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FetchCoder = a CLI tool for real dev work. 3 demos, 3 everyday use cases: 1️⃣ Create: generate a full Express app (UI + API) you can run immediately 2️⃣ Refactor: modernize legacy code with async/await, behavior preserved 3️⃣ Optimize: analyze SQL queries + suggest performance improvements Creation, refactoring,... show more

Fetch.ai

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10,942 views • 6 months ago •via X (Twitter)

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DeepLearning.AI

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Julian Goldie SEO

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Markandey Sharma

49,777 views • 1 month ago

Claude Code on desktop just added an embedded loop that can run your dev server, view the app, review local changes, and watch pull requests until they merge. It can now preview a running app inside the desktop UI while it iterates on fixes. It can also monitor CI for an open PR and either fix failures or merge when checks go green. Earlier “agentic coding” workflows usually bounced between a terminal, a browser, and GitHub, so the model could not directly see what the app rendered or stay attached to CI results without manual babysitting. The new server preview flow starts and stops dev servers from a Preview control, shows the app in an embedded browser, reads logs, and can interact with the page to verify changes. That closes a common gap where a model edits code but cannot reliably confirm the UI and runtime behavior match the intent. The local “Review code” step runs before pushing, and it leaves inline comments on the diff so basic bugs and risky changes get caught earlier. For PR monitoring, the desktop app will track status and CI checks, and it can use auto-fix to attempt repairs and auto-merge to land the PR once checks pass, using GitHub tooling under the hood. Session mobility adds a /desktop command to move a CLI session into the desktop app, and sessions can be pushed to the cloud to resume from web or phone.

Claude Code on desktop just added an embedded loop that can run your dev server, view the app, review local changes, and watch pull requests until they merge. It can now preview a running app inside the desktop UI while it iterates on fixes. It can also monitor CI for an open PR and either fix failures or merge when checks go green. Earlier “agentic coding” workflows usually bounced between a terminal, a browser, and GitHub, so the model could not directly see what the app rendered or stay attached to CI results without manual babysitting. The new server preview flow starts and stops dev servers from a Preview control, shows the app in an embedded browser, reads logs, and can interact with the page to verify changes. That closes a common gap where a model edits code but cannot reliably confirm the UI and runtime behavior match the intent. The local “Review code” step runs before pushing, and it leaves inline comments on the diff so basic bugs and risky changes get caught earlier. For PR monitoring, the desktop app will track status and CI checks, and it can use auto-fix to attempt repairs and auto-merge to land the PR once checks pass, using GitHub tooling under the hood. Session mobility adds a /desktop command to move a CLI session into the desktop app, and sessions can be pushed to the cloud to resume from web or phone.

Rohan Paul

33,431 views • 5 months ago

New Video Tutorial - Deploying a personal merchant AI agent with payment processing on EigenCompute @eigenlayer ⚡️ • Zero to live with HTTP endpoint in 10 minutes • Sell arbitrary items, receive payments, all via X402 • Natural language API • Easily extendable to any commerce use case • Includes full codebase for both client and merchant (server) EigenCompute lets you deploy apps which generate proof of exactly what code is executing. Users can verify your app’s behavior cryptographically, not just trusting the developer or the company. So you get smart contract-like guarantees like transparency and verifiability, but with the programmability and performance of Web2 infrastructure. You containerize your app with Docker, and it runs in secure enclaves called TEEs that generate these proofs.

New Video Tutorial - Deploying a personal merchant AI agent with payment processing on EigenCompute @eigenlayer ⚡️ • Zero to live with HTTP endpoint in 10 minutes • Sell arbitrary items, receive payments, all via X402 • Natural language API • Easily extendable to any commerce use case • Includes full codebase for both client and merchant (server) EigenCompute lets you deploy apps which generate proof of exactly what code is executing. Users can verify your app’s behavior cryptographically, not just trusting the developer or the company. So you get smart contract-like guarantees like transparency and verifiability, but with the programmability and performance of Web2 infrastructure. You containerize your app with Docker, and it runs in secure enclaves called TEEs that generate these proofs.

nader dabit

41,295 views • 9 months ago

the video is live. full app built in under 3 hours. raw. uncut. bugs and all. the stack: → Idea Browser — idea to full PRD in minutes → Mobbin — production grade UI patterns from pre-existing apps → — built full stack with plain english (i specialize in frontend but you can also use superbase) what you'll see: HOUR 1: PRD generation (user flows, features, edge cases) HOUR 2: reference hunting (real apps > dribbble fantasy) HOUR 3: full build (frontend and you can use superbase for backend) this isn't a polished tutorial. it's the actual workflow. live. you'll see where it breaks. you'll see how I fix it. the gap between "idea" and "shipped product" is now 3 hours. watch it. then go build something. like + RT if this changes how your new workflow looks like..

