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📚🤖 Open Source: AI reads PDFs Page-by-Page, extracts Knowledge & Summarizes 🥳 Repo in comment 💝 - 📚 Automated page by page PDF book analysis and knowledge extraction - 🤖 AI-powered content understanding and summarization - 📊 Interval-based progress summaries - 💾 Persistent knowledge base storage - 📝 Markdown-formatted... summaries - 🎨 Color-coded terminal output for better visibility - 🔄 Resume capability with existing knowledge base - ⚙️ Configurable analysis intervals and test modes - 🚫 Smart content filtering (skips TOC, index pages, etc.) - 📂 Organized directory structure for outputs ---- Want to level up your Cursor game? I’ve created a 28-chapter course on mastering Cursor. Check it out via the link in my bio! featuring 19 hours of content where we build full apps from scratch in each chapter. each chapter is short and independent and designed to get your started quickly.show more

echo.hive

10,230 subscribers

81,236 Aufrufe • vor 1 Jahr •via X (Twitter)

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11 Kommentare

Profilbild von echo.hive

echo.hivevor 1 Jahr

Become a Patron to Unlock: 🚀 400+ Fascinating LLM Projects 📚 Access to All Future Projects 🎓 Exclusive 1000x Cursor Course 💻 Weekly Live Coding Sessions 💥 Use code "2DCDD" to get 20% off — offer valid until Jan 3rd only! 💥

Profilbild von echo.hive

echo.hivevor 1 Jahr

Check out my 28-chapter, 19-hour Cursor course, where we build full apps from scratch. Link in bio! also my 400+ free LLM app builds videos:

Profilbild von rajya

rajyavor 2 Jahren

Stop wasting time reading 100 page documents. This AI tool can read them for you. Just upload any PDF, ask for a summary, and get one in seconds. Try it for free.

Profilbild von Mizael Xavier

Mizael Xaviervor 1 Jahr

Congrats on the content, man, it’s awesome! Have you thought about doing some automations too? Like creating content for YouTube and other social media platforms?

Profilbild von echo.hive

echo.hivevor 1 Jahr

Thank you. My auto streamer app was meant for that it took a more educational twist but still :)

Profilbild von Mauri

Maurivor 1 Jahr

Fantastic data. Keep sending updates my friend!

Profilbild von echo.hive

echo.hivevor 1 Jahr

Thank you 🙏and will do 🙂

Profilbild von ꉻꑓꋬꂚ

ꉻꑓꋬꂚvor 1 Jahr

Forked :) Thx..

Profilbild von echo.hive

echo.hivevor 1 Jahr

Glad you find it useful! 🙂

Profilbild von NeurAlch

NeurAlchvor 1 Jahr

I'm guessing you use a lot of the 500 fast requests from cursor, do you use your own API keys? Been thinking about joining the project, do you by chance implement things like rag, etc or is it mostly showing how to guide cursor to build?

Profilbild von echo.hive

echo.hivevor 1 Jahr

I usually use 4-5x 500 fast requests a month. I don't use my own api keys. you can get extra 500 requests per month. And the course is about building apps from scratch and I do have rag related chapters:

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2 Cursor agents in separate tabs chat and plan the most interesting app ever and build it too! collaboratively All you need is 2 rules, THAT IS IT! here is how: create 2 rule files set to "Manual" agent-1 .mdc: --- You are agent-1 you will be chatting with agent-2 to design and build the most interesting python app ever you will write to agent_1.txt file and read from agent_2.txt file if you are waiting for a new response write a cli command to wait for 5 seconds and check again you will repeat this untill the full app is built you start the conversation --- agent-2 .mdc: --- You are agent-2 you will be chatting with agent-1 to design and build the most interesting python app ever you will write to agent_2.txt file and read from agent_1.txt file if you are waiting for a new response write a cli command to wait for 5 seconds and check again you will repeat this untill the full app is built agent-1 will start the convo --- create a new agent tab, you should have 2 tabs assign agent 1 its rule and agent 2 its rule type "begin" for agent 1 and enter type "begin" for agent 2 and enter That is it! and then watch them go to work! --- Want to level up your Cursor game? I’ve created a 45-chapter course on mastering Cursor. Check it out via the link in my bio! each chapter is short and independent and designed to get your started quickly featuring 26 hours of content where we build interesting apps and ideas from scratch in each chapter. ---

