Documentation

The Assistant

A chat pane that lives beside the dashboard on every tab — and, once connected to a language model, becomes an analyst that sees your holdings, your screen and your last actions, and can drive the dashboard for you. You choose the model: a cloud API or a fully private local one.

Connecting a provider

Open ⚙ Settings → Assistant and pick one of four providers:

Local-server detection is live: while the Assistant tab is open with Ollama or LM Studio picked, it re-probes the server every few seconds — load a model in LM Studio and it appears in the dropdown without reopening Settings. On the hosted app the backend calls the LLM server-side and cannot reach localhost on your machine; connecting a local model there needs a tunnel — see Local models with the cloud app for the full setup and the risks.

API keys you enter never live in your browser — they're stored encrypted on the server for your session or account (see Privacy & your data). Local models keep everything on your machine: the portfolio snapshot travels to your own Ollama/LM Studio server and nowhere else.

Press Save and the assistant arms itself — the red/green status dot in the chat pane's header shows the live state (hover it for the reason when it's red), re-checked every minute. Nothing calls an LLM and no tokens are spent while it's red. A session token counter (in/out) sits in the pane's corner.

Locking your API key down

An API key you paste into Settings works only for your own portfolio's calls — but for extra security you can restrict the key itself at your provider, so it is useless anywhere else even if it ever leaked:

• Spend caps and usage limits. Both the OpenAI and Anthropic consoles (and most OpenRouter-style aggregators) let you cap a key's monthly spend or rate — set one that matches your expected assistant usage.
• Scoped keys. Where the console offers project- or scope-restricted keys, create a dedicated key for Fortunest rather than reusing an account-wide one.
• IP allow-listing. Where the provider supports restricting a key to caller addresses, whitelist the app server's egress IP. The hosted app shows the current address right where you need it: it sits directly under the API key field in ⚙ Settings → Assistant, with a Copy button — paste it into the provider's allow-list as-is.

One nuance: the IP applies to the hosted app only, where the server makes the LLM calls. The desktop app in local mode talks to the provider straight from your own computer — there is no server in between, so an IP allow-list would have to cover your own network instead (usually not practical on a home connection; prefer spend caps there).

The two model slots

• The conversation model answers your chat questions and writes the one-click reports. Thinking/effort controls appear where the model supports them (thinking on, high effort by default).
• A subordinate fast model — background insights & search (same model list, thinking off by default for speed) runs everything else: proactive comments, the ✨ AI insights, ✨ smart search in the finder and Explore, the semantic holdings filter, and article/filing summaries. Metric and top-N holdings filters ("top 5 holdings", "my best PEG holdings", "7 of my highest free cash flow holdings") are answered locally without calling any model.

Both picks persist per provider; changing either model (or its thinking settings) clears the AI caches so one model's output is never served as another's.

Setting up a local model from scratch

A local model means the whole conversation — your portfolio included — never leaves your machine, and it costs nothing per token. You need three things: a server app (LM Studio or Ollama, both free), a model your hardware can hold, and a large enough context window — the last one is the step most guides skip and the most common reason a local setup "connects but fails".

LM Studio, step by step

1. Download LM Studio from lmstudio.ai (macOS, Windows, Linux) and open it.
2. Get a model: the 🔍 search tab lists them with download sizes. Good starting points: openai/gpt-oss-20b on a 16 GB+ machine, or an 8B-class model (Llama, Qwen) on 8–16 GB.
3. Load it with a bigger window. When loading the model, open its load settings and raise Context Length — the pane shows what the model supports (often 131 072); set at least ~32 000, ideally 50 000+. This is a per-load setting: reloading the model with a new length is what actually applies it. The app notices a reload within a minute — the header's context: … / N follows the live value.
4. Start the server: Developer tab → toggle Status: Running. The address next to Reachable at — http://127.0.0.1:1234 by default — is the server URL. Running the app on the same machine, you don't need to enter it anywhere: that's exactly where Fortunest looks.
5. In Fortunest: ⚙ Settings → Assistant → LM Studio. Your loaded model appears in the dropdowns (detection is live — no reopening needed); pick the conversation and fast models yourself and press Save.

