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One AI skill that changes how you read academic papers. It turns every reference into a reusable writing block. I call this method: Atomic sentences. Atomic sentence = One sentence. One claim. One source. You can extract them directly from a peer-reviewed paper, vetted by authors and reviewers before... it ever reaches you. It is a form of summarising the content, but the output can be reused and recombined in your writing. Here's how it works 👇 1. Upload any paper to Claude and trigger the /ea-atomic-sentences skill. It breaks down every cited reference into a single compressed claim. (Repeat on as many papers as needed) 2. Pick 5–6 sentences relevant to an argument and arrange them coherently, so it makes sense. Add your own ideas/findings as well. 3. Ask Claude to combine them into a coherent paragraph. What you get is surprisingly well-written paragraphs. Use the skill /ea-academic-writer for even better results. ✅ Every reference in that paragraph is real. ✅ Every claim was made by a human researcher. ✅ You control the narrative of what is being said in your paper, but build on the findings of others. Disclaimer: Don't skip reading, as you need to understand your domain to use this and don't rely 100% on AI, as it can make mistakes.show more

Ilya Shabanov

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16,002 просмотров • 2 месяцев назад •via X (Twitter)

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How to extract data from papers for literature review in seconds? 1. Go to 2. Upload 2-3 papers you already know are relevant 3. Start with clicking on one paper 4. ResearchCollab do the following for the paper ✦ Provide a short overview of the paper ✦ Identify weaknesses in the paper ✦ Share papers that contrast the existing paper ✦ Extract meta data about the paper ✦ Evaluate each part (e.g., methodology) of the paper ✦ Provide AI chat to extract any other data 5. Now click on the Related Papers button at top right 6. You will get papers related to the seed paper 7. You can click on each paper and read its abstract 8. If you find it relevant, just add it to your list of papers 9. Do the same for the other 2 seed papers 10. This way you will collect all relevant papers 11. And extract data from those papers After this, analyse the data and report the findings. Try ResearchCollab today:

How to extract data from papers for literature review in seconds? 1. Go to 2. Upload 2-3 papers you already know are relevant 3. Start with clicking on one paper 4. ResearchCollab do the following for the paper ✦ Provide a short overview of the paper ✦ Identify weaknesses in the paper ✦ Share papers that contrast the existing paper ✦ Extract meta data about the paper ✦ Evaluate each part (e.g., methodology) of the paper ✦ Provide AI chat to extract any other data 5. Now click on the Related Papers button at top right 6. You will get papers related to the seed paper 7. You can click on each paper and read its abstract 8. If you find it relevant, just add it to your list of papers 9. Do the same for the other 2 seed papers 10. This way you will collect all relevant papers 11. And extract data from those papers After this, analyse the data and report the findings. Try ResearchCollab today:

Faheem Ullah

26,494 просмотров • 5 месяцев назад

how to use Google's NEW open source Design.md + AI Skills to make your startup look like a $100 million company in 1 hour: 1. Design.md is an open source file from Google that captures the soul of a design. Typography, colors, spacing, all in one markdown file. You attach it to your prompt and your agent builds beautiful things every time. 2. Think of it this way. The HTML is the finished dish. The design.md is the recipe. The skills are the ingredients. Put them together and everything you build looks consistent and professional. 3. Don't create a design system from scratch. Find a brand you love. Linear, Stripe, Vercel, whatever resonates. Study it. Use ChatGPT or Claude to help you extract the design language into your own design.md file. 4. Build skills on top of your design.md. A landing page skill. A mobile app skill. A motion design skill. A slide deck skill. Each one references the same design.md so everything looks like it came from the same designer. 5. The biggest mistake people make: they nail one screen and then everything else looks generic. Design.md solves this. One file keeps every page, every format, every medium consistent. 6. Use it across everything. Your landing page. Your app. Your pitch deck. Your promo videos. Same DNA. Same taste. Same system. That's what separates a startup that looks real from one that looks vibe-coded. 7. Build a second brain for design inspiration. When you see something beautiful in the real world or online, capture it. Save it. When you're building something new, reference it. Taste is developed, not downloaded. 8. It's obvious but the difference between a product people trust and a product people bounce from is how it looks and feels. Design.md gives you that edge. you can watch below shoutout to Meng To for coming on The Startup Ideas Podcast (SIP) 🧃 and walking through his full workflow. if you want to use AI to actually build gorgeous designs, you'll want to use see this. watch

GREG ISENBERG

503,527 просмотров • 2 месяцев назад

How to read a research paper 10x faster? PhD Students are often under time pressure. Many research papers but little time to read. Meet Bohrium – a tool that can 10x your paper reading. 𝐇𝐨𝐰 𝐜𝐚𝐧 𝐁𝐨𝐡𝐫𝐢𝐮𝐦 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐫𝐞𝐚𝐝 𝐬𝐦𝐚𝐫𝐭𝐞𝐫? 1. Go to and log in. 2. Click on 𝑁𝑒𝑤 𝐶ℎ𝑎𝑡 and then on 𝐿𝑖𝑡𝑇𝑎𝑙𝑘 3. Upload the paper you want to read 4. Click on 𝑆𝑢𝑚𝑚𝑎𝑟𝑖𝑧𝑒 from the list. 5. Bohirum will summarize the paper for you. 6. This will help you understand the gist of the paper. 7. Now click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝐹𝑖𝑛𝑑𝑖𝑛𝑔𝑠 8. Bohrium will extract key findings from the paper. 9. Go through each of the key finding. 10. Bohrium links each finding to respective part 11. This way you can see the links to the paper. 12. In the similar manner, you can also ↳ Extract information about research methods ↳ Academic concepts used in the paper 13. Bohrium can also help you in your writing 14. For example, you can use it to create diagrams 15. You can use Bohrium to create ✓ Graphical abstract ✓ Mechanism diagram ✓ Flowchart ✓ Concept cover 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐬𝐩𝐞𝐜𝐢𝐚𝐥 𝐚𝐛𝐨𝐮𝐭 𝐁𝐨𝐡𝐫𝐢𝐮𝐦? → You can upload and chat with multiple papers together → Instead of only summarizing, it helps you in deep understanding → It links key findings to exact sections of the paper → It works like research-focused but free NotebookLLM Try it today:

How to read a research paper 10x faster? PhD Students are often under time pressure. Many research papers but little time to read. Meet Bohrium – a tool that can 10x your paper reading. 𝐇𝐨𝐰 𝐜𝐚𝐧 𝐁𝐨𝐡𝐫𝐢𝐮𝐦 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐫𝐞𝐚𝐝 𝐬𝐦𝐚𝐫𝐭𝐞𝐫? 1. Go to and log in. 2. Click on 𝑁𝑒𝑤 𝐶ℎ𝑎𝑡 and then on 𝐿𝑖𝑡𝑇𝑎𝑙𝑘 3. Upload the paper you want to read 4. Click on 𝑆𝑢𝑚𝑚𝑎𝑟𝑖𝑧𝑒 from the list. 5. Bohirum will summarize the paper for you. 6. This will help you understand the gist of the paper. 7. Now click on 𝐸𝑥𝑡𝑟𝑎𝑐𝑡 𝐹𝑖𝑛𝑑𝑖𝑛𝑔𝑠 8. Bohrium will extract key findings from the paper. 9. Go through each of the key finding. 10. Bohrium links each finding to respective part 11. This way you can see the links to the paper. 12. In the similar manner, you can also ↳ Extract information about research methods ↳ Academic concepts used in the paper 13. Bohrium can also help you in your writing 14. For example, you can use it to create diagrams 15. You can use Bohrium to create ✓ Graphical abstract ✓ Mechanism diagram ✓ Flowchart ✓ Concept cover 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐬𝐩𝐞𝐜𝐢𝐚𝐥 𝐚𝐛𝐨𝐮𝐭 𝐁𝐨𝐡𝐫𝐢𝐮𝐦? → You can upload and chat with multiple papers together → Instead of only summarizing, it helps you in deep understanding → It links key findings to exact sections of the paper → It works like research-focused but free NotebookLLM Try it today:

Faheem Ullah

27,133 просмотров • 7 месяцев назад

Skills are the quickest way to 10x the quality and consistency of what you get from Claude Code. And you don't need to be a developer to use them. Anthropic just published how they use hundreds of skills internally every day. Most skill tutorials are made for developers — if you're in marketing, sales, content ops, or GTM, you probably watched those and moved on. But skills are just as important for non-developers. A skill is just a reusable prompt with clear instructions for a specific task. Instead of prompting Claude the same way over and over, you build it once and invoke it every time. I have a skill for writing on LinkedIn. A different one for YouTube outlines. Another for X. Each platform has different rules, different voice, different structure — so each one gets its own skill. If you're doing something repeatedly, it's time to make a skill. The biggest mistake most people make: building skills as a single .md file. A single file dumps everything into context whether Claude needs it or not. Wastes tokens. Gets worse results. Skills should be folders. Here's the structure that works: skill.md — the orchestrator. Tells Claude which files to read and when. It doesn't contain rules itself — it's the playbook. instructions/ — separate files for voice, structure, scope. Claude only loads the one it needs for the current step. examples/ — good AND bad. Good examples show what success looks like. Bad examples show patterns to avoid — AI writing tells, weak hooks, generic CTAs. Most people skip bad examples. Don't. eval/ — a checklist that scores every output before you see it. "Does it have a clear hook?" "Is it free of AI buzzwords?" Pass or fail on each item. templates/ — output formatting so you get consistent structure every time. The three types of skills that matter most for non-developers: 1. Business automation. Writing a newsletter. Checking reports and drafting follow-ups. Running programmatic ad campaigns. Any workflow you repeat — build a skill for it. 2. Content templates. Landing page copy, meta ads, email sequences, SEO briefs. Each one has specific requirements. Each one gets its own skill. 3. Thinking partners. This is the one people miss. Skills don't have to produce output. They can help you think — an advisory board that reviews your work from your ICP's perspective, a coach that pressure-tests your strategy, an ideation partner that researches competitors before suggesting your next move. If you already have skills as .md files, here's the exact prompt to restructure them in the Anthropic approved format: "I want to restructure my Claude Code skill file. Right now my skill is a single .md file and I want to break it into a folder system following Anthropic's best practices. Read my current skill file, then restructure it into a folder with: a skill.md orchestrator, an instructions/ folder with separate files for each concern (voice, structure, scope), an examples/ folder with good and bad examples, an eval/ folder with a quality checklist, and a templates/ folder for output formatting. Keep all my existing rules and intent — just reorganize them into the modular structure." Paste that into Claude Code pointed at the folder where your skill lives. It handles the rest. A few caveats: 1. Don't add too many skills. Every skill adds context Claude has to process. 50 skills loaded means everything slows down. Start with 3-5 covering your most repeated workflows. 2. Vet skills before downloading. If you grab a skill from the internet, read what's inside first. Skills can include shell commands and scripts. Check what you're running. 3. Share what works. Build a skill that performs well, put it in a shared GitHub repo. Your marketing org gets shared skills for copywriting, SEO, ad copy — new hires invoke the skill instead of learning every playbook from scratch. Onboarding time drops dramatically. 4. Keep your skills updated. When you see output you love, add it as a good example. When you see a pattern you hate, add it as a bad example. The skill gets sharper every time. I made a full video walking through all of this — including a live build of two skills from scratch (no terminal, no code), the exact prompt I use to restructure old skills, and 5 pro tips from Anthropic's internal playbook. Share this with your non-developer friends that want to do more with AI; or bookmark it to come back to at a later time.