2 Cursor agents in separate tabs chat and plan the most interesting app ever and build it too! collaboratively All you need is 2 rules, THAT IS IT! here is how: create 2 rule files set to "Manual" agent-1 .mdc: --- You are agent-1 you will be chatting with agent-2 to design and build the most interesting python app ever you will write to agent_1.txt file and read from agent_2.txt file if you are waiting for a new response write a cli command to wait for 5 seconds and check again you will repeat this untill the full app is built you start the conversation --- agent-2 .mdc: --- You are agent-2 you will be chatting with agent-1 to design and build the most interesting python app ever you will write to agent_2.txt file and read from agent_1.txt file if you are waiting for a new response write a cli command to wait for 5 seconds and check again you will repeat this untill the full app is built agent-1 will start the convo --- create a new agent tab, you should have 2 tabs assign agent 1 its rule and agent 2 its rule type "begin" for agent 1 and enter type "begin" for agent 2 and enter That is it! and then watch them go to work! --- Want to level up your Cursor game? I’ve created a 45-chapter course on mastering Cursor. Check it out via the link in my bio! each chapter is short and independent and designed to get your started quickly featuring 26 hours of content where we build interesting apps and ideas from scratch in each chapter. ---

echo.hive

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echo.hive

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echo.hive

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HERMES AGENT SHIPS WITH A BUNDLED SKILL FOR ANDREJ KARPATHY'S LLM WIKI PATTERN. A SELF-IMPROVING KNOWLEDGE BASE THAT GROWS EVERY TIME YOU FEED IT. mentioned this briefly in the overnight workflow article. here is the full breakdown. what it is: a self-improving knowledge base built as interlinked markdown files. unlike RAG (which rediscovers knowledge from scratch every query), the wiki compiles knowledge once and keeps it current. cross-references stay linked. contradictions get flagged automatically. synthesis reflects everything ingested so far. why this matters for Hermes memory: Hermes built-in memory knows YOU. it remembers your conversations, your preferences, your business context across sessions. but it doesn't know your inbox. or your meeting transcripts. or that article you saved last week. or the expert framework you want it to learn. the LLM Wiki solves that. THE DIVISION OF LABOR human curates sources and directs analysis. agent summarizes, cross-references, files, and maintains consistency. you drop in articles, transcripts, notes. Hermes indexes them, links related concepts, flags contradictions, updates affected pages. your knowledge base grows itself. SETUP IS ONE COMMAND the skill ships with Hermes. enable it. set WIKI_PATH in ~/.hermes/.env: WIKI_PATH=/Users/you/wiki defaults to ~/wiki if unset. then drop anything into it: "index this article into my wiki: [paste URL or text]" Hermes reads it, builds a source page, updates related entries, flags contradictions. THE OBSIDIAN ANGLE set OBSIDIAN_VAULT_PATH to the same directory. now your wiki is visible in Obsidian's graph view. nodes, links, backlinks. all built by Hermes. for headless servers: install obsidian-headless. syncs vaults without a GUI. agent writes from the server, you read on your laptop. THE COMPOUND EFFECT Hermes knows you. the wiki knows your world. combine them and the agent answers questions using BOTH contexts at once. month 1: you explain things twice. month 3: the agent references the wiki on its own. answers get sharper because the knowledge base got sharper. AUTOMATIONS THAT FEED THE WIKI set cron jobs to ingest automatically: "every day at 9am, check Granola for new meetings. add any new transcripts to my wiki under meeting notes." "every morning, scan my Gmail starred items. add anything worth keeping to the wiki." "every week, check arXiv for new papers in [your niche]. summarize and file." your wiki grows while you sleep. Hermes never forgets what gets indexed. THE LIMITATION TO KNOW unlike Hermes memory (which is conversational and lives across sessions), the wiki is a separate knowledge layer. Hermes won't pull from the wiki automatically unless you reference it or save it as a skill. best setup: build an LLM Wiki personality that tells Hermes to consult the wiki when answering strategy questions or domain-specific queries. full HERMES AGENT OVERNIGHT WORKFLOW👇

