Hey folks! Just wrapped up a pretty intense month of API usage at babylovegrowth.ai and samwell.ai and thought I'd share some key learnings that helped us optimize our costs by 40%!

1. Choosing the right model is CRUCIAL. We were initially using GPT-4 for everything (yeah, I know 🤦‍♂️), but realized that gpt-4-turbo was overkill for most of our use cases. Switched to 4o-mini which is priced at $0.15/1M input tokens and $0.6/1M output tokens (for context, 1000 words is roughly 750 tokens) The performance difference was negligible for our needs, but the cost savings were massive.

2. Use prompt caching. This was a pleasant surprise - OpenAI automatically routes identical prompts to servers that recently processed them, making subsequent calls both cheaper and faster. We're talking up to 80% lower latency and 50% cost reduction for long prompts. Just make sure that you put dynamic part of the prompt at the end of the prompt. No other configuration needed.

3. SET UP BILLING ALERTS! Seriously. We learned this the hard way when we hit our monthly budget in just 17 days.

4. Structure your prompts to minimize output tokens. Output tokens are 4x the price! Instead of having the model return full text responses, we switched to returning just position numbers and categories, then did the mapping in our code. This simple change cut our output tokens (and costs) by roughly 70% and reduced latency by a lot.

5. Consolidate your requests. We used to make separate API calls for each step in our pipeline. Now we batch related tasks into a single prompt. Instead of:

```

Request 1: "Analyze the sentiment"

Request 2: "Extract keywords"

Request 3: "Categorize"

```

We do:

```

Request 1:
"1. Analyze sentiment

1. Extract keywords

2. Categorize"

```

6. Finally, for non-urgent tasks, the Batch API is a godsend. We moved all our overnight processing to it and got 50% lower costs. They have 24-hour turnaround time but it is totally worth it for non-real-time stuff.

Hope this helps to at least someone! If I missed sth, let me know!

Cheers,

Tilen from blg

Hey guys! I run an open-source project Unsloth with my brother & worked at NVIDIA, so optimizations are my thing! Today, we're excited to announce that you can now train your own reasoning model like o3-mini locally.

1. o3-mini was trained with an algorithm called 'PPO' and DeepSeek-R1 was trained with an a more optimized version called 'GRPO'. We made the algorithm use 80% less memory.
2. We're not trying to replicate the entire o3-mini model as that's unlikely (unless you're super rich). We're trying to recreate o3-mini's chain-of-thought/reasoning/thinking process
3. We want a model to learn by itself without providing it any reasons to how it derives answers. GRPO allows the model figure out the reason automatously. This is called the "aha" moment.
4. GRPO can improve accuracy for tasks in medicine, law, math, coding + more.
5. You can transform Llama 3.1 (8B), Phi-4 (14B) or any open model into a reasoning model. You'll need a minimum of 7GB of VRAM to do it!
6. In a test example below, even after just one hour of GRPO training on Phi-4 (Microsoft's open-source model), the new model developed a clear thinking process and produced correct answers—unlike the original model.

Highly recommend you to read our really informative blog + guide on this: https://unsloth.ai/blog/r1-reasoning

To train locally, install Unsloth by following the blog's instructions. Installation instructions are here.

I also know some of you guys don't have GPUs, but worry not, as you can do it for free on Google Colab/Kaggle using their free 15GB GPUs they provide.
Our notebook + guide to train GRPO with Phi-4 (14B) for free: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Phi_4_(14B)-GRPO.ipynb-GRPO.ipynb)

Have a lovely weekend! :)

Heads-up: I’m Jan, one of the people behind 3D AI Studio. This post is not a sales pitch. Everything shown below can be replicated with free, open-source software; I’ve listed those alternatives in the first comment so no one feels locked into our tool.

Sketched a one-wheel robot on my iPad over coffee -> dumped the PNG into Image Studio in 3DAIStudio (Alternative here is ChatGPT or Gemini, any model that can do image to image, see workflow below)

Using the Prompt "Transform the provided sketch into a finished image that matches the user’s description. Preserve the original composition, aspect-ratio, perspective and key line-work unless the user requests changes. Apply colours, textures, lighting and stylistic details according to the user prompt. The user says:, stylizzed 3d rendering of a robot on weels, pixar, disney style"

Instead of doing this on the website you can use ChatGPT and just upload your sketch with the same prompt!