Skills are the quickest way to 10x the quality and consistency of what you get from Claude Code. And you don't need to be a developer to use them. Anthropic just published how they use hundreds of skills internally every day. Most skill tutorials are made for developers — if you're in marketing, sales, content ops, or GTM, you probably watched those and moved on. But skills are just as important for non-developers. A skill is just a reusable prompt with clear instructions for a specific task. Instead of prompting Claude the same way over and over, you build it once and invoke it every time. I have a skill for writing on LinkedIn. A different one for YouTube outlines. Another for X. Each platform has different rules, different voice, different structure — so each one gets its own skill. If you're doing something repeatedly, it's time to make a skill. The biggest mistake most people make: building skills as a single .md file. A single file dumps everything into context whether Claude needs it or not. Wastes tokens. Gets worse results. Skills should be folders. Here's the structure that works: skill.md — the orchestrator. Tells Claude which files to read and when. It doesn't contain rules itself — it's the playbook. instructions/ — separate files for voice, structure, scope. Claude only loads the one it needs for the current step. examples/ — good AND bad. Good examples show what success looks like. Bad examples show patterns to avoid — AI writing tells, weak hooks, generic CTAs. Most people skip bad examples. Don't. eval/ — a checklist that scores every output before you see it. "Does it have a clear hook?" "Is it free of AI buzzwords?" Pass or fail on each item. templates/ — output formatting so you get consistent structure every time. The three types of skills that matter most for non-developers: 1. Business automation. Writing a newsletter. Checking reports and drafting follow-ups. Running programmatic ad campaigns. Any workflow you repeat — build a skill for it. 2. Content templates. Landing page copy, meta ads, email sequences, SEO briefs. Each one has specific requirements. Each one gets its own skill. 3. Thinking partners. This is the one people miss. Skills don't have to produce output. They can help you think — an advisory board that reviews your work from your ICP's perspective, a coach that pressure-tests your strategy, an ideation partner that researches competitors before suggesting your next move. If you already have skills as .md files, here's the exact prompt to restructure them in the Anthropic approved format: "I want to restructure my Claude Code skill file. Right now my skill is a single .md file and I want to break it into a folder system following Anthropic's best practices. Read my current skill file, then restructure it into a folder with: a skill.md orchestrator, an instructions/ folder with separate files for each concern (voice, structure, scope), an examples/ folder with good and bad examples, an eval/ folder with a quality checklist, and a templates/ folder for output formatting. Keep all my existing rules and intent — just reorganize them into the modular structure." Paste that into Claude Code pointed at the folder where your skill lives. It handles the rest. A few caveats: 1. Don't add too many skills. Every skill adds context Claude has to process. 50 skills loaded means everything slows down. Start with 3-5 covering your most repeated workflows. 2. Vet skills before downloading. If you grab a skill from the internet, read what's inside first. Skills can include shell commands and scripts. Check what you're running. 3. Share what works. Build a skill that performs well, put it in a shared GitHub repo. Your marketing org gets shared skills for copywriting, SEO, ad copy — new hires invoke the skill instead of learning every playbook from scratch. Onboarding time drops dramatically. 4. Keep your skills updated. When you see output you love, add it as a good example. When you see a pattern you hate, add it as a bad example. The skill gets sharper every time. I made a full video walking through all of this — including a live build of two skills from scratch (no terminal, no code), the exact prompt I use to restructure old skills, and 5 pro tips from Anthropic's internal playbook. Share this with your non-developer friends that want to do more with AI; or bookmark it to come back to at a later time.

JJ Englert

29,322 просмотров • 3 месяцев назад

Read 100 paywalled research papers for free every month! You don't even need a university account to do this. Here's how to read paywalled papers on JSTOR for free: 1. Go to jstor(dot)org and click on "Register" in the top-right corner. You can register with your personal Google or Outlook account. Or, you can create a JSTOR account manually. 2. Once you've logged in to your JSTOR account, click on "Workspace" in the menu bar. Then click on "Create folder." Choose a name for your folder and click on "Create."Creating folders in Workspace is a great way to keep your papers organized. 3. Type in the keywords in the search bar to find relevant papers. JSTOR willl give you a list of papers. To read a paper for free, click on "Read online." You will see a preview of the paper. Scroll down a bit and click on "Read Online" again. 4. If you find the paper super-relevant to your project, click on "Save" on the top of the article. Choose the folder you just created in your Workspace and save the paper in it. If you go to your Workspace, the paper will show up in the relevant folder. 5. You can also take notes on papers in your Workspace. To do so, click on the "Add Note" button under a paper and start typing. Click on "Save" to your save your note. 6. If you already have a paper and you want to related to it, you can use Text Analyzer. To do so, click on "Tools" and select "Text Analyzer." Upload the paper you have and JSTOR will give you a list of papers related to you original paper. 7. Text Analyzer also lets you callibrate your search parameters. Adjust the priority for different terms by moving the priority scale left or right. You can more related terms and adjust their priority. Text Analyzer will update the results accordingly. 8. If you find a paper interesting, simply click on it and then select "Read Online." 9. You can also add papers to your Zotero library. Open the paper you want to add and click on the Zotero Connector in the top-right corner of your browser. Choose the Zotero collection you want to save the paper in and click on "Done." The paper will show up in your Zotero. Found this post on JSTOR helpful? • Repost to share it with your friends and colleagues. •Follow me for more posts on academic writing.

Read 100 paywalled research papers for free every month! You don't even need a university account to do this. Here's how to read paywalled papers on JSTOR for free: 1. Go to jstor(dot)org and click on "Register" in the top-right corner. You can register with your personal Google or Outlook account. Or, you can create a JSTOR account manually. 2. Once you've logged in to your JSTOR account, click on "Workspace" in the menu bar. Then click on "Create folder." Choose a name for your folder and click on "Create."Creating folders in Workspace is a great way to keep your papers organized. 3. Type in the keywords in the search bar to find relevant papers. JSTOR willl give you a list of papers. To read a paper for free, click on "Read online." You will see a preview of the paper. Scroll down a bit and click on "Read Online" again. 4. If you find the paper super-relevant to your project, click on "Save" on the top of the article. Choose the folder you just created in your Workspace and save the paper in it. If you go to your Workspace, the paper will show up in the relevant folder. 5. You can also take notes on papers in your Workspace. To do so, click on the "Add Note" button under a paper and start typing. Click on "Save" to your save your note. 6. If you already have a paper and you want to related to it, you can use Text Analyzer. To do so, click on "Tools" and select "Text Analyzer." Upload the paper you have and JSTOR will give you a list of papers related to you original paper. 7. Text Analyzer also lets you callibrate your search parameters. Adjust the priority for different terms by moving the priority scale left or right. You can more related terms and adjust their priority. Text Analyzer will update the results accordingly. 8. If you find a paper interesting, simply click on it and then select "Read Online." 9. You can also add papers to your Zotero library. Open the paper you want to add and click on the Zotero Connector in the top-right corner of your browser. Choose the Zotero collection you want to save the paper in and click on "Done." The paper will show up in your Zotero. Found this post on JSTOR helpful? • Repost to share it with your friends and colleagues. •Follow me for more posts on academic writing.

