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GPT‑5.3‑Codex‑Spark

openai.com

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Re: GPT‑5.3‑Codex‑Spark

#61

Post by raw_anon_1111 »

XCSme wrote: post #34
nikkwong wrote: post #29

> 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).

Their ability to burn through tokens non-stop for hours, days or weeks without intervention.

You’re mixing up Open AI for Anthropic.

Anthropic is actually sort of concerned with not burning through cash and charging people a reasonable price. Open AI doesn’t care. I can use Codex CLI all day and not approach any quotas with just my $20 a month ChatGPT subscription.

I treat coding agents like junior developers and never take my hand off the wheel except for boilerplate refactoring.

Re: GPT‑5.3‑Codex‑Spark

#62

Post by beklein »

orochimaaru wrote: post #53
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…

How do you handle the diagrams?

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.

Re: GPT‑5.3‑Codex‑Spark

#63

Post by Aurornis »

antirez wrote: post #20

The search for speed is vain. Often Claude Code Opus 4.6, on hard enough problems, can do the impression of acting fast without really making progresses because of lack of focus on what matters. Then you spin the much slower GPT 5.3-Codex and it fixes everything in 3 minutes of doing the right thing.

I will always take more speed. My use of LLMs always comes back to doing something manually, from reviewing code to testing it to changing direction. The faster I can get the LLM part of the back-and-forth to complete, the more I can stay focused on my part.

Re: GPT‑5.3‑Codex‑Spark

#64

Post by nikkwong »

gamegoblin wrote: post #38
nikkwong wrote: post #29

> 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).

I routinely leave codex running for a few hours overnight to debug stuff If you have a deterministic unit test that can reproduce the bug through your app front door, but you have no idea how the bug is actually happening, having a coding agent just grind through the slog of sticking debug prints everywhere, testing hypotheses, etc — it's an ideal usecase

I have a hard time understanding how that would work — for me, I typically interface with coding agents through cursor. The flow is like this: ask it something -> it works for a min or two -> I have to verify and fix by asking it again; etc. until we're at a happy place with the code. How do you get it to stop from going down a bad path and never pulling itself out of it?

The important role for me, as a SWE, in the process, is verify that the code does what we actually want it to do. If you remove yourself from the process by letting it run on its own overnight, how does it know it's doing what you actually want it to do?

Or is it more like with your usecase—you can say "here's a failing test—do whatever you can to fix it and don't stop until you do". I could see that limited case working.

Re: GPT‑5.3‑Codex‑Spark

#65

Post by sva_ »

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.

Re: GPT‑5.3‑Codex‑Spark

#66

Post by pjs_ »

Continue to believe that Cerebras is one of the most underrated companies of our time. It's a dinner-plate sized chip. It actually works. It's actually much faster than anything else for real workloads. Amazing

Re: GPT‑5.3‑Codex‑Spark

#67

Post by addaon »

gamegoblin wrote: post #38
nikkwong wrote: post #29

> 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).

I routinely leave codex running for a few hours overnight to debug stuff If you have a deterministic unit test that can reproduce the bug through your app front door, but you have no idea how the bug is actually happening, having a coding agent just grind through the slog of sticking debug prints everywhere, testing hypotheses, etc — it's an ideal usecase

> it's an ideal usecase

This is impressive, you’ve completely mitigated the risk of learning or understanding.

Re: GPT‑5.3‑Codex‑Spark

#68

Post by andai »

wahnfrieden wrote: post #56
cheema33 wrote: post #25

Earlier quoted context omitted.

Why use MCP instead of an agent skill for something like this when MCP is typically context inefficient?

Models haven't been trained enough on using skills yet, so they typically ignore them

Is that true? I had tool use working with GPT-4 in 2023, before function calling or structured outputs were even a thing. My tool instructions were only half a page though. Maybe the long prompts are causing problems?

Re: GPT‑5.3‑Codex‑Spark

#69

Post by arcanemachiner »

pjs_ wrote: post #66

Continue to believe that Cerebras is one of the most underrated companies of our time. It's a dinner-plate sized chip. It actually works. It's actually much faster than anything else for real workloads. Amazing

Just wish they weren't so insanely expensive...

Re: GPT‑5.3‑Codex‑Spark

#70

Post by [deleted] »

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…

[deleted]
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