Notebook LM

Notebook LM AI

Notebook LM AI

Notebook LM AI

Notebook LM AI

Notebook LM AI

Notebook LM AI

Notebook LM AI

Notebook LM AI

Notebook LM AI

Notebook LM AI

built a Claude Code plugin that turns any website into a Python CLI (19 generated so far)

most web apps don't have public APIs. so I built a plugin that watches you use a site in a browser, captures all the HTTP traffic, figures out the protocol, and writes a full Python CLI from it. auth, tests, --json everywhere. it also writes a SKILL.md for each generated CLI, so Claude can call them on its own without extra prompting. ask "find me a hotel in Paris under 200", it runs the booking CLI by itself. the harder parts: bypassing Cloudflare and AWS WAF, decoding Google's batchexecute RPC, handling auth cookie refresh without user interaction. 19 sample CLIs in the repo so people can see how each protocol is handled (Reddit, NotebookLM, Booking, Airbnb, ChatGPT, Stitch, Capitol Trades, LinkedIn, and others). open source, MIT, no affiliation with any of those sites. repo: https://github.com/ItamarZand88/CLI-Anything-WEB would love feedback, especially on which sites you'd want it pointed at next. submitted by /u/zanditamar [link] [comments]

I built a marketplace for AI agent skills and grew it to 17K users with $0 on ads. ChatGPT did all the SEO and content. Here's the full playbook.

I'm a solo non-technical founder. I built a marketplace called Agensi for SKILL.md skills (the files that teach AI coding agents like Codex CLI, Claude Code, and Cursor new capabilities). I'm not a developer. The entire product was built with AI tools. But this post isn't about that. This post is about how I used ChatGPT to build and execute a content strategy that took the site from zero to 17K active users, 559K Google impressions per month, and 509 indexed pages in about 8 weeks. No ad spend. No marketing team. No SEO consultant. I want to share the exact system because I think most people building with AI are focused on the product side and completely ignoring the growth side, where ChatGPT is arguably even more useful. I don't write content. I write data analysis prompts. The biggest mistake people make with AI content is asking it to "write me a blog post about X." That produces generic slop that Google doesn't rank and nobody reads. Instead, I export my Google Search Console data every week. Queries, impressions, click-through rates, average positions. I dump it into ChatGPT and ask it to find three things: Queries where I have high impressions but almost zero clicks (meaning my title doesn't match what people are searching for) Queries where I have zero content but Google is already showing my site (meaning Google thinks I should rank but I have nothing to rank with) Queries where multiple pages on my site compete against each other (cannibalization) ChatGPT comes back with a prioritized list. Today it found 42 queries about SKILL.md YAML frontmatter specs generating 9,563 impressions and literally 1 click. My existing page didn't answer what people were actually searching for. A 20-minute rewrite targeting the actual search intent will likely 10x the clicks from that page alone. That's not content creation. That's data analysis that happens to produce content as output. The AEO angle that most people are sleeping on Here's what surprised me. ChatGPT, Gemini, Perplexity, and Claude are now sending us direct traffic. Real users clicking through from AI-generated answers. Last 28 days: AI Source Users ChatGPT 159 Gemini 75 Perplexity 69 Claude.ai 60 Others (Doubao, Copilot, You.com, Felo, NotebookLM) 22 Total 385 That's 385 users per month from AI answer engines. More than LinkedIn, Instagram, and all newsletters combined. And it's growing fast. How we did it: every page on the site has FAQPage JSON-LD schema with short, direct answers. When someone asks ChatGPT "where can I find SKILL.md skills" or asks Perplexity "what is the best AI agent skills marketplace," the structured data makes it easy for the model to cite and link to us. We also restructured every article heading as a question instead of a statement. Not "Claude Code Skill Locations" but "Where Does Claude Code Store Skills?" AI Overviews and answer engines prefer extracting from question-format sections. This is basically SEO for LLMs. I'm calling it AEO (answer engine optimization). Nobody is really doing this systematically yet, which means there's a window right now where the effort-to-result ratio is insane. ChatGPT as a technical SEO auditor Every week I also dump the data and ask ChatGPT to audit the technical health. Things it's caught that I never would have found on my own: It found that 121 queries where I ranked position 1-3 had zero clicks because AI Overviews were answering the question directly from my content. Google was showing the answer without users needing to click. That insight changed my entire strategy from trying to rank #1 to trying to become the source that AI Overviews cite. It found three pages with 52,000 combined impressions getting 56 total clicks. The content was fine. The titles were wrong. ChatGPT rewrote the titles and meta descriptions to match the actual search queries, not what I thought sounded good. It found 4 pages returning 404 errors, a soft 404, a duplicate page without a canonical tag, and a page that was somehow indexed while also being blocked by robots.txt. Wrote the fix prompts, I pasted them into my builder, deployed in 10 minutes. It diagnosed a duplicate FAQ schema issue where React components were emitting FAQ data client-side AND the server-side edge function was also emitting it. Google was seeing double schemas on 90 pages. ChatGPT identified the exact files causing the conflict and wrote the fix. None of these are things I would have caught manually. ChatGPT finds patterns in the data that a human eye just skips over. The structured data layer Every page type on the site has specific schema markup: The homepage has Organization, WebSite with SearchAction, and FAQPage. Individual skill pages have SoftwareApplication with pricing, BreadcrumbList, and conditional FAQPage. Article pages have Article, FAQPage, HowTo where relevant, BreadcrumbList, and Organization. The /about page has Organization, AboutPage, and Person schema for

