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bro created an AI job search system for Claude Code that scored 700+ job applications and actually got him a job. AND IT'S NOW OPEN-SOURCE. It scans multiple company career pages, rewrites your CV per job, and even fills application forms. The repo has: > 14 skill modes (evaluate,... show more

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5,809,787 просмотров • 4 месяцев назад •via X (Twitter)

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Una persona montó un sistema de búsqueda de empleo con IA para Claude Code que llegó a mandar más de 700 candidaturas… y acabó consiguiéndo trabajo. Lo mejor de todo es que ahora es open source. Automatiza todo el proceso: revisa páginas de empleo de distintas empresas, adapta tu CV para cada oferta e incluso completa formularios por ti. El repo incluye: 14 modos distintos (evaluación, scraping, PDFs, etc.) Un dashboard en terminal hecho en Go Generación de CVs en PDF optimizados para ATS usando Playwright Más de 45 empresas ya configuradas (Anthropic, OpenAI, ElevenLabs, Stripe…) (Guárdalo antes de que sea tarde) Os dejo el link en los comentarios

Una persona montó un sistema de búsqueda de empleo con IA para Claude Code que llegó a mandar más de 700 candidaturas… y acabó consiguiéndo trabajo. Lo mejor de todo es que ahora es open source. Automatiza todo el proceso: revisa páginas de empleo de distintas empresas, adapta tu CV para cada oferta e incluso completa formularios por ti. El repo incluye: 14 modos distintos (evaluación, scraping, PDFs, etc.) Un dashboard en terminal hecho en Go Generación de CVs en PDF optimizados para ATS usando Playwright Más de 45 empresas ya configuradas (Anthropic, OpenAI, ElevenLabs, Stripe…) (Guárdalo antes de que sea tarde) Os dejo el link en los comentarios

xIA

17,786 просмотров • 3 месяцев назад

🚨 ANTHROPIC QUIETLY DROPPED THEIR OWN OFFICIAL SKILLS REPO Everyone's chasing third-party Claude Code configs. Meanwhile Anthropic open-sourced their own, and it's already at 149,000 stars. These are skills: little folders that teach Claude how to do a specific job, the right way, every time. You install them once and Claude just knows how. What's in the box: > the actual docx, pdf, pptx and xlsx skills that power Claude's own file creation > skills for building and testing web apps > MCP server generation > creative ones for art, music and design > enterprise stuff like branding and comms People are paying for setups that try to copy this. The source is sitting on Anthropic's own GitHub for free. Repo: anthropics/skills Full breakdown in the article above

🚨 ANTHROPIC QUIETLY DROPPED THEIR OWN OFFICIAL SKILLS REPO Everyone's chasing third-party Claude Code configs. Meanwhile Anthropic open-sourced their own, and it's already at 149,000 stars. These are skills: little folders that teach Claude how to do a specific job, the right way, every time. You install them once and Claude just knows how. What's in the box: > the actual docx, pdf, pptx and xlsx skills that power Claude's own file creation > skills for building and testing web apps > MCP server generation > creative ones for art, music and design > enterprise stuff like branding and comms People are paying for setups that try to copy this. The source is sitting on Anthropic's own GitHub for free. Repo: anthropics/skills Full breakdown in the article above

Fokki

33,601 просмотров • 1 месяц назад

Someone paid $6,000 (!) to get OpenClaw setup. So I built a free, open-source alternative: > OpenClaw Operator It's an agent skill + AGENTS.md/CLAUDE.md file that gives Codex/Claude Code the playbooks + validation flow needed to configure your local OpenClaw install. You open your OpenClaw repo/workspace in your coding agent and say things like: - “set up a cron job” - “create a custom skill” - “fix my provider config” - “troubleshoot this install and validate it” Nothing magical. Uses context from the OpenClaw open-source repo + docs to provide an effective skill. Includes: - SKILL.md - AGENTS.md/CLAUDE.md - validation checklist - task playbooks Free and open source. Contributions welcome. Demo below. Repo in reply.

Someone paid $6,000 (!) to get OpenClaw setup. So I built a free, open-source alternative: > OpenClaw Operator It's an agent skill + AGENTS.md/CLAUDE.md file that gives Codex/Claude Code the playbooks + validation flow needed to configure your local OpenClaw install. You open your OpenClaw repo/workspace in your coding agent and say things like: - “set up a cron job” - “create a custom skill” - “fix my provider config” - “troubleshoot this install and validate it” Nothing magical. Uses context from the OpenClaw open-source repo + docs to provide an effective skill. Includes: - SKILL.md - AGENTS.md/CLAUDE.md - validation checklist - task playbooks Free and open source. Contributions welcome. Demo below. Repo in reply.

Dan McAteer

17,809 просмотров • 5 месяцев назад

Boris Cherny, Head of Claude Code at Anthropic: "I do not prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops." The person who built the most used AI coding tool stopped prompting entirely. Most people using it every day are still typing one request at a time. This Github repo changes that. ↳ Seven production loop patterns with real token cost estimates ↳ CLI that scaffolds your entire loop in five minutes ↳ Starters for Grok, Claude Code, Codex, and Opencode The repo gives you the framework. My article covers the mental shift that makes it actually work.

Boris Cherny, Head of Claude Code at Anthropic: "I do not prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops." The person who built the most used AI coding tool stopped prompting entirely. Most people using it every day are still typing one request at a time. This Github repo changes that. ↳ Seven production loop patterns with real token cost estimates ↳ CLI that scaffolds your entire loop in five minutes ↳ Starters for Grok, Claude Code, Codex, and Opencode The repo gives you the framework. My article covers the mental shift that makes it actually work.