Ollama, step by step

1. Install from ollama.com (macOS, Windows, Linux; brew install ollama also works). After install it runs quietly in the background at http://localhost:11434 — again the default the app probes, nothing to configure on the same machine.
2. Get a model either right from Fortunest — Settings → Assistant → Ollama offers a curated list with disk sizes and downloads with a progress bar — or on the command line: ollama pull llama3.1:8b.
3. Context is handled for you: the app requests a 32 768-token window on every Ollama call, so there's no slider to remember — but your machine still needs the memory to serve it.
4. Pick the models in ⚙ Settings → Assistant and press Save.

A hardware reality check

The model file has to fit in memory (RAM, or GPU memory where you have it), and a big context window adds to that. Rough quantized-model guide: an 8B model wants ~6–8 GB free, a 20B-class model ~13–16 GB, 30B+ upwards of 20 GB. If loading with a 50k context fails or answers crawl, try a smaller model before a smaller window — the window is load-bearing for this app.

The Server field in Settings → Assistant exists for the exceptions: a non-default port, the model running on another machine on your network, or the hosted app reaching your machine through a tunnel — that last one is its own chapter below, risks included.

Local models with the cloud app — advanced

Why this needs anything at all: the hosted app calls your model server-side. When app.fortunest.ai asks for localhost:1234 it reaches itself, never your machine — so the app must be given a public address that forwards to your computer: a tunnel. When you pick Ollama or LM Studio on the hosted app, a confirmation dialog first makes sure you know what you're opting into; the Server field then takes the tunnel URL. The hosted app accepts only publicly routable Server URLs — a private address could never reach your laptop from the cloud anyway, and allowing one would let visitors aim the backend at its own internal network.

Option A — ngrok

1. Create a free account at ngrok.com and install the agent: brew install ngrok on macOS, winget install ngrok.ngrok on Windows, or the download on their site.
2. Connect the agent to your account (one-time): ngrok config add-authtoken YOUR_TOKEN — the token is on your ngrok dashboard.
3. In LM Studio, load a model and start the server (Developer → Start server, port 1234).
4. Start the tunnel: ngrok http 1234. It prints a https://…ngrok-free.app address.
5. In the app: ⚙ Settings → Assistant, pick LM Studio, confirm the warning, paste the address into Server and press Save. Detection follows within a few seconds and your loaded model appears in the dropdowns.
6. When you're done, stop the tunnel (Ctrl-C). The address dies with it; the next start mints a fresh one — just paste the new one over the old.

Option B — cloudflared (no account needed)

1. Install the agent: brew install cloudflared on macOS, winget install Cloudflare.cloudflared on Windows, or the package from Cloudflare's downloads page. Quick tunnels need no account or sign-in.
2. Start LM Studio's server as above, then: cloudflared tunnel --url http://localhost:1234. It prints a https://…trycloudflare.com address.
3. Paste that address into Settings → Assistant → Server (after picking LM Studio and confirming the warning) and press Save.

Using Ollama instead of LM Studio

Same idea, port 11434 — but Ollama refuses requests whose Host header isn't local (its protection against DNS rebinding), so tell the tunnel to rewrite the header:

1. ngrok http 11434 --host-header=localhost:11434, or
2. cloudflared tunnel --url http://localhost:11434 --http-host-header localhost:11434

The risks, honestly — and how to shrink them

LM Studio's and Ollama's servers have no authentication: while a tunnel is up, the URL is the credential.

For calibration, what a URL-holder cannot get: your portfolio data, your chat history, your app session, or files on your disk (short of an unpatched server vulnerability). The exposure is your compute, its availability, and the server's own attack surface — for exactly as long as the tunnel runs.

Two closing notes: putting basic-auth on the tunnel (ngrok http 1234 --basic-auth …) does not work — the app's backend sends no credentials and refuses URLs with credentials embedded in them. And once more: the setup with none of these trade-offs is running Fortunest locally, where your model and your data never leave the machine.

Chatting

The assistant sees everything on the Portfolio tab — holdings, performance, allocation, insights, events, dividends, recent news, your strategy and watchlist, benchmark returns, per-holding fundamentals — and what you're doing: your live view (tab, currency, filters, whatever drawer or dialog is open on top, the simulation you ran) plus a log of your last 50 actions travels with every message. Ask about "this position" without any further context.