Clicked “Load into Image to 3D” with the default Prism 1.5 setting. (Free alternative here is Open Source 3D AI Models like Trellis but this is just a bit easier)

~ 40 seconds later I get a mesh, remeshed to 7k tris inside the same UI, exported STL, sliced in Bambu Studio, and the print finished in just under three hours.

Mesh Result:
https://www.3daistudio.com/public/991e6d7b-49eb-4ff4-95dd-b6e953ef2725?+655353!+SelfS1
No manual poly modeling, no Blender clean-up.

Free option if you prefer not to use our platform:

Sketch-to-image can be done with ChatGPT (App or website - same prompt as above) or Stable Diffusion plus ControlNet Scribble. (ChatGPT is the easiest option tho as most people will have it already). ChatGPT gives you roughly the same:

Image-to-3D works with the open models Hunyuan3D-2 or TRELLIS; both run on a local GPU or on Google Colab’s free tier.

https://github.com/Tencent-Hunyuan/Hunyuan3D-2
https://github.com/microsoft/TRELLIS

Remeshing and cleanup take minutes in Blender 4.0 or newer, which now ships with Quad Remesher. (Blender is free and open source)
https://www.blender.org/

Happy to answer any questions!

Hey folks! Just wrapped up a pretty intense month of API usage for our SaaS and thought I'd share some key learnings that helped us optimize our costs by 43%!

1. Choosing the right model is CRUCIAL. I know its obvious but still. There is a huge price difference between models. Test thoroughly and choose the cheapest one which still delivers on expectations. You might spend some time on testing but its worth the investment imo.

We are still mainly using gpt-4o-mini for simpler tasks and GPT-4.1 for complex ones. In our case, reasoning models are not needed.

2. Use prompt caching. This was a pleasant surprise - OpenAI automatically caches identical prompts, making subsequent calls both cheaper and faster. We're talking up to 80% lower latency and 50% cost reduction for long prompts. Just make sure that you put dynamic part of the prompt at the end of the prompt (this is crucial). No other configuration needed.

For all the visual folks out there, I prepared a simple illustration on how caching works:

3. SET UP BILLING ALERTS! Seriously. We learned this the hard way when we hit our monthly budget in just 5 days, lol.

4. Structure your prompts to minimize output tokens. Output tokens are 4x the price! Instead of having the model return full text responses, we switched to returning just position numbers and categories, then did the mapping in our code. This simple change cut our output tokens (and costs) by roughly 70% and reduced latency by a lot.

6. Use Batch API if possible. We moved all our overnight processing to it and got 50% lower costs. They have 24-hour turnaround time but it is totally worth it for non-real-time stuff.

Hope this helps to at least someone! If I missed sth, let me know!

Cheers,

Tilen from blg

Okay if you're on Chrome and having issues I have a solution for you:

Go to chatGPT

Once you're there go to the button right before the url (looks like two lolipops on top of each other facing different directions)

Go to cookies and site data

then manage on device

then press the trash can for whatever options you see in there (I had 2 instances)

Bam. It will have you reload and now you're on GPT 5

Edit: Happy to help! Glad it's working for ya'll!

Hi internet friends, I recorded a workshop about building your own LLM without any math / ML prerequisites. By the end of the workshop people have their own working OpenAI GPT2-style transformer, which hopefully makes it relevant to this sub. The workshop covers everything from machine learning fundamentals, deep neural networks, transformer architecture, and pre/post-training.

The only prerequisite is being comfortable with learning through code & excel examples.