Kanika

20,576 просмотров • 1 месяц назад

I built a Harvard-style resume builder Claude Skill that does it for you, and I’m giving it away free. Claude Applying for jobs without tailoring your resume should be a taboo by now. Most job seekers still treat applications like a volume game. Send the same CV everywhere. Make tiny edits. Hope something lands. But every strong application follows the same workflow: → understand the role → match your experience to it → rewrite your CV around that fit → refine it → repeat for the next role Same process. Every single time. So I stopped doing it manually and built a Claude Skill for each part of that thinking. Here’s what this one does: → Takes your current CV → Structures it properly → Outputs a clean Harvard-style resume → Tailors it to any job description in seconds → You just have to click download. What used to take a lot of effort now takes seconds. And the result is better because its Harvard style, ready to go and works in Claude, no new tool needed. Even we ditched our old model at hellocv and adopted the Harvard Style Resume Builder. Want it? -> Like this post -> RT + Comment “RESUME” I’ll DM it to you.

I built a Harvard-style resume builder Claude Skill that does it for you, and I’m giving it away free. Claude Applying for jobs without tailoring your resume should be a taboo by now. Most job seekers still treat applications like a volume game. Send the same CV everywhere. Make tiny edits. Hope something lands. But every strong application follows the same workflow: → understand the role → match your experience to it → rewrite your CV around that fit → refine it → repeat for the next role Same process. Every single time. So I stopped doing it manually and built a Claude Skill for each part of that thinking. Here’s what this one does: → Takes your current CV → Structures it properly → Outputs a clean Harvard-style resume → Tailors it to any job description in seconds → You just have to click download. What used to take a lot of effort now takes seconds. And the result is better because its Harvard style, ready to go and works in Claude, no new tool needed. Even we ditched our old model at hellocv and adopted the Harvard Style Resume Builder. Want it? -> Like this post -> RT + Comment “RESUME” I’ll DM it to you.

Opeyemi Awoyemi | hello.cv/whatsapp

14,741 просмотров • 5 месяцев назад

My whole company runs on skills I bundled into a GitHub repo. Here's how to build the same thing for your team: - Put your skills in a GitHub repo - Get Claude to write the couple of JSON files that make it readable as a plugin - In Claude Code, type /plugin, go to marketplaces, add marketplace, paste your repo URL - Split the plugins by department, so your copywriter isn't installing the finance skills It works in Codex too. I've got the same team skills installed there and I use them the same way. Now when I build something like my Notion formatting skill, I add it to the repo and it lands on everyone's Claude and Codex. If someone wrecks a skill, we roll it back to an older version. The key is enabling auto-update, so when someone improves a skill everyone benefits. I don't have a clue how the JSON works. I got Claude to set the whole thing up for me. Start with one skill and before you know it your whole team will be AI pilled.

My whole company runs on skills I bundled into a GitHub repo. Here's how to build the same thing for your team: - Put your skills in a GitHub repo - Get Claude to write the couple of JSON files that make it readable as a plugin - In Claude Code, type /plugin, go to marketplaces, add marketplace, paste your repo URL - Split the plugins by department, so your copywriter isn't installing the finance skills It works in Codex too. I've got the same team skills installed there and I use them the same way. Now when I build something like my Notion formatting skill, I add it to the repo and it lands on everyone's Claude and Codex. If someone wrecks a skill, we roll it back to an older version. The key is enabling auto-update, so when someone improves a skill everyone benefits. I don't have a clue how the JSON works. I got Claude to set the whole thing up for me. Start with one skill and before you know it your whole team will be AI pilled.

The Startup Ideas Podcast (SIP) 🧃

18,942 просмотров • 4 дней назад

Claude Opus 4.7 autopilot PR machine. Tired of spamming journalists for backlinks that never come? I open-sourced a Claude Code skill that 1. logs into Qwoted (the new HARO), 2. scans real journalist requests, 3. instantly builds custom statistics pages or PDFs journalists actually want to cite, 4. sends personalized pitches from your Gmail, reads the replies, tracks wins… and can run on a daily schedule while you sleep. No more manual outreach. Real press mentions. Real backlinks. On autopilot. (Yes, it even got Manychat to link to me. And and an Interview from Morning Brew ☕️ ...proof in the video ) Comment skill and I’ll send you the GitHub repo instantly.

Claude Opus 4.7 autopilot PR machine. Tired of spamming journalists for backlinks that never come? I open-sourced a Claude Code skill that 1. logs into Qwoted (the new HARO), 2. scans real journalist requests, 3. instantly builds custom statistics pages or PDFs journalists actually want to cite, 4. sends personalized pitches from your Gmail, reads the replies, tracks wins… and can run on a daily schedule while you sleep. No more manual outreach. Real press mentions. Real backlinks. On autopilot. (Yes, it even got Manychat to link to me. And and an Interview from Morning Brew ☕️ ...proof in the video ) Comment skill and I’ll send you the GitHub repo instantly.

borja

43,504 просмотров • 4 месяцев назад

Here is why Nimble has 1.1M Claude plugin installations. Ask Claude what your competitor's pricing looks like right now and it will tell you it can't browse the web. Install the Nimble skill and ask the same question. You get live, structured data back in seconds. That's the whole story. Nimble turns Claude into an agent that can actually search the live internet, extract specific data from any page, and return it in clean, structured format ready for analysis. No setup beyond the one-time install. → Search any public webpage from inside Claude → Extract structured data from product pages, news, job boards, social profiles → Get AI-ready output, not raw HTML It crossed 1.1M installs because it solves the exact limitation that makes Claude frustrating for real research tasks. Link:

