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GPT‑5.3‑Codex‑Spark

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Re: GPT‑5.3‑Codex‑Spark

#51
post #47

Earlier quoted context omitted.

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

How can you afford that?

It costs $200 for a month

Re: GPT‑5.3‑Codex‑Spark

#52
post #43

> 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).

How hard have you tried? I've been finding that the Opus 4.5/4.6 and GPT-5.2/5.3 models really have represented a step-change in how good they are at running long tasks. I can one-shot prompt all sorts of useful coding challenges now that previously I would have expected to need multiple follow-ups to fix mistakes the agents made. I got all of this from a single prompt, for example: https://github.com/simonw/research…

Can you share any examples of these one-shot prompts? I've not gotten to the point where I can get those kind of results yet.

Re: GPT‑5.3‑Codex‑Spark

#53
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?

Re: GPT‑5.3‑Codex‑Spark

#55

Off topic but how is it always this HN user sharing model releases within a couple of minutes of their announcement?

The account isn’t a normal user. They literally only post stuff like this. Their comments are just official links back to said announcements.

Re: GPT‑5.3‑Codex‑Spark

#56

Earlier quoted context omitted.

I built something similar using an MCP that allows claude to "outsource" development to GLM 4.7 on Cerebras (or a different model, but GLM is what I use). The tool allows Claude to set the system prompt, instructions, specify the output file to write to and crucially allows it to list which additional files (or subsections of files) should be included as context for the prompt. Ive had great success with it, and it r…

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

Re: GPT‑5.3‑Codex‑Spark

#57
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 idea of a living slide deck. This feels like a product that needs to exist!

Re: GPT‑5.3‑Codex‑Spark

#59
Does anyone want this? Speed has never been the problem for me, in fact, higher latency means less work for me as a replaceable corporate employee. What I need is the most intelligence possible; I don't care if I have to wait a day for an answer if the answer is perfect. Small code edits, like they are presented as the use case here, I can do much better myself than trying to explain to some AI what exactly I want done.

Re: GPT‑5.3‑Codex‑Spark

#60
post #45

Earlier quoted context omitted.

They were afraid for the untested tech but it looks like a leap in speed now

This is nonsense what do you mean? Mistral uses Cerebras for their LLMs as well. [0] It's certainly not "untested". [0] https://www.cerebras.ai/blog/mistral-le-chat

Tested at Mistral’s scale is a very different thing to tested at OpenAI’s scale.
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