Transform ANY Content into Clean Markdown with Markdownify MCP... Server Think about the last time you struggled with file conversions. Now imagine: File Conversions: - PDFs → Markdown - Images → Text - Audio → Transcripts - Office docs → Clean text Web Content: - YouTube videos - Search results - Web pages - Existing Markdown Why it matters: • Single solution for multiple formats • Clean, readable output • Simple integration • Developer-friendly setup The beauty isn't just WHAT it converts, but HOW EASILY it does it. Ready to transform your content? Three steps using Cline: 1. Clone the repo 2. Install dependencies 3. Build & Run Remember: Great tools don't just solve problems—they eliminate them entirely.show more

Here's how you can use the Firecrawl MCP server... to turn online docs into a functional .clinerule in minutes. This makes Cline a pro in any library you're using.show more

Web Scraping is dead. Web Agenting is here. Writing... selectors (div > .class > span) breaks every time a site updates. Building custom bots for every new target is a waste of engineering hours. TinyFish turns the entire real-time web into a single API. Input: Natural Language ("Find availability for X"). Target: 1 or 100 URLs. Output: Structured JSON. This isn't a simulation. It visits the Real-Time Web. 1./ One API, Many Sites - Same contract whether you hit 1 URL or 50. You focus on the Goal (Business Logic). TinyFish handles the How (Navigation, Clicks, Inputs). 2./ Real Automation - It doesn't just "read." It interacts. It fills forms, navigates multi-step flows, and handles dynamic JS content. 3./ Production Ready - This is the same infrastructure used by large enterprises, now exposed as a clean developer primitive. Logs, error handling, and structured data are built-in. The web is finally a proper API.show more

1. Model agnostic. 2. Inference agnostic. & now, 3.... Platform agnostic. Cline for JetBrains is here. (install it below)show more

A New Era with V3🪄 V3's new engine introduces... significant advancements in output generation. Unlike V2, where the multi-model system processed prompts to produce a single output, V3 is designed to generate multiple outputs and logically link them together. This enhancement effectively removes limitations on output size, enabling more complex and expansive results. Key Features Seamless Multi-Output Generation: V3 has been trained to generate separate outputs and connect them logically. This advancement ensures that there are no longer any limitations on output size. Intelligent Image Creation: V3 improves image generation with better tools, allowing the AI to create as many images as needed and place them within the project’s context. It supports various formats like PNG, JPEG, SVG, and GLB. Web-Integrated Intelligence: V3 can now search the web for documentation and data, providing real-time context and up-to-date references. For example, if you run a restaurant and want to update your website, simply ask Alchemist AI to “generate this website in a more modern style,” and it will update all content accordingly. Improved Creativity and Output Quality: V3's creative capacity has significantly increased. Simple prompts now generate more complete and refined results, with the system efficiently combining multiple elements into cohesive outputs.show more

my team didn't want me to give this away... for free. But I'm going to do it anyway it's the SEO & AI search dashboard I built in Claude Code it connects to your Google Analytics (GA4) and Google Search Console and Claude Code builds it in 5 minutes and I made a Notion document and a skill file so you can build this in Claude Code yourself in literally minutes the dashboard has three tabs: 1. AI Search - How much traffic is coming from ChatGPT, Perplexity, and Gemini ETC. It aggregates the GA4 data and gives single number 2. Paid ads - which keywords rank top 3 for but still pay for ads on, you should cut these to save budget 3. Organic overview - sessions, conversions, top landing pages, demographics. The single view for what is working I built this because this is how I drive our SEO and AEO forward it gives me the insights I need to allocate budget and prioritize what content to work on next I decided to give it away because most companies have no idea AI search is already sending them traffic like this post and comment "AEOdashboard" and I'll send it overshow more

Here's how you can use the Gemini CLI provider... in Cline: 1. Run the following in your terminal: npm install -g @google/gemini-cli gemini 2. Follow the prompts to log in with your personal Google account 3. Select the "Gemini CLI" provider in Cline's settingsshow more