Here is why Nimble has 1.1M Claude plugin installations. Ask Claude what your competitor's pricing looks like right now and it will tell you it can't browse the web. Install the Nimble skill and ask the same question. You get live, structured data back in seconds. That's the whole story. Nimble turns Claude into an agent that can actually search the live internet, extract specific data from any page, and return it in clean, structured format ready for analysis. No setup beyond the one-time install. → Search any public webpage from inside Claude → Extract structured data from product pages, news, job boards, social profiles → Get AI-ready output, not raw HTML It crossed 1.1M installs because it solves the exact limitation that makes Claude frustrating for real research tasks. Link:

Rimsha Bhardwaj

61,011 просмотров • 3 месяцев назад

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.

Jensen Huang just explained why every company cutting engineers over AI is asking the entirely wrong question. Huang: “People say, I don’t need software engineers because apparently coding is going to be automated.” That was the narrative. Here is what Huang actually did. Huang: “I’ve given AIs to every one of my software engineers and hardware engineers and engineers period. 100% of NVIDIA has AI assistants, AI coders, and they’re busier than ever.” Not fewer engineers. Not smaller teams. Busier than ever. That is the line most companies are getting completely wrong right now. They hear “AI can write code” and immediately start cutting headcount. Huang did the opposite. He armed everyone. Huang: “And so the question is, what is the task versus what is the job? No different than a financial analyst; the task is mess around with spreadsheets, but the job is to make financial advice. The job is to help a customer.” Writing code was always the task. It was never the job. The job is architecture. Knowing what to build. Why it matters. How it fits into a system that actually creates value. Code is the execution layer between the idea and the outcome. Nothing more. When you automate that layer, you don’t eliminate the engineer. You eliminate the bottleneck between what they can envision and what they can ship. The companies using AI to cut headcount are optimizing for cost. The companies using AI to multiply output are optimizing for territory. Nvidia chose territory. Every engineer at the most valuable semiconductor company on Earth now operates with an AI assistant. Not a pilot program. Not an experiment. Company-wide. Every function. Every team. And the result is not less work. It is more work. Faster. At a scale that was physically impossible twelve months ago. The companies that understand the difference between eliminating engineers and unleashing them will build what comes next. The ones that don’t will watch their best talent walk out the door to the ones that did.

Dustin

82,757 просмотров • 5 месяцев назад

I spent months doing this manually. Open the Home Office sponsor register. Cross-reference job boards. Hunt each company's careers page. Check whether the salary meets the CoS threshold. Track it all in a spreadsheet. Repeat every week. The data exists. The problem is nobody has turned it into a proper job search tool. So I built one. VisaPath UK combines everything a skilled worker actually needs in one place: 🔍 Search 125,572 verified UK sponsor companies by name, city and sector 💷 Salary threshold checker — know instantly if an offer meets the CoS requirement 📋 Personal application tracker — no more spreadsheets ✉️ Weekly job alerts for new roles at sponsor companies (coming soon) 🤖 AI CV tailoring per role (coming soon) The register search already exists in other forms. What doesn't exist is a platform built around the full job search experience, from finding sponsors to applying with confidence. That's what I'm building. I'm Babatunde Oloko — MSc Business Analytics, Glasgow. I built this because I lived the problem. Hundreds of thousands of skilled workers in the UK are living it right now. Launching publicly next week. Drop a comment or DM me if you want early access. If this comes across your TL. Kindly repost for wider reach. Thank you #SkilledWorkerVisa #UKImmigration #VisaSponsorship #JobSearch #VisaPathUK #BuiltThis

I spent months doing this manually. Open the Home Office sponsor register. Cross-reference job boards. Hunt each company's careers page. Check whether the salary meets the CoS threshold. Track it all in a spreadsheet. Repeat every week. The data exists. The problem is nobody has turned it into a proper job search tool. So I built one. VisaPath UK combines everything a skilled worker actually needs in one place: 🔍 Search 125,572 verified UK sponsor companies by name, city and sector 💷 Salary threshold checker — know instantly if an offer meets the CoS requirement 📋 Personal application tracker — no more spreadsheets ✉️ Weekly job alerts for new roles at sponsor companies (coming soon) 🤖 AI CV tailoring per role (coming soon) The register search already exists in other forms. What doesn't exist is a platform built around the full job search experience, from finding sponsors to applying with confidence. That's what I'm building. I'm Babatunde Oloko — MSc Business Analytics, Glasgow. I built this because I lived the problem. Hundreds of thousands of skilled workers in the UK are living it right now. Launching publicly next week. Drop a comment or DM me if you want early access. If this comes across your TL. Kindly repost for wider reach. Thank you #SkilledWorkerVisa #UKImmigration #VisaSponsorship #JobSearch #VisaPathUK #BuiltThis

Babatunde. O.

