Free & open · read it before you run it
A local-first personal AI operating system

Synta is the whole map. Not a screenshot.

Not a feature list. The actual system map, annotated — read the tree and you have read the system. Plain folders and Markdown files on your disk. No account, no cloud, nothing to buy.

The map · click any node

Read the tree. That's the product.

Every node below maps to a real folder or file in the Synta system. Expand a branch, open a node, and read what it is, why it exists, and how it works. Follow the links between related nodes — the same shape the system uses to think.

~/synta
How you start

Bring your own AI tool. Synta gives it memory and operating rules.

Synta is not another AI subscription. You run it on your machine with the AI tools you already use: Claude, Codex, Cursor, an Ollama local model, or a similar agent. Your subscription, API key, or local model stays yours. Your files stay yours.

Step 1

Put the folder on your disk

Download or clone the public repo into a normal folder. Nothing starts in the cloud. The system begins as files you can open, move, back up, and inspect.

Step 2

Run the browser dashboard

The dashboard gives non-technical users a web interface for the system: projects, tasks, health checks, knowledge graph, and agent sessions. More tech-savvy users can use the command line instead. There is nothing to be scared of: either way, the same files and rules drive the work.

Step 3

Go through onboarding

An AI-led setup creates your DNA: profile, preferences, first projects, and the rules for how the system should work with you. After that, you learn by using it and feeding it real data.

The operating loop

It is probably not the model. It is your loop.

Most people do not fail with AI because the model is weak. They fail because they skip the basic loop: context, goal, action, feedback, and conscious decisions. Synta makes that loop visible and repeatable.

1

Give identity and context

Tell the AI who you are, what matters, how you work, and what world it is stepping into.

2

Define the goal first

If the goal is unclear, use AI to make it precise before asking it to produce anything.

3

Stop predicting the model

Do not overthink what AI might do. Give it context, run the session, and inspect the result.

4

Do the next step

AI is useful only when you act on the recommendation, test it, or turn it into a decision.

5

Give feedback

Correct it, refine it, and tell it what was useful. No feedback means no learning loop.

6

Read and decide

The worst failure mode is skimming the answer and letting the AI decide by accident. You own the decision.

7

Automate last

Do not automate the fantasy workflow. First prove the pattern in live AI sessions, then automate it.

Daily operating layer

The value appears when your work starts flowing through it.

The map explains the structure. The tools make it useful every day: they help you plan, prepare, review, start focused sessions, and notice what you forgot to write down. A founder, manager, consultant, or developer will use different routines, but the underlying system stays the same: projects, context, tasks, agents, and decisions in one place.

Dashboard

Run the system from a browser

Open projects, review tasks, inspect the knowledge graph, and start agent sessions from one web interface. With Tailscale, the dashboard can be reached from any trusted laptop, desktop, or phone.

Pulse

Start the day with a brief

Pulse sends a daily focus brief to Telegram: what changed, what needs attention, which tasks are stale, and where yesterday's work created new decisions or follow-ups.

Adapters

Bring calendar and messages in

Calendar, email, and Slack adapters can add a layer of automation: upcoming meetings, raw context, and routing hints arrive before you have to hunt for them manually.

Kanban

Turn tasks into sessions

Add tasks, plan them, review progress, and start an AI session from a card. The board is not just tracking work; it is a launchpad for making progress.

The idea, made literal

Levels and links.
Folders inside folders. Notes connected to notes.

Synta is named for the way it works. Knowledge is organized in levels — areas hold projects, projects hold files. And it's connected by links — the system's synapses — so it can pull the right context and spot what's missing. The map above navigates exactly that shape.

Levels

Root, system, agents, areas, projects, files. Every level is a real folder you can open. Context loads by level: a handful of files on startup, the rest only when a task needs them.

Synapses

Files link to related files. The system walks those links to gather context, and flags dead links, missing files, and contradictions on every read. In the map, every "see also" is one of these links.

Plain text

It's all Markdown in folders you own. Open it in any editor, back it up like any normal folder, and use Git if you want version history. Walk away tomorrow and you keep every byte — because it was always just text.

What the tree guarantees

You can inspect all of it before you commit to any of it.

The system is readable before you download it. Here is what you can verify for yourself.

Auditable

Every rule is a file

The startup routine, the routing logic, the rule that stops the AI from inventing facts, and the limits on what it may change — none of it is hidden inside an app. Open the file and read the rule.

Yours

No account, no cloud, no lock-in

It runs as a folder on your machine. Data only leaves if you connect Claude, OpenAI, or another AI provider yourself. Synta does not send your files back to a server you do not control.

Reusable

Take the parts you want

A public project with a standard open license. Use the whole system, or copy one useful rule into your own setup. It's built to be read and reused, not to trap you.

Durable

Plain Markdown outlives the tool

The format has been stable for twenty years. Whatever happens to Synta, your files still open in every text editor ever made.

Who is behind this

Built by someone who needed the system to work.

I am Emil Majkowski, a CIO / systems architect and former founder.

I spent 15 years building both the business and the technology of a vacation-rental company from zero to acquisition. We were building infrastructure for a market that was still learning what it needed.

Today I work as a CIO / systems architect and spend most of my thinking time on AI: how models work, how people use them, where AI workflows break, and what it takes to make AI useful inside real companies.

Security is one of the main criteria in everything I build. That is one reason Synta is local-first: your memory, rules, and work context should stay under your control.

Synta is the personal operating system that came out of that work: local memory, clear rules, specialist roles, and a protocol that makes AI useful beyond a single chat.

I am sharing it because I believe AI creates real value only when it has context, boundaries, feedback, and a human making conscious decisions.

Free & open

Read it. Copy it. Take what you want.

No price, no checkout, no fit-call. The map above shows the structure and explains the key parts; when the public repo is ready, you will be able to inspect the full prompts and files yourself.

It's free.

The map is readable here today. The full system — prompts, rules, roles, protocols, and files — goes public shortly in the GitHub repo.

Read the map now; download it, run it, or just steal the ideas the moment the repo is up.

Back to the map
The map is free to read today. The full repo goes public shortly. Nothing to buy, ever.