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👶🤖 Introducing `gpt-engineer` ▸ One prompt generates a codebase ▸ Asks clarifying questions ▸ Generates technical spec ▸ Writes all necessary code ▸ Easy to add your own reasoning steps, modify, and experiment ▸ open source: ▸ Lets you finish a coding project in minutes. swyx scooped me on... show more

Anton Osika

81,935 subscribers

664,715 次观看 • 3 年前 •via X (Twitter)

科学技术教育艺术

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10 条评论

Alex Mizrahi 的头像

Alex Mizrahi3 年前

Generating a whole code base at once is a nice trick but it works only for tiny code bases. Basically as soon as things don't fit into the context the quality will drop sharply and the tool won't be useful. To make a real GPT engineer you need to make it incremental. It should work only on a small task at a time. For example it might work in a loop: 1. Select parts of the code base which are relevant for a particular task. 2. Generate new code taking the context into consideration. 3. Update the summary of the code base. Of course, with a larger context size it might become feasible to update the whole code base at once. But I doubt that quality will be the same as with a smaller context. And you hit a hard limit once you exceed the context size.

Sid⚡️ 的头像

Sid⚡️3 年前

Bonus feature: Over promise and under deliver like a true consulting engineer

spink 的头像

spink3 年前

This looks cool! I’m working on something pretty similar

SQUIP 的头像

SQUIP3 年前

this is amazing 🔥🔥. . any tips on how to make this work with chatgpt-3.5 turbo for the plebs on the waitlist for gpt4 api access? I'm getting an error bc of the context length token limits of the 3.5 model. I want to experiment with this soo bad.

Daniel Deveney 的头像

Daniel Deveney3 年前

But did the code work? And does it have the ability to clarify that it works, and debug it if it doesn’t?

Mihai ᛋ 的头像

Mihai ᛋ3 年前

bro why didn't you hit run

Jacob Valdez 的头像

Jacob Valdez3 年前

Thank you thank you thank you But at the same time 😭😭😭😭😭😭😭. There goes my profession

Robin Ranjit Singh Chauhan @ Neurips 的头像

Robin Ranjit Singh Chauhan @ Neurips3 年前

So did the snake game work? Demo ended for it ran. Anyways cool project!

dj spry 的头像

dj spry3 年前

@SteveMoraco @Memdotai mem it

Mem 的头像

Mem3 年前

@antonosika @SteveMoraco Saved! Here's the compiled thread: 🪄 AI-generated summary: "gpt-engineer is an open source tool that allows users to generate a codebase, ask clarifying questions, create a technical spec, and write all necessary code with ease. It also...

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GPT-5 is live in Cline. We've been working with OpenAI to get this model ready, and here's our take: it's disciplined, persistent, & highly competent. It's collaborative in planning & and a diligent operator while acting. It plans thoroughly, asks optioned follow-ups when needed, & then gets out of the way and ships code. On long tasks it keeps going before pausing to check in. It follows instructions to the letter. And most importantly -- it writes good code. GPT-5 is like "The Wolf" from Pulp Fiction. Comes in, assesses the situation, then executes. Here's what you can expect from GPT-5 in Cline: > verbose while planning; terse while executing > asks a lot of good clarification questions, & frequently provides options when appropriate > strong context retention and persistence over long horizons (256k context window) > good at diff-style edits and multi-file changes (we'll monitor as more usage data comes in) > quiet in Act mode -- writes code without yapping Metaprompting is another strength. We tested early with OpenAI and used GPT-5 to tune our own prompt for GPT-5. Here's a pattern we like: “Answer from your own perspective: what changes or additions would help you better follow this prompt? Here is the prompt (or snippet): [snippet]. Users have complained about X and Y. What minimal edits would you make while keeping the rest intact?” Do you need to change any of your existing patterns in Cline? No -- it's good out of the box. Give a clear goal and constraints, let it plan, then let it cook. Expect more clarifying questions than most models. Pricing: $1.25/M input tokens (+90% cache), $10/M output. Roughly half of Sonnet 4 ($3/$15). Want to try GPT-5? Use it in Cline today for pure, unfiltered inference via the OpenAI, Cline, or OpenRouter providers. (fyi -- GPT-5 one-shotted this browser DAW below on the prompt "build something impressive to show me what you're capable of")

GPT-5 is live in Cline. We've been working with OpenAI to get this model ready, and here's our take: it's disciplined, persistent, & highly competent. It's collaborative in planning & and a diligent operator while acting. It plans thoroughly, asks optioned follow-ups when needed, & then gets out of the way and ships code. On long tasks it keeps going before pausing to check in. It follows instructions to the letter. And most importantly -- it writes good code. GPT-5 is like "The Wolf" from Pulp Fiction. Comes in, assesses the situation, then executes. Here's what you can expect from GPT-5 in Cline: > verbose while planning; terse while executing > asks a lot of good clarification questions, & frequently provides options when appropriate > strong context retention and persistence over long horizons (256k context window) > good at diff-style edits and multi-file changes (we'll monitor as more usage data comes in) > quiet in Act mode -- writes code without yapping Metaprompting is another strength. We tested early with OpenAI and used GPT-5 to tune our own prompt for GPT-5. Here's a pattern we like: “Answer from your own perspective: what changes or additions would help you better follow this prompt? Here is the prompt (or snippet): [snippet]. Users have complained about X and Y. What minimal edits would you make while keeping the rest intact?” Do you need to change any of your existing patterns in Cline? No -- it's good out of the box. Give a clear goal and constraints, let it plan, then let it cook. Expect more clarifying questions than most models. Pricing: $1.25/M input tokens (+90% cache), $10/M output. Roughly half of Sonnet 4 ($3/$15). Want to try GPT-5? Use it in Cline today for pure, unfiltered inference via the OpenAI, Cline, or OpenRouter providers. (fyi -- GPT-5 one-shotted this browser DAW below on the prompt "build something impressive to show me what you're capable of")