261,333 просмотров • 4 месяцев назад

$THIS MIGHT BE THE #1 OPEN-SOURCE REPO FOR CLAUDE CODE RIGHT NOW. IT GIVES CLAUDE A MEMORY AND SLASHES YOUR TOKEN COST ON EVERY QUESTION The repo is safishamsi/graphify, a free open-source skill that turns any codebase into a knowledge graph Claude Code can read instantly. Instead of grepping through your files every session, Claude gets a map of how everything connects The problem it fixes: Every time you ask Claude Code about a big repo, it does the same thing, greps through dozens of files like a brute-force Ctrl+F, blows through your context window, and sometimes still misses the answer hiding in a file nobody searched. Claude Code has no memory of how your project is structured. Every session starts from zero What it does: It maps your entire codebase into a knowledge graph, capturing not just which files exist, but which functions depend on which, which modules are central, and which files cluster around the same concern. Claude queries the map instead of scanning files How it works, three passes: 1. Code structure, free and local. Tree-sitter parses your files and pulls out classes, functions, imports and call graphs. No LLM, no tokens, just your actual code mapped deterministically 2. Audio and video, if you have them. Transcribed locally and folded into the graph 3. Docs, papers, images. Here an LLM does semantic analysis, figuring out what each document means and where it fits. Only the meaning gets sent up, never your raw source It saves you money: Normally a question about a big repo makes Claude spawn explore agents that scan file after file, eating your context window and your token budget before you get an answer. With the graph already built, Claude queries the map instead of re-reading the codebase every time. Same answer, a fraction of the tokens. The graph only gets built once, then a hook rebuilds it after each commit for free, so you never pay that scanning cost again. The bigger the repo, the bigger the gap The best parts: it's a skill, so once installed Claude knows when to use it without you memorizing commands. It works on non-code folders too, point it at docs or notes and it can spin up an Obsidian vault How to add it to your Claude: 1. Install Claude Code if you haven't: npm install -g Paul Jankura-ai/claude-code 2. Add the skill: claude skill add safishamsi/graphify 3. Open your project folder and run /graphify . to build the graph 4. Optional, make it automatic: graphify hook install so the graph rebuilds after every commit That's it. Ask Claude about your repo and it reads the map instead of burning tokens on a file hunt Bookmark this$

THIS MIGHT BE THE #1 OPEN-SOURCE REPO FOR CLAUDE CODE RIGHT NOW. IT GIVES CLAUDE A MEMORY AND SLASHES YOUR TOKEN COST ON EVERY QUESTION The repo is safishamsi/graphify, a free open-source skill that turns any codebase into a knowledge graph Claude Code can read instantly. Instead of grepping through your files every session, Claude gets a map of how everything connects The problem it fixes: Every time you ask Claude Code about a big repo, it does the same thing, greps through dozens of files like a brute-force Ctrl+F, blows through your context window, and sometimes still misses the answer hiding in a file nobody searched. Claude Code has no memory of how your project is structured. Every session starts from zero What it does: It maps your entire codebase into a knowledge graph, capturing not just which files exist, but which functions depend on which, which modules are central, and which files cluster around the same concern. Claude queries the map instead of scanning files How it works, three passes: 1. Code structure, free and local. Tree-sitter parses your files and pulls out classes, functions, imports and call graphs. No LLM, no tokens, just your actual code mapped deterministically 2. Audio and video, if you have them. Transcribed locally and folded into the graph 3. Docs, papers, images. Here an LLM does semantic analysis, figuring out what each document means and where it fits. Only the meaning gets sent up, never your raw source It saves you money: Normally a question about a big repo makes Claude spawn explore agents that scan file after file, eating your context window and your token budget before you get an answer. With the graph already built, Claude queries the map instead of re-reading the codebase every time. Same answer, a fraction of the tokens. The graph only gets built once, then a hook rebuilds it after each commit for free, so you never pay that scanning cost again. The bigger the repo, the bigger the gap The best parts: it's a skill, so once installed Claude knows when to use it without you memorizing commands. It works on non-code folders too, point it at docs or notes and it can spin up an Obsidian vault How to add it to your Claude: 1. Install Claude Code if you haven't: npm install -g Paul Jankura-ai/claude-code 2. Add the skill: claude skill add safishamsi/graphify 3. Open your project folder and run /graphify . to build the graph 4. Optional, make it automatic: graphify hook install so the graph rebuilds after every commit That's it. Ask Claude about your repo and it reads the map instead of burning tokens on a file hunt Bookmark this

Yarchi

56,177 просмотров • 2 месяцев назад

Marc Andreessen explains why AI coding won't replace programmers, but fundamentally change what they do. He argues that AI coding is just the latest abstraction layer, and the job of a programmer has always evolved with each one. Andreessen's key reframe of what's actually happening: "AI coding actually abstracts away the process of actually writing the scripting code... This is the next layer of the task redefinition under the job of programmer." He's clear that the best programmers aren't being replaced. They're already adapting, even if their day-to-day looks radically different now. Their job has shifted from writing code line by line to managing dozens of AI agents working in parallel. "The world's best programmers today will tell you, 'My job is I'm sitting there and I'm orchestrating 10 code bots running in parallel.' Their day job now is kind of arguing with the AI bots to try to get them to write the right code." But Marc Andreessen 🇺🇸 is adamant this doesn't make foundational knowledge obsolete — it makes it more important. "You need to still fully understand and learn how to write and understand code, because if it doesn't work or it's not doing what you expect, you need to be able to understand the results of what the AI is giving you." He draws a direct parallel: Just as someone writing scripting languages still needs to understand how a microprocessor works, someone orchestrating AI bots needs to understand the code those bots produce. "It's this upleveling of capability where you actually want the depth to go down and understand what the thing is actually doing, even if you're not spending your day doing that by hand." The result, in his view, is transformative: "Now programmers are going to be 10 times or 100 times or a thousand times more productive. And that is overwhelmingly a good thing." The pattern: New abstraction layer emerges → tasks change → the job gets redefined upward → productivity explodes It raises a question every programmer should be sitting with... Are you building the depth to evaluate what AI gives you, or just accepting the output?