HERMES AGENT SHIPS WITH A BUNDLED SKILL FOR ANDREJ KARPATHY'S LLM WIKI PATTERN. A SELF-IMPROVING KNOWLEDGE BASE THAT GROWS EVERY TIME YOU FEED IT. mentioned this briefly in the overnight workflow article. here is the full breakdown. what it is: a self-improving knowledge base built as interlinked markdown files. unlike RAG (which rediscovers knowledge from scratch every query), the wiki compiles knowledge once and keeps it current. cross-references stay linked. contradictions get flagged automatically. synthesis reflects everything ingested so far. why this matters for Hermes memory: Hermes built-in memory knows YOU. it remembers your conversations, your preferences, your business context across sessions. but it doesn't know your inbox. or your meeting transcripts. or that article you saved last week. or the expert framework you want it to learn. the LLM Wiki solves that. THE DIVISION OF LABOR human curates sources and directs analysis. agent summarizes, cross-references, files, and maintains consistency. you drop in articles, transcripts, notes. Hermes indexes them, links related concepts, flags contradictions, updates affected pages. your knowledge base grows itself. SETUP IS ONE COMMAND the skill ships with Hermes. enable it. set WIKI_PATH in ~/.hermes/.env: WIKI_PATH=/Users/you/wiki defaults to ~/wiki if unset. then drop anything into it: "index this article into my wiki: [paste URL or text]" Hermes reads it, builds a source page, updates related entries, flags contradictions. THE OBSIDIAN ANGLE set OBSIDIAN_VAULT_PATH to the same directory. now your wiki is visible in Obsidian's graph view. nodes, links, backlinks. all built by Hermes. for headless servers: install obsidian-headless. syncs vaults without a GUI. agent writes from the server, you read on your laptop. THE COMPOUND EFFECT Hermes knows you. the wiki knows your world. combine them and the agent answers questions using BOTH contexts at once. month 1: you explain things twice. month 3: the agent references the wiki on its own. answers get sharper because the knowledge base got sharper. AUTOMATIONS THAT FEED THE WIKI set cron jobs to ingest automatically: "every day at 9am, check Granola for new meetings. add any new transcripts to my wiki under meeting notes." "every morning, scan my Gmail starred items. add anything worth keeping to the wiki." "every week, check arXiv for new papers in [your niche]. summarize and file." your wiki grows while you sleep. Hermes never forgets what gets indexed. THE LIMITATION TO KNOW unlike Hermes memory (which is conversational and lives across sessions), the wiki is a separate knowledge layer. Hermes won't pull from the wiki automatically unless you reference it or save it as a skill. best setup: build an LLM Wiki personality that tells Hermes to consult the wiki when answering strategy questions or domain-specific queries. full HERMES AGENT OVERNIGHT WORKFLOW👇

YanXbt

30,804 Aufrufe • vor 2 Monaten

HOW TO SELL "SECOND BRAIN AS A SERVICE" FOR $5K + (FULL COURSE) The model: build businesses a structured knowledge base out of plain markdown files, then charge to maintain it. No fancy database, Obsidian, or RAG. It becomes the foundation for every other AI project you sell them afterward. A $750 audit turns into a $3,500 build turns into a client who keeps paying you to add more. Adam Sandler came on the pod and walked through the entire playbook live. Here's what I learned: 1. Lead with the knowledge base, not the agent. Every prospect wants a CFO agent. Nobody has the context to make one work. Solve "where does the info live?" first and you've teed up every future build. 2. Sell it with AI out of the pitch. "I'll organize all your scattered company knowledge into one living asset." Their files are everywhere and they hate it. That closes on its own. 3. Zero technical friction. Just markdown files on their machine. No signup, no vault, no vectors. Technical drop-off kills AI deals. This deletes it. 4. Seven note types are your starting schema. Snapshot, people, rules, project history, decisions, open loops, links. Walk in with this and you already have a point of view. 5. Find the spine. The one thing everything ladders up to. Usually their annual goals. 6. Portability is the pitch. Fable got pulled and everyone panicked. A knowledge base outside Anthropic or OpenAI plugs into any model. Sells itself. 7. Token savings close the enterprise. One source means you stop re-pasting context every session. Real money, especially with pay-as-you-go API pricing. 8. The audit is the tripwire. Charge to map where their knowledge lives, then roll the fee into the build. Half the build is already done. 9. Two skills run it on autopilot. Ingest pulls from Gmail, calendar, and CRM. Curate does a weekly cleanup pass. That's the flywheel. 10. The knowledge base tells you what to sell next. Ask it for the top 3 AI opportunities. It finds the gap ("inbound takes 22 hours") and the next build sells itself. His 2 key takeaways: 1. Build one for yourself first. One source of truth, built once, every tool connected. Live the unlock and you'll sell it with conviction. 2. Don't let startups scare you off. Off-the-shelf can't learn how your client's business works. That domain knowledge is the moat you charge for. Adam is quietly building one of the best AI service offers out there and we had a blast going deep on it. Go follow Adam Sandler Full video below. (Also available on the Build With AI podcast wherever you get your pods)