the video is live. full app built in under 3 hours. raw. uncut. bugs and all. the stack: → Idea Browser — idea to full PRD in minutes → Mobbin — production grade UI patterns from pre-existing apps → — built full stack with plain english (i specialize in frontend but you can also use superbase) what you'll see: HOUR 1: PRD generation (user flows, features, edge cases) HOUR 2: reference hunting (real apps > dribbble fantasy) HOUR 3: full build (frontend and you can use superbase for backend) this isn't a polished tutorial. it's the actual workflow. live. you'll see where it breaks. you'll see how I fix it. the gap between "idea" and "shipped product" is now 3 hours. watch it. then go build something. like + RT if this changes how your new workflow looks like..

MotionViz

21,716 views • 6 months ago

I built a mobile app to check Paddle revenue (because they don't have one): 👉 - Use your Paddle API key (read-only and scoped) - Live data with beautiful and useful graphs built with native Swift UI. - Multi-account supported, unified revenue metrics. - Data stay on device, no server (api requests are sent directly from your phone) - Home widgets - I made it free to download on App Store (once it's approved) - Buy the source code for $19 and customize it however you want (save 5hrs of prompting if you try to do it yourself). Some interesting facts about this side project: - I vibe coded with 100% claude code remotely on my Mac Mini (with my AI assistant setup) in less than 24 hours. - I have read 0 line of code in this project and never opened Xcode myself. - My AI assistant designed the app with GPT Image 2, built the app with Swift UI, test it on simulator (via screenshots), send the test build to TestFlight for me to test, and invited me to the app store connect account so I can test on my phone, then the AI submitted the app to App Store and currently waiting for approval. - For the website, I ask it to come up with a domain name, I bought it via manually and give it access via Cloudflare API, the AI design and create a static website with GitHub, test it with lighthouse CLI, deploy via GitHub pages, config the domain DNS, deploy the website. - Then I sign up an account with Polar payment, create an API key and ask the AI to setup a store, add payment, link with the account, and add the payment to the website. The entire process happened in the last 24 hours with me only talking to the AI via Telegram. This is such a fun side project not only to create an app that I wish exists, but also to push the limit of what I can use AI for, and so far I'm very impressed. I'll create so much more apps! It feels like I have unlocked a super power.

I built a mobile app to check Paddle revenue (because they don't have one): 👉 - Use your Paddle API key (read-only and scoped) - Live data with beautiful and useful graphs built with native Swift UI. - Multi-account supported, unified revenue metrics. - Data stay on device, no server (api requests are sent directly from your phone) - Home widgets - I made it free to download on App Store (once it's approved) - Buy the source code for $19 and customize it however you want (save 5hrs of prompting if you try to do it yourself). Some interesting facts about this side project: - I vibe coded with 100% claude code remotely on my Mac Mini (with my AI assistant setup) in less than 24 hours. - I have read 0 line of code in this project and never opened Xcode myself. - My AI assistant designed the app with GPT Image 2, built the app with Swift UI, test it on simulator (via screenshots), send the test build to TestFlight for me to test, and invited me to the app store connect account so I can test on my phone, then the AI submitted the app to App Store and currently waiting for approval. - For the website, I ask it to come up with a domain name, I bought it via manually and give it access via Cloudflare API, the AI design and create a static website with GitHub, test it with lighthouse CLI, deploy via GitHub pages, config the domain DNS, deploy the website. - Then I sign up an account with Polar payment, create an API key and ask the AI to setup a store, add payment, link with the account, and add the payment to the website. The entire process happened in the last 24 hours with me only talking to the AI via Telegram. This is such a fun side project not only to create an app that I wish exists, but also to push the limit of what I can use AI for, and so far I'm very impressed. I'll create so much more apps! It feels like I have unlocked a super power.