Marc Andreessen explains why AI coding won't replace programmers, but fundamentally change what they do. He argues that AI coding is just the latest abstraction layer, and the job of a programmer has always evolved with each one. Andreessen's key reframe of what's actually happening: "AI coding actually abstracts away the process of actually writing the scripting code... This is the next layer of the task redefinition under the job of programmer." He's clear that the best programmers aren't being replaced. They're already adapting, even if their day-to-day looks radically different now. Their job has shifted from writing code line by line to managing dozens of AI agents working in parallel. "The world's best programmers today will tell you, 'My job is I'm sitting there and I'm orchestrating 10 code bots running in parallel.' Their day job now is kind of arguing with the AI bots to try to get them to write the right code." But Marc Andreessen 🇺🇸 is adamant this doesn't make foundational knowledge obsolete — it makes it more important. "You need to still fully understand and learn how to write and understand code, because if it doesn't work or it's not doing what you expect, you need to be able to understand the results of what the AI is giving you." He draws a direct parallel: Just as someone writing scripting languages still needs to understand how a microprocessor works, someone orchestrating AI bots needs to understand the code those bots produce. "It's this upleveling of capability where you actually want the depth to go down and understand what the thing is actually doing, even if you're not spending your day doing that by hand." The result, in his view, is transformative: "Now programmers are going to be 10 times or 100 times or a thousand times more productive. And that is overwhelmingly a good thing." The pattern: New abstraction layer emerges → tasks change → the job gets redefined upward → productivity explodes It raises a question every programmer should be sitting with... Are you building the depth to evaluate what AI gives you, or just accepting the output?

Big Brain AI

45,325 просмотров • 6 месяцев назад

-> If you’re looking for a job -> right now, there are three -> Claude certifications that -> you should do this week -> and then put them on your -> LinkedIn, they are all -> completely free, and they -> come from Anthropic, -> which is the company -> that's behind Claude. -> And jobs that need AI skills -> pay 56% more than jobs -> that don’t, So, I think -> spending a few hours this -> week to knock out these -> three courses and then -> add them to your LinkedIn -> will really go a long way -> The first one is called -> Claude 101, and this -> essentially just goes over -> what Claude is and when -> you should use chat versus -> co-work versus code, how -> projects and skills work, -> and how to connect all -> of your tools and apps -> like Gmail, Notion, Slack, -> and other tools that you use -> And the second course -> is called AI Fluency -> Framework and Foundations -> Inside this one, there are -> 13 lessons on how to -> actually work with AI. -> It goes over things like -> effective prompting, critical -> thinking on the outputs, -> and it has a vocabulary -> sheet that you’ll want -> to read and save for later. -> And the third one is -> Intro to Claude Cowork -> Claude Cowork is where -> you can actually get stuff -> done with Claude. -> So, this course covers -> projects, skills, plug-ins, -> scheduling tasks, handling -> files, and then also how -> to pick the right model -> for the job, and then when -> you finish these courses, -> just go to your LinkedIn -> and go to your profile -> Click "Add section," and -> then go to "Licenses -> and certifications" and -> add all three of these. -> And then when you land -> the interview, you should -> talk about your AI fluency -> often as you possibly can -> I feel pretty confident that -> you’ll truly be able to -> differentiate yourself -> from other candidates -> if you do this

BeingInvested

13,130 просмотров • 2 месяцев назад

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

New course: MCP: Build Rich-Context AI Apps with Anthropic. Learn to build AI apps that access tools, data, and prompts using the Model Context Protocol in this short course, created in partnership with Anthropic Anthropic and taught by Elie Schoppik Elie Schoppik, its Head of Technical Education. Connecting AI applications to external systems that bring rich context to LLM-based applications has often meant writing custom integrations for each use case. MCP is an open protocol that standardizes how LLMs access tools, data, and prompts from external sources, and simplifies how you provide context to your LLM-based applications. For example, you can provide context via third-party tools that let your LLM make API calls to search the web, access data from local docs, retrieve code from a GitHub repo, and so on. MCP, developed by Anthropic, is based on a client-server architecture that defines the communication details between an MCP client, hosted inside the AI application, and an MCP server that exposes tools, resources, and prompt templates. The server can be a subprocess launched by the client that runs locally or an independent process running remotely. In this hands-on course, you'll learn the core architecture behind MCP. You’ll create an MCP-compatible chatbot, build and deploy an MCP server, and connect the chatbot to your MCP server and other open-source servers. Here’s what you’ll do: - Understand why MCP makes AI development less fragmented and standardizes connections between AI applications and external data sources - Learn the core components of the client-server architecture of MCP and the underlying communication mechanism - Build a chatbot with custom tools for searching academic papers, and transform it into an MCP-compatible application - Build a local MCP server that exposes tools, resources, and prompt templates using FastMCP, and test it using MCP Inspector - Create an MCP client inside your chatbot to dynamically connect to your server - Connect your chatbot to reference servers built by Anthropic’s MCP team, such as filesystem, which implements filesystem operations, and fetch, which extracts contents from the web as markdown - Configure Claude Desktop to connect to your server and others, and explore how it abstracts away the low-level logic of MCP clients - Deploy your MCP server remotely and test it with the Inspector or other MCP-compatible applications - Learn about the roadmap for future MCP development, such as multi-agent architecture, MCP registry API, server discovery, authorization, and authentication MCP is an exciting and important technology that lets you build rich-context AI applications that connect to a growing ecosystem of MCP servers, with minimal integration work. Please sign up here!