zettelkasten style knowledge matrix

Hi friends, I've been circling through a personal curriculum for a while now and I'm trying to set up a system where Claude helps me synthesize memory dumps into an atomic note system I can refer back to. Similar to NotebookLM, but with Claude as the interface and my personal files as the source base, with proper citations. I know in theory how to take notes and how to adjust note structure based on topic. What I'm not sure about is the folder architecture. How do I set up a Google Drive system that functions as a second brain across all of it? I've read about Obsidian as a second brain solution. Has anyone built something comparable in Drive (Google or Microsoft), and what would you carry forward into a personal knowledge base set up this way? I have Claude doing a deep search + report on his take with the following prompt: "Research Obsidian & Zettelkaten methodology and compare to Google Drive + Zettelkasten Atomic Notes system design. I'm looking to design a second brain system that prioritizes keeping my files either on a hard drive or a cloud system (or both, for the sake of back up and safety). Compare and contrast different designs for Second Brain systems and give the report in a mark down file with citations at the bottom so it's Speechify friendly. The goal is to help me design a system that compliments the Zettelkaten & knowledge-synthesis skills we've designed previously." (I then uploaded the two skill files so Claude knew what to calibrate the system suggestions toward). While waiting for him, I thought I would poke Reddit's brain and see what fellow humans set up and think about second brain systems. submitted by /u/Crazy_Buffalo3782 [link] [comments]

AI Podcasts made learning economics way less painful for me

I’m basically a total beginner when it comes to finance and economics maybe 2 or 3 months ago, and honestly trying to learn from reports or books used to completely destroy me. Too many charts, numbers, random terms I have to Google every 2 minutes. And I started using AI Podcast to kind of brute force my way into learning this stuff, and I’m honestly surprised by how much it helped. Instead of sitting there suffering through a 70-page report, I can turn it into conversational audio and just listen while driving or walking around. But those tools actually feel slightly different. Like NotebookLM feels more “AI teacher explains the document to you.” It’s really good at organizing information and walking through the important points clearly. And I enjoy Genspark AI Pods more because it feels more like an actual show or podcast episode. The tone feels lighter, less dry, less like I’m studying for an exam. Sometimes it genuinely just sounds like casually discussing the topic instead of reading a report at me. Not saying this magically turned me into some economics genius lol. But it definitely made learning feel way less painful and boring. submitted by /u/EHOON [link] [comments]

Mind Maps are getting a major glow up 💅 These new features are rolling out today: 🚗Customization: Steer your map with specific user prompts 📂Organization: Rename and Share your maps instantly 🗺️