Cline

63,496 次观看 • 11 个月前

Someone open-source harness that lets Claude Code and Codex generate real 3D CAD models from plain English. It’s called text-to-cad, an open source harness that turns your coding agent into a CAD engineer.. Building a robot used to mean stitching 6+ tools, CAD software, URDF exporters, kinematics solvers, viewers. Days of work. Broken pipelines. Files no agent can read. text-to-cad kills all of it. → Exports STEP, STL, DXF, GLB, 3MF → Generates URDF robot descriptions → Slices straight to G-code → Sends jobs to your Bambu Lab printer → Runs fully offline, no backend One demo generates a full 7-DoF robot arm, working kinematics, custom GUI, the whole thing, almost entirely through prompts. 100% open source.

Someone open-source harness that lets Claude Code and Codex generate real 3D CAD models from plain English. It’s called text-to-cad, an open source harness that turns your coding agent into a CAD engineer.. Building a robot used to mean stitching 6+ tools, CAD software, URDF exporters, kinematics solvers, viewers. Days of work. Broken pipelines. Files no agent can read. text-to-cad kills all of it. → Exports STEP, STL, DXF, GLB, 3MF → Generates URDF robot descriptions → Slices straight to G-code → Sends jobs to your Bambu Lab printer → Runs fully offline, no backend One demo generates a full 7-DoF robot arm, working kinematics, custom GUI, the whole thing, almost entirely through prompts. 100% open source.

Superman

51,642 次观看 • 17 小时前

The next change mentioned in the last AMA on Discord is the addition of "Mention GPTs" and a more agent-like approach to GPTs This new feature allows you to inline tag any other custom GPT, which will then be displayed as "Talking to [Grimoire/custom GPT name]". This feature also enables using different GPTs in the same conversation, taking an agents-like approach (although it seems that this feature may not work properly yet). To mention a GPT and add it directly into your conversation, simply type "@" followed by the name of the GPT. h/t to Glenn 'devalias' Grant & his amazing ChatGPT Source Watch project

The next change mentioned in the last AMA on Discord is the addition of "Mention GPTs" and a more agent-like approach to GPTs This new feature allows you to inline tag any other custom GPT, which will then be displayed as "Talking to [Grimoire/custom GPT name]". This feature also enables using different GPTs in the same conversation, taking an agents-like approach (although it seems that this feature may not work properly yet). To mention a GPT and add it directly into your conversation, simply type "@" followed by the name of the GPT. h/t to Glenn 'devalias' Grant & his amazing ChatGPT Source Watch project

Tibor Blaho

12,776 次观看 • 2 年前

ONE PROMPT IN CHATGPT IMAGE 2 JUST REPLACED AN ENTIRE $5,000-$50,000 BRAND DESIGN TEAM Full brand identity. Colors, typography, logo style, aesthetic. All generated in one single prompt. Here's the exact workflow: Step 1: Paste one prompt asking for a complete brand identity. GPT Image 2 generates everything – color palette, fonts, logo direction, overall visual style. Step 2: Use that output as context for every asset you generate after. Campaign visuals, shop graphics, product mockups, social posts – all stay consistent and on-brand automatically. The result: A full visual identity that looks like a real design team built it. Clean text rendering. Consistent style across every single asset. Design agencies charge $5,000-$50,000 for brand identity work. GPT Image 2 does it in one prompt. The full prompt that makes this work is in the video below. Bookmark this post, you'll want to come back to it.

SCOTTY BEAM

12,023 次观看 • 1 个月前

Nothing beats Augment Code answering questions about large codebases. It's a free extension (works in VSCode, JetBrains, and NeoVim), and has a community edition you can run for free forever. Try this experiment: 1. Download any large open-source project from GitHub 2. Augment will index the code 3. Start asking questions about the codebase 4. Do the same using any other coding agent 5. Compare the speed and the depth of the answers you get If you have a small codebase, the differences with other coding agents won't be obvious, but if you are working on a large project, things will click immediately. Suggestion: download the Keras repository (~180,000 lines of code). I know the library well, and the answers I got from Augment were pretty impressive. By the way, Augment is a fully-fledged AI coding assistant. You can use its agent in every imaginable way: • To implement brand new functionality • To make quick edits to your code • To refactor existing functionality • To generate unit tests • To generate documentation • To handle GitHub—pull requests, commits, branching, etc My favorite feature is their "Next Edit" functionality: 1. You edit the code 2. Augment analyzes the change (across the entire codebase!) 3. Augment determines the ripple effects of that change 4. Augment suggests everything you need to update Here is a link to the tool: Thanks to the team at Augment Code for partnering with me on this post.

