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The best way to use o1 in Cursor: Let it analyze and explain the codebase to you o1 is extremely good at this. You can jump into any new codebase and quickly get a good understanding of it

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Moritz Kremb

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87,335 görüntüleme • 1 yıl önce •via X (Twitter)

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ok first impressions of claude 3.7 sonnet max on cursor VERY good at planning out your project first - seems to have way more of an understanding of the codebase i had it whip up a set of instructions to create a cursor rules tool that lets you mix/match and output in the format you want (including .cursor/rules/{rule}.mdc, .windsurfrules, .clinerules and legacy .cursorrules) still struggled with new versions of packages and i need to riff on the ui but this is impressive and i'll be using it more to plan features with!
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ok first impressions of claude 3.7 sonnet max on cursor VERY good at planning out your project first - seems to have way more of an understanding of the codebase i had it whip up a set of instructions to create a cursor rules tool that lets you mix/match and output in the format you want (including .cursor/rules/{rule}.mdc, .windsurfrules, .clinerules and legacy .cursorrules) still struggled with new versions of packages and i need to riff on the ui but this is impressive and i'll be using it more to plan features with!

Ian Nuttall

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We built the best codebase search to give any agent grounded information from GitHub repos. Introducing Sandbox Search. Point it at any repo, and we’ll spin up a secure coding agent in its own sandbox to do research for you. Use inside claude code, openclaw, cursor, and more.
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We built the best codebase search to give any agent grounded information from GitHub repos. Introducing Sandbox Search. Point it at any repo, and we’ll spin up a secure coding agent in its own sandbox to do research for you. Use inside claude code, openclaw, cursor, and more.

Arlan

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My project has 39,205 lines of code, and Cursor can't answer questions about it. Cursor's context seems to be capped at around 10,000 tokens. Unfortunately, this is not enough for any decent-sized project. If you have a large codebase, check out Augment Code. This thing is faaaast! I'm currently using their Visual Studio Code plugin, but you can also use them on JetBrains, Neovim, and even Vim. (I'm a Neovim fan, but Copilot's implementation for Neovim is nowhere as good as Augment Code.) Augment Code was gracious enough to sponsor this post. After you install their extension and run it for the first time, it will index your entire codebase. This is why it can answer questions as fast as it does, regardless of the size of your codebase. Augment Code supports chat and completions like every other AI coding assistant, but its killer feature is "Next Edit." When you make a change, two things happen: 1. The model analyzes the change to determine the ripple effects across your *entire* codebase. 2. The model suggests everything you need to update to ensure everything works correctly. This is pretty wild!
0:19

My project has 39,205 lines of code, and Cursor can't answer questions about it. Cursor's context seems to be capped at around 10,000 tokens. Unfortunately, this is not enough for any decent-sized project. If you have a large codebase, check out Augment Code. This thing is faaaast! I'm currently using their Visual Studio Code plugin, but you can also use them on JetBrains, Neovim, and even Vim. (I'm a Neovim fan, but Copilot's implementation for Neovim is nowhere as good as Augment Code.) Augment Code was gracious enough to sponsor this post. After you install their extension and run it for the first time, it will index your entire codebase. This is why it can answer questions as fast as it does, regardless of the size of your codebase. Augment Code supports chat and completions like every other AI coding assistant, but its killer feature is "Next Edit." When you make a change, two things happen: 1. The model analyzes the change to determine the ripple effects across your *entire* codebase. 2. The model suggests everything you need to update to ensure everything works correctly. This is pretty wild!

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The new Cursor agent is INSANE It can run any python func in your codebase. The idea being being to check output & debug, but fuck that CRAZY SIDE EFFECT: this means you can customize the agent w/ callable tools! Like GPT Store actions or Claude MCP Cursor Agent Builder!
0:17

The new Cursor agent is INSANE It can run any python func in your codebase. The idea being being to check output & debug, but fuck that CRAZY SIDE EFFECT: this means you can customize the agent w/ callable tools! Like GPT Store actions or Claude MCP Cursor Agent Builder!

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Excited to introduce OpenAI o1—a new series of reasoning models. Developers, we’d love for you to kick the tires, but don’t just hot-swap GPT-4o with o1! Send o1-preview and o1-mini your coding tasks or hard challenges, and share your feedback and most interesting results.
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Excited to introduce OpenAI o1—a new series of reasoning models. Developers, we’d love for you to kick the tires, but don’t just hot-swap GPT-4o with o1! Send o1-preview and o1-mini your coding tasks or hard challenges, and share your feedback and most interesting results.