Mind Maps are getting a major glow up 💅 These new features are rolling out today: 🚗Customization: Steer your map with specific user prompts 📂Organization: Rename and Share your maps instantly 🗺️ Navigation: Silky smooth transitions between nodes Let us know what you think! https://t.co/zwnmkt0oYo

How to effectively use Cowork

I have backround in Engineering/DevOps. I have used Claude Code for past six months. Now i try to shift and upgrade my flow with centralized knowledge vault with Obsidian, custom skills, hooks etc. It feels naturally to split work around vault to Cowork and leave Code for implementation, but for now I have big issues with Cowork as it cannot: - run anything on my machine, even simple bash scripts are spitted in text with instruction how to run them. - sandbox limited to specific folder so the only way is to give it access to ~/ which sounds like a horrible idea. - not shared config, everything like plugins, skills etc have to be installed twice for both Cowork and Code. I see a benefit of sandbox but it seems the best use for me would be to use it simillar to NotebookLM mcp, where I would call Cowork for precise operation or query on my vault. submitted by /u/Valgav [link] [comments]

Building A Claude Brain

I learned how someone was using NotebookLM to use valuable sources to ask how to effectively prompt Claude and when to use Chat, CoWork, and/or Code. What are some important documentation I should use to build something similar? I’m open to online documentation and YouTube links. I know this is something that would need to be updated often but starting with insightful and detailed information. Thanks. submitted by /u/roncee [link] [comments]

How to Export Claude Conversations to PDF or Markdown

I've seen people asking how to save or share their Claude chats, copy-pasting the whole thing manually is painful, and Claude doesn't have a native export option. I built a Chrome extension that adds this. It's called Superpower for Claude. What the export does: Claude to PDF: Clean, formatted output. Good for saving a thread as a proper document, printing it, or sharing it with someone outside Claude. Claude to Markdown: Great for feeding it into another AI as context, droping it into NotebookLM, Obsidian, Notion, wherever you work. How to use it: Install the extension here Open any Claude conversation Click the export button (choose PDF or Markdown) Done It runs locally in your browser (privacy-first). I built this because I needed a way to save my coding sessions without copy-pasting manually. Let me know if you run into any bugs or have feature requests! Link: Superpower for Claude on Chrome Web Store submitted by /u/Kindly_Revenue3077 [link] [comments]

Problem with MCPs and authentication error in HPC system

Hi, I'm working on my university's HPC and want to connect NotebookLM MCP to Claude code. The HPC lacks X11 forwarding, so I can't log in via browser during authentication, and my personal computer is too constrained for the current project. I'm seeking alternatives. I came up with the idea of automating the sync between the local folder and the HPC to maintain authentication, but I'm unsure whether it will work. submitted by /u/Alive_Society2375 [link] [comments]

Best suited model for solo Dev

Hey everyone! I've kinda new to Claude, I've only had few chats with it but nothing too deep like projects etc. I have an upcoming interview for a Frontend Developer role which specifically states using Claude. I do not particularly know for which part of their product they use it as they don't have the role advertised, headhunter got me. I'm doing their free courses as fast as possible so I won't go in blindly. I'm using AI to code, learn, research, daily questions, brainstorming. But I try to avoid full agentic coding so I can actually get good fundamentals and be a proper programmer. For coding I mainly used it inside VSCode as I paid for Copilot so had a set amount of requests. Mostly the number of requests were enough for me, I only went over once when I got locked in developing an app I can use myself daily so it grow a bit bigger than a simple MVP. For a general Chat interface I've started with ChatGPT and ended up with Google AI Pro as I'm in that ecosystem so don't want to pay for the ChatGPT too. I've never paid for Claude directly or for Claude Code so here I'm asking your advice. For learning purposes and develop 1-2 apps as projects for learning and to my portfolio which plan would be sufficient enough? I'm talking about few months only paid out of my own pocket. I still have Copilot until end of June. (then I will cancel it probably) I'm currently not working as a Dev but doing all in my free time next to an irrelevant job. My coding time will be pretty much limited to a few hours a day. I've not used Antigravity or Jules or any other AI Google has, I've only used Gemini and NotebookLM, I went for the AI Pro plan mostly because needed more storage from 200 GB to 2 TB and now they just added 3 TB extra to it so ended with 5 TB. As I said I'm in the Google ecosystem so there is that. I plan to use their whole services but I'm not particulary keen on just going full on with AI coding as I've only had 4 months professional Developer experience and it sadly ended with the project being stopped. I'm learning Frontend and a bit of Backend for years next to a full time job focusing on Angular mostly. So any advice would help to be a bit better with Claude so I can be a successful Junior in a team. If you have course advice on the top of their free courses feel free to recommend. Thanks in advance! submitted by /u/syzgod [link] [comments]