Mushtaq Bilal, PhD

31,810 просмотров • 2 лет назад

Notch Clips. every other ad tool ignores the footage sitting in your dropbox and generates new clips from a prompt. notch opens your library instead. it watches every clip you have ever shot and understands what is inside each one. the mood. who is in frame. what they are doing. the quality. so when you ask it to build an ad, it does not start from scratch. it reaches into your past shoots, finds the clip that fits, and slots it into the exact moment that needs it. here is what that looks like. you had thirteen versions of one reaction shot. notch picked the one that worked and left the other twelve. not by filename. not by guessing from a reference image. it watched all thirteen and could tell the difference. that is the whole idea. the right clip was already in your library. every other tool was about to make a worse one. footage you already shot, turned into every ad you still need. drop your site and a few clips you have. notch builds the first ad from them.

Notch Clips. every other ad tool ignores the footage sitting in your dropbox and generates new clips from a prompt. notch opens your library instead. it watches every clip you have ever shot and understands what is inside each one. the mood. who is in frame. what they are doing. the quality. so when you ask it to build an ad, it does not start from scratch. it reaches into your past shoots, finds the clip that fits, and slots it into the exact moment that needs it. here is what that looks like. you had thirteen versions of one reaction shot. notch picked the one that worked and left the other twelve. not by filename. not by guessing from a reference image. it watched all thirteen and could tell the difference. that is the whole idea. the right clip was already in your library. every other tool was about to make a worse one. footage you already shot, turned into every ad you still need. drop your site and a few clips you have. notch builds the first ad from them.

Vinay Jain

406,286 просмотров • 1 месяц назад

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

57,975 просмотров • 1 месяц назад

Remember that paper that started with ‘Certainly, here is a possible introduction for your topic’? How did that get past peer review?! I don’t want AI tools to do my research for me. I want AI tools to speed up boring tasks that take up my time, so I can focus on the important stuff. Anara moved to a new handle (formerly Unriddle) does exactly that. Here’s how you can use it for your research. 🧵👇 #SponsoredWalkthrough One of the biggest challenges in research is time. A solid literature review takes at least 2-3 months… sometimes even longer, depending on the depth of analysis needed. Reading, organising, and synthesising information is a slow process, but it’s absolutely necessary for high-quality work. AI can help speed it up. Not by replacing your critical thinking. It’s your PhD, your ideas need to be your own—but by automating the tedious, repetitive parts of research so you can focus on deep understanding, analysis, and writing. Unlike other AI tools, Anara works with almost any document format. This is what makes it really stand out from the rest. For instance, you can upload: ✅PDFs and other word-based documents ✅Images and presentations ✅Handwritten notes, voice memos, even videos There are so many resources out there that we can learn from. You can upload everything from research papers to YouTube videos and even your own notes and scribbles. It actually understands handwriting surprisingly well! You get automatic summaries when you upload documents. The AI extracts key information immediately, giving you quick insights. It can also help you keep your documents organised. Use the Groups feature to sort and categorise your resources. Create a group for your literature review and keep these papers separate from your other projects or chapters. Tip: Overwhelmed by the number of papers in your "to-be-read" folder? Upload your papers to Anara for immediate insights on each of them, then use these to decide which ones you want to read in more detail. Quickly identify which papers are worth your time—thank me later! You can also go deeper into the papers with Anara’s chat feature. Instead of endlessly scrolling through documents to find relevant sections, just ask the AI a question based on your uploaded files. The chat provides direct answers, all with citations. ✅Suggests questions based on your prompt, helping you refine your focus ✅Everything is sourced directly from your documents. So no random AI-generated nonsense ✅Switch between different AI models to suit your needs. Some are better for summarisation, others for deeper contextual analysis It actually sticks to the sources you give it. My favourite feature is the ability to make flashcards! After you upload a document, Anara can create flashcards to help you test your understanding. Perfect for revision and retention. But… can you trust it? The problem with many AI research tools is hallucination... meaning that they make things up. Anara doesn’t do that. It reduces hallucinations by only referencing the documents you upload. Plus, it provides detailed references and hyperlinks so you can check the original source down to the exact page number. This doesn’t mean you shouldn’t read the paper for yourself. It does mean that you can find what you need much faster, and then verify it with automatic citations. At the end of the day, these tools are here to help you, not replace you. If you’ve made it this far, then it’s (definitely) time to go to 👇 anara(dot)so and give it a try. Use code THEPHDPLACE20 for 20% off

Remember that paper that started with ‘Certainly, here is a possible introduction for your topic’? How did that get past peer review?! I don’t want AI tools to do my research for me. I want AI tools to speed up boring tasks that take up my time, so I can focus on the important stuff. Anara moved to a new handle (formerly Unriddle) does exactly that. Here’s how you can use it for your research. 🧵👇 #SponsoredWalkthrough One of the biggest challenges in research is time. A solid literature review takes at least 2-3 months… sometimes even longer, depending on the depth of analysis needed. Reading, organising, and synthesising information is a slow process, but it’s absolutely necessary for high-quality work. AI can help speed it up. Not by replacing your critical thinking. It’s your PhD, your ideas need to be your own—but by automating the tedious, repetitive parts of research so you can focus on deep understanding, analysis, and writing. Unlike other AI tools, Anara works with almost any document format. This is what makes it really stand out from the rest. For instance, you can upload: ✅PDFs and other word-based documents ✅Images and presentations ✅Handwritten notes, voice memos, even videos There are so many resources out there that we can learn from. You can upload everything from research papers to YouTube videos and even your own notes and scribbles. It actually understands handwriting surprisingly well! You get automatic summaries when you upload documents. The AI extracts key information immediately, giving you quick insights. It can also help you keep your documents organised. Use the Groups feature to sort and categorise your resources. Create a group for your literature review and keep these papers separate from your other projects or chapters. Tip: Overwhelmed by the number of papers in your "to-be-read" folder? Upload your papers to Anara for immediate insights on each of them, then use these to decide which ones you want to read in more detail. Quickly identify which papers are worth your time—thank me later! You can also go deeper into the papers with Anara’s chat feature. Instead of endlessly scrolling through documents to find relevant sections, just ask the AI a question based on your uploaded files. The chat provides direct answers, all with citations. ✅Suggests questions based on your prompt, helping you refine your focus ✅Everything is sourced directly from your documents. So no random AI-generated nonsense ✅Switch between different AI models to suit your needs. Some are better for summarisation, others for deeper contextual analysis It actually sticks to the sources you give it. My favourite feature is the ability to make flashcards! After you upload a document, Anara can create flashcards to help you test your understanding. Perfect for revision and retention. But… can you trust it? The problem with many AI research tools is hallucination... meaning that they make things up. Anara doesn’t do that. It reduces hallucinations by only referencing the documents you upload. Plus, it provides detailed references and hyperlinks so you can check the original source down to the exact page number. This doesn’t mean you shouldn’t read the paper for yourself. It does mean that you can find what you need much faster, and then verify it with automatic citations. At the end of the day, these tools are here to help you, not replace you. If you’ve made it this far, then it’s (definitely) time to go to 👇 anara(dot)so and give it a try. Use code THEPHDPLACE20 for 20% off