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Truth: AI coding models are good for code execution, but not good at planning. If you let AI plan next step while coding, it will mess up the codebase. You can solve this by attaching a detailed "Implementation Plan" Here's how I create my plan: ↓
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Truth: AI coding models are good for code execution, but not good at planning. If you let AI plan next step while coding, it will mess up the codebase. You can solve this by attaching a detailed "Implementation Plan" Here's how I create my plan: ↓

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Introducing o1 web crawler 🕸️ It crawls entire websites with OpenAI’s new o1 reasoning model and . Just state an objective and it will navigate + return the requested data in a JSON schema. Check it out:
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Introducing o1 web crawler 🕸️ It crawls entire websites with OpenAI’s new o1 reasoning model and . Just state an objective and it will navigate + return the requested data in a JSON schema. Check it out:

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im not as good as it as i used to but the gist is: jump and hold forward, when you land you have to let go of forward and flick your mouse to the side and then jump again and then press forward again. it's really fun when you get the hang of it. i dont have revolvers on dis sav
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im not as good as it as i used to but the gist is: jump and hold forward, when you land you have to let go of forward and flick your mouse to the side and then jump again and then press forward again. it's really fun when you get the hang of it. i dont have revolvers on dis sav

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This was us haha. Sreejith PP Arpit Saxena and I built Andrej Karpathy-fy! It takes a paperswithcode link, your current proficiency and gives you a tailored 5 lesson plan to go from where you are to the meaty parts of the paper implementation - like the karpathy video series / labml style. This is a task none of the previous models could do, and O1 was really good at. The best part? The output is an executable ipynb notebook (which you can use minusxai to work through :) ) The below video has apple's recent monocular depth paper. I could actually go through and understand it, with loss functions from the paper and everything!
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This was us haha. Sreejith PP Arpit Saxena and I built Andrej Karpathy-fy! It takes a paperswithcode link, your current proficiency and gives you a tailored 5 lesson plan to go from where you are to the meaty parts of the paper implementation - like the karpathy video series / labml style. This is a task none of the previous models could do, and O1 was really good at. The best part? The output is an executable ipynb notebook (which you can use minusxai to work through :) ) The below video has apple's recent monocular depth paper. I could actually go through and understand it, with loss functions from the paper and everything!

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Wow.. you can now instantly turn any GitHub repo into AI-generated documentation, just by changing the URL. Replace “github” with “deepwiki” in any GitHub URL and you’ll see: → Instant docs for any codebase → Chat with it like a conversation
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Wow.. you can now instantly turn any GitHub repo into AI-generated documentation, just by changing the URL. Replace “github” with “deepwiki” in any GitHub URL and you’ll see: → Instant docs for any codebase → Chat with it like a conversation

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The then and now of this Country! Everyone can literally look this up to fact check it too! Just to let some of you know the Blue is good and the red is bad!

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The way you track token unlocks is about to change. Our new Token Unlocks Dashboard gives you a market-wide view of upcoming unlocks. This way you catch and analyze crucial events before they impact prices: 🔗 Here’s how to use it to stay ahead 🧵

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setting. cadence. influencer realism. story. product. audience. that's what actually makes AI UGC work seedance 2.0 is the best at all of those things good start frame + my V2 prompting system and it handles b-roll, cuts, voiceover, everything you don't need better tools. we're already at the best it can get you just need to know how to use it properly...

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short thread to recap this new workflow on Kling O1 1. choose an image or create it 2. create more cinematic angles in a grid (Higgsfield cinema studio or directly in NanoBanana) 3. add the grid into Kling O1 (add more references if needed)

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Before you really can be good at the game, you gotta love the game. Just let them get out there and enjoy the game because once they develop that love, then they want to learn and listen to the people that want to teach them. You gotta have a passion for something to be good at it.
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Before you really can be good at the game, you gotta love the game. Just let them get out there and enjoy the game because once they develop that love, then they want to learn and listen to the people that want to teach them. You gotta have a passion for something to be good at it.

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Code reviews Codex goes beyond static analysis—it checks a PR against its intent, reasons across the codebase and dependencies, and can run code to validate the behavior of changes. Set it up to auto-review new PRs in your repo, or tag @ codex.
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Code reviews Codex goes beyond static analysis—it checks a PR against its intent, reasons across the codebase and dependencies, and can run code to validate the behavior of changes. Set it up to auto-review new PRs in your repo, or tag @ codex.

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This Wyatt Langford catch from yesterday is a great example of why catch probability is good to know. Just a 45% catch probability here. Why? Any MLB outfielder catches this ball if they get there. Most can't get there. Langford's jump is elite, +8.7 feet above the average outfielder. The jump is so good he doesn't even have to get up to top speed. This is just a routine double into the gap much of the time, and the TV viewer thinks nothing of it:

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