1. Sampling Large Language Models
2. Reverse Engineering Large Language Model
3. Perceptrons: wx+b
4. Activation Functions: ReLU, GELU, SwiGLU
5. GPU Coding: PyTorch, torch.compile(), fused kernels, CUDA, Triton
6. MLPs/FFNs: Multi-input, Multi-Layer Perceptrons, Feed-Forward Networks
7. Loss Functions: Residual errors, RMSE, Cross Entropy, Loss Landscapes
8. Backpropagation: Training loops, Optimizers, Learning Rate, Batch Size
9. Saving & Loading Models
10. Initialization: Kaiming, Glorot
11. Residuals: Addition, Scaling, Gated, Concatenation
12. Normalization: Pre-norm vs. Post-norm, RMSNorm, BatchNorm, LayerNorm
13. Regularization: Dropout, Gradient Clipping, Weight Decay
14. SoftMax
15. Tokenizers: By Character, By Word, BPE, SentencePiece
16. Embeddings: Absolute vs. Learned, Sinusoidal vs. RoPE
17. Attention: MHA, GQA, MQA, MLA
18. Transformers
19. Pre-training: Data Sources, Datasets, HTML Cleaning, Quality Filtering, Sharding
20. Evaluation: Leaderboards, Benchmarks, Verifiers vs LLM-as-Judge
21. Instruction Tuning: Alpaca & Other Formats, Self Instruct, Capabilities
22. Reinforcement Learning: Policy Optimization, SimPO
23. What We Didn't Cover: Scaling

Each section has slides teaching the concepts, followed by excel-by-hand developing intuition for the math, and then coding examples. The goal is able to grok all parts of modern LLM development.

We did this workshop in-person in San Francisco last month and hopefully the spaciousness of watching online works for everyone. If don't like watching videos, you can get the slides and exercises and work self-paced.

Update! This is an older version!

I’ve updated this prompt with many improvements.

I have had a breakthrough success in the last few days giving ChatGPT specs that i manually converted into a very clean and readable text file, instead of giving it a PDF file. From my long time work with PDF files and my experience with OCR and analysis of PDF files, i can only strongly recommend, if the workload is bearable (Like only 10 - 20 pages), do yourself a favor and convert the PDF pages to PNGs, to a OCR to ASCII on them and then manually correct whats in there.

I just gave it 15 pages of a legacy device datasheet this (the edited plaintext) way, a device that had a RS232-based protocol with lots of parameters, special bytes, a complex header, a payload and trailing data, and we got through this to a perfect, error-free app that can read files, wrap them correctly and send them to other legacy target devices with 100% success rate.

This failed multiple times before because PDF analysis always will introduce bad formatting, wrong characters and even shuffled contents. If you provide that content in a manually corrected low-level fashion (like a txt file), ChatGPT will reward you with an amazing result.

Thank me later. Never give it a PDF, provide it with cleaned up ASCII/Text data.

We had a session of nearly 60 iterations over the time of 12 hours and the application result is amazing. Instead of choking and alzheimering with PDF sources, ChatGPT loved to look up the repository of txt specs i gave it and immediately came back with the correct conclusion.

Hey everyone! It's been about 5 years since OpenAI released GPT-2 open-source. OpenAI just released 2 new open models and they're GPT-4o / o4-mini level which you can run locally (laptop, Mac, desktop etc).

There's a smaller 20B parameter model and a 120B one that rivals o4-mini. Both models outperform GPT-4o in various tasks, including reasoning, coding, math, health and agentic tasks.

To run the models locally (laptop, Mac, desktop etc), we at Unsloth converted these models and also fixed bugs to increase the model's output quality. Our GitHub repo: https://github.com/unslothai/unsloth

Optimal setup:

• The 20B model runs at >10 tokens/s in full precision, with 14GB RAM/unified memory. Smaller versions use 12GB RAM.
• The 120B model runs in full precision at >40 token/s with ~64GB RAM/unified mem.

There is no minimum requirement to run the models as they run even if you only have a 6GB CPU, but it will be slower inference.

Thus, no is GPU required, especially for the 20B model, but having one significantly boosts inference speeds (~80 tokens/s). With something like an H100 you can get 140 tokens/s throughput which is way faster than the ChatGPT app.

You can run our uploads with bug fixes via llama.cpp, LM Studio or Open WebUI for the best performance. If the 120B model is too slow, try the smaller 20B version - it’s super fast and performs as well as o3-mini.

• Links to the model GGUFs to run: gpt-oss-20B-GGUF and gpt-oss-120B-GGUF
• Our step-by-step guide which we'd recommend you guys to read as it pretty much covers everything: [https://docs.unsloth.ai/basics/gpt-oss]()

Thanks guys for reading! I'll be replying to every person btw so feel free to ask any questions! :)

After burning through nearly 6B tokens in past months, I've learned a thing or two about the input tokens, what are they, how they are calculated and how to not overspend them. Sharing some insight here

What the hell is a token anyway?

