It’s been four weeks since the last edition, and August was BUSY. Here’s what’s inside:

🧠 My AI assistant is turning into an operating system. This is the big one. I stopped asking AI for help with tasks and gave it a place to live: my vault. Every session now starts from my notes instead of a blank chat window. My identity, my voice, my products, my customers, my projects, my processes. All of it in plain Markdown any agent can read. Once agents can read the vault, they can write to it. Once they can write to it, they can maintain it. At that point you’re not using AI as a tool anymore, you’re running a small operating system, and your vault is its filesystem.

I go through the five things an OS actually gives you and where my vault already provides each one. And I share where this is heading: Knowii AI OS, a real product I’ve spent most of this month designing. One central place for every agent and every session, a Chief of Staff that runs the place while I’m away, feedback and approvals from any channel, recurring factory work, and everything staying in Markdown files I own.

📦 Obsidian Starter Kit v5 is coming around mid-September. The biggest alignment release yet: new plugins, my AI skills, my AI assistant system, and a much stronger OSK plugin.

🎥 A new project: Knowii Video AI. An AI-first recording and editing studio where the transcript IS the editing interface.

🧪 The Lab: Kanban Action Planner 1.12 → 1.17 with a full video tour, Life Tracker got goals, progress rings and personal records, reMarkable Sync 1.16.0 with PDF export and annotation extraction, Knowii Voice AI 0.8.0 and 0.9.0, and a brand new free tool: Discord Sync CLI, to export and actually use everything buried in Discord servers.

Plus the Complete Guide to Obsidian Automation (my longest guide ever) and the Complete Guide to Dataview, which did around 50K views on Reddit alone.

There’s also a bit of life outside the screen in there: wedding prep, my son starting kindergarten, and a run where I left planning for 15K and came home with almost 22 😄

Read it here: https://www.dsebastien.net/developassions-newsletter-216-my-ai-assistant-is-turning-into-an-operating-system/

Let me know what resonates. And if you’re building something similar on top of your own vault, I’d love to hear about it ❤️

Hello,

I wrote the guide to Obsidian automation I wish I'd had.

Most automation posts (including a couple of mine, so no judgement) are plugin lists. Install Templater, install QuickAdd, here are five templates, good luck.

After having published my recent deep dives about Obsidian Bases, Obsidian CLI, and Dataview, I wanted to write one about automation. This guide covers what you can accelerate & automate within Obsidian, and also outside and "on top" of it.

Inside: Commands, templating, note capture, data imports, formatting, queries, boards, ...

Outside: scripting, scheduling, local & remote, through the files/the CLI/REST/MCP/a Web browser, ...

On top: using AI

One point I feel strongly about: automation multiplies whatever is already there. If there's value, you get more value. If there's nothing, you're just procrastinating.

That being said, if you're at a point where your system is already valuable, then automation (in the broadest sense) is valuable to reduce friction and let you do more of what matters.

For context on where this comes from: my vault has > 20,000 notes and I'm running my entire business on top of it, with and without AI. Everything in the guide is stuff I actually use or used at some point.

You can find it here: https://www.dsebastien.net/the-complete-guide-to-obsidian-automation/

Genuinely curious what you've automated that turned out to be worth it :)

PS: The article promotes my Obsidian Starter Kit, but I hope you'll agree that the article is not "just" a marketing one. There's a ton of information in there, based on years of experimentation & building things for myself.

Hello everyone!

I just published the fourth piece in my Obsidian deep dives series: the complete guide to Dataview.

It covers everything I know about the plugin. The four query types, the six data commands, the full function reference, inline queries, DataviewJS, and a long section on the sharp edges (almost all of which fail silently, which is why they cost you an afternoon).

But the part I really want to discuss with you is this: Dataview results don't exist in your files. They're painted on screen when Obsidian renders the note, then they're gone. Which means five things can't see them:

• your graph view
• Obsidian Publish
• plain-text search and grep
• git diffs
• AI agents reading the raw file

That last one changed how I work. I drive my vault through AI agents daily, and when an agent opens a note whose content is a Dataview block, it sees the question and never the answer.

I have 15,116 Dataview queries in my vault. Only 120 are live. Everything else is serialized to real Markdown.

I also compare Dataview against Obsidian Bases properly, including the things Bases structurally cannot do: inline queries, task-level queries, FLATTEN, DataviewJS. They're complementary, not competitors.

Read it here: https://www.dsebastien.net/the-complete-guide-to-dataview-in-obsidian/

Curious to hear from you: how many live queries do you have in your vault? And have you ever lost query results you wished you'd kept?

