, instead of just eyeballing the lists of extracted and official titles to manually match them, it relied purely on the script's logging as its eyes. When the script failed to match all 52 episodes perfectly, it went into a six-iteration loop of writing increasingly convoluted regex until it found 52 matches (which ended up incorrectly matching episodes). It was frustrating behavior, I stopped the loop after four minutes.
In my mind, the "right way" was straightforward but that wasn't borne out by how differently the llms behaved.
Wow, I wish we could post pictures to HN. That chip is HUGE!!!! The WSE-3 is the largest AI chip ever built, measuring 46,255 mm² and containing 4 trillion transistors. It delivers 125 petaflops of AI compute through 900,000 AI-optimized cores — 19× more transistors and 28× more compute than the NVIDIA B200. From https://www.cerebras.ai/chip : https://cdn.sanity.io/images/e4qjo92p/production/78c94c67be9... https://cd…
I was under the impression that often times chip manufacture at the top of the lines failed to be manufactured perfectly to spec and those with say, a core that was a bit under spec or which were missing a core would be down clocked or whatever and sold as the next in line chip.
Is that not a thing anymore? Or would a chip like this maybe be so specialized that you'd use say a generation earners transistor width and thus have more certainty of a successful cast?
Or does a chip this size just naturally ebb around 900,000 cores and that's not always the exact count?
20kwh! Wow! 900,000 cores. 125 teraflops of compute. Very neat