Best AI to "teach" me from a PDF textbook? (Self-studying Uni course)

I’m currently self-studying a university course and hitting a wall just reading the textbook. I have the PDFs, but I’m looking for an AI where I can upload the files and have it actually teach me interactively—not just give me "key points" or summaries. Ideally, I want to be able to: Go through the book section by section. Ask it to "explain this like I'm 5" or give real-world examples. Have it quiz me on specific details to make sure I actually get it before moving on. Ask follow-up questions when a concept doesn't click. Has anyone found a tool that handles large PDFs well and acts more like a tutor than a search engine? I've started using NotebookLM, the podcast feature is cool but looking for something I can have a conversation with that can go through the pdf completely unit by unit. submitted by /u/StoTonho [link] [comments]

Mo sources mo problems? Not anymore: Rolling out now, NotebookLM can auto-label & categorize sources (when you have 5+), so you can spend less time scrolling and more time thinking/learning/philo

Mo sources mo problems? Not anymore: Rolling out now, NotebookLM can auto-label & categorize sources (when you have 5+), so you can spend less time scrolling and more time thinking/learning/philosophizing, etc. Rename, reorganize, & personalize (emojis!) to your ❤️'s content. https://t.co/WiY58zkQJU

FOSS NotebookLM with no data limits

NotebookLM is one of the best and most useful AI platforms out there, but once you start using it regularly you also feel its limitations leaving something to be desired more. There are limits on the amount of sources you can add in a notebook. There are limits on the number of notebooks you can have. You cannot have sources that exceed 500,000 words and are more than 200MB. You are vendor locked in to Google services (LLMs, usage models, etc.) with no option to configure them. Limited external data sources and service integrations. NotebookLM Agent is specifically optimised for just studying and researching, but you can do so much more with the source data. Lack of multiplayer support. ...and more. SurfSense is specifically made to solve these problems. For those who dont know, SurfSense is open source, privacy focused alternative to NotebookLM for teams with no data limit's. It currently empowers you to: Control Your Data Flow - Keep your data private and secure. No Data Limits - Add an unlimited amount of sources and notebooks. No Vendor Lock-in - Configure any LLM, image, TTS, and STT models to use. 25+ External Data Sources - Add your sources from Google Drive, OneDrive, Dropbox, Notion, and many other external services. Real-Time Multiplayer Support - Work easily with your team members in a shared notebook. Desktop App - Get assistance in your OS. Check us out at https://github.com/MODSetter/SurfSense if this interests you or if you want to contribute to a open source software submitted by /u/Uiqueblhats [link] [comments]

Are “AI stacks” actually better than using a single model for academic work?

Hey everyone, I’ve been experimenting with different AI tools for university work, and I keep seeing people recommend using a “stack” (e.g., ChatGPT + Claude + Perplexity + NotebookLM), where each tool is used for a specific task. However, I’m starting to wonder if this is actually more efficient, or just overcomplicating things. From my experience, switching between tools can: Break workflow continuity Create inconsistencies in outputs Add friction when managing sources and drafts At the same time, different models clearly excel at different things (reasoning, writing style, sourcing, etc.). So I’m curious: 👉 Do you think using multiple AI tools is genuinely better for academic work, or is it mostly overkill? 👉 Has anyone tried sticking to a single model and optimizing around it instead? Interested in hearing real experiences, especially from students or researchers. submitted by /u/Party_Advantage_5136 [link] [comments]