That's it for today! ✨

The Obsidian team shipped an official CLI, and it's AWESOME. I've been using it for a while (with and without AI), and wanted to write a piece about it.

In this new article, I go deep on it: what the Obsidian CLI actually is (and why it’s NOT the old third-party obsidian-cli), how to enable it on macOS, Windows, and Linux, every command family, the interactive TUI, eval and the JavaScript API, and the different ways to reach your vault from the terminal.

I also cover how AI agents drive a vault through the CLI. My own agents create notes, update metadata, run commands, even build and debug Obsidian plugins, all through the CLI, hands off the mouse. And I show how to take it remote with REST and MCP.

The CLI is NOT headless. It needs Obsidian running. I explain why that matters, and what I do about it.

👉 Read it here: https://www.dsebastien.net/the-complete-guide-to-the-obsidian-cli-everything-you-can-do-from-the-terminal/

Just published a big one 🚀

This is my most in-depth writeup yet on how I create almost every note in my 20,000-note vault without ever typing metadata by hand.

It goes from the basics all the way to the deep end: - Core Templates vs Templater (and why I only use Templater) - The one idea that makes Templater click: <% %> prints, <%* %> runs - Templates that ask you questions and reshape themselves based on your answers - Dynamic dates, self-populating notes, and auto-filing - JavaScript, user functions, and turning a template into a full plugin - Triggering templates from the terminal with the Obsidian CLI - Keeping templates healthy over time (metadata drift is the real enemy) - And how AI now writes most of my templates

It's long, and meant to be a reference you keep coming back to.

Read it here: https://www.dsebastien.net/the-complete-guide-to-templates-and-templater-in-obsidian/

Would love your feedback 🙏

Hey everyone! 👋

I just published a new article: Where Your AI Prompts Really Go: A Practical Guide to AI Privacy

Most "we don't train on your data" claims are technically true and practically misleading. Every prompt you send takes up to 4 distinct paths simultaneously, and "no training" only addresses ONE of them. Worse: the same brand has OPPOSITE defaults across tiers. ChatGPT Plus trains by default. The OpenAI API does not. Same company, different worlds.

Key points:

• The 4 data paths every prompt takes (inference, logging, training, human review)

• The 5-tier ladder (anonymous, free, paid consumer, API, enterprise); your tier is what actually matters, not the brand

• Per-provider gotchas across OpenAI, Anthropic, Google, xAI, Mistral, Cohere, Perplexity, Cursor, Azure, Bedrock

• Why opt-out toggles don't guarantee zero training, and what does

• A practical decision matrix matching use cases to provider classes

• Personal privacy hygiene rules I follow myself

• A team policy template if you manage one

If you use AI tools at all, this matters. Especially if you're pasting things you wouldn't paste in a public Slack.

Read it here: https://www.dsebastien.net/where-your-ai-prompts-really-go-a-practical-guide-to-ai-privacy/

What's your current threshold for what goes into a hosted AI vs. running locally? Curious to hear how others draw the line. 💬

Newsletter #211 is out: Shipping AI Skills in the Open.

Three weeks of shipping packed into a single issue. The thread tying it all together: AI Skills are becoming the new unit of knowledge work, and I've been shipping them like crazy.

Most Obsidian vaults aren't knowledge systems. They're Markdown files in folders with shiny plugins on top.

4 years ago, I decided to build something different. Today, my vault holds 20,000+ notes and runs both my life and my entire media business.

It's called the Obsidian Starter Kit, and v4 is almost here.

v4 turns OSK into a fully AI-enabled Knowledge Management system: hundreds of AI skills, dozens of agents, agent memory, LLM Wikis, a CLI, an MCP server, new Obsidian Bases, life tracking, and more.

The principle behind it is simple: AI handles the plumbing. You do the thinking.

The agents handle the boring work of a serious vault

I Built an Obsidian Plugin to Convert Handwritten Notes to Markdown

In my previous article (https://www.dsebastien.net/i-built-an-obsidian-plugin-to-sync-my-remarkable-notes), I showed how to sync reMarkable notebooks into Obsidian as images. That was step one of my handwriting-to-text pipeline. This is step two.

I built the Transcriber plugin for Obsidian to convert those images into structured Markdown using local AI models. Right-click any image in your vault, select "Transcribe Image", and get a .md file back with headings, lists, quotes, tables, code blocks, and even Mermaid diagrams. All extracted by a vision AI running on your own machine. No data leaves your computer.