HOW TO SELL "SECOND BRAIN AS A SERVICE" FOR $5K + (FULL COURSE) The model: build businesses a structured knowledge base out of plain markdown files, then charge to maintain it. No fancy database, Obsidian, or RAG. It becomes the foundation for every other AI project you sell them afterward. A $750 audit turns into a $3,500 build turns into a client who keeps paying you to add more. Adam Sandler came on the pod and walked through the entire playbook live. Here's what I learned: 1. Lead with the knowledge base, not the agent. Every prospect wants a CFO agent. Nobody has the context to make one work. Solve "where does the info live?" first and you've teed up every future build. 2. Sell it with AI out of the pitch. "I'll organize all your scattered company knowledge into one living asset." Their files are everywhere and they hate it. That closes on its own. 3. Zero technical friction. Just markdown files on their machine. No signup, no vault, no vectors. Technical drop-off kills AI deals. This deletes it. 4. Seven note types are your starting schema. Snapshot, people, rules, project history, decisions, open loops, links. Walk in with this and you already have a point of view. 5. Find the spine. The one thing everything ladders up to. Usually their annual goals. 6. Portability is the pitch. Fable got pulled and everyone panicked. A knowledge base outside Anthropic or OpenAI plugs into any model. Sells itself. 7. Token savings close the enterprise. One source means you stop re-pasting context every session. Real money, especially with pay-as-you-go API pricing. 8. The audit is the tripwire. Charge to map where their knowledge lives, then roll the fee into the build. Half the build is already done. 9. Two skills run it on autopilot. Ingest pulls from Gmail, calendar, and CRM. Curate does a weekly cleanup pass. That's the flywheel. 10. The knowledge base tells you what to sell next. Ask it for the top 3 AI opportunities. It finds the gap ("inbound takes 22 hours") and the next build sells itself. His 2 key takeaways: 1. Build one for yourself first. One source of truth, built once, every tool connected. Live the unlock and you'll sell it with conviction. 2. Don't let startups scare you off. Off-the-shelf can't learn how your client's business works. That domain knowledge is the moat you charge for. Adam is quietly building one of the best AI service offers out there and we had a blast going deep on it. Go follow Adam Sandler Full video below. (Also available on the Build With AI podcast wherever you get your pods)

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New Short Course: Getting Structured LLM Output! Learn how to get structured outputs from your LLM applications in this course, built in partnership with .txt, and taught by Will Kurt, a Founding Engineer, and , Developer Relations Engineer. It's challenging for software to automatically parse through an LLM's freeform text outputs. Structured outputs—like JSON—solve this by converting natural language into consistent, clear, data that a machine can read and process. This course teaches you how to generate structured outputs while building several use cases, including a social media analysis agent. You’ll learn about structured outputs and efficient ways to generate outputs in your defined schema or format. You’ll begin by using structured output APIs, then use re-prompting libraries like “instructor” to generate structured output. Finally, you’ll learn how constrained decoding works; this is a very clever technique in which constraints are applied on each subsequent token generated, blocking any tokens that don’t fit your defined schema. In detail, you’ll: - Learn why structured outputs are important, how they allow for scalable software development, and the different approaches to generate them, including vendor-provided APIs, re-prompting libraries, and structured generation. - Build a simple social media agent using OpenAI’s structured output API, learn how to define a model's desired structured output using Pydantic, and perform basic programming with your outputs, such as importing structured data into a data frame using pandas. - Learn how to use the open-source library "instructor," which checks the structured output of the model and re-prompts the model until it validates the desired output, and explore the limitations of this approach. - Understand how structured generation by the “outlines” library works by modifying LLM logits, on a per-generated-token basis based on the desired format, to give a particular output structure. - Learn how regular expressions, which outlines works with, are represented as finite-state machines, and how they can be used to develop a range of structured outputs beyond JSON. By the end of this course, you’ll have broadened your knowledge of the approaches you can use to get structured outputs from your LLM applications. Please sign up here:

