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GPT‑5.3‑Codex‑Spark

openai.com

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Re: GPT‑5.3‑Codex‑Spark

#161

Post by sleazebreeze »

orochimaaru wrote: post #112
beklein wrote: post #62

Earlier quoted context omitted.

In my AGENTS.md file i have a _rule_ that tells the model to use Apache ECharts, the data comes from the prompt and normally .csv/.json files. Prompt would be like: "After slide 3 add a new content slide that shows a bar chart with data from @data/somefile.csv" ... works great and these charts can be even interactive.

What about other ad hoc diagrams like systems architecture, roadmaps, mind maps, etc. These are the bane of any staff engineers life - lol. Because people above need to know a plan in art form. So seriously interested on how I can make it easier

Claude code can output Excalidraw format files which can be imported directly into the webapp. You can MCP it too if you want.

Re: GPT‑5.3‑Codex‑Spark

#162

Post by gaigalas »

foobar10000 wrote: post #126
gaigalas wrote: post #37

Earlier quoted context omitted.

You always want faster feedback. If not a human leveraging the fast cycles, another automated system (eg CI). Slow, deep tasks are mostly for flashy one-shot demos that have little to no practical use in the real world.

I mean, yes, one always does want faster feedback - cannot argue with that! But some of the longer stuff - automating kernel fusion, etc, are just hard problems. And a small model - or even most bigger ones, will not get the direction right…

From my experience, larger models also don't get the direction right a surprising amount of times. You just take more time to notice when it happens, or start to be defensive (over-specing) to account for the longer waits. Even the most simple task can appear "hard" with that over spec'd approach (like building a react app).

Iterating with a faster model is, from my perspective, the superior approach. Doesn't matter the task complexity, the quick feedback more than compensates for it.

Re: GPT‑5.3‑Codex‑Spark

#163

Post by p1esk »

zozbot234 wrote: post #148
p1esk wrote: post #111

Earlier quoted context omitted.

By perf I mean how much does it cost to serve 1T model to 1M users at 50 tokens/sec.

All 1T models are not equal. E.g. how many active parameters? what's the native quantization? how long is the max context? Also, it's quite likely that some smaller models in common use are even sub-1T. If your model is light enough, the lower throughput doesn't necessarily hurt you all that much and you can enjoy the lightning-fast speed.

Just pick some reasonable values. Also, keep in mind that this hardware must still be useful 3 years from now. What’s going to happen to cerebras in 3 years? What about nvidia? Which one is a safer bet?

On the other hand, competition is good - nvidia can’t have the whole pie forever.

Re: GPT‑5.3‑Codex‑Spark

#164

Post by beklein »

esafak wrote: post #83
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…

Can you show one?

The end result would be a normal PPT presentation, check https://sli.dev as an easy start, ask Codex/Claude/... to generate the slides using that framework with data from something.md. The interesting part here is generating these otherwise boring slide decks not with PowerPoint itself but with AI coding agents and a master slides, AGENTS.md context. I’ll be showing this to a small group (normally members only) at IPAI in Heilbronn, Germany on 03/03. If you’re in the area and would like to join, feel free to send me a message I will squeeze you in.

Re: GPT‑5.3‑Codex‑Spark

#166

Post by mbreese »

deepGem wrote: post #154
onionisafruit wrote: post #147

Earlier quoted context omitted.

Every presentation becomes improv

Isn't that such a great outcome. No more robotic presentations. The best part is that you can now practice Improv at the comfort of your home.

And this product will work great for any industry... can I get a suggestion for an industry from the crowd?

Audience: Transportation... Education... Insurance...

Speaker: Great! I heard "Healthcare".

Right... as we can see from this slide, this product fits the "Healthcare" industry great because of ...

Re: GPT‑5.3‑Codex‑Spark

#167

Post by snovv_crash »

Sohcahtoa82 wrote: post #130
moralestapia wrote: post #105

Earlier quoted context omitted.

This comment doesn't make sense.

One wafer will turn into multiple chips. Defects are best measured on a per-wafer basis, not per-chip. So if if your chips are huge and you can only put 4 chips on a wafer, 1 defect can cut your yield by 25%. If they're smaller and you fit 100 chips on a wafer, then 1 defect on the wafer is only cutting yield by 1%. Of course, there's more to this when you start reading about "binning", fusing off cores, etc. There's…

That's why you typically fuse off defective sub-units and just have a slightly slower chip. GPU and CPU manufacturers have done this for at least 15 years now, that I'm aware of.

Re: GPT‑5.3‑Codex‑Spark

#168

Post by alexdobrenko »

postalcoder wrote: post #108

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…

can we plese make the bluey bench the gold standard for all models always

Re: GPT‑5.3‑Codex‑Spark

#169

Post by zozbot234 »

p1esk wrote: post #163
zozbot234 wrote: post #148

Earlier quoted context omitted.

All 1T models are not equal. E.g. how many active parameters? what's the native quantization? how long is the max context? Also, it's quite likely that some smaller models in common use are even sub-1T. If your model is light enough, the lower throughput doesn't necessarily hurt you all that much and you can enjoy the lightning-fast speed.

Just pick some reasonable values. Also, keep in mind that this hardware must still be useful 3 years from now. What’s going to happen to cerebras in 3 years? What about nvidia? Which one is a safer bet? On the other hand, competition is good - nvidia can’t have the whole pie forever.

> Just pick some reasonable values.

And that's the point - what's "reasonable" depends on the hardware and is far from fixed. Some users here are saying that this model is "blazing fast" but a bit weaker than expected, and one might've guessed as much.

> On the other hand, competition is good - nvidia can’t have the whole pie forever.

Sure, but arguably the closest thing to competition for nVidia is TPUs and future custom ASICs that will likely save a lot on energy used per model inference, while not focusing all that much on being super fast.

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