The PhD Place

23,135 просмотров • 1 год назад

You can build an AI second brain in 15 minutes. No coding experience needed. no $1000 course [Here is how you can do it in 5 mins:] Step 1: Download Claude Desktop. Step 2: Download Obsidian Desktop. Step 3: Create a new vault and start dropping .MD files into it. Step 4: Tell Claude Code to connect directly to your vault using Andrej Karpathy's prompt: That is it. Your entire knowledge base becomes searchable, connectable, and queryable by the most powerful AI model on earth. Every note you have ever written. Every idea you have ever captured. Every resource you have ever saved. Claude can now read all of it, find connections you missed, and surface insights from your own thinking that you forgot you had. Most people are using Claude as a search engine. The people building second brains with it are using it as an intelligence layer on top of everything they know. The gap between those two use cases is the gap between asking Google a question and having a research partner who has read everything you have ever written. Bookmark this. Build it tonight.

You can build an AI second brain in 15 minutes. No coding experience needed. no $1000 course [Here is how you can do it in 5 mins:] Step 1: Download Claude Desktop. Step 2: Download Obsidian Desktop. Step 3: Create a new vault and start dropping .MD files into it. Step 4: Tell Claude Code to connect directly to your vault using Andrej Karpathy's prompt: That is it. Your entire knowledge base becomes searchable, connectable, and queryable by the most powerful AI model on earth. Every note you have ever written. Every idea you have ever captured. Every resource you have ever saved. Claude can now read all of it, find connections you missed, and surface insights from your own thinking that you forgot you had. Most people are using Claude as a search engine. The people building second brains with it are using it as an intelligence layer on top of everything they know. The gap between those two use cases is the gap between asking Google a question and having a research partner who has read everything you have ever written. Bookmark this. Build it tonight.

Avid

465,882 просмотров • 17 дней назад

You can build an AI second brain in 15 minutes. No coding experience needed. no $1000 course [Here is how you can do it in 5 mins:] Step 1: Download Claude Desktop. Step 2: Download Obsidian Desktop. Step 3: Create a new vault and start dropping .MD files into it. Step 4: Tell Claude Code to connect directly to your vault using Andrej Karpathy's prompt: That is it. Your entire knowledge base becomes searchable, connectable, and queryable by the most powerful AI model on earth. Every note you have ever written. Every idea you have ever captured. Every resource you have ever saved. Claude can now read all of it, find connections you missed, and surface insights from your own thinking that you forgot you had. Most people are using Claude as a search engine. The people building second brains with it are using it as an intelligence layer on top of everything they know. The gap between those two use cases is the gap between asking Google a question and having a research partner who has read everything you have ever written. Bookmark this. Build it tonight.

You can build an AI second brain in 15 minutes. No coding experience needed. no $1000 course [Here is how you can do it in 5 mins:] Step 1: Download Claude Desktop. Step 2: Download Obsidian Desktop. Step 3: Create a new vault and start dropping .MD files into it. Step 4: Tell Claude Code to connect directly to your vault using Andrej Karpathy's prompt: That is it. Your entire knowledge base becomes searchable, connectable, and queryable by the most powerful AI model on earth. Every note you have ever written. Every idea you have ever captured. Every resource you have ever saved. Claude can now read all of it, find connections you missed, and surface insights from your own thinking that you forgot you had. Most people are using Claude as a search engine. The people building second brains with it are using it as an intelligence layer on top of everything they know. The gap between those two use cases is the gap between asking Google a question and having a research partner who has read everything you have ever written. Bookmark this. Build it tonight.

Avid

35,283 просмотров • 1 месяц назад

PhD Students – How to convert your rough notes into a paper draft in 1 hour? Let’s first quickly understand the concept of rough notes. As a PhD student, you often read papers and make notes. Sometimes, you extract data from the papers you read. You record this data in an excel sheet. Irrespective of how you record your notes or data, you can convert it into a paper draft. More interesting, you can do this automatically. 𝐖𝐡𝐲 𝐭𝐨 𝐜𝐨𝐧𝐯𝐞𝐫𝐭 𝐲𝐨𝐮𝐫 𝐧𝐨𝐭𝐞𝐬 𝐢𝐧𝐭𝐨 𝐚 𝐩𝐚𝐩𝐞𝐫 𝐝𝐫𝐚𝐟𝐭? ➟ Difficult to comprehend your notes/data ➟ The notes are scattered in different places ➟ Need to synthesize a broader idea from your notes ➟ Need to write paper based on data noted 𝐇𝐨𝐰 𝐭𝐨 𝐜𝐨𝐧𝐯𝐞𝐫𝐭 𝐲𝐨𝐮𝐫 𝐧𝐨𝐭𝐞𝐬 𝐢𝐧𝐭𝐨 𝐚 𝐩𝐚𝐩𝐞𝐫 𝐝𝐫𝐚𝐟𝐭? 1. Go to and log in. 2. Open a blank document to start. 3. Click on 𝑊𝑟𝑖𝑡𝑒 on the right menu. 4. Click on 𝑢𝑝𝑙𝑜𝑎𝑑/𝑎𝑡𝑡𝑎𝑐ℎ button to upload your rough notes. 5. Use either pre-defined prompts or write your own. 6. For example, use the prompt – 𝐷𝑟𝑎𝑓𝑡 𝑎 𝑙𝑖𝑡𝑒𝑟𝑎𝑡𝑢𝑟𝑒 𝑟𝑒𝑣𝑖𝑒𝑤. 7. Paperpal will convert your notes a first draft of literature review. 8. Unlike ChatGPT, it will include real references your notes have. 9. You can give follow up instruction to modify the output. 10. For example, you can instruct – 𝑚𝑎𝑘𝑒 𝑜𝑢𝑡𝑝𝑢𝑡 𝑚𝑜𝑟𝑒 𝑐𝑜𝑛𝑐𝑖𝑠𝑒. 11. Paperpal will provide a concise alternative. 12. You can continue writing using the following options. ➝ Keep Writing ➝ Expand on ➝ Write a counter argument ➝ Write a transition sentence ➝ Write an impact statement 13. You can either accept or discard the generated text. Following this process will generate a paper draft for you. However, it is important to note that it is only a draft. You need to put your own human insight into it to polish it. You also need to carefully cross check the outputs. Please note this tutorial and tool is to facilitate you. It does not intend or even can replace you as a researcher. Try the Write 2.0 feature of Paperpal. Here is the link: Use code – FAHPP30 to get 30% off on Paperpal Prime. Anything you'd like to add?