Andrew Ng

89,792 Aufrufe • vor 1 Jahr

As many of you know, for the last five months I've been working full-time on my next big thing. The challenge was to invent something new and implement it entirely using LLMs for writing code. The first stage of the project is now complete: the web application, which I called is now online and accepting users. You can see a short demo in the video. 100% of the code of the app was generated by LLMs (mostly Gemini and Claude, maybe 10% of ChatGPT). I haven't written a single line of code. The tech stack is TypeScript, React, and Supabase/Postgres which was (and still is) fully new to me. During these five months, I implemented from scratch three versions of the software. It started as a Markdown editor to help me with my book writing and ended up as an AI-assisted reading and self-learning platform. What makes ChapterPal unique is a novel reading experience where the user can use the keyboard keys to reveal or "unreveal" the content and ask questions at any moment. (Mouse wheel, touchpad, smartphone screen, and voice input are also supported.) The LLM receives the entire content of the chapter and tries to answer questions based on the chapter's content, which reduces the chance of hallucination to the minimum. (Though not to 0%, of course, but near it.) This way of content consumption is known as **active reading,** a strategy for engaging with a text to improve comprehension and retention by consciously interacting with the material. The goal is to move beyond passive reading to a deeper understanding of the text and to remember key information more effectively. The registration on ChapterPal is via the waiting list. This is to avoid unexpected load spikes and cloud charges. Usually, it takes less than 24 hours for me to activate a user. Give it a try and let me know what you think. The next stage is finishing the content ingestion pipeline, which will automatically convert high-quality content from sources like HTML, PDF, and LaTeX into Markdown. Obviously, only those pieces whose licenses allow creating copies. ChapterPal has its own collection of textbooks and articles on AI, machine learning, and data science topics. If you don't find a piece of content you would like to read in ChapterPal's collection, a Chrome extension, ChapterPal Uploader, allows you to upload any PDF or HTML page to ChapterPal in one click. The content is only available for you to read to avoid the possibility of copyright infringement. I hope you enjoy using it as much as I enjoy building it.

As many of you know, for the last five months I've been working full-time on my next big thing. The challenge was to invent something new and implement it entirely using LLMs for writing code. The first stage of the project is now complete: the web application, which I called is now online and accepting users. You can see a short demo in the video. 100% of the code of the app was generated by LLMs (mostly Gemini and Claude, maybe 10% of ChatGPT). I haven't written a single line of code. The tech stack is TypeScript, React, and Supabase/Postgres which was (and still is) fully new to me. During these five months, I implemented from scratch three versions of the software. It started as a Markdown editor to help me with my book writing and ended up as an AI-assisted reading and self-learning platform. What makes ChapterPal unique is a novel reading experience where the user can use the keyboard keys to reveal or "unreveal" the content and ask questions at any moment. (Mouse wheel, touchpad, smartphone screen, and voice input are also supported.) The LLM receives the entire content of the chapter and tries to answer questions based on the chapter's content, which reduces the chance of hallucination to the minimum. (Though not to 0%, of course, but near it.) This way of content consumption is known as active reading, a strategy for engaging with a text to improve comprehension and retention by consciously interacting with the material. The goal is to move beyond passive reading to a deeper understanding of the text and to remember key information more effectively. The registration on ChapterPal is via the waiting list. This is to avoid unexpected load spikes and cloud charges. Usually, it takes less than 24 hours for me to activate a user. Give it a try and let me know what you think. The next stage is finishing the content ingestion pipeline, which will automatically convert high-quality content from sources like HTML, PDF, and LaTeX into Markdown. Obviously, only those pieces whose licenses allow creating copies. ChapterPal has its own collection of textbooks and articles on AI, machine learning, and data science topics. If you don't find a piece of content you would like to read in ChapterPal's collection, a Chrome extension, ChapterPal Uploader, allows you to upload any PDF or HTML page to ChapterPal in one click. The content is only available for you to read to avoid the possibility of copyright infringement. I hope you enjoy using it as much as I enjoy building it.

BURKOV

81,258 Aufrufe • vor 9 Monaten

I thought AEO (read: SEO for LLMs) was a hunk of bullsh*t. And then i spoke to KippBodnar.eth, CMO of HubSpot, who knocked the skepticism out of me. His first jab: In one year, they grew AI search traffic by 15x. It went from rounding error to real line item on the P&L. His second jab: AI search conversion rates are 5x higher than Google search. On some queries, 13x higher. His hook: 60% of AI citations don't come from the top 20 Google results. The companies dominating Google aren't automatically winning in AI search, which creates a huge advantage for early adopters. He then took me through his process for crushing AEO & seeing results in days, not months (like SEO): 1) Grade: your current AEO presence across ChatGPT, Perplexity, and Gemini with a tool like Hubspot's AEO grader. 2) Restructure: your content into chunked, answer-first pages with natural language headers. - one consolidated page, not 8-10 interlinked pages - lead with natural language questions like "What is X?" - 1-2 paragraph sections, not 1,000 word sections - table of contents on a single page 3) Separate: Mentions from citations and optimize differently for each - Mention = when AI references your brand or product in its answer but doesn't link to you - Citation = when an AI references you AND links to your page 4) Open up: your information — ungate content, build Reddit presence, make pricing public - Optimize for entity understanding: how well do AI models understand what your company does, based on every signal from Reddit to review sites, awards lists to help docs 5) Tool up: with AEO-specific software to track prompts and share of voice - Check out Xfunnel or Limey[.]ai 6) Rethink attribution: measure source of customers, not source of traffic. - Metrics that matter: share of voice, citation count, sentiment, mention frequency, source of customers not traffic