Big moment for text-to-speech. Qwen just open-sourced a text-to-speech... model that lets you clone voices, design new ones, and control speech using natural language. Let me explain what I mean: You can literally tell it "speak in a cheerful tone with slight nervousness," and it actually does that. No complex audio engineering needed. What makes this special: - 3-second voice cloning - Covers 10 languages: English, German, French, and more - Latency as low as 97ms for real-time applications - Supports both streaming and non-streaming generation The model comes in two sizes (0.6B and 1.7B parameters), so you can pick based on your hardware and quality needs. Three modes to work with: 1. Custom Voice: Use pre-built premium voices with instruction-based style control 2. Voice Design: Describe the voice you want in plain English (or Chinese), and the model creates it 3. Voice Clone: Provide a 3-second reference audio and clone that voice The best part? It integrates with vLLM for production deployment and has a simple Python package you can pip install. I've shared a link to the GitHub repo in the next tweet.show more

HERMES AGENT HAS A SECOND BRAIN. 1,100+ KNOWLEDGE FILES.... AUTO-LINKED. SELF-IMPROVING. GROWING EVERY NIGHT. THIS IS THE OBSIDIAN GRAPH BEHIND IT. every dot = one knowledge file (markdown) every line = one wiki-link between files every color = one category (skills, notes, decisions, sources, entities) HOW IT BUILDS ITSELF: Hermes ships with a bundled LLM Wiki skill. based on Andrej Karpathy's pattern. unlike RAG (rediscovers knowledge from scratch every query), the wiki compiles knowledge once and keeps it current. when you feed the agent a source: → it reads the content → writes a structured markdown page → auto-links to every related existing page → flags contradictions with previous entries → updates all affected pages one source in. multiple connections created. the graph grows denser with every entry. WHAT FEEDS THE WIKI: → articles and URLs you find interesting → meeting transcripts → PDF documents and research papers → conversation history from Hermes sessions → Claude Code and Codex session history → Slack logs, email threads, saved notes → YouTube transcripts → raw text dropped into a _raw/ folder the obsidian-wiki package supports multi-agent ingest from Hermes, Claude Code, Codex, OpenClaw, Pi, Windsurf, and ChatGPT exports. install: pip install obsidian-wiki obsidian-wiki setup --vault ~/wiki AUTOMATE THE GROWTH: set cron jobs to feed the wiki overnight: "every day at 9am, check for new meetings. ingest transcripts into the wiki." "every week, check arXiv for new papers in [niche]. summarize and file into the wiki." "every day, ingest today's Hermes sessions into the wiki under session-history." month 1: 50 entries. scattered. month 3: 300+ entries. cross-referenced. month 6: 1,000+ entries. the agent surfaces patterns you never searched for. WHY OBSIDIAN: the wiki is plain markdown files. no database. no lock-in. open it in Obsidian for graph view: → nodes show knowledge density → links show how ideas connect → clusters reveal your strongest domains → orphan nodes reveal gaps Hermes writes from a VPS. Obsidian reads on your laptop. obsidian-headless syncs without a GUI. agent writes from the server, you browse on your device. FOUR MEMORY LAYERS: Layer 1: memory.md + user.md (~2,200 + 1,375 chars. short-term.) Layer 2: SQLite with FTS5 (full session transcripts. searchable.) Layer 3: external providers (Mem0, SuperMemory, Honcho. optional.) Layer 4: Obsidian wiki via LLM Wiki skill (unlimited. compounding. the long-term brain.) layers 1-3 handle memory. layer 4 handles knowledge. the graph in this post is layer 4. SETUP: set in Desktop app, Dashboard, or config.yaml: WIKI_PATH=~/wiki OBSIDIAN_VAULT_PATH=~/wiki first run: Hermes asks for your domain. answer with your niche. the skill builds SCHEMA.md with tag taxonomy. after that: "index this into my wiki: [URL or text]" the wiki grows. the graph densifies. the agent gets smarter because the knowledge base got smarter. full 15 levels breakdown in the article 👇show more