Tony Dinh

43,787 views • 2 months ago

8 rules to improve your AI coding agent. All of these rules work with Claude Code, Cursor, VS Code, and with most programming languages. Automating these rules will 10x the code quality and security produced by your AI coding agents. 1. Dependency checks - Prevent your agent from suggesting insecure libraries based on outdated training data. 2. Secret exposure - Auto-fix the use of hardcoded credentials introduced by your coding agent. 3. File and function size - Automatically refactor any files or functions that exceed a reasonable length. 4. Complexity and parameter limits - Simplify overly complex code written by the agent. 5. SQL Injection - Auto-fix all database interactions with unsanitized user input. 6. Unused variables and imports - Detect and remove dead code. 7. Detect invisible unicode characters in AI rules files - Remove zero-width spaces, direction overrides, and other invisible characters that can hide malicious behavior. 8. Insecure OpenAI API usage - Enforce use of secure OpenAI endpoints, proper authentication, and context isolation Here is how you can automate this: Install the Codacy extension. This will give you access to a CLI for local scanning and an MCP server for agent communication. From here on out, every time you need to generate some code: 1. Your agent will write the code 2. It will then call Codacy's CLI to check it 3. It will find any issues in real time 4. Your coding agent will fix the issues 5. When the code passes all checks, you are done Level of effort on your side: literally zero! Code quality and security because of this: 100x better! Here is the link to download the extension for your IDE: Thanks to the Codacy team for collaborating with me on this post.

8 rules to improve your AI coding agent. All of these rules work with Claude Code, Cursor, VS Code, and with most programming languages. Automating these rules will 10x the code quality and security produced by your AI coding agents. 1. Dependency checks - Prevent your agent from suggesting insecure libraries based on outdated training data. 2. Secret exposure - Auto-fix the use of hardcoded credentials introduced by your coding agent. 3. File and function size - Automatically refactor any files or functions that exceed a reasonable length. 4. Complexity and parameter limits - Simplify overly complex code written by the agent. 5. SQL Injection - Auto-fix all database interactions with unsanitized user input. 6. Unused variables and imports - Detect and remove dead code. 7. Detect invisible unicode characters in AI rules files - Remove zero-width spaces, direction overrides, and other invisible characters that can hide malicious behavior. 8. Insecure OpenAI API usage - Enforce use of secure OpenAI endpoints, proper authentication, and context isolation Here is how you can automate this: Install the Codacy extension. This will give you access to a CLI for local scanning and an MCP server for agent communication. From here on out, every time you need to generate some code: 1. Your agent will write the code 2. It will then call Codacy's CLI to check it 3. It will find any issues in real time 4. Your coding agent will fix the issues 5. When the code passes all checks, you are done Level of effort on your side: literally zero! Code quality and security because of this: 100x better! Here is the link to download the extension for your IDE: Thanks to the Codacy team for collaborating with me on this post.

Santiago

49,331 views • 9 months ago

While your team is just getting started vibe coding prototypes, the team at Stripe is vibe coding a full vibe coding platform. Owen Williams ⚡ is a design manager at Stripe who built Protodash - an internal AI prototyping tool that lets designers and PMs spin up real, clickable prototypes in minutes. In this ep he walks through: - why generic AI tools produce "blurple slop" that doesn't match your design system - how he built a full prototyping studio on top of dev boxes with cursor + cc - why more PMs than designers now use Protodash - the design review mode that lets teams ship feedback direct in the app Plus, we talk about getting to the ultimate dream: demos, not memos. ty to our sponsors! 🧠 CESAR - Intelligent automation built for AI 💻 Cursor - The best way to code with AI Full episode on yt:

While your team is just getting started vibe coding prototypes, the team at Stripe is vibe coding a full vibe coding platform. Owen Williams ⚡ is a design manager at Stripe who built Protodash - an internal AI prototyping tool that lets designers and PMs spin up real, clickable prototypes in minutes. In this ep he walks through: - why generic AI tools produce "blurple slop" that doesn't match your design system - how he built a full prototyping studio on top of dev boxes with cursor + cc - why more PMs than designers now use Protodash - the design review mode that lets teams ship feedback direct in the app Plus, we talk about getting to the ultimate dream: demos, not memos. ty to our sponsors! 🧠 CESAR - Intelligent automation built for AI 💻 Cursor - The best way to code with AI Full episode on yt:

claire vo 🖤

92,074 views • 2 months ago

Want to create beautiful, high-converting landing pages like this without spending days in Figma or Webflow? Here's my AI-first workflow: Start with Claude Have Claude interview you about the product, audience, positioning, features, concerns and tradeoffs using this prompt: "Create me a plan file for [Product Idea] and interview me in detail using the AskUserQuestionTool about literally anything: technical implementation, UI & UX, concerns, tradeoffs, etc." Turn the plan into a prototype Use the plan file to generate a working prototype and pull inspiration, layouts and motion ideas from Generate the landing page in code Skip static mockups and build directly in the browser. Run a design review Use the Design Review skill to identify hierarchy, spacing, branding, usability and accessibility issues. Iterate relentlessly Keep refining until it feels designed and polished, not generated. The future isn't designers vs AI. It's builders with great taste using AI as leverage. Anyone can generate. Taste is the moat. Follow for more AI design and vibe coding workflows. And if you want more practical systems like this, my book 'Think. Prompt. Create.' is available now on Amazon