Andrew Ng

142,137 просмотров • 1 год назад

Jensen Huang just told the world something nobody wants to hear. AI is not coming for your job. It is coming for the part of your job you mistakenly believe IS your job. Huang: “The purpose of your job and the tasks that you do in your job are related but not the same.” That one sentence is the fault line between the people who thrive in the next decade and the people who vanish from it. Huang used himself as proof. Reduce the CEO of Nvidia to his raw outputs and his entire career is typing and talking. Both have been automated to superhuman levels. Huang: “Typing and talking have both been automated to a superhuman level by AI. And yet, I’m busier than ever.” The man building the infrastructure that automates human labor has never worked harder. That should stop you cold. We look at a profession and see the tasks. The motions. The mechanical friction. We never see the intent underneath. And when AI arrives, we panic. Because we confuse the task with the job. The task was never the job. It was always the bottleneck between a human and their actual purpose. Now the bottleneck is dissolving. Years ago, the experts declared radiology dead. The algorithm could read a scan better than any human. A generation of medical students listened. They walked away from the field. The result was catastrophic. Huang: “We need more radiologists than ever, and we don’t have enough.” The algorithm did not replace the doctor. It armed the doctor. Suddenly the department could see more patients. Catch more anomalies. Generate more revenue. The hospital did not fire the radiologists. It tried to hire more. And could not find them. Because we terrified an entire generation out of a career with a prediction that landed exactly backwards. Now the same hysteria is consuming software engineering. The timeline is screaming that coding is dead. Meanwhile, inside the very company building the hardware that automates code. Huang: “The software engineers that know how to use AI, know how to work with agentic systems, are the most popular and the most successful.” The tool did not replace the architect. It replaced the shovel. This is the pattern nobody wants to confront. AI does not eliminate the human. It eliminates the friction that made the human slow. And when the friction disappears, demand for the human explodes. But only if the human shows up. The ones who defined themselves by the mechanical act of writing code are fading. The ones who defined themselves by what the code was meant to build are now the most valuable people on the planet. That is not a nuance. That is the entire dividing line. The machine will write the script. Read the scan. Draft the brief. It will never possess the reason any of it needed to exist. The task was never the job. And nobody who figures that out last gets the privilege of figuring it out twice.

Jensen Huang just told the world something nobody wants to hear. AI is not coming for your job. It is coming for the part of your job you mistakenly believe IS your job. Huang: “The purpose of your job and the tasks that you do in your job are related but not the same.” That one sentence is the fault line between the people who thrive in the next decade and the people who vanish from it. Huang used himself as proof. Reduce the CEO of Nvidia to his raw outputs and his entire career is typing and talking. Both have been automated to superhuman levels. Huang: “Typing and talking have both been automated to a superhuman level by AI. And yet, I’m busier than ever.” The man building the infrastructure that automates human labor has never worked harder. That should stop you cold. We look at a profession and see the tasks. The motions. The mechanical friction. We never see the intent underneath. And when AI arrives, we panic. Because we confuse the task with the job. The task was never the job. It was always the bottleneck between a human and their actual purpose. Now the bottleneck is dissolving. Years ago, the experts declared radiology dead. The algorithm could read a scan better than any human. A generation of medical students listened. They walked away from the field. The result was catastrophic. Huang: “We need more radiologists than ever, and we don’t have enough.” The algorithm did not replace the doctor. It armed the doctor. Suddenly the department could see more patients. Catch more anomalies. Generate more revenue. The hospital did not fire the radiologists. It tried to hire more. And could not find them. Because we terrified an entire generation out of a career with a prediction that landed exactly backwards. Now the same hysteria is consuming software engineering. The timeline is screaming that coding is dead. Meanwhile, inside the very company building the hardware that automates code. Huang: “The software engineers that know how to use AI, know how to work with agentic systems, are the most popular and the most successful.” The tool did not replace the architect. It replaced the shovel. This is the pattern nobody wants to confront. AI does not eliminate the human. It eliminates the friction that made the human slow. And when the friction disappears, demand for the human explodes. But only if the human shows up. The ones who defined themselves by the mechanical act of writing code are fading. The ones who defined themselves by what the code was meant to build are now the most valuable people on the planet. That is not a nuance. That is the entire dividing line. The machine will write the script. Read the scan. Draft the brief. It will never possess the reason any of it needed to exist. The task was never the job. And nobody who figures that out last gets the privilege of figuring it out twice.

Dustin

52,882 просмотров • 4 месяцев назад

I found a skill that turns Claude into an AI ad creative maker. It’s called Goose Ads by Gooseworks, and it works directly inside Claude, Claude Code, Cowork, or Codex. You can now create ad creatives for your brand with one command. How it works: → Install Gooseworks npx gooseworks install --all → Run this prompt /goose-ads create ads for my brand → Optional Pick templates you like on the platform Goose Ads finds creatives that companies are already spending money on, studies what is working, and generates new ad creatives for your brand. It also keeps everything aligned with your website, messaging, logo, assets, and positioning, so the final output feels brand-ready instead of generic. And this is bigger than one ad skill. Gooseworks has 100+ open-source growth skills for ads, content, competitor research, GTM, SEO, lead generation, and more. Claude is slowly becoming more than a chatbot. It is becoming a growth operator. Comment 'Goose' and I’ll DM you the full open-source skill library.