An AI Equity Research Framework: Design Principles and Setup

I used Claude Code + two MCP tools + a ~400-line protocol file to run deep equity research on Chinese A-share stocks. The workflow starts by enabling Plan Mode, then giving the agent a single instruction: "Follow Deep Research.md strictly, and conduct deep research on [ticker]." This post covers: the core principles behind the protocol design, and the two MCP tools that support it. Note — A-share specific: This setup is built around the Chinese A-share market. The protocol (Deep Research.md) is market-agnostic, but the two MCP tools are not. For US equities, you'll need to swap out ashare-mcp for a different financial data pipeline (SEC EDGAR, a brokerage API, or something like SimFin / Financial Modeling Prep). NotebookLM MCP works as-is for any language. A few other differences worth considering: A-share filings are in Chinese, so your retrieval and parsing tools need to handle that; liquidity and disclosure standards differ; and the grassroots signal sources (Phase 3.5 in the full protocol) will need to be substituted with US equivalents. Part 1: Core Principles The protocol is built around four principles. Each one exists to counter a specific failure mode of LLMs doing open-ended research. 1. Define "Done" Before You Start LLMs are next-token predictors. Without a concrete target, "analyze this company" matches the most statistically common pattern in training data: introduce the company, list metrics, discuss competitive landscape, give a vague conclusion. That output is probable, not useful. The protocol requires a falsifiable research question with a measurable threshold before any retrieval begins — locked in a research contract the agent cannot skip. "CAGR ≥8%" is verifiable. "Decent growth" is not. The agent needs to know what counts as done, or it will optimize for looking like a complete analysis rather than being one. 2. Anchor Hypotheses Before Seeing the Data Once you've read everything, rationalization is easy. The protocol requires the agent to write 3–5 hypotheses and assign prior probabilities to each before any retrieval — and to specify, for each one, what evidence would trigger a revision. Using probabilities rather than just listing hypotheses matters for a specific reason: a list of hypotheses costs nothing — the model can write five and "confirm" all of them. A probability externalizes judgment. Before any evidence arrives, you've committed to a degree of belief for each hypothesis. This forces belief updating to be explicit. If H2 starts at 40% and ends at 40% after a full investigation, that's a finding. If it drops to 15%, that's the conclusion. The prior is the anchor that prevents the model from reading everything and then working backwards to fit the hypotheses to the answer. 3. Grade Claims, Not Just Sources Not all assertions carry the same weight. The protocol classifies every claim into three tiers: C1 (Critical): Revenue, profit, cashflow figures; market share; core thesis; valuation inputs — requires ≥2 independent sources, or must be explicitly flagged as "single source, high uncertainty" C2 (Supporting): Industry trends, event timelines, non-critical comparisons C3 (Background): Definitions and common knowledge "Independent" means different organizations and different data collection methods. Two sell-side analysts citing the same company filing are not two independent sources. Citation drift in practice looks like this: the agent writes "the company holds approximately 35% market share," attaches a link to an analyst report, and all subsequent reasoning builds on that 35% — but that report was itself citing the company's own IR materials, and management's market share definition differs from third-party research firms by 8 percentage points. This rule exists to catch that before it propagates. 4. Isolate the Red Team Confirmation bias in LLMs is structural, not incidental. An agent that built the bull case has its context loaded with supporting reasoning chains. Ask it to challenge itself and its next-token predictions are already anchored toward the thesis it just constructed. The protocol runs a mandatory adversarial review using a separate subagent with a clean context — one that reads only raw data and working notes, never the main report. This subagent must produce ≥3 substantive rebuttals with evidence. The main context then adjudicates each challenge with a formal verdict (accept / partial accept / reject), and "reject" requires ≥2 sourced counter-evidence entries. Context isolation is a structural defense against confirmation bias, not a stylistic choice. Part 2: The Two MCP Tools The principles define what the protocol should do. The tools determine what data it can reach. Both are necessary: a rigorous protocol running on patchwork data will exhaust its context window assembling financials before any analysis begins; abundant data without protocol constraints just produces wrong answers faster and with more confidence. The two