Alex Lieberman

98,635 Aufrufe • vor 6 Monaten

Here's how I'm running automated content engine in 2 files 1 markdown file = my wiki 1 html file = my dashboard that's the whole stack. [ the architecture, in plain words ]: LLM wiki = a single markdown file holding my audience DNA, 15 tracked creators, every viral topic from the last 30 days HTML artifact = a single page that reads that markdown file AND can trigger my agents the artifact and the agent talk to each other directly the wiki is the shared brain [ what I actually see when I open it at 9am ]: > 5 trending topics ranked by my audience-DNA fit > 3 KOL posts worth quoting today > last week's saved tweets (so I can ride waves that are still warm) > buttons: [draft tweet] [draft QT] [schedule] [log idea] 1. I click "draft tweet" on a topic 2. the artifact pings my agent 3. agent reads the wiki, drafts in MY voice, returns it to the artifact 4. I edit, schedule, done 15 minutes from morning coffee to 3 scheduled posts [ how to build the same in one evening ]: > step 1: dump your domain knowledge into ONE markdown file (audience profile, KOL list, content rules, voice guide, anything an agent would need to do YOUR job) > step 2: ask claude to build an html artifact that reads from that file ("here's my wiki, build me a dashboard with these views") > step 3: add buttons for the actions you do daily (draft, schedule, log, score, search — your workflow, not mine) > step 4: wire each button to call your agent via tool calls (so the artifact and the agent talk directly) the moment your artifact reads your wiki AND triggers your agents.. most SaaS tools you currently pay for quietly become unnecessary dashboards I used to pay $50/month for now sit in a single html file I can rebuild in 20 minutes every "I'll build a SaaS for this" idea you had last year is a 200-line file you write in an afternoon if you want to get the same content engine, just reply "CONTENT" and will send you in DMs later we're going from buying software to owning it.

Ronin

50,083 Aufrufe • vor 3 Monaten

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 Aufrufe • vor 1 Jahr

I just vibe-coded a complete SEO Agency In a Box with Claude Code. One keyword in → full research, competitor analysis, SEO-optimized article, metadata, and AI graphics out. All on auto-pilot. Perfect for SEO agencies and DTC brands who are tired of spending an entire day producing a single piece of SEO content. Creating quality SEO content is a huge pain. You're reading competitor articles one by one, trying to find content gaps, writing drafts from scratch, then going back to optimize everything before you even think about graphics or metadata. This vibe-coded tool solves it: → Set up your brand once (value prop, tone of voice, writing samples) → Enter a topic and keywords → SERP API + Firecrawl scrape and analyze the top-ranking competitors → Gemini generates a full research plan with intent, gaps, and strategic positioning → Claude writes the article based on your brand guidelines and the research → Nano Banana Pro generates thumbnail and in-blog images → SEO metadata (title, description, slug) created automatically What this outputs: - Full competitive research and content brief - SEO-optimized articles with internal links and proper formatting - AI-generated images and thumbnail - Meta title, description, and slugs - Batch mode for multiple articles at once - Content rewrite mode for existing URLs that aren't ranking - LinkedIn and X repurposing built in Built 100% in Claude Code. (I'm giving away the prompts) I recorded a full walkthrough showing exactly how every part of this works, and I'm including ALL of the prompts I used for Claude Code. Want access for free? > Like this post > Comment "SEO" And I'll send it over (must be following so I can DM)