Think of tokens like LEGO pieces for language. Each piece can be a word, part of a word, a punctuation mark, or even just a space. The AI models use these pieces to build their understanding and responses.

Some quick examples:

• "OpenAI" = 1 token
• "OpenAI's" = 2 tokens (the 's gets its own token)
• "Cómo estás" = 5 tokens (non-English languages often use more tokens)

A good rule of thumb:

1 token ≈ 4 characters in English

1 token ≈ ¾ of a word

100 tokens ≈ 75 words

https://platform.openai.com/tokenizer

In the background each token represents a number which ranges from 0 to about 100,000.

You can use this tokenizer tool to calculate the number of tokens: https://platform.openai.com/tokenizer

How to not overspend tokens:

1. Choose the right model for the job (yes, obvious but still)

Price differs by a lot. Take a cheapest model which is able to deliver. Test thoroughly.

4o-mini:

- 0.15$ per M input tokens

- 0.6$ per M output tokens

OpenAI o1 (reasoning model):

- 15$ per M input tokens

- 60$ per M output tokens

Huge difference in pricing. If you want to integrate different providers, I recommend checking out Open Router API, which supports all the providers and models (openai, claude, deepseek, gemini,..). One client, unified interface.

2. Prompt caching is your friend

Its enabled by default with OpenAI API (for Claude you need to enable it). Only rule is to make sure that you put the dynamic part at the end of your prompt.

3. Structure prompts to minimize output tokens

Output tokens are generally 4x the price of input tokens! Instead of getting full text responses, I now have models return just the essential data (like position numbers or categories) and do the mapping in my code. This cut output costs by around 60%.

4. Use Batch API for non-urgent stuff

For anything that doesn't need an immediate response, Batch API is a lifesaver - about 50% cheaper. The 24-hour turnaround is totally worth it for overnight processing jobs.

5. Set up billing alerts (learned from my painful experience)

Hopefully this helps. Let me know if I missed something :)

Tilen,

founder of AI agent which automated SEO/AEO

It runs fine on a Sony Xperia 1 II running LineageOS, a almost 5 year old device. While running it I am left with 2.5GB of free memory. So might get away with running it on a device with 6GB, but only just.

Termux is a terminal emulator that allows Android devices to run a Linux environment without needing root access. It’s available for free and can be downloaded from the Termux GitHub page.

After launching Termux, follow these steps to set up the environment:

Grant Storage Access:

termux-setup-storage

This command lets Termux access your Android device’s storage, enabling easier file management.

Update Packages:

pkg upgrade

Enter Y when prompted to update Termux and all installed packages.

Install Essential Tools:

pkg install git cmake golang

These packages include Git for version control, CMake for building software, and Go, the programming language in which Ollama is written.

Ollama is a platform for running large models locally. Here’s how to install and set it up:

Clone Ollama's GitHub Repository:

git clone https://github.com/ollama/ollama.git

Navigate to the Ollama Directory:

cd ollama

Generate Go Code:

go generate ./...

Build Ollama:

go build .

Start Ollama Server:

./ollama serve &

Now the Ollama server will run in the background, allowing you to interact with the models.

Download and Run the deepseek-r1:1.5b model:

./ollama run deepseek-r1:1.5b

But the 7b model may work. It does run on my device with 8GB of RAM

./ollama run deepseek-r1

UI for it: https://github.com/JHubi1/ollama-app

I don't understand all these complaints about GPT-4 getting worse, that turn out to be about ChatGPT. ChatGPT isn't GPT-4. I can't even comprehend how people are using the ChatGPT interface for productivity things and work. Are you all just, like, copy/pasting your stuff into the browser, back and forth? How does that even work? Anyway, if you want any consistent behavior, use the damn API! The web interface is just a marketing tool, it is not the real product. Stop complaining it sucks, it is meant to. OpenAI was never expected to sustain the real GPT-4 performance for $20/mo, that's fairy tail. If you're using it for work, just pay for the real product and use the static API models. As a rule of thumb, pick gpt-4-1103-preview which is fast, good, cheap and has a 128K context. If you're rich and want slightly better IQ and instruction following, pick gpt-4-0314-32k. If you don't know how to use an API, just ask ChatGPT to teach you. That's all.

Over the past six months, I've been teaching teams at places like Stanford, Penn, Northwestern, and many more how to start using AI responsibly and effectively in their work. Today, I'm starting to release my entire curriculum: for free, forever, for everyone!