Santiago

34,977 次观看 • 1 年前

Claude Code is unusable. So I built my own. Introducing BridgeCode. Launching next Wednesday. Everything you love about Claude Code. No rate limits. No 529 errors. No 500 errors. No nerfing. Connect to any provider. Claude Opus 4.6. GPT 5.4. Gemini 3.1 Pro. GLM 5.1. All of them. Switch models in seconds. And one more thing. Swarm mode. One prompt. Ten agents. Working together to ship your code. Claude Code rate limited me into building a competitor. Thank you, Anthropic. Launching next Wednesday.

Claude Code is unusable. So I built my own. Introducing BridgeCode. Launching next Wednesday. Everything you love about Claude Code. No rate limits. No 529 errors. No 500 errors. No nerfing. Connect to any provider. Claude Opus 4.6. GPT 5.4. Gemini 3.1 Pro. GLM 5.1. All of them. Switch models in seconds. And one more thing. Swarm mode. One prompt. Ten agents. Working together to ship your code. Claude Code rate limited me into building a competitor. Thank you, Anthropic. Launching next Wednesday.

BridgeMind

47,103 次观看 • 3 个月前

This workflow combining Loom’s AI features + a custom ChatGPT GPT is saving me hours. Instead of creating onboarding Docs for new team members, I film a Loom → generate SOP → train a GPT to answer questions Game changer for businesses to delegate faster. Here's how to do it: First, I record a video of whatever task I want to delegate to the new team member with Loom. The more in-depth, the better, but I just used a 7-minute video. Then, I use Loom's new AI 'Write a document' feature. Upgrading to Loom AI from the standard Loom plan cost me $2. Loom AI can generate an entire SOP, PR description, step-by-step guide, QA, and more from a simple Loom video in <5 seconds. In the past, I’ve spent 2 hours+ hand-writing each one of these docs to onboard new team members, so Loom AI is already a massive timesaver. But it gets even better! Next, we can take that data from the SOP document, and we use it as 'Knowledge' to train a Custom GPT that can answer the new team members' questions. The more SOP docs/Knowledge you feed the GPT, the better. But one is fine if that's all you have because the GPT will pull any unknown answers from the web or its training data. Here are the prompt Instructions you want to put into the Custom GPT (copy and paste this): You are an expert CEO, specialized in onboarding and training new team members. Using the Knowledge provided, you will help new team members with any questions or stipulations they may have about their new role. Stick as true to the data provided as possible, but if= they ask any questions that the Knowledge base does not have a specific answer for, you are permitted to use your pre-trained data and/or web browsing capabilities. That's it! It can't replace you entirely, but it'll save you 90% of the time you would've wasted on writing an SOP doc and answering questions. Simple AI workflows here and there really add up. There's also a workflow to help with the job screening process, but I'll save that for another day :^)

This workflow combining Loom’s AI features + a custom ChatGPT GPT is saving me hours. Instead of creating onboarding Docs for new team members, I film a Loom → generate SOP → train a GPT to answer questions Game changer for businesses to delegate faster. Here's how to do it: First, I record a video of whatever task I want to delegate to the new team member with Loom. The more in-depth, the better, but I just used a 7-minute video. Then, I use Loom's new AI 'Write a document' feature. Upgrading to Loom AI from the standard Loom plan cost me $2. Loom AI can generate an entire SOP, PR description, step-by-step guide, QA, and more from a simple Loom video in <5 seconds. In the past, I’ve spent 2 hours+ hand-writing each one of these docs to onboard new team members, so Loom AI is already a massive timesaver. But it gets even better! Next, we can take that data from the SOP document, and we use it as 'Knowledge' to train a Custom GPT that can answer the new team members' questions. The more SOP docs/Knowledge you feed the GPT, the better. But one is fine if that's all you have because the GPT will pull any unknown answers from the web or its training data. Here are the prompt Instructions you want to put into the Custom GPT (copy and paste this): You are an expert CEO, specialized in onboarding and training new team members. Using the Knowledge provided, you will help new team members with any questions or stipulations they may have about their new role. Stick as true to the data provided as possible, but if= they ask any questions that the Knowledge base does not have a specific answer for, you are permitted to use your pre-trained data and/or web browsing capabilities. That's it! It can't replace you entirely, but it'll save you 90% of the time you would've wasted on writing an SOP doc and answering questions. Simple AI workflows here and there really add up. There's also a workflow to help with the job screening process, but I'll save that for another day :^)

Rowan Cheung

129,424 次观看 • 2 年前

I just asked Claude Fable 5 to show me its muscles and build the best game it could with PRD and Goal in 1 shot Done, Torched 90% of my tokens in two 5 hours sessions 🤦 ( it spend 2x the token than opus confirmed) Come admire Claude's 5 hour master piece - an ink-wash calligraphy defense game with roguelike, skills, bossfight and end game delight. Your finger IS the brush: any stroke is a blade. What I found: - Fables is a lot more like a Artist during desiging and have its own taste. it created all the game art assets and even the guqin music. - During testing, it is also showing GPT 5.5 level testing, fired up Playwright on its own, screenshot-tested every boss, caught and fixed its own bugs if GPT 5.5 is a solid engineer, Fables 5 is a Artist and a solid Engineer all in one check out the game in comments 👇

