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I Built a website with a front end & a backend by prompting Claude and either... 1. Pasting the code into replit (or) 2. Following its instructions on Firebase I filmed the whole thing.... But, this is by no means a tutorial lol. so I recommend skipping around. This... can be best described as a worthwhile struggle. Theres 3 hours of cut footage of me getting annoyed, but I was able to create something that will serve as a home page for my business, where I can house the AI apps I create going forward. And by the end, you'll see that I actually know my way around the different parts of Firebase, and my confidence level working with code has gone up 1,000,000%. I'm beginning to know what each file does, and what context Claude needs to be successful in creating what I want to build. Ready for my next project. See you later this week, i'm tired lol... (video was harder than the site....) TimeStamps ------------------------------------------------------ 00:00 - Introduction and project overview 07:21 - Using Claude Artifact 21:00 - Setting up Firebase backend and user authentication 30:00 - Adding Firebase Collections (database) 39:10 - WE DID IT! 45:20 - Implementing lesson editing functionality 54:00 - Separating code into manageable chunks for easier editing 56:00 - Access control for signed-in users 01:01:32 - Connecting the custom domain (create dot inc) 01:02:41 - Showcasing additional features (user count, music feature) 01:05:26 - Discussing future plans and improvements ------------------------------------------------------show more

Riley Brown

179,533 subscribers

210,639 次观看 • 1 年前 •via X (Twitter)

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10 条评论

Riley Brown 的头像

Riley Brown1 年前

Create Dot Inc

Sabrina Ramonov 🍄 的头像

Sabrina Ramonov 🍄1 年前

Built this and filmed myself, inspired by watching you:

Samuel Castillo 的头像

Samuel Castillo1 年前

Riley, thanks for sharing this. I’d been looking for this kind of Claude-based code generation driven by a non-technical person and it’s great to follow along what you’re doing

Damon-Elliott 的头像

Damon-Elliott1 年前

this is gold. thank you Riley!

Frank lin 的头像

Frank lin1 年前

4 hours, that’s really fast,I try a similar job with ChatGPT and it took me one week 😅:

Hassan 的头像

Hassan1 年前

I’m getting frustrated with Claude giving a lot of errors - I’m just trying to build HTML front ends before I move them to replit. I have tabs on my admin section - could that be the issue? Each tab has different data. Did you face this at all?

Riley Brown 的头像

Riley Brown1 年前

it actually gets easier once you move off artifacts with claude... it needs access to other libraries. learning curve but now i don't use artifact that much.

Teodora P L 的头像

Teodora P L1 年前

I will check out the video. I did the same with ChatGpt, but Claude looks so much funnier.

Leo Antunes 的头像

Leo Antunes1 年前

Thank you so much for sharing 💜

Conwic 的头像

Conwic1 年前

Casually dropping terms like 'front end' & 'back end'? Building a full-stack site with Replit & Firebase? All in one session? This 'beginner' journey smells 🐟🐟🐟… AI can code, but it can't fake authentic learning.

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The Matt Walsh Show

19,034 次观看 • 7 个月前

OMG!!!!! 6 Days Ago I had never written a line of code in my life. But I just deployed my first note taking web app with a back end that stores the data, using Claude, Replit, and Google Firebase. Just brute forced asked Claude questions alternating between 2 accounts to get around the limits, AND STILL running out of credits on both accounts. Just about 4.5 hours later I had a custom Notes app in the style that I wanted. I obviously don't have any programming "hard skills", but my understanding of what is possible with code has increased 1,000,000%... I would share link but i don't think my code protects my API tokens or whatever... I got confused on that part. --- Next step is adding perplexity and claude API into the app. :) Posting full video making this on youtube at some point, idk when, its 3AM, off to bed... much love.