Want to create beautiful, high-converting landing pages like this without spending days in Figma or Webflow? Here's my AI-first workflow: Start with Claude Have Claude interview you about the product, audience, positioning, features, concerns and tradeoffs using this prompt: "Create me a plan file for [Product Idea] and interview me in detail using the AskUserQuestionTool about literally anything: technical implementation, UI & UX, concerns, tradeoffs, etc." Turn the plan into a prototype Use the plan file to generate a working prototype and pull inspiration, layouts and motion ideas from Generate the landing page in code Skip static mockups and build directly in the browser. Run a design review Use the Design Review skill to identify hierarchy, spacing, branding, usability and accessibility issues. Iterate relentlessly Keep refining until it feels designed and polished, not generated. The future isn't designers vs AI. It's builders with great taste using AI as leverage. Anyone can generate. Taste is the moat. Follow for more AI design and vibe coding workflows. And if you want more practical systems like this, my book 'Think. Prompt. Create.' is available now on Amazon

Joey Primiani

470,774 views • 1 month ago

Task Master CLI will makes Cursor 10x better at building projects. I created a full video demo of how it works. For those that don't want to watch. Here's what happens: 1️⃣ Install npm i task-master-ai CLI 2️⃣ Give it a description of your project. It will generate a PRD 3️⃣ It will then convert the PRD into tasks (10 detailed .txt files and a .json that acts as a local db to task track status) 4️⃣ Ask it to do a task. It will do the task and mark it off as done. Each task can also be broken down into small tasks using "expand" command. Following these steps it will build out an entire project. This is the future of dev and how tools like Manus work. But you can run it locally and follow step by step to make sure you're happy with what's happening. Seems a fitting to be posting about Task Master the day before Passover starts. Good job by Eyal Toledano who built this project.

Task Master CLI will makes Cursor 10x better at building projects. I created a full video demo of how it works. For those that don't want to watch. Here's what happens: 1️⃣ Install npm i task-master-ai CLI 2️⃣ Give it a description of your project. It will generate a PRD 3️⃣ It will then convert the PRD into tasks (10 detailed .txt files and a .json that acts as a local db to task track status) 4️⃣ Ask it to do a task. It will do the task and mark it off as done. Each task can also be broken down into small tasks using "expand" command. Following these steps it will build out an entire project. This is the future of dev and how tools like Manus work. But you can run it locally and follow step by step to make sure you're happy with what's happening. Seems a fitting to be posting about Task Master the day before Passover starts. Good job by Eyal Toledano who built this project.

Elie Steinbock

36,636 views • 1 year ago

THIS GUY VIBE CODED A FULL CAPYBARA FOOD DELIVERY GAME IN 2 WEEKS WITH CLAUDE CODE you play as a capybara delivering food on a bike. orders stack on the back, you have a phone with apps in-game and the whole delivery system is realistic 2 weeks, zero game dev experience, and ENTIRELY AI generated the full stack: > claude code for all the code > three.js for the 3D engine > suno for original music > elevenlabs for sound effects and voice > GPT images-2 and grok for textures and illustrations > tripo3d for generating all the 3D assets the cinematics are all in-game too. he asked claude to build a cinematic editor with timeline controls, camera animation, and transitions. then he just placed the cameras himself his workflow was more planning than coding (obviously): > come up with the core mechanic > plan every feature using claude /plan mode > generate assets with AI tools > spend most of his time on the final polish, prop placement, and making the design feel right he said the human part is what most vibe coded games are missing. AI can generate everything but having taste for what looks good and what feels right is still on you the game is playable right now in the browser this is what vibe coding is actually capable of in the game dev space right now a year ago this would have taken a small team of developers, a sound designer, and an artist working together for months now one person with no experience can ship a polished playable game with story, music, and mechanics in 14 days the tools keep getting better and the barrier to making real games keeps getting lower