Big moment for Postgres! Search has always been Postgres'... weak spot, and everyone just accepted it. If you needed a real relevance-ranked keyword search, the default answer was to spin up Elasticsearch or add Algolia and deal with the data sync headaches forever. The problem isn't that Postgres can't do text search. It can. But the built-in `ts_rank` function uses a basic term frequency algorithm that doesn't come close to what modern search engines deliver. So teams end up: - Running a separate Elasticsearch cluster just for search - Building sync pipelines that inevitably drift out of consistency - Paying for managed search services that charge per query - Accepting mediocre search relevance because "good enough" ships faster But this is actually a solvable problem. You can realistically bring industry-standard search ranking directly into Postgres, which eliminates the need for external infra entirely. This exact solution is now available with the newly open-sourced pg_textsearch by Tiger Data - Creators of TimescaleDB, a Postgres extension that brings true BM25 relevance ranking into the database. BM25 is the algorithm behind Elasticsearch, Lucene, and most modern search engines. Now it runs natively in Postgres. Here's what pg_textsearch enables: - True BM25 ranking with configurable parameters (the same algorithm powering production search systems) - Simple SQL syntax: `ORDER BY content 'search terms'` - Works with Postgres text search configurations for multiple languages - Pairs naturally with pgvector for hybrid keyword + semantic search That last point matters a lot for RAG apps. The video below shows this in action, and I worked with the team to put this together. You can now do hybrid retrieval (combining keyword matching with vector similarity) in a single database, without stitching together multiple systems. The syntax is clean enough that you can add relevance-ranked search to existing queries in minutes. pg_textsearch is fully open-source under the PostgreSQL license. You can find a link to their GitHub repo in the next tweet.show more

Kimi just wiped out Google Docs, Excel, and PowerPoint... in one quiet update. While most people still treat AI like a fancy typing assistant, Kimi Premium shipped something completely different: AI that creates finished professional files directly from conversation. Not exports. Not drafts. Real Excel, Word, PPT, and PDFs built at the structural level. Ask for five years of financials on the Magnificent Seven and it doesn’t summarize. It builds a multi-sheet Excel workbook with balance sheets, income statements, cash flows, clean formatting, and sourced data like a senior analyst prepared it. Someone asked for a DCF on Apple. Kimi produced a full valuation model with assumptions, discount rates, scenario analysis, and citations. Another user fed in one payroll Excel file and got 30 individualized, encrypted PDF payslips out. End-to-end automation. It gets crazier. It can generate images inside spreadsheets for storyboards. One prompt produced a 20-shot video storyboard Excel with timing, visuals, voiceover text, sound cues, and consistent sketch images per frame. This isn’t better chat. It’s execution replaced. Kimi doesn’t give you content to paste into tools. It gives you the tools’ final output. That’s the shift everyone missed. Try it here:show more

This is how frontend should be built in 2025.... With 21stdev Magic UI MCP + Cline, describe what you need and get beautiful components instantly. It taps into a curated library of professional UI elements at conversation speed. Here's how you can use it 🧵show more

🚀 New in Cline v3.5: improved MCP interactions with... visual, content-rich displays. Enjoy automatic image previews, rich link previews, and enhanced tool outputs like graphs and charts directly in your conversation. Toggle seamlessly between rich display and plain text modes for a tailored experience.show more

Some of our power users have a weird habit:... they make Cline say 'YARRR!' before writing any code. Sounds crazy, but there's genius behind it. Here's what they discovered about getting 10x better output 👇show more

Available in the MCP Marketplace: Apple Native Tools Watch... Cline use the new Apple MCP server to read project details from Mail, automatically create tasks in Reminders, make a related code edit, and then send an update email -- all without leaving VS Code. Stop juggling apps and losing focus. Automate the admin work that pulls you away from coding. See how Cline integrates your personal productivity tools into your development workflow.show more

Selectable options transform how you work with Cline --... let us explain: Instead of typing open-ended responses, you can now choose from curated paths that Cline presents. This shifts Cline from just a tool to a true collaborative partner -- offering guidance while keeping you in control of the direction. Small UI change, profound workflow difference.show more

here's HOW you're going to get your $10k MRR... startup idea: 1/ the reddit json hack > go to any subreddit in a niche of your choice > take any reddit thread and add /.json to the url > you get the full conversation instantly > every reply, every deep thread, every nested comment > all the context and metadata in a clean format > no scraping tools needed. it's just there. 2/ now do something with it (analyze it) > pass it through claude > get the real pain points and problems users are facing > spot intent without guessing > find patterns no one reads > extract the exact words people use to describe their problems 3/ why this works > small subreddits are untapped opportunities > people openly explain what they need > they literally tell you what they'd pay for > no surveys, no interviews, just raw frustration 4/ what to look for > "i wish someone would build..." > "i'd pay for..." > "why doesn't X exist?" > "frustrated with..." > same complaint appearing 10+ times = validated idea the advantage isn't speed. it's listening better than everyone else. while founders are brainstorming in the shower, you're reading exactly what people are begging for. the internet is telling you what to build. you just have to listen.show more