Jaynit Makwana

53,943 просмотров • 1 месяц назад

I applied to Y Combinator . solo founder. no team, no funding. just me and a project i built because i got sick of how broken job hunting has become. it's called JustHireMe (JustHireMeAi ). it runs your whole job search locally, on your own machine: pulls real jobs straight from company hiring systems (greenhouse, lever, and more) scores each one against your actual CV tells you in plain english why it fits or doesn't drafts the application for you to approve no API key. nothing ever leaves your computer. why i built it: AI made applying free, so everyone started spraying hundreds of applications and the signal just died. everyone's building a louder megaphone. i wanted to build the filter. open sourced it in May and it kind of took off on its own, zero ads: ~2,180 github stars ~24 people sending in PRs 2,000 downloads across windows, mac and linux a new release every week or two every single line written by me for the application i recorded a founder video and a full walkthrough of the whole system running end to end. figured why not just share them, so here they are. funny part: i dictated my entire YC application using another project i built, literally just spoke all my answers out loud and it turned them into clean text. saved me hours. easily the scariest and most exciting thing i've done. let's see how it goes.

I applied to Y Combinator . solo founder. no team, no funding. just me and a project i built because i got sick of how broken job hunting has become. it's called JustHireMe (JustHireMeAi ). it runs your whole job search locally, on your own machine: pulls real jobs straight from company hiring systems (greenhouse, lever, and more) scores each one against your actual CV tells you in plain english why it fits or doesn't drafts the application for you to approve no API key. nothing ever leaves your computer. why i built it: AI made applying free, so everyone started spraying hundreds of applications and the signal just died. everyone's building a louder megaphone. i wanted to build the filter. open sourced it in May and it kind of took off on its own, zero ads: ~2,180 github stars ~24 people sending in PRs 2,000 downloads across windows, mac and linux a new release every week or two every single line written by me for the application i recorded a founder video and a full walkthrough of the whole system running end to end. figured why not just share them, so here they are. funny part: i dictated my entire YC application using another project i built, literally just spoke all my answers out loud and it turned them into clean text. saved me hours. easily the scariest and most exciting thing i've done. let's see how it goes.

Vasu-Devs

41,849 просмотров • 25 дней назад

Make your CLI 10x more powerful with custom subagents! Mistral Vibe is an open-source coding agent that runs in your terminal. Most terminal agents give you one assistant that does everything. With Vibe, you can build custom subagents for targeted tasks like deploy scripts, PR reviews, and test generation, then invoke them on demand. Here's how it works: Each subagent runs independently and handles its job without cluttering your main workflow. You might have one for PR reviews, another for testing, and another for deployment. The agent also has full access to your codebase. It scans your project structure and Git status automatically, so it understands context across multiple files, not just the one you're editing. Key capabilities: • Web search and fetching - query and fetch content from the web mid-session for documentation, or API references. • Multi-choice clarifications - when your instruction is ambiguous, Vibe prompts with options instead of guessing. You pick the right path before it acts. • Full skills support - slash commands for preconfigured workflows, custom subagents for specialized tasks, and extensibility through the Agent Skills standard. Vibe works in your terminal or connects to VS Code, JetBrains, and Zed through the Agent Client Protocol. Mistral Vibe is available through Le Chat Pro and Team plans. It's 100% open source Link to Mistral Vibe in the comments!

Make your CLI 10x more powerful with custom subagents! Mistral Vibe is an open-source coding agent that runs in your terminal. Most terminal agents give you one assistant that does everything. With Vibe, you can build custom subagents for targeted tasks like deploy scripts, PR reviews, and test generation, then invoke them on demand. Here's how it works: Each subagent runs independently and handles its job without cluttering your main workflow. You might have one for PR reviews, another for testing, and another for deployment. The agent also has full access to your codebase. It scans your project structure and Git status automatically, so it understands context across multiple files, not just the one you're editing. Key capabilities: • Web search and fetching - query and fetch content from the web mid-session for documentation, or API references. • Multi-choice clarifications - when your instruction is ambiguous, Vibe prompts with options instead of guessing. You pick the right path before it acts. • Full skills support - slash commands for preconfigured workflows, custom subagents for specialized tasks, and extensibility through the Agent Skills standard. Vibe works in your terminal or connects to VS Code, JetBrains, and Zed through the Agent Client Protocol. Mistral Vibe is available through Le Chat Pro and Team plans. It's 100% open source Link to Mistral Vibe in the comments!