I just asked Claude Fable 5 to show me its muscles and build the best game it could with PRD and Goal in 1 shot Done, Torched 90% of my tokens in two 5 hours sessions 🤦 ( it spend 2x the token than opus confirmed) Come admire Claude's 5 hour master piece - an ink-wash calligraphy defense game with roguelike, skills, bossfight and end game delight. Your finger IS the brush: any stroke is a blade. What I found: - Fables is a lot more like a Artist during desiging and have its own taste. it created all the game art assets and even the guqin music. - During testing, it is also showing GPT 5.5 level testing, fired up Playwright on its own, screenshot-tested every boss, caught and fixed its own bugs if GPT 5.5 is a solid engineer, Fables 5 is a Artist and a solid Engineer all in one check out the game in comments 👇

Nil Ni 🦊🗝🎮🐷💎🎆

412,524 次观看 • 1 个月前

Today we’re excited to feature RAGApp v0.1 - which lets any user construct a multi-agent application 🎨🤖 without writing a single line of code 💫 Add any number of agents that you wish, and assign each agent a role, system prompt, and a set of tools. In this example, use a researcher, analyst, and report generation agent to write a news article. This directly generates a full chat interface where you can ask questions and get back answers with full streaming and sources. Huge shoutout to Marcus Schiesser for working on this! RAGApp: create-llama: If you want finer control, you can define your own agentic workflows through code:

Today we’re excited to feature RAGApp v0.1 - which lets any user construct a multi-agent application 🎨🤖 without writing a single line of code 💫 Add any number of agents that you wish, and assign each agent a role, system prompt, and a set of tools. In this example, use a researcher, analyst, and report generation agent to write a news article. This directly generates a full chat interface where you can ask questions and get back answers with full streaming and sources. Huge shoutout to Marcus Schiesser for working on this! RAGApp: create-llama: If you want finer control, you can define your own agentic workflows through code:

LlamaIndex 🦙

93,022 次观看 • 1 年前

OpenAI just announced API access to o1 (advanced reasoning model) yesterday. I'm delighted to announce today a new short course, Reasoning with o1, built with OpenAI, and taught by Colin Jarvis, Head of AI Solutions at OpenAI, to show you how to use this effectively! Unlike previous language models which generate output directly, o1 “thinks before it responds,” and generates many reasoning tokens before returning a more thoughtful and accurate response. It is great at complex reasoning -- including planning for agentic workflows, coding, and domain-specific reasoning in STEM fields like law. But how you should use it is quite different from other LLMs. I think o1 will be a game changer for many AI applications; and in this course, you'll learn how to use it effectively. In detail, you’ll: - Learn to recognize what tasks o1 is suited for, and when to use a smaller model, or combine o1 with a smaller model - Understand the new principles of prompting reasoning models: Be simple and direct; no explicit chain-of-thought required; use structure; show rather than tell - Implement multi-step orchestration in which o1 plans, and hands tasks over to gpt-4o-mini to execute specific steps; this illustrates a design pattern to optimize intelligence (accuracy) and cost - Use o1 for a coding task to build a new application, edit existing code, and test performance by running a coding competition between o1-mini and GPT 4o - Use o1 for image understanding and learn how it performs better with a "hierarchy of reasoning," in which it incurs the latency and cost upfront, preprocessing the image and indexing it with rich details so it can be used for Q&A later - Learn a technique called meta-prompting, in which you use o1 to improve your prompts. Using a customer support evaluation set, you'll iteratively use o1 to modify a prompt to improve performance You'll also learn about how OpenAI used reinforcement learning to produce a model that uses "test-time compute" to improve performance. I think you'll find this course enjoyable and valuable. Please sign up for it here:

OpenAI just announced API access to o1 (advanced reasoning model) yesterday. I'm delighted to announce today a new short course, Reasoning with o1, built with OpenAI, and taught by Colin Jarvis, Head of AI Solutions at OpenAI, to show you how to use this effectively! Unlike previous language models which generate output directly, o1 “thinks before it responds,” and generates many reasoning tokens before returning a more thoughtful and accurate response. It is great at complex reasoning -- including planning for agentic workflows, coding, and domain-specific reasoning in STEM fields like law. But how you should use it is quite different from other LLMs. I think o1 will be a game changer for many AI applications; and in this course, you'll learn how to use it effectively. In detail, you’ll: - Learn to recognize what tasks o1 is suited for, and when to use a smaller model, or combine o1 with a smaller model - Understand the new principles of prompting reasoning models: Be simple and direct; no explicit chain-of-thought required; use structure; show rather than tell - Implement multi-step orchestration in which o1 plans, and hands tasks over to gpt-4o-mini to execute specific steps; this illustrates a design pattern to optimize intelligence (accuracy) and cost - Use o1 for a coding task to build a new application, edit existing code, and test performance by running a coding competition between o1-mini and GPT 4o - Use o1 for image understanding and learn how it performs better with a "hierarchy of reasoning," in which it incurs the latency and cost upfront, preprocessing the image and indexing it with rich details so it can be used for Q&A later - Learn a technique called meta-prompting, in which you use o1 to improve your prompts. Using a customer support evaluation set, you'll iteratively use o1 to modify a prompt to improve performance You'll also learn about how OpenAI used reinforcement learning to produce a model that uses "test-time compute" to improve performance. I think you'll find this course enjoyable and valuable. Please sign up for it here:

Andrew Ng

357,661 次观看 • 1 年前

For those of you tired of yet another AI-based IDE: Check out Traycer. It's a Visual Studio Code extension (free forever for anyone working on open-source repositories), and it does something different from everyone else. You write a high-level prompt outlining the changes you want to make to your codebase, and the extension generates a plan for you before you touch the code. (It reminds me of GitHub Copilot Workspaces.) You can review the plan, understand exactly the changes the extension will make to your code, and make any modifications. When you are ready, you execute the plan, and Traycer stages all of the changes so you have a new opportunity to review everything. Only then does Traycer apply the changes. This is ideal for large codebases where you'd rather study the changes the AI plans to make before they make it into your code. Look at the video I recorded to see how it works. Thanks to the Traycer team for collaborating with me on this post!

For those of you tired of yet another AI-based IDE: Check out Traycer. It's a Visual Studio Code extension (free forever for anyone working on open-source repositories), and it does something different from everyone else. You write a high-level prompt outlining the changes you want to make to your codebase, and the extension generates a plan for you before you touch the code. (It reminds me of GitHub Copilot Workspaces.) You can review the plan, understand exactly the changes the extension will make to your code, and make any modifications. When you are ready, you execute the plan, and Traycer stages all of the changes so you have a new opportunity to review everything. Only then does Traycer apply the changes. This is ideal for large codebases where you'd rather study the changes the AI plans to make before they make it into your code. Look at the video I recorded to see how it works. Thanks to the Traycer team for collaborating with me on this post!

Santiago

84,445 次观看 • 1 年前

I tested a new coding assistant on 34,568 lines of code. (Cursor could not achieve what this tool did.) AI models are improving every day: - Faster inference - Longer context lengths But are AI coding assistants advancing at the same rate? Can they truly understand your entire project? I tested Augment Code on my ai-engineering repo with ~35k lines of code. Augment Code is a powerful AI Assistant built for developers working with large, evolving codebases—needing an assistant that understands the full context of their projects. In the video demo below, I asked it to: - Merge two projects. - Answer a global context question. Here’s what happened: - It understood dependencies across both projects. - It merged them intelligently—without breaking anything. - It even created a README file for the merged project. - It answered my global query instantly, pulling from the full codebase. This is the difference between AI autocomplete and an AI engineer that truly understands your repo. Augment Code is powerful because it indexes your entire repo upfront. This way, it can answer questions instantly, no matter how large your project is. Lastly, Augment Code is fully compatible with VSCode, JetBrains, Vim, Slack, and more. Try them now, I have shared a link in the next tweet! Thanks to Augment Code for showing me their powerful AI coding assistant and working with me on this post.

I tested a new coding assistant on 34,568 lines of code. (Cursor could not achieve what this tool did.) AI models are improving every day: - Faster inference - Longer context lengths But are AI coding assistants advancing at the same rate? Can they truly understand your entire project? I tested Augment Code on my ai-engineering repo with ~35k lines of code. Augment Code is a powerful AI Assistant built for developers working with large, evolving codebases—needing an assistant that understands the full context of their projects. In the video demo below, I asked it to: - Merge two projects. - Answer a global context question. Here’s what happened: - It understood dependencies across both projects. - It merged them intelligently—without breaking anything. - It even created a README file for the merged project. - It answered my global query instantly, pulling from the full codebase. This is the difference between AI autocomplete and an AI engineer that truly understands your repo. Augment Code is powerful because it indexes your entire repo upfront. This way, it can answer questions instantly, no matter how large your project is. Lastly, Augment Code is fully compatible with VSCode, JetBrains, Vim, Slack, and more. Try them now, I have shared a link in the next tweet! Thanks to Augment Code for showing me their powerful AI coding assistant and working with me on this post.

Akshay 🚀

30,806 次观看 • 1 年前

Anthropic engineer: «You're not supposed to sit and re-prompt Claude all day. You build one loop, and it does the work while you sleep.» • 00:00 - Build your first agent that runs on its own • 05:02 - The loop that fixes its own mistakes • 16:43 - Why Claude forgets you between chats, and the fix You can learn it in one afternoon. 30 minutes here replace what most people spend a year figuring out. Watch it today, then read the guide on building loops below.

Anthropic engineer: «You're not supposed to sit and re-prompt Claude all day. You build one loop, and it does the work while you sleep.» • 00:00 - Build your first agent that runs on its own • 05:02 - The loop that fixes its own mistakes • 16:43 - Why Claude forgets you between chats, and the fix You can learn it in one afternoon. 30 minutes here replace what most people spend a year figuring out. Watch it today, then read the guide on building loops below.