OMG!!!!! 6 Days Ago I had never written a line of code in my life. But I just deployed my first note taking web app with a back end that stores the data, using Claude, Replit, and Google Firebase. Just brute forced asked Claude questions alternating between 2 accounts to get around the limits, AND STILL running out of credits on both accounts. Just about 4.5 hours later I had a custom Notes app in the style that I wanted. I obviously don't have any programming "hard skills", but my understanding of what is possible with code has increased 1,000,000%... I would share link but i don't think my code protects my API tokens or whatever... I got confused on that part. --- Next step is adding perplexity and claude API into the app. :) Posting full video making this on youtube at some point, idk when, its 3AM, off to bed... much love.

Riley Brown

621,843 次观看 • 2 年前

I built an AI powered writing app with Cursor Agent without writing a single line of code in a little over an hour. Features: - AI powered autocomplete - AI powered image search with autosuggestions based on what i'm writing - Ai powered research based on what i'm writing - Block based writing (which is useful for my workflow as a video content creator) !!! And I ran it locally this time. Normally I use replit, so if you got stuck on connecting cursor and replit, this video is for you. Template pinned on @senior_swc (starting point you can download) Tools used - Cursor (Claude for ai model) - Firebase - Deepseek DeepSeek - Exa ( Exa ) - Search API These videos serve as R&D for the app we're building at @yapthread. We're building the cursor for content creators. Sections 00:00 Introduction 03:24 Introduction to the Template 03:54 Setting Up Locally 04:04 Downloading and Running Locally 05:34 Firebase Setup 06:10 first prompt in cursor + continuing firebase 08:11 Full Plan for the app I'm building 10:44 Outlining the app hotkeys 11:55 Testing First Feature 16:54 Adding Autocomplete Feature Deepseek 22:23 Adding Images to Blocks (Search API) 22:42 Implementing and Testing Image Search (Search API) 25:32 Adjusting Image Sizes and Social Media Discussion 28:11 Adding Relevant Links (Exa API) 30:34 Testing and Debugging Link Highlights 32:27 Finalizing Image and Link Integration 40:37 Conclusion and Future Plans

I built an AI powered writing app with Cursor Agent without writing a single line of code in a little over an hour. Features: - AI powered autocomplete - AI powered image search with autosuggestions based on what i'm writing - Ai powered research based on what i'm writing - Block based writing (which is useful for my workflow as a video content creator) !!! And I ran it locally this time. Normally I use replit, so if you got stuck on connecting cursor and replit, this video is for you. Template pinned on @senior_swc (starting point you can download) Tools used - Cursor (Claude for ai model) - Firebase - Deepseek DeepSeek - Exa ( Exa ) - Search API These videos serve as R&D for the app we're building at @yapthread. We're building the cursor for content creators. Sections 00:00 Introduction 03:24 Introduction to the Template 03:54 Setting Up Locally 04:04 Downloading and Running Locally 05:34 Firebase Setup 06:10 first prompt in cursor + continuing firebase 08:11 Full Plan for the app I'm building 10:44 Outlining the app hotkeys 11:55 Testing First Feature 16:54 Adding Autocomplete Feature Deepseek 22:23 Adding Images to Blocks (Search API) 22:42 Implementing and Testing Image Search (Search API) 25:32 Adjusting Image Sizes and Social Media Discussion 28:11 Adding Relevant Links (Exa API) 30:34 Testing and Debugging Link Highlights 32:27 Finalizing Image and Link Integration 40:37 Conclusion and Future Plans

Riley Brown

82,974 次观看 • 1 年前

For the past 9 days I've been creating websites using AI with no coding experience In this video: [1] 5 examples of websites I've built w/ Claude [2] My 4 step process to easily create a website [3] Example: create a landing page from scratch Timestamps: - 00:00 - introduction - 00:54 - 5 Websites I've created in last 9 days - 03:00 - 4 Step Process for creating website - 06:37 - Building a website with own custom domain - 16:40 - Adding Custom Domain to our site

