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GPT‑5.3‑Codex‑Spark

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Re: GPT‑5.3‑Codex‑Spark

#271

Post by codyb »

sva_ wrote: post #65
beklein wrote: post #41

I love this! I use coding agents to generate web-based slide decks where “master slides” are just components, and we already have rules + assets to enforce corporate identity. With content + prompts, it’s straightforward to generate a clean, predefined presentation. What I’d really want on top is an “improv mode”: during the talk, I can branch off based on audience questions or small wording changes, and the system p…

I love the probabilistic nature of this. Presentations could be anywhere from extremely impressive to hilariously embarrassing.

I guess you could have two people per presentation, one person who confirms whether to slide in the generated slide or maybe regenerate. And then of course, eventually that's just an agent

Re: GPT‑5.3‑Codex‑Spark

#272

Post by seaal »

mediaman wrote: post #266
neya wrote: post #250

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.

Others clarified the kW versus kWh, but to re-visit the comparison to a household: 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…

Which honestly doesn't sound that bad given how many users one server is able to serve.

Re: GPT‑5.3‑Codex‑Spark

#273

Post by mzl »

simonw wrote: post #170

My stupid pelican benchmark proves to be genuinely quite useful here, you get a visual representation of the quality difference between GPT-5.3-Codex-Spark and full GPT-5.3-Codex: https://simonwillison.net/2026/Feb/12/codex-spark/

I find it interesting that the spark version seems worse than the gpt-oss version (https://simonwillison.net/2025/Aug/5/gpt-oss/)

Re: GPT‑5.3‑Codex‑Spark

#274

Post by m_mueller »

cebert wrote: post #242
m_mueller wrote: post #192

Earlier quoted context omitted.

You're describing almost verbatim what we're building at Octigen [1]! Happy to provide a demo and/or give you free access to our alpha version already online. [1] https://octigen.com

Claude Code is pretty good at making slides already. What’s your differentiator?

* ability to work with your own PowerPoint native templates; none of the AI slide makers I've seen have any competency in doing that.

* ability to integrate your corporate data.

* repeatable workflows for better control over how your decks look like.

Re: GPT‑5.3‑Codex‑Spark

#275

Post by dyauspitr »

elorant wrote: post #233
ElijahLynn wrote: post #210

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…

There have been discussions about this chip here in the past. Maybe not that particular one but previous versions of it. The whole server if I remember correctly eats some 20KWs of power.

That’s wild. That’s like running 15 indoor heaters at the same time.

Re: GPT‑5.3‑Codex‑Spark

#276

Post by zozbot234 »

elorant wrote: post #233
ElijahLynn wrote: post #210

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…

There have been discussions about this chip here in the past. Maybe not that particular one but previous versions of it. The whole server if I remember correctly eats some 20KWs of power.

A first-gen Oxide Computer rack puts out max 15 kW of power, and they manage to do that with air cooling. The liquid-cooled AI racks being used today for training and inference workloads almost certainly have far higher power output than that.

(Bringing liquid cooling to the racks likely has to be one of the biggest challenges with this whole new HPC/AI datacenter infrastructure, so the fact that an aircooled rack can just sit in mostly any ordinary facility is a non-trivial advantage.)

Re: GPT‑5.3‑Codex‑Spark

#277

Post by dotancohen »

ElijahLynn wrote: post #210

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…

  > 46,255 mm²
To be clear: that's the thousandths separator, not the Nordic decimal. It's the size of a cat, not the size of a thumbnail.

Re: GPT‑5.3‑Codex‑Spark

#278

Post by fulafel »

codyb wrote: post #270
ElijahLynn wrote: post #210

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…

Wow, I'm staggered, thanks for sharing 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 g…

Designing to tolerate the defects is well trodden territory. You just expect some rate of defects and have a way of disabling failing blocks.

Re: GPT‑5.3‑Codex‑Spark

#279

Post by dotancohen »

clickety_clack wrote: post #117
sva_ wrote: post #65

Earlier quoted context omitted.

I love the probabilistic nature of this. Presentations could be anywhere from extremely impressive to hilariously embarrassing.

It would be so cool if it generated live in the presentation and adjusted live as you spoke, so you’d have to react to whatever popped on screen!

That would make a great Wii game.

PowerPoint Hero.

Re: GPT‑5.3‑Codex‑Spark

#280

Post by shwetanshu21 »

dotancohen wrote: post #277
ElijahLynn wrote: post #210

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…

> 46,255 mm² To be clear: that's the thousandths separator, not the Nordic decimal. It's the size of a cat, not the size of a thumbnail.

Thanks, I was acutally wondering how would someone even manage to make that big a chip.
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