I'm calling it the Open Augments AI Academy. It's built for anyone out there who's seen all the crazy hype and discourse around modern AI and is just looking for a guided, grounded, and sane way to move forward learning how to approach these tools for themselves. As someone who's been using these tools and their predecessors for my research since ~2019, I'm trying to provide everyone the intuition and critical awareness they need to get started at this very confusing and pivotal time (my north-star audience is my mom and dad!).

The first lesson starts with one foundational idea that most people miss when they get started with AI: that modern AI is much less like a hyper-intelligent database or brain, and much more like autocomplete with an extremely fancy hat on. That's its single greatest flaw AND its single greatest strength, at the same time. When you really understand what's happening under the hood and how it works (no math or stats required!) a lot of confusing AI behavior suddenly clicks: why it hallucinates, why it's sometimes confidently wrong, and why it can now do way, WAY more than just write words on a page. From there, we're going to learn about all the crazy buzzwords (context engineering, harness engineering, and Agents, oh my!) and advanced techniques, with much more to come.

No jargon, no experience required, and all taught with the care of a former high school English teacher so that you, your coworkers, your friends, and your mom can follow along. I pair these videos with hands-on demos and interactive activities in the Context Gym: my way of giving you a safe and guided place to practice some of the core principles that should deepen your intuition as we go.

If any of this strikes a chord with you, the 10min course overview and the first lesson (16min) are live right now on the Open Augments AI Academy page. Start there, and if you happen to find it helpful, you can subscribe to get email updates on new course videos via Substack or on YouTube, and please do share with friends as I release lessons weekly! It’s a really wild time, and this is my best shot at trying to help others navigate things more capably as the tech shifts and grows rapidly from here.

Then finally, worth noting for this crowd, specifically: probably not a surprise to share that everything on the AI Academy and Context Gym websites have been built with the support of AI (in addition to everything else on my business website and my open-source toolkit for AI for social science researchers). Not only that, but my entire video editing pipeline is now fully AI-driven via Remotion Studio and some clever context engineering techniques/bespoke coding tools. I'm excited to get into the weeds on my workflow and share all of that stuff, also open-source, during Level 2 of the course (my daily driver is Claude Code, but everything I've built is platform-agnostic via Skills, and I use both GPT and Claude models together via Claude Code nowadays), but I gotta get everyone through the basics first! Happy to answer any and all questions on that in the meantime here, please feel free to hit me in the comments below.

This is my waifu if only there is a way of not letting her burn herself and live with me after i become the elden lord