PhD Students – How to convert your rough notes into a paper draft in 1 hour? Let’s first quickly understand the concept of rough notes. As a PhD student, you often read papers and make notes. Sometimes, you extract data from the papers you read. You record this data in an excel sheet. Irrespective of how you record your notes or data, you can convert it into a paper draft. More interesting, you can do this automatically. 𝐖𝐡𝐲 𝐭𝐨 𝐜𝐨𝐧𝐯𝐞𝐫𝐭 𝐲𝐨𝐮𝐫 𝐧𝐨𝐭𝐞𝐬 𝐢𝐧𝐭𝐨 𝐚 𝐩𝐚𝐩𝐞𝐫 𝐝𝐫𝐚𝐟𝐭? ➟ Difficult to comprehend your notes/data ➟ The notes are scattered in different places ➟ Need to synthesize a broader idea from your notes ➟ Need to write paper based on data noted 𝐇𝐨𝐰 𝐭𝐨 𝐜𝐨𝐧𝐯𝐞𝐫𝐭 𝐲𝐨𝐮𝐫 𝐧𝐨𝐭𝐞𝐬 𝐢𝐧𝐭𝐨 𝐚 𝐩𝐚𝐩𝐞𝐫 𝐝𝐫𝐚𝐟𝐭? 1. Go to and log in. 2. Open a blank document to start. 3. Click on 𝑊𝑟𝑖𝑡𝑒 on the right menu. 4. Click on 𝑢𝑝𝑙𝑜𝑎𝑑/𝑎𝑡𝑡𝑎𝑐ℎ button to upload your rough notes. 5. Use either pre-defined prompts or write your own. 6. For example, use the prompt – 𝐷𝑟𝑎𝑓𝑡 𝑎 𝑙𝑖𝑡𝑒𝑟𝑎𝑡𝑢𝑟𝑒 𝑟𝑒𝑣𝑖𝑒𝑤. 7. Paperpal will convert your notes a first draft of literature review. 8. Unlike ChatGPT, it will include real references your notes have. 9. You can give follow up instruction to modify the output. 10. For example, you can instruct – 𝑚𝑎𝑘𝑒 𝑜𝑢𝑡𝑝𝑢𝑡 𝑚𝑜𝑟𝑒 𝑐𝑜𝑛𝑐𝑖𝑠𝑒. 11. Paperpal will provide a concise alternative. 12. You can continue writing using the following options. ➝ Keep Writing ➝ Expand on ➝ Write a counter argument ➝ Write a transition sentence ➝ Write an impact statement 13. You can either accept or discard the generated text. Following this process will generate a paper draft for you. However, it is important to note that it is only a draft. You need to put your own human insight into it to polish it. You also need to carefully cross check the outputs. Please note this tutorial and tool is to facilitate you. It does not intend or even can replace you as a researcher. Try the Write 2.0 feature of Paperpal. Here is the link: Use code – FAHPP30 to get 30% off on Paperpal Prime. Anything you'd like to add?

Faheem Ullah

34,237 просмотров • 1 год назад

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

GREG ISENBERG

192,483 просмотров • 3 месяцев назад

Once you learn these three things, you can build nearly anything yourself. Skills, chains, and plugins. Learn them once and you can automate a real part of your week yourself. Here's the system: A skill is a standard operating procedure. It's one long, reusable prompt that does one thing well, like writing a newsletter, setting up a PPC campaign, triaging your inbox, or drafting a note to the board. You write it once and reuse it forever. The trick is to feed it your real work. For my writing skill, I gave Claude posts I admire and my own exported analytics with the winners marked, so it could see the patterns. Show it what good looks like and it nails your voice. Skip that step and it guesses. A chain connects skills into an automation. One skill's output feeds the next, in order. A copywriting skill writes the messaging, then hands it to a PPC-setup skill that builds the campaign. That's an automation, and you built it without writing code. A plugin is the harness that holds it all. It bundles your skills and chains into one thing you share with your team. Instead of sending 15 separate prompts around, you send one plugin, and it knows when to use each skill on its own. Skill, chain, plugin. It's one step up the ladder, and once you're up there it's easy. Now the worked example. I call it the Daily Driver. It's five skills chained together: email triage, a writer, Slack triage, a thinking partner, and a setup skill that connects my tools and personalizes everything. I chained them into one morning brief and scheduled it to run at 9am and message me the list. Two things made the biggest difference. Give it memory. I made plain docs for About Me, my Brand Voice, and my working preferences. I had Claude interview me and saved each answer as a file. Now every output sounds like me and knows my projects and my team. Connect your tools. The plugin reads my inbox, Slack, and calendar through MCP connectors for Gmail, Slack, and Google Calendar. That's what turns "summarize my morning" into a real brief instead of an empty wish. This is the highest-ROI thing an operator can set up this week. I run my YouTube channel, podcast, community, and newsletter on plugins I built myself, all on the $100 Max plan with no engineer in sight. I made a full walkthrough that shows the whole build start to finish. Want the Daily Driver plugin to start from? Comment PLUGIN below and I'll send it to you. #AI #automation #ClaudeCode