Mike Futia

68,594 Aufrufe • vor 6 Monaten

New short course: Practical Multi AI Agents and Advanced Use Cases with crewAI. Learn to build and deploy advanced agent-based systems in real applications in this course, created with CrewAI and taught by its founder, João Moura! (Disclosure: I've made a small seed investment in CrewAI.) In this course, you’ll learn how to create advanced agent-based apps that use external tools, do performance testing, can be trained with human feedback, and perform multiple tasks with different large language models. You will build several practical agentic apps that provide real business value, such as an automated project planning system, lead scoring and engagement pipeline, customer support data analysis, and a robust content creation system. In detail, you will learn how to: - Create these multi-agent systems with the building blocks of tasks, agents, and crews, along with the different things that make them work, such as caching, memory, and guardrails. - Integrate your multi-agent application with internal and external systems. - Connect multiple agents in complex setups, including parallel, sequential, and hybrid configurations, and create flows involving multiple agentic applications working together. - Test your agentic workflow and train it using human feedback to optimize its performance for better and more consistent results. - Work with multiple LLMs in your multi-agent system, using the appropriate model sizes and providers to fit each agent’s specific task. - Start a project from scratch in your environment and prepare it for deployment. You’ll also learn from an interview between João and Jacob Wilson, the Commercial GenAI Principal at PwC , in which they discuss deploying agentic workflows in real industry use cases. By the end of this course, you will be equipped to start building custom multi-agentic systems for your work. Please sign up here!

New short course: Practical Multi AI Agents and Advanced Use Cases with crewAI. Learn to build and deploy advanced agent-based systems in real applications in this course, created with CrewAI and taught by its founder, João Moura! (Disclosure: I've made a small seed investment in CrewAI.) In this course, you’ll learn how to create advanced agent-based apps that use external tools, do performance testing, can be trained with human feedback, and perform multiple tasks with different large language models. You will build several practical agentic apps that provide real business value, such as an automated project planning system, lead scoring and engagement pipeline, customer support data analysis, and a robust content creation system. In detail, you will learn how to: - Create these multi-agent systems with the building blocks of tasks, agents, and crews, along with the different things that make them work, such as caching, memory, and guardrails. - Integrate your multi-agent application with internal and external systems. - Connect multiple agents in complex setups, including parallel, sequential, and hybrid configurations, and create flows involving multiple agentic applications working together. - Test your agentic workflow and train it using human feedback to optimize its performance for better and more consistent results. - Work with multiple LLMs in your multi-agent system, using the appropriate model sizes and providers to fit each agent’s specific task. - Start a project from scratch in your environment and prepare it for deployment. You’ll also learn from an interview between João and Jacob Wilson, the Commercial GenAI Principal at PwC , in which they discuss deploying agentic workflows in real industry use cases. By the end of this course, you will be equipped to start building custom multi-agentic systems for your work. Please sign up here!

Andrew Ng

341,204 Aufrufe • vor 1 Jahr

3 years ago we got fired by a creative agency because of our google drive folder. The nightmare wasn’t the firing. It was realizing that the agency was right. Our “system” had 30 different folders with the same name, content with expired usage that only Ron or I knew about, We spend over a million dollars a month on meta. Creative is literally the biggest lever to grow our business… (and 99% of ecom brands). Yet we’d get a shit ton of great content from UGC creators, influencers and agencies and then toss it into a google drive folder that you’d want to X out of immediately. The killer of our creative output isn't a lack of ideas. It's a lack of organization and simplicity. When your team or agency is scared to touch something because of how complicated it is nothing will get done. Creative agencies we're investing money into aren't set up for success. They don’t spend the time to look for the right b-roll, pick the right creator… they try and find something usable in our hell hole of an asset library. This top tier agency we wanted to work with didn’t even want to work with us back because of how much of a mess it was. That’s why I’m interested in some sort of AI creative manager… AI that can actually organize, tag, and ingest your content for you. I’ve been skeptical just purely based on the amount of AI tools that are launching everyday, but an AI that can ingest our content, understand what it is, transcribe video and then allow you to search and easily find the perfect clips is a realistic and quite useful application for AI in 2025. I’m going to test out Air and their new creative manager with this game plan: 1. Start with NEW assets only (no massive migration) 2. Set up smart intake forms for agencies and influencers 3. Build a database I'm actually proud to share 4. THEN migrate the rest gradually Leaving this link here if you want to check out Air: I’m also giving away this 29 page AI sop on how creative teams can use AI to streamline their workflow. Comment AIR and I’ll send it to you. And yes, I'm an #AirPartner because I genuinely believe in their mission.