Raytar

27,480 次观看 • 8 天前

prompting tip: How to write hybrid english & code prompts while building custom GPTs -Turn on code interpreter -"write code to ..." -Regenerate a few times until you get one you like -Copy paste it back into your prompt -tweak it to improve Now your ai can write consistent code Plus, you can add & combine with all sorts of english rules on top. Essentially adding english code logic "If the user asks for ..., then run this code" "If the user needs help with ..., use this code snippet and ask the user for all required information" Guess what thats a working if statement in english! if userAsksFor { // run this code } Prompts are code! English is a coding language Prompt-gramming is abstract coding

prompting tip: How to write hybrid english & code prompts while building custom GPTs -Turn on code interpreter -"write code to ..." -Regenerate a few times until you get one you like -Copy paste it back into your prompt -tweak it to improve Now your ai can write consistent code Plus, you can add & combine with all sorts of english rules on top. Essentially adding english code logic "If the user asks for ..., then run this code" "If the user needs help with ..., use this code snippet and ask the user for all required information" Guess what thats a working if statement in english! if userAsksFor { // run this code } Prompts are code! English is a coding language Prompt-gramming is abstract coding

Nick Dobos

117,034 次观看 • 2 年前

Ok, so the new Midjourney Style Tuner is here and it's a MASSIVE update. There is so much to unpack, no way I can do it all in one post, but here is an overview. You start by typing in /tune followed by your prompt. MJ will generate a range of sample images (between 16-128) showing different visual styles based on your prompt. Then you choose your favorite images from the sample to generate a unique code you can use to customize the look of future jobs. (link below) So for example, I just tried it with the prompt: 💬 1990s, a photo of a woman dressed in fuzzy velour, shimmering midnight hues reflecting sadness by the lake, a contemporary nightmare on cross-processed film --ar 4:5 --c 4 I selected 10 images, which generated the code zlPhgLCHDXol64kPlwSUzu97. I can then add it to my prompt using --style {code} But it makes more sense if you try it yourself, so try playing around with my Style Tuner for that prompt and generate your own code from your selections: Some quick notes: -You can control the level of style applied by adding the --s parameter to your prompt. -You can combine multiple styles together by separating with a hyphen, i.e --style code1-code2 I will be obsessing over this all day tomorrow and sharing everything I learn along the way. This is an incredible update.

Ok, so the new Midjourney Style Tuner is here and it's a MASSIVE update. There is so much to unpack, no way I can do it all in one post, but here is an overview. You start by typing in /tune followed by your prompt. MJ will generate a range of sample images (between 16-128) showing different visual styles based on your prompt. Then you choose your favorite images from the sample to generate a unique code you can use to customize the look of future jobs. (link below) So for example, I just tried it with the prompt: 💬 1990s, a photo of a woman dressed in fuzzy velour, shimmering midnight hues reflecting sadness by the lake, a contemporary nightmare on cross-processed film --ar 4:5 --c 4 I selected 10 images, which generated the code zlPhgLCHDXol64kPlwSUzu97. I can then add it to my prompt using --style {code} But it makes more sense if you try it yourself, so try playing around with my Style Tuner for that prompt and generate your own code from your selections: Some quick notes: -You can control the level of style applied by adding the --s parameter to your prompt. -You can combine multiple styles together by separating with a hyphen, i.e --style code1-code2 I will be obsessing over this all day tomorrow and sharing everything I learn along the way. This is an incredible update.

Nick St. Pierre

377,958 次观看 • 2 年前

The most expensive part of a startup isn't the code. It's finding out nobody wanted it. 💀 This is what Code Xero and Cluster Protocol are trying to change. 🔥 You can build a working dApp on Base in minutes, test it before you hiring anyone. All you need is your Idea and a prompt. Just watch a video. Got any questions or need help? I am open to conversations (Tags to the projects in first comment 👇🏼)

The most expensive part of a startup isn't the code. It's finding out nobody wanted it. 💀 This is what Code Xero and Cluster Protocol are trying to change. 🔥 You can build a working dApp on Base in minutes, test it before you hiring anyone. All you need is your Idea and a prompt. Just watch a video. Got any questions or need help? I am open to conversations (Tags to the projects in first comment 👇🏼)

Anastasiia

20,681 次观看 • 1 个月前

What if we set GPT-4 free in Minecraft? ⛏️ I’m excited to announce Voyager, the first lifelong learning agent that plays Minecraft purely in-context. Voyager continuously improves itself by writing, refining, committing, and retrieving *code* from a skill library. GPT-4 unlocks a new paradigm: “training” is code execution rather than gradient descent. “Trained model” is a codebase of skills that Voyager iteratively composes, rather than matrices of floats. We are pushing no-gradient architecture to its limit. Voyager rapidly becomes a seasoned explorer. In Minecraft, it obtains 3.3× more unique items, travels 2.3× longer distances, and unlocks key tech tree milestones up to 15.3× faster than prior methods. We open-source everything. Let generalist agents emerge in Minecraft! Welcome you all to try today: Paper: Code: Deep dive with me: 🧵