JJ Englert

53,778 просмотров • 27 дней назад

YOU CAN BUILD AN AI SECOND BRAIN IN 15 MINUTES. No coding experience. No $1000 course. Here is the entire setup. Step 1: Download Claude Desktop. Step 2: Download Obsidian. Step 3: Create a new vault and start dropping .MD files into it. Step 4: Tell Claude Code to connect to your vault using Karpathy's prompt: That is it. Your entire knowledge base becomes searchable, connectable, and queryable by the most powerful AI model on earth. Every note you have ever written. Every idea you have ever captured. Every resource you have ever saved. Claude reads all of it, finds connections you missed, and surfaces insights from your own thinking that you FORGOT you had. Most people use Claude as a search engine. The people building second brains use it as an INTELLIGENCE LAYER on top of everything they know. That gap is the gap between asking Google a question and having a research partner who has read everything you have ever written. Bookmark this. Build it tonight. Follow CyrilXBT

YOU CAN BUILD AN AI SECOND BRAIN IN 15 MINUTES. No coding experience. No $1000 course. Here is the entire setup. Step 1: Download Claude Desktop. Step 2: Download Obsidian. Step 3: Create a new vault and start dropping .MD files into it. Step 4: Tell Claude Code to connect to your vault using Karpathy's prompt: That is it. Your entire knowledge base becomes searchable, connectable, and queryable by the most powerful AI model on earth. Every note you have ever written. Every idea you have ever captured. Every resource you have ever saved. Claude reads all of it, finds connections you missed, and surfaces insights from your own thinking that you FORGOT you had. Most people use Claude as a search engine. The people building second brains use it as an INTELLIGENCE LAYER on top of everything they know. That gap is the gap between asking Google a question and having a research partner who has read everything you have ever written. Bookmark this. Build it tonight. Follow CyrilXBT

CyrilXBT

114,070 просмотров • 3 дней назад

Don't use ChatGPT for academic writing. It may get flagged as AI-generated text. Instead, use Paperpal — an AI-powered personal writing assistant. Especially if your first language is not English. Think of Paperl as the elder brother of Grammarly, especially designed for academic writing. Go to and sign up. You can either start writing in Paperpal or paste a text you have already written. Paperpal will evaluate your writing and give you detailed suggestions to improve it. If English is not your first language, you can write in your own language and Paperpal will translate it for you. You can also install a Paperpal add-in in MS Word. Click on "Insert" and then "Get Add-ins." Search for the Paperpal add-in, and press "Add." And you will have Paperpal inside MS Word. Paperpal will give you helpful suggestion as you write. You can write in your own language and get Paperpal to translate it into English. Paperpal supports following languages: Bulgarian, Chinese, Czech, Danish, Dutch, Estonian, Finnish, French, German, Greek, Hungarian, Indonesian, Italian, Japanese, Korean, Latvian, Lithuanian, Polish, Portuguese, Romanian, Russian, Slovak, Slovenian, Spanish, Swedish, Turkish, Ukrainian. Please note Paperpal's give you 500 free suggestions a month after which you will have to buy a subscription. If you use it judiciously, you won't have to pay any money. ----- Found this post helpful? 1. Retweet to share it with your friends. 2. Bookmark it so you can come back to it later. 3. Follow me for regular posts on how to supercharge your academic writing with AI-powered apps.

Don't use ChatGPT for academic writing. It may get flagged as AI-generated text. Instead, use Paperpal — an AI-powered personal writing assistant. Especially if your first language is not English. Think of Paperl as the elder brother of Grammarly, especially designed for academic writing. Go to and sign up. You can either start writing in Paperpal or paste a text you have already written. Paperpal will evaluate your writing and give you detailed suggestions to improve it. If English is not your first language, you can write in your own language and Paperpal will translate it for you. You can also install a Paperpal add-in in MS Word. Click on "Insert" and then "Get Add-ins." Search for the Paperpal add-in, and press "Add." And you will have Paperpal inside MS Word. Paperpal will give you helpful suggestion as you write. You can write in your own language and get Paperpal to translate it into English. Paperpal supports following languages: Bulgarian, Chinese, Czech, Danish, Dutch, Estonian, Finnish, French, German, Greek, Hungarian, Indonesian, Italian, Japanese, Korean, Latvian, Lithuanian, Polish, Portuguese, Romanian, Russian, Slovak, Slovenian, Spanish, Swedish, Turkish, Ukrainian. Please note Paperpal's give you 500 free suggestions a month after which you will have to buy a subscription. If you use it judiciously, you won't have to pay any money. ----- Found this post helpful? 1. Retweet to share it with your friends. 2. Bookmark it so you can come back to it later. 3. Follow me for regular posts on how to supercharge your academic writing with AI-powered apps.

Mushtaq Bilal, PhD

344,393 просмотров • 3 лет 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

55,345 просмотров • 1 месяц назад

PhD Students – How to identify related papers for your research paper? 1. Go to 2. Paste your DOI or title of your research paper 3. ResearchCollab will display the network of papers 4. Explore the network in following ways → Filter the network of papers based on research field → Filter the network based on thematic clusters → Filter the network based on keywords → and much more As you find related papers, keep saving them. This use case has multiple advantages. ➟ You won’t miss a related paper to cite in your paper ➟ You can identify related papers for your lit review ➟ You can explore new research directions and trends ➟ You can visualize the entire collection of papers As a researcher, work smarter not just harder. For this, try ResearchCollab:

PhD Students – How to identify related papers for your research paper? 1. Go to 2. Paste your DOI or title of your research paper 3. ResearchCollab will display the network of papers 4. Explore the network in following ways → Filter the network of papers based on research field → Filter the network based on thematic clusters → Filter the network based on keywords → and much more As you find related papers, keep saving them. This use case has multiple advantages. ➟ You won’t miss a related paper to cite in your paper ➟ You can identify related papers for your lit review ➟ You can explore new research directions and trends ➟ You can visualize the entire collection of papers As a researcher, work smarter not just harder. For this, try ResearchCollab:

Faheem Ullah

46,249 просмотров • 1 месяц назад

I just built a Brand Operating System inside Claude Cowork 🤯 A connected system of files that every skill automatically reads from, so your hook writer, brief generator, and script writer all speak in your exact brand voice. All inside Claude Cowork. Perfect for DTC brands and agencies tired of generic AI output that sounds like every other brand in their category. If you're opening a new Claude chat and re-explaining your brand, re-pasting your voice guidelines, and re-describing your customers every single time, a Brand OS fixes the entire loop: → Build 3 foundation files once per brand → Every skill you create reads from the Brand OS automatically → Hook writer pulls your voice + customer pain points → Brief generator pulls your positioning + angles → Script writer pulls the brief + brand DNA → Every output is calibrated to your brand on the first pass No re-briefing Claude on every chat. No editing for an hour to fix generic AI phrasing. No creative that sounds like it could belong to any brand. What you get in the playbook: → The exact Brand OS file structure I use → Templates for all 3 files you can fill in for any brand → The architecture that makes every Claude skill 10x sharper → The exact setup for agencies running a Brand OS per client For agencies: this is how you build a perfect, reusable knowledge base for every client on your roster. Set up the Brand OS once per client, and every campaign after that is already calibrated. I put together a full playbook with the file templates, the architecture, and the exact setup process so you can build your own Brand OS for your brand or your clients. Want it for free? > Like this post > Comment "OS" And I'll send it over (must be following so I can DM)

Mike Futia

30,159 просмотров • 3 месяцев назад

I built a Claude skill that turns Claude Code into your personal coding tutor. The core insight: Claude Opus 4.5 is already the best tutor in the world. Anthropic cooked with this model! It has incredible emotional intelligence and deep coding knowledge. What this skill does is just provide a harness—a way for Claude to agentically build the right context about YOU so it can personalize the tutoring experience in exactly the right way. Here's what makes it work: Learner profile from day one. The first time you use it, Claude interviews you. It asks about your programming background, your goal (where do you want this to take you?), and who you are as a person. This gets saved and informs every single tutorial it ever writes for you. From the very first interaction, everything is 100% personalized. Tutorials that use YOUR code. When you ask to learn something, Claude doesn't give you generic examples from some blog post. It finds examples in the actual codebase you're working in. This makes concepts stick in a way abstract examples never do. Quiz mode with spaced repetition. You can run "/quiz-me" and Claude will test you on concepts you've learned. It tracks your understanding score for each tutorial. Then it uses spaced repetition to prioritize the next quiz—concepts you're shaky on come back in 2 days, concepts you've mastered fade to 55+ day intervals. It literally builds retention into the learning process. One central knowledge base across all your projects. Whether you're joining a new company and want to understand their codebase, learning from an open source project, or leveling up on your own vibe-coded project—all your tutorials live in one place (~/coding-tutor-tutorials/). So your personal coding-tutor accompanies you across all your coding adventures. The whole thing is a feedback loop: learn → quiz → retain → learn more → quiz → retain. Your tutorials evolve, your knowledge compounds, and Claude gets better at teaching YOU specifically over time. To install it in Claude Code: • Run /plugin to open the plugin manager • Add marketplace nityeshaga/claude-code-essentials • Enable coding-tutor plugin Here's the Github: And here's 20-mins of me walking you through how to use this plugin and how it works 👇🏽 Let me know if you use it to teach yourself something cool!

Nityesh

64,494 просмотров • 6 месяцев назад

REAL ESTATE PEOPLE WILL HATE HIM FOR THIS. HE BUILT A CLAUDE AGENT THAT TURNS ANY LISTING INTO A SELLABLE VIDEO ON ITS OWN Playbook: connect Claude to a video generator, paste a listing, get a cinematic tour of every room, sell it to the agent But typing the prompt for every listing doesn't scale. He turned it into a skill his Claude runs on its own Here's how to build the automated version: 1. Connect the video engine once. In Claude, go to Customize, Connectors, Add Custom Connector, name it Higgsfield, and paste the server URL from higgsfield. ai/mcp. Authenticate through your account. No API keys. Now Claude can generate video straight from chat 2. Turn the workflow into a skill. Instead of pasting the same prompt every time, have Claude build a skill. Tell it: "Create a skill called listing-to-video. When I give it a listing URL, scrape the room photos, generate a cinematic clip of each room with Higgsfield, and save them to a folder." Now the whole process is one command, not a wall of text 3. Let the agent run the listing. Hand it a URL and say "run listing-to-video on this." It pulls the photos, fires each room through the video model, and brings the clips back. You wrote the prompt once, inside the skill. You never write it again 4. Stitch and deliver. Drop the clips together into one tour. Send a free sample to the listing's agent, then charge per video or a monthly rate for ongoing listings 5. Scale it with your team. Add a skill that drafts the outreach email and one that builds a simple landing page for the agent. Now one operator runs sourcing, production, and pitching from a single Claude session The edge isn't generating one video. It's building the skill once so every future listing runs itself Bookmark this

REAL ESTATE PEOPLE WILL HATE HIM FOR THIS. HE BUILT A CLAUDE AGENT THAT TURNS ANY LISTING INTO A SELLABLE VIDEO ON ITS OWN Playbook: connect Claude to a video generator, paste a listing, get a cinematic tour of every room, sell it to the agent But typing the prompt for every listing doesn't scale. He turned it into a skill his Claude runs on its own Here's how to build the automated version: 1. Connect the video engine once. In Claude, go to Customize, Connectors, Add Custom Connector, name it Higgsfield, and paste the server URL from higgsfield. ai/mcp. Authenticate through your account. No API keys. Now Claude can generate video straight from chat 2. Turn the workflow into a skill. Instead of pasting the same prompt every time, have Claude build a skill. Tell it: "Create a skill called listing-to-video. When I give it a listing URL, scrape the room photos, generate a cinematic clip of each room with Higgsfield, and save them to a folder." Now the whole process is one command, not a wall of text 3. Let the agent run the listing. Hand it a URL and say "run listing-to-video on this." It pulls the photos, fires each room through the video model, and brings the clips back. You wrote the prompt once, inside the skill. You never write it again 4. Stitch and deliver. Drop the clips together into one tour. Send a free sample to the listing's agent, then charge per video or a monthly rate for ongoing listings 5. Scale it with your team. Add a skill that drafts the outreach email and one that builds a simple landing page for the agent. Now one operator runs sourcing, production, and pitching from a single Claude session The edge isn't generating one video. It's building the skill once so every future listing runs itself Bookmark this

Yarchi

54,531 просмотров • 1 месяц назад