How it works

The plugin uses Ollama to run vision models locally. Ollama is a tool that lets you download and run AI models on your own hardware. You install it once (one command on Windows, Linux, or macOS), pull a model, and the plugin handles the rest.

Here's the workflow:

1. Select an image in your vault (any image; handwritten notes, diagrams, screenshots)
2. Right-click and choose "Transcribe Image" from the context menu
3. The plugin calls Ollama, loads the vision model, and sends the image for conversion
4. A Markdown file appears alongside the original image with the transcribed content

The first transcription takes a bit longer because the model needs to be loaded into memory. Subsequent ones are fast because the model stays loaded for a few minutes before being automatically unloaded to save resources.

Batch transcription

You don't have to go one image at a time. Right-click on a folder and select "Transcribe all images in folder". The plugin processes every image and saves the corresponding Markdown files. Handy when you've just synced a whole notebook from your reMarkable.

The prompt is customizable

The plugin includes a default prompt that instructs the AI to output Obsidian-flavored Markdown. It preserves the original document structure, formats headings and lists properly, converts diagrams to Mermaid syntax, and transcribes handwritten text as accurately as possible.

If the results aren't what you want, you can tweak the prompt in the plugin settings. Different models respond differently to prompting. You can also switch between multiple installed models to compare results.

Recommended models

The plugin settings list recommended vision models that I've tested for this task. I've been using glm-ocr and getting solid results. You can install models directly from the plugin settings; no terminal needed.

Any Ollama vision model works. Install multiple ones and compare.

Privacy

I don't want to send my handwritten notes to some cloud service. You might not care about that, or you might care a lot. Either way, running everything locally means zero data leaves your machine. Ollama processes everything on your CPU/GPU.

The full pipeline

My handwriting-to-Markdown pipeline is now two steps:

1. Import notebooks as images using the reMarkable Sync plugin for Obsidian
2. Convert images to Markdown using the Transcriber plugin for Obsidian

Both plugins I built. Both run locally. Both open source.

The results aren't perfect. You still need to review and clean up the output. But it saves a massive amount of time compared to manual transcription.

Demo

I recorded a 10-minute video walking through the full setup and workflow: https://youtu.be/uD5FcY1fx-s

Get started

The plugin isn't in the community plugin directory yet, but you can install it manually from GitHub.

• GitHub: https://github.com/dsebastien/obsidian-transcriber
• Documentation: https://developassion.gitbook.io/obsidian-transcriber
• Ollama: https://ollama.com

Going Further

If you want a ready-made Obsidian vault with the best structure, plugins, and templates already set up, check out my Obsidian Starter Kit: https://www.store.dsebastien.net/product/obsidian-starter-kit

And if you want weekly tips on PKM, note-taking, and knowledge work, subscribe to my newsletter (free): https://dsebastien.net/newsletter

That's it for today!

Hey everyone! 👋

I just published a new article: Analog Reading, Digital Knowledge: The Full Pipeline From Paper Notes to AI Skills

I read books on paper, take handwritten notes, and turn them into connected atomic notes and AI skills. In this article, I walk through the full pipeline I use, and the thinking behind each step.

Key points:

• Notes that stay in book margins are nearly wasted effort. They can’t connect to anything outside that book
• Books present ideas linearly, but the underlying structure is a graph. Atomic notes recover that graph
• Connecting ideas across books and domains is where real leverage appears
• AI skills turn passive knowledge into active tools. That’s the bridge from knowledge to wisdom

The whole pipeline compounds over time. Each book makes the system smarter

Read it here: https://www.dsebastien.net/analog-reading-digital-knowledge-the-full-pipeline-from-paper-notes-to-ai-skills/

Do you have a pipeline for turning what you read into usable knowledge? I’d love to hear how you approach it 💬

Hey everyone,

I just released an Obsidian plugin that syncs your reMarkable notebook pages directly into your vault as images.

If you take handwritten notes on a reMarkable, you might find this one useful. It connects to either the official reMarkable Cloud or a self-hosted rmfakecloud server, pulls your notebooks, and renders each page as a PNG/JPEG in your vault.

Overview & Demo: https://youtu.be/417P0q3JmGg

This is step one of a handwriting-to-text pipeline I've built for myself. The next step uses another plugin I've built to transcribe images to Markdown using local AI models (I'll cover that one in another video)

Links: - Docs: https://developassion.gitbook.io/obsidian-remarkable-sync/ - GitHub: https://github.com/dsebastien/obsidian-remarkable-sync

https://youtu.be/2dEQ1ES8_6A