What if we set GPT-4 free in Minecraft? ⛏️ I’m excited to announce Voyager, the first lifelong learning agent that plays Minecraft purely in-context. Voyager continuously improves itself by writing, refining, committing, and retrieving code from a skill library. GPT-4 unlocks a new paradigm: “training” is code execution rather than gradient descent. “Trained model” is a codebase of skills that Voyager iteratively composes, rather than matrices of floats. We are pushing no-gradient architecture to its limit. Voyager rapidly becomes a seasoned explorer. In Minecraft, it obtains 3.3× more unique items, travels 2.3× longer distances, and unlocks key tech tree milestones up to 15.3× faster than prior methods. We open-source everything. Let generalist agents emerge in Minecraft! Welcome you all to try today: Paper: Code: Deep dive with me: 🧵

Jim Fan

3,822,275 次观看 • 3 年前

How does tooling and promotions work at Google, and what are tactics to become a more influential engineer? Ex-Google engineer Irina Stanescu shares insights in the latest The Pragmatic Engineer Podcast episode. Watch the full episode: A special thanks to the fantastic sponsors of the podcast: • WorkOS — The modern identity platform for B2B SaaS. Check it out at • Sonar — Trust your developers – verify your AI-generated code. Learn more at In the episode, dive into these following topics: • An inside look at Google’s unique working processes • How to build credibility as a new engineer • Tactical tips for getting promoted • The importance of having a career plan and guidance in designing one • Having influence vs. influencing—and how to become more influential • Essential leadership skills to develop • And so much more Check it out, and thank you to Irina for the insightful and honest chat! Irina also writes the newsletter The Caring Techie, and you can read it here: (If your company is interested in sponsoring the podcast, you can reach out at sponsor@pragmaticengineer.com. 2024 is fully booked, but there are open spaces for the first quarter of 2025).

How does tooling and promotions work at Google, and what are tactics to become a more influential engineer? Ex-Google engineer Irina Stanescu shares insights in the latest The Pragmatic Engineer Podcast episode. Watch the full episode: A special thanks to the fantastic sponsors of the podcast: • WorkOS — The modern identity platform for B2B SaaS. Check it out at • Sonar — Trust your developers – verify your AI-generated code. Learn more at In the episode, dive into these following topics: • An inside look at Google’s unique working processes • How to build credibility as a new engineer • Tactical tips for getting promoted • The importance of having a career plan and guidance in designing one • Having influence vs. influencing—and how to become more influential • Essential leadership skills to develop • And so much more Check it out, and thank you to Irina for the insightful and honest chat! Irina also writes the newsletter The Caring Techie, and you can read it here: (If your company is interested in sponsoring the podcast, you can reach out at [email protected]. 2024 is fully booked, but there are open spaces for the first quarter of 2025).

Gergely Orosz

17,617 次观看 • 1 年前

Your one-minute clock starts now. Prompt 1 to Grok: "Review the video in the post. Create a detailed project requirements document to exactly recreate the app in the video" - about 15 seconds of typing Prompt 2 to Claude Code: "Go into planning mode and create a detailed plan to implement the app described in the attached PRD" - about 15 seconds of typing. Although I did have to come back and type "1" to give it permission to execute the plan. Prompt 3 to Claude Code: "Push this to a public repo." - about 5 seconds of typing All coding about 60 seconds of my time. The longest part was trying to figure out how to screen capture a video with system audio. That flummoxed me for three to four minutes.

Your one-minute clock starts now. Prompt 1 to Grok: "Review the video in the post. Create a detailed project requirements document to exactly recreate the app in the video" - about 15 seconds of typing Prompt 2 to Claude Code: "Go into planning mode and create a detailed plan to implement the app described in the attached PRD" - about 15 seconds of typing. Although I did have to come back and type "1" to give it permission to execute the plan. Prompt 3 to Claude Code: "Push this to a public repo." - about 5 seconds of typing All coding about 60 seconds of my time. The longest part was trying to figure out how to screen capture a video with system audio. That flummoxed me for three to four minutes.