3 years ago we got fired by a creative agency because of our google drive folder. The nightmare wasn’t the firing. It was realizing that the agency was right. Our “system” had 30 different folders with the same name, content with expired usage that only Ron or I knew about, We spend over a million dollars a month on meta. Creative is literally the biggest lever to grow our business… (and 99% of ecom brands). Yet we’d get a shit ton of great content from UGC creators, influencers and agencies and then toss it into a google drive folder that you’d want to X out of immediately. The killer of our creative output isn't a lack of ideas. It's a lack of organization and simplicity. When your team or agency is scared to touch something because of how complicated it is nothing will get done. Creative agencies we're investing money into aren't set up for success. They don’t spend the time to look for the right b-roll, pick the right creator… they try and find something usable in our hell hole of an asset library. This top tier agency we wanted to work with didn’t even want to work with us back because of how much of a mess it was. That’s why I’m interested in some sort of AI creative manager… AI that can actually organize, tag, and ingest your content for you. I’ve been skeptical just purely based on the amount of AI tools that are launching everyday, but an AI that can ingest our content, understand what it is, transcribe video and then allow you to search and easily find the perfect clips is a realistic and quite useful application for AI in 2025. I’m going to test out Air and their new creative manager with this game plan: 1. Start with NEW assets only (no massive migration) 2. Set up smart intake forms for agencies and influencers 3. Build a database I'm actually proud to share 4. THEN migrate the rest gradually Leaving this link here if you want to check out Air: I’m also giving away this 29 page AI sop on how creative teams can use AI to streamline their workflow. Comment AIR and I’ll send it to you. And yes, I'm an #AirPartner because I genuinely believe in their mission.

Ash

12,835 Aufrufe • vor 11 Monaten

This is Farzapedia. I had an LLM take 2,500 entries from my diary, Apple Notes, and some iMessage convos to create a personal Wikipedia for me. It made 400 detailed articles for my friends, my startups, research areas, and even my favorite animes and their impact on me complete with backlinks. But, this Wiki was not built for me! I built it for my agent! The structure of the wiki files and how it's all backlinked is very easily crawlable by any agent + makes it a truly useful knowledge base. I can spin up Claude Code on the wiki and starting at index.md (a catalog of all my articles) the agent does a really good job at drilling into the specific pages on my wiki it needs context on when I have a query. For example, when trying to cook up a new landing page I may ask: "I'm trying to design this landing page for a new idea I have. Please look into the images and films that inspired me recently and give me ideas for new copy and aesthetics". In my diary I kept track of everything from: learnings, people, inspo, interesting links, images. So the agent reads my wiki and pulls up my "Philosophy" articles from notes on a Studio Ghibli documentary, "Competitor" articles with YC companies whose landing pages I screenshotted, and pics of 1970s Beatles merch I saved years ago. And it delivers a great answer. I built a similar system to this a year ago with RAG but it was ass. A knowledge base that lets an agent find what it needs via a file system it actually understands just works better. The most magical thing now is as I add new things to my wiki (articles, images of inspo, meeting notes) the system will likely update 2-3 different articles where it feels that context belongs, or, just creates a new article. It's like this super genius librarian for your brain that's always filing stuff for your perfectly and also let's you easily query the knowledge for tasks useful to you (ex. design, product, writing, etc) and it never gets tired. I might spend next week productizing this, if that's of interest to you DM me + tell me your usecase!

This is Farzapedia. I had an LLM take 2,500 entries from my diary, Apple Notes, and some iMessage convos to create a personal Wikipedia for me. It made 400 detailed articles for my friends, my startups, research areas, and even my favorite animes and their impact on me complete with backlinks. But, this Wiki was not built for me! I built it for my agent! The structure of the wiki files and how it's all backlinked is very easily crawlable by any agent + makes it a truly useful knowledge base. I can spin up Claude Code on the wiki and starting at index.md (a catalog of all my articles) the agent does a really good job at drilling into the specific pages on my wiki it needs context on when I have a query. For example, when trying to cook up a new landing page I may ask: "I'm trying to design this landing page for a new idea I have. Please look into the images and films that inspired me recently and give me ideas for new copy and aesthetics". In my diary I kept track of everything from: learnings, people, inspo, interesting links, images. So the agent reads my wiki and pulls up my "Philosophy" articles from notes on a Studio Ghibli documentary, "Competitor" articles with YC companies whose landing pages I screenshotted, and pics of 1970s Beatles merch I saved years ago. And it delivers a great answer. I built a similar system to this a year ago with RAG but it was ass. A knowledge base that lets an agent find what it needs via a file system it actually understands just works better. The most magical thing now is as I add new things to my wiki (articles, images of inspo, meeting notes) the system will likely update 2-3 different articles where it feels that context belongs, or, just creates a new article. It's like this super genius librarian for your brain that's always filing stuff for your perfectly and also let's you easily query the knowledge for tasks useful to you (ex. design, product, writing, etc) and it never gets tired. I might spend next week productizing this, if that's of interest to you DM me + tell me your usecase!

Farza 🇵🇰🇺🇸

2,099,275 Aufrufe • vor 4 Monaten