This would be interesting if it was an open weights model.
GPT‑5.3‑Codex‑Spark
151–160 of 415 posts
Re: GPT‑5.3‑Codex‑Spark
#152Continue 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
Nvidia seems cooked. Google is crushing them on inference. By TPUv9, they could be 4x more energy efficient and cheaper overall (even if Nvidia cuts their margins from 75% to 40%). Cerebras will be substantially better for agentic workflows in terms of speed. And if you don't care as much about speed and only cost and energy, Google will still crush Nvidia. And Nvidia won't be cheaper for training new models either.…
Man I hope someone drinks Nvidia's milk shake. They need to get humbled back to the point where they're desperate to sell gpus to consumers again.
Only major road block is cuda...
Re: GPT‑5.3‑Codex‑Spark
#153Great move by OpenAI. With coding agents, if you have access to a fast and cheap model, you can afford to let it rip, making lots of mistakes, and iterate until it gets things right. With the right scaffolding (AGENTS.md, SKILLS.md, etc.), a fast and light model can do great things. And when it's done, you can still have the heavyweight model come in to clean up any messes.
Re: GPT‑5.3‑Codex‑Spark
#154Earlier quoted context omitted.
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!
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.
Re: GPT‑5.3‑Codex‑Spark
#155Earlier quoted context omitted.
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!
Every presentation becomes improv
I had a butterfly take over my live DreamScape slide show demo at the 1995 WWDC.
Re: GPT‑5.3‑Codex‑Spark
#156Earlier 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!
Some consulting firms do this, one guy is giving the presentation live while others are in the next meeting room still banging out the slides.
Re: GPT‑5.3‑Codex‑Spark
#157Earlier 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
Not my normal use-case, but you can always fall back and ask the AI coding agent to generate the diagram as SVG, for blocky but more complex content like your examples it will work well and still is 100% text based, so the AI coding agents or you manually can fix/adjust any issues.
An image generation skill is a valid fallback, but in my opinion it's hard to change details (json style image creation prompts are possible but hard to do right) and you won't see changes nicely in the git history.
In your use case you can ask the AI coding agent to run a script.js to get the newest dates for the project from a page/API, then it should only update the dates in the roadmap.svg file on slide x with the new data.
This way you will automagically have the newest numbers and can track everything within git in one prompt. Save this as a rule in AGENTS.md and run this every month to update your slides with one prompt.
Re: GPT‑5.3‑Codex‑Spark
#158Earlier quoted context omitted.
I'm definitely bullish on LLM's for coding. It sounds to me as though getting it to run on its own for hours and produce something usable requires more careful thought and setup than just throwing a prompt at it and wishing for the best—but I haven't seen many examples in the wild yet
It needs a closed loop. Strategy -> [ Plan -> [Execute -> FastVerify -> SlowVerify] -> Benchmark -> Learn lessons] -> back to strategy for next big step. Claude teams and a Ralph wiggum loop can do it - or really any reasonable agent. But usually it all falls apart on either brittle Verify or Benchmark steps. What is important is to learn positive lessons into a store that survives git resets, machine blowups, etc… A…
I do it easily just by asking Codex
Re: GPT‑5.3‑Codex‑Spark
#159I 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 built something similar at a hackathon, a dynamic teleprompter that adjusts the speed of tele-prompting based on speaker tonality and spoken wpm. I can see extending the same to an improv mode. This is a super cool idea.
Re: GPT‑5.3‑Codex‑Spark
#160First 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 you compare it to Opus 4.6 with thinking disabled? It seems to have very impressive benchmark scores. Could also be pretty fast.
Added a thinking-disabled Opus 4.6 timing. It took 1m 4s – coincidentally the same as 5.3-codex-low.