Wayne Culbreth

614,913 次观看 • 3 个月前

✨ I spent the last 48 hours making GPT-4 read the entire Solana validator codebase and write documentation, so doesn't have to. Introducing — an AI-powered chatbot trained on nothing but code that can answer deep technical questions. How it works 👇 But first... A huge shoutout to , Zahid Khawaja, and Sean. Their hard work made prototyping this thing a breeze. Without further ado... Devs like to write code, not documentation. Tribal knowledge is lost when devs move on to other projects, leaving future devs to sort through mountains of code and figure out not just how it works, but why it works that way. This is all about to change. GPT-4's ability to write code is stunning. It seems to understand something fundamental about writing software that previous models just didn't. This comprehension of the principles that drive the design behind a complex system carries over into its ability to document existing codebases in a truly impressive way. With the enlarged context window(s), it's now feasible to feed GPT-4 entire files of code and ask it to write documentation about how the code works. Taking this as a starting point, the process looks something like this: 1. Download repo. 2. Depth-first traversal of repo contents, ignoring things like package-lock and binary files. 3. For each file, feed to GPT-4 and ask it to write documentation in markdown. 4. Save the output in a separate location as [outputRoot]/[inputFilepath][inputFilename].md 5. For each folder, we ask GPT-4 to write a summary of the folder, taking the newly generated documentation for all files in the folder and the summaries from each of its subfolders as context. Write this to the filesystem as markdown. Now we have a filesystem that matches the structure of the input repo, but all files in the tree are markdown documentation of the corresponding code file. From here, we: 1. Load markdown documents into LangChain. 2. Embed all documents via OpenAI embeddings. 3. Store embeddings in Pinecone. When a user sends a query: 1. Embed query. 2. Find k-nearest markdown files. 3. Feed to GPT-4 with a prompt asking to answer the query based on k-nearest markdown documents provided. The craziest part of all this? GPT-4 actually wrote ~30% of the code. The results are pretty good for 2 days of work. There is certainly room for improvement. Some items that are top of mind: 1. TolyGPT will occasionally hallucinate answers. It is especially bad with links to external sources, like GitHub. The base model seems to know a bit about Solana already, and sometimes this creeps in. Fine-tuning the prompt can solve some of this. 2. Context selection is difficult in a codebase this large. For example, sometimes it will pull in details about the Solana SDK when asked about transaction processing. The SDK files can seem relevant depending on the phrasing of the question. It may be worth breaking the documentation into subsystems to limit this. 3. Not all files fit into the 32k token window. As of now, there are 23 (out of ~1,100) files that cannot be documented in their entirety. Some of these files are very important to how Solana works. Final thoughts: 1. GPT-4 is super powerful, and we're going to see a ton of tools that supercharge the entire software development lifecycle. This is not 12 months away. For the people that can afford it, these tools are here now. And they're only getting better. Act accordingly. 2. The price of inference has to come down for this to go mainstream. I spent about $300 prototyping this project, and the final crawl cost about the same. The high cost of GPT-4 will push developers to other, cheaper alternatives with similar performance. This is coming very soon. If you have a large software project and you're interested in something like this for your codebase, fill out this form and we'll be in touch this week. Or just DM me :)

✨ I spent the last 48 hours making GPT-4 read the entire Solana validator codebase and write documentation, so doesn't have to. Introducing — an AI-powered chatbot trained on nothing but code that can answer deep technical questions. How it works 👇 But first... A huge shoutout to , Zahid Khawaja, and Sean. Their hard work made prototyping this thing a breeze. Without further ado... Devs like to write code, not documentation. Tribal knowledge is lost when devs move on to other projects, leaving future devs to sort through mountains of code and figure out not just how it works, but why it works that way. This is all about to change. GPT-4's ability to write code is stunning. It seems to understand something fundamental about writing software that previous models just didn't. This comprehension of the principles that drive the design behind a complex system carries over into its ability to document existing codebases in a truly impressive way. With the enlarged context window(s), it's now feasible to feed GPT-4 entire files of code and ask it to write documentation about how the code works. Taking this as a starting point, the process looks something like this: 1. Download repo. 2. Depth-first traversal of repo contents, ignoring things like package-lock and binary files. 3. For each file, feed to GPT-4 and ask it to write documentation in markdown. 4. Save the output in a separate location as [outputRoot]/[inputFilepath][inputFilename].md 5. For each folder, we ask GPT-4 to write a summary of the folder, taking the newly generated documentation for all files in the folder and the summaries from each of its subfolders as context. Write this to the filesystem as markdown. Now we have a filesystem that matches the structure of the input repo, but all files in the tree are markdown documentation of the corresponding code file. From here, we: 1. Load markdown documents into LangChain. 2. Embed all documents via OpenAI embeddings. 3. Store embeddings in Pinecone. When a user sends a query: 1. Embed query. 2. Find k-nearest markdown files. 3. Feed to GPT-4 with a prompt asking to answer the query based on k-nearest markdown documents provided. The craziest part of all this? GPT-4 actually wrote ~30% of the code. The results are pretty good for 2 days of work. There is certainly room for improvement. Some items that are top of mind: 1. TolyGPT will occasionally hallucinate answers. It is especially bad with links to external sources, like GitHub. The base model seems to know a bit about Solana already, and sometimes this creeps in. Fine-tuning the prompt can solve some of this. 2. Context selection is difficult in a codebase this large. For example, sometimes it will pull in details about the Solana SDK when asked about transaction processing. The SDK files can seem relevant depending on the phrasing of the question. It may be worth breaking the documentation into subsystems to limit this. 3. Not all files fit into the 32k token window. As of now, there are 23 (out of ~1,100) files that cannot be documented in their entirety. Some of these files are very important to how Solana works. Final thoughts: 1. GPT-4 is super powerful, and we're going to see a ton of tools that supercharge the entire software development lifecycle. This is not 12 months away. For the people that can afford it, these tools are here now. And they're only getting better. Act accordingly. 2. The price of inference has to come down for this to go mainstream. I spent about $300 prototyping this project, and the final crawl cost about the same. The high cost of GPT-4 will push developers to other, cheaper alternatives with similar performance. This is coming very soon. If you have a large software project and you're interested in something like this for your codebase, fill out this form and we'll be in touch this week. Or just DM me :)

Sam Hogan 🇺🇸

374,577 次观看 • 3 年前