THIS GUY VIBE CODED A FULL CAPYBARA FOOD DELIVERY GAME IN 2 WEEKS WITH CLAUDE CODE you play as a capybara delivering food on a bike. orders stack on the back, you have a phone with apps in-game and the whole delivery system is realistic 2 weeks, zero game dev experience, and ENTIRELY AI generated the full stack: > claude code for all the code > three.js for the 3D engine > suno for original music > elevenlabs for sound effects and voice > GPT images-2 and grok for textures and illustrations > tripo3d for generating all the 3D assets the cinematics are all in-game too. he asked claude to build a cinematic editor with timeline controls, camera animation, and transitions. then he just placed the cameras himself his workflow was more planning than coding (obviously): > come up with the core mechanic > plan every feature using claude /plan mode > generate assets with AI tools > spend most of his time on the final polish, prop placement, and making the design feel right he said the human part is what most vibe coded games are missing. AI can generate everything but having taste for what looks good and what feels right is still on you the game is playable right now in the browser this is what vibe coding is actually capable of in the game dev space right now a year ago this would have taken a small team of developers, a sound designer, and an artist working together for months now one person with no experience can ship a polished playable game with story, music, and mechanics in 14 days the tools keep getting better and the barrier to making real games keeps getting lower

Om Patel

240,916 views • 2 months ago

SQL has levels to it: - level 1 SELECT, FROM, WHERE, GROUP BY, HAVING, LIMIT Master these basic keywords and you’ll be well on your way to mastering SQL. - level 2 Mastering JOINs: Most common JOINs: INNER and LEFT Less common JOINs: FULL OUTER Joins you should avoid almost always: RIGHT and CROSS JOIN Mastering common table expressions (CTEs). The WITH keyword defines a CTE which you can imagine as a “variable” that you can query later. Using variables like this you can master algorithm techniques like recursion, breadth first search and more! CTEs also make your SQL much more readable and make your coworkers hate you less compared to nested sub queries. - level 3 Mastering window functions Window functions have 3 pieces: The function (i.e. SUM, RANK, AVG) The over clause to start the window The window definition which has 3 pieces: - how to split the window up with PARTITION BY - how to order the window with ORDER BY - how to restrict the window size with ROWS clause (useful for rolling monthly averages) Understand RANK vs DENSE_RANK vs ROW_NUMBER, I have been asked this in interviews a million times. - level 4 You understand table scans, b-tree indexes, and partitioning schemes to increase performance. Doing something like COUNT(CASE WHEN) is much better than doing multiple queries with a UNION ALL. UNION ALL is terrible for all sorts of reasons that I don’t want to get into in this post. B-trees indexes allow for efficient scanning of data in the WHERE clause. Use explain plans to understand if an index is actually being used or not! Partitioning is similar to indexes except it’s a “poor mans” index. It just keeps data in specific folders and skips the folders that don’t include the data I question. What else did I miss for mastering SQL?

SQL has levels to it: - level 1 SELECT, FROM, WHERE, GROUP BY, HAVING, LIMIT Master these basic keywords and you’ll be well on your way to mastering SQL. - level 2 Mastering JOINs: Most common JOINs: INNER and LEFT Less common JOINs: FULL OUTER Joins you should avoid almost always: RIGHT and CROSS JOIN Mastering common table expressions (CTEs). The WITH keyword defines a CTE which you can imagine as a “variable” that you can query later. Using variables like this you can master algorithm techniques like recursion, breadth first search and more! CTEs also make your SQL much more readable and make your coworkers hate you less compared to nested sub queries. - level 3 Mastering window functions Window functions have 3 pieces: The function (i.e. SUM, RANK, AVG) The over clause to start the window The window definition which has 3 pieces: - how to split the window up with PARTITION BY - how to order the window with ORDER BY - how to restrict the window size with ROWS clause (useful for rolling monthly averages) Understand RANK vs DENSE_RANK vs ROW_NUMBER, I have been asked this in interviews a million times. - level 4 You understand table scans, b-tree indexes, and partitioning schemes to increase performance. Doing something like COUNT(CASE WHEN) is much better than doing multiple queries with a UNION ALL. UNION ALL is terrible for all sorts of reasons that I don’t want to get into in this post. B-trees indexes allow for efficient scanning of data in the WHERE clause. Use explain plans to understand if an index is actually being used or not! Partitioning is similar to indexes except it’s a “poor mans” index. It just keeps data in specific folders and skips the folders that don’t include the data I question. What else did I miss for mastering SQL?

Zach Wilson

79,538 views • 1 year ago