Sumanth

10,483 просмотров • 5 месяцев назад

OpenAI just admitted Anthropic is KILLING their business. Their own applications chief told employees it was a "code red." Said Anthropic was a "wake-up call." Then admitted OpenAI had been "spreading efforts across too many apps" and it was "slowing them down." This is an internal confession. Here's why Anthropic is eating up OpenAI: 12 months ago, OpenAI owned 50% of all enterprise AI spending. Today it's just 27%. Anthropic went from nearly ZERO to winning 70% of every first-time enterprise AI deal. Seven out of ten companies buying AI tools for the first time are choosing Claude over ChatGPT. A year ago, one in 25 businesses on Ramp paid for Anthropic. Today it's one in four. OpenAI just had its biggest single-month adoption decline ever recorded. And Anthropic literally charges MORE than OpenAI for roughly the same performance. And businesses are STILL choosing them. In enterprise software, that never happens. The cheaper product usually wins. But Claude became something OpenAI never figured out how to be: Cool. Celebrities publicly switched to Claude. Senators are tweeting about using it. Engineers are shipping entire products with Claude Code in hours that used to take weeks. It started to became an identity signal. Like blue bubble vs green bubble in iMessage. Choosing Claude says something about you now. Meanwhile OpenAI went the opposite direction: They took the Pentagon contract that Anthropic refused. Greg Brockman donated $25 million to fund wars. ChatGPT uninstalls jumped 295% in a single day. Reddit posts saying "Cancel and Delete ChatGPT" got 30,000 upvotes. Anthropic said no to mass surveillance and autonomous weapons. Got blacklisted by the Pentagon. Trump called them a "Radical Left AI company." And their downloads went to #1 on the App Store the next day. Turns out refusing to build weapons is good marketing. But the real damage isn't consumer downloads. It's the MONEY. Claude Code hit $2.5 billion in annual revenue in six months. OpenAI's competing product Codex just barely crossed $1 billion. And Anthropic literally cannot meet demand. They're turning away paying customers because they don't have enough compute to serve them. A company REJECTING revenue because it's growing too fast. While OpenAI scrambles to consolidate. Last week OpenAI announced they're merging ChatGPT, Codex, and their browser into one "superapp." But what this really means: "We launched too many products, none of them worked well enough alone, so now we're cramming everything together and hoping it sticks." And remember their video tool Sora? Launched standalone. Hit #1 on the App Store. Usage flatlined within weeks. Now they're forced to shut it down. Their browser Atlas? Still hasn't launched publicly. Their IPO? Polymarket odds dropped from 55% to 35%. OpenAI has 900 million users. Anthropic has maybe 10 million daily actives. But here's the thing... OpenAI won the consumer war. ChatGPT is where your mom asks about recipes and your cousin makes memes. Anthropic won the war that actually MATTERS. The developers. The engineers. The enterprises writing 7 figure checks. OpenAI built the biggest chatbot on Earth. Anthropic built the tool that companies can't stop paying for. This is Yahoo vs Google all over again. Yahoo had the users. Google had the product. And we all know how that ended. OpenAI has 12 months to prove the superapp works, land the IPO, and stop the enterprise bleeding. If they can't, the most valuable startup in history becomes the most cautionary tale in tech. 900 million users don't mean anything if the people who actually pay are walking out the door. What do you think?

OpenAI just admitted Anthropic is KILLING their business. Their own applications chief told employees it was a "code red." Said Anthropic was a "wake-up call." Then admitted OpenAI had been "spreading efforts across too many apps" and it was "slowing them down." This is an internal confession. Here's why Anthropic is eating up OpenAI: 12 months ago, OpenAI owned 50% of all enterprise AI spending. Today it's just 27%. Anthropic went from nearly ZERO to winning 70% of every first-time enterprise AI deal. Seven out of ten companies buying AI tools for the first time are choosing Claude over ChatGPT. A year ago, one in 25 businesses on Ramp paid for Anthropic. Today it's one in four. OpenAI just had its biggest single-month adoption decline ever recorded. And Anthropic literally charges MORE than OpenAI for roughly the same performance. And businesses are STILL choosing them. In enterprise software, that never happens. The cheaper product usually wins. But Claude became something OpenAI never figured out how to be: Cool. Celebrities publicly switched to Claude. Senators are tweeting about using it. Engineers are shipping entire products with Claude Code in hours that used to take weeks. It started to became an identity signal. Like blue bubble vs green bubble in iMessage. Choosing Claude says something about you now. Meanwhile OpenAI went the opposite direction: They took the Pentagon contract that Anthropic refused. Greg Brockman donated $25 million to fund wars. ChatGPT uninstalls jumped 295% in a single day. Reddit posts saying "Cancel and Delete ChatGPT" got 30,000 upvotes. Anthropic said no to mass surveillance and autonomous weapons. Got blacklisted by the Pentagon. Trump called them a "Radical Left AI company." And their downloads went to #1 on the App Store the next day. Turns out refusing to build weapons is good marketing. But the real damage isn't consumer downloads. It's the MONEY. Claude Code hit $2.5 billion in annual revenue in six months. OpenAI's competing product Codex just barely crossed $1 billion. And Anthropic literally cannot meet demand. They're turning away paying customers because they don't have enough compute to serve them. A company REJECTING revenue because it's growing too fast. While OpenAI scrambles to consolidate. Last week OpenAI announced they're merging ChatGPT, Codex, and their browser into one "superapp." But what this really means: "We launched too many products, none of them worked well enough alone, so now we're cramming everything together and hoping it sticks." And remember their video tool Sora? Launched standalone. Hit #1 on the App Store. Usage flatlined within weeks. Now they're forced to shut it down. Their browser Atlas? Still hasn't launched publicly. Their IPO? Polymarket odds dropped from 55% to 35%. OpenAI has 900 million users. Anthropic has maybe 10 million daily actives. But here's the thing... OpenAI won the consumer war. ChatGPT is where your mom asks about recipes and your cousin makes memes. Anthropic won the war that actually MATTERS. The developers. The engineers. The enterprises writing 7 figure checks. OpenAI built the biggest chatbot on Earth. Anthropic built the tool that companies can't stop paying for. This is Yahoo vs Google all over again. Yahoo had the users. Google had the product. And we all know how that ended. OpenAI has 12 months to prove the superapp works, land the IPO, and stop the enterprise bleeding. If they can't, the most valuable startup in history becomes the most cautionary tale in tech. 900 million users don't mean anything if the people who actually pay are walking out the door. What do you think?

Ricardo

35,020 просмотров • 5 месяцев назад