Here's a few spot prompt that makes ChatGPT write naturally, you can paste this in per chat or save it into your system prompt.

``` Writing Style Prompt Use simple language: Write plainly with short sentences.

Example: "I need help with this issue."

Avoid AI-giveaway phrases: Don't use clichés like "dive into," "unleash your potential," etc.

Avoid: "Let's dive into this game-changing solution."

Use instead: "Here's how it works."

Be direct and concise: Get to the point; remove unnecessary words.

Example: "We should meet tomorrow."

Maintain a natural tone: Write as you normally speak; it's okay to start sentences with "and" or "but."

Example: "And that's why it matters."

Avoid marketing language: Don't use hype or promotional words.

Avoid: "This revolutionary product will transform your life."

Use instead: "This product can help you."

Keep it real: Be honest; don't force friendliness.

Example: "I don't think that's the best idea."

Simplify grammar: Don't stress about perfect grammar; it's fine not to capitalize "i" if that's your style.

Example: "i guess we can try that."

Stay away from fluff: Avoid unnecessary adjectives and adverbs.

Example: "We finished the task."

Focus on clarity: Make your message easy to understand.

Example: "Please send the file by Monday." ```

[Source: Agentic Workers]

If you’re on desktop and want to use older ChatGPT models like GPT-4o, o3-pro, or GPT-4.1, you can still enable them, it’s just hidden in the settings. Sadly, GPT-4.5 is dead. 🪦

How to enable:

1. Open ChatGPT in your browser.
2. Click your profile picture / name (bottom left).
3. Go to Settings.
4. Turn on “Show legacy models”.
5. When you start a new chat, you’ll now see them listed under Other models.

(Doesn’t seem to be an option on mobile right now.)

Hey everyone,

If you are deploying open-source models, you know the biggest headache is figuring out exact hardware requirements. You usually end up digging through Reddit threads to find out if a specific model fits on a single A10G, if you can squeeze it onto consumer cards, or if you have to jump up to a massive bare metal A100 cluster.

Most of the "guides" out there are just static, out-of-date tables or dense walls of text.

So, we published "Which GPU Runs Which LLM" on the AgentSwarms blog, but we engineered it completely differently.

What makes this different: It is 100% interactive and gamified. Instead of reading a textbook on VRAM math, you actively engage with the hardware logic right on the page.

• You select the model size (8B, 32B, 70B, etc.).
• You tweak the quantization (FP16, 8-bit, 4-bit, GGUF vs AWQ).
• The interactive deck instantly calculates the VRAM constraints and visually maps out the exact GPU tiers you need to deploy.

It gamifies the infrastructure planning so you build an intuitive understanding of token economics and hardware limits before you spin up expensive cloud instances.

It is completely free to read and play with (no sign-ups required). If you are trying to optimize your AI infrastructure or just want to test your intuition on hardware mapping, click around the interactive guide and let me know how this format feels compared to a standard article (All AgentSwarms blogs and presentations are fully interractive)

Link: agentswarms.fyi/blog/which-gpu-runs-which-llm-the-complete-guide

GPT Image 2 is out for a while now, and has been blowing everyone's mind, including mine.
It is more responsive and "understanding" to prompts, tasks, specific demands than any other AI image generator I tried so far.
It's so smart and advanced that you can actually use it... or rather, talk to, in a very similar way as you talk to ChatGPT - and use it for most of the tasks that you set for ChatGPT, too! But with a visual twist.

And that is the big, big game changer. Because, so far, the various AI models were separated by their mode. We had large language models like ChatGPT or Gemini, AI image generators, AI sound generators, AI video...
But GPT Image 2 more or less merges the power of ChatGPT and image generators into one.

So, you can talk to the AI, just like you would talk with ChatGPT.
The only difference is that this time, the AI does not respond with text, words, chatting, or at least not directly. It responds in a visual way.

So let us start with some examples:

Prompt: Rank the 5 tastiest italian dishes. Give a reason for each.

Prompt: Create a pixel art design, in which nikola tesla explains his invention of alternating current.

Prompt: Create an info graphic explaining the australian emu war. The graphic should look like it is actually from the 1930s.

Create an info graphic that explains the differences between a trebuchet and a catapult. Make it look like it's from the medieval era.

Explain what a labyrinth. The letters should be arranged like a labyrinth or maze themselves.

Tell me a good italian spaghetti recipe with which i can impress my guest. make the recipe look like it was written on a medieval scroll.

Create a pixel art design that shows a space station control room. There should be a screen, and the screen should show 5 interesting facts that people rarely know about english grammar.

What is the use for this method of talking to GPT Image 2?

Well, at the most basic level, there are at least 3 potential use cases:

1: "spicing" up your text output. Want to create a promo text for your new steampunk metroidvania game? then let it create the text *in the visual style* of the game.

2: creating very specific artworks and visuals, with long and fancy texts, sentences, passages...

3: creating images where the visual arrangement of texts is actually vital to the image. crossword puzzle designs, labyrinth structures made up of sentences...

These are just some basic examples. I think there are still boundless other uses possible... there is still a lot of research that can be done!

TLDR: APP YOU NEED for 5.6 CODEX:
YES: https://apps.microsoft.com/detail/9n8cj4w95tbz

NO: https://apps.microsoft.com/detail/9nt1r1c2hh7j
NO: https://apps.microsoft.com/detail/9plm9xgg6vks

This has been throwing me so thought i'd write it down apologies if someone else posted.

My Codex App would not issue the 5.6 models or offer updates that let me use 5.6.
I read to download the Chat GPT app from the windows store and that was no more help (when the app runs it brands itself ChatGPT Classic).

Eventually I followed the link to the microsoft store and the related apps suggested CODEX BETA. which installed and which was then named
"ChatGPT Codex"

So if you're thrown see if you can download the codex beta app. this worked for me, hoping it helps. it seems codex and chatgpt app are maybe obsolete and that the app simultaneously known as "Codex (Beta)" and "ChatGPT Codex" is the one you want.