For the past 9 days I've been creating websites using AI with no coding experience In this video: [1] 5 examples of websites I've built w/ Claude [2] My 4 step process to easily create a website [3] Example: create a landing page from scratch Timestamps: - 00:00 - introduction - 00:54 - 5 Websites I've created in last 9 days - 03:00 - 4 Step Process for creating website - 06:37 - Building a website with own custom domain - 16:40 - Adding Custom Domain to our site

Riley Brown

73,710 次观看 • 2 年前

Hot off the press! 📰🔥 The Firebase Release Notes for April are here. Timecodes: 00:00 - Firebase Studio 01:53 - Firebase App Hosting 03:10 - Firebase Data Connect 04:20 - Genkit: The Model Context Protocol 04:54 - Vertex AI in Firebase updates 05:40 - Interactive Solutions

Hot off the press! 📰🔥 The Firebase Release Notes for April are here. Timecodes: 00:00 - Firebase Studio 01:53 - Firebase App Hosting 03:10 - Firebase Data Connect 04:20 - Genkit: The Model Context Protocol 04:54 - Vertex AI in Firebase updates 05:40 - Interactive Solutions

Firebase

28,042 次观看 • 1 年前

Pi was built when there were already agent harnesses around. Here’s why Mario Zechner(Mario Zechner), found them suboptimal and built Pi, a minimalist self-modifying agent: #1 - Mario initially was a believer in Claude Code: "I was a believer in Claude code because they were the first that packaged agentic search up in a really compelling package. And at the time that fit my workflow really well. Everything around the LLM was kind of nice and tidy and easy to understand. I was super happy. I was proselytising Claude code." #2 - Reverse engineering Claude Code highlighted the degradation that Mario felt as a user: "I personally like simple tools that are stable and that I can rely on. Even if they have non-deterministic parts, all the deterministic parts should be as stable as possible. That was just not the experience with Claude Code around summer 2025. They would take away your control of the context. They would inject stuff behind your back, which is bad. Then, your workflows stopped working because there's now a system reminder that you don't even see in the UI that would modify the behaviour of the model. They would also do this to the system prompt. I built a little service where I can track the progression or evolution of the system, prompt and tool definitions and, with every release, it was messing with stuff. That just messed with my workflows and I don't appreciate that." #3 - PI was built with an appreciation for simple and reliable tools: "If I commit to a development tool, I want it to be a stable, reliable thing like a hammer. I don't want my hammer to break a different spot every day. That's terrible. We need somebody who goes the full velocity kind of way. But I don't want to work with a tool like that."

Pi was built when there were already agent harnesses around. Here’s why Mario Zechner(Mario Zechner), found them suboptimal and built Pi, a minimalist self-modifying agent: #1 - Mario initially was a believer in Claude Code: "I was a believer in Claude code because they were the first that packaged agentic search up in a really compelling package. And at the time that fit my workflow really well. Everything around the LLM was kind of nice and tidy and easy to understand. I was super happy. I was proselytising Claude code." #2 - Reverse engineering Claude Code highlighted the degradation that Mario felt as a user: "I personally like simple tools that are stable and that I can rely on. Even if they have non-deterministic parts, all the deterministic parts should be as stable as possible. That was just not the experience with Claude Code around summer 2025. They would take away your control of the context. They would inject stuff behind your back, which is bad. Then, your workflows stopped working because there's now a system reminder that you don't even see in the UI that would modify the behaviour of the model. They would also do this to the system prompt. I built a little service where I can track the progression or evolution of the system, prompt and tool definitions and, with every release, it was messing with stuff. That just messed with my workflows and I don't appreciate that." #3 - PI was built with an appreciation for simple and reliable tools: "If I commit to a development tool, I want it to be a stable, reliable thing like a hammer. I don't want my hammer to break a different spot every day. That's terrible. We need somebody who goes the full velocity kind of way. But I don't want to work with a tool like that."

The Pragmatic Engineer

62,825 次观看 • 2 个月前

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11

Dan Shipper 📧

66,339 次观看 • 2 个月前