• To see exactly what it knows, open the Context window (click the context: indicator in the assistant's header): every section — live view, strategy, recent actions, portfolio snapshot, favourites, simulations, news, events, instructions — with its token estimate.
• Ticker symbols in replies are live — known symbols render as chips: click to open the asset's details from any tab, hover for a one-week mini chart; for symbols you hold, also your units, value and portfolio share.
• It can draw charts — interactive Vega-Lite charts render inline, expand to a modal and download as PNG or JSON.
• Answers render as markdown — when structure genuinely helps, replies come back with real tables (ask for "the values driving each pick's scores in one table"; wide ones scroll sideways inside the bubble), short headers, nested lists, italic, inline code and code blocks — and the live ticker chips keep working inside table cells and list items. Simple questions still get simple prose.
• It knows this manual — ask how something works ("how do I export my Revolut history?") and it looks up or fulltext-searches the documentation you are reading right now before answering, then grounds the reply in it. Answers can cite the docs with real links — hover one for a thumbnail preview of the page — and include the manual's screenshots (like the broker export walkthroughs) right below the answer.
• Attachments: 📎 or paste from the clipboard. Images go to multimodal models (marked by a picture-frame badge next to the model name) and are downscaled client-side; PDF and text files work with any model — their text is extracted and travels inline. Switching to a text-only model mid-conversation silently drops earlier images instead of erroring.
• You can keep the conversation — the ⇩ button beside ⟲ saves what is in the chat window as a self-contained HTML file (fortunest-chat-2026-08-13.html), or opens your browser's print dialog so you can Save as PDF. Both are produced in your browser: nothing is uploaded, the file needs no internet connection to open, and it carries only what the bubbles show — the messages, their formatting, their tables and code blocks, the charts as pictures, the attachment chips. No portfolio context, no model or provider name, no token counts, no account details, no keys.
• Enter sends, Shift+Enter breaks a line; the box grows with long questions. ⟲ resets the conversation.

The dock

The assistant opens maximized by default: a full-height side pane that pushes the dashboard left so nothing is hidden — and nothing ever covers it: drawers, dialogs and maximized charts all stop at its edge. A window control turns it into a small floating window bottom-right (drag its top-left corner to resize); minimize drops it to a launcher bubble. Phones get a 💬 bottom-sheet drawer instead. Float/minimize choices stick for the browser-tab session and reset to maximized on the next visit.

While the pane is docked, the dashboard is its own scrolling column, so its scrollbar sits between the dashboard and the chat rather than at the far right of the window, past the chat. The keys work as usual — PageDown, Space, End and the arrows scroll the dashboard — and maximizing or restoring the pane keeps you where you were reading. A floating or minimized chat, and the phone's bottom sheet, overlay the page instead of standing beside it, so there the page scrolls as it always did.

Driving the dashboard from chat

The assistant doesn't just describe the dashboard — it can drive it. Type a command like "Go to allocation" and the app switches to the right tab, smoothly scrolls to the card, expands it — and the assistant comments in the same message, using the very numbers the card shows and whatever you discussed before. Three starter buttons under the one-click report cards suggest example commands to try.

Custom assets by conversation

You can also create data by talking: ask for "add my apartment in Prague, bought 2019 for 200k EUR" and the assistant collects whatever is still missing — name, purchase date, price, currency, an optional yearly appreciation — asking follow-up questions until the entry is complete, then creates the asset. It appears under ⚙ Settings → Data exactly like a manually added asset: part of your allocation and history, toggleable and editable any time.

Market-priced assets work the same way, live-priced: "add 2 oz of gold futures" or "add 10 shares of RHM.DE" resolves your words through the same Yahoo Finance symbol search the Settings bar uses and adds the position — priced from live market data in the holdings table, allocation and charts, like any broker holding. An exact ticker adds directly; when several listings match a name, the assistant lists the candidates and asks which one you meant before anything is added. The acquisition date defaults to today and the per-unit price to the market close on that date unless you state them. It also reads positions out of an attached file or a screenshot — paste a brokerage position list and it adds each line as its own asset.

What you can rely on

• Graceful degradation — if an action can't be performed (an unknown card, an asset you don't hold), the assistant says so in the chat and answers the question textually instead. Never a silent failure.
• Real numbers — the chat commentary always comes from the same data the card on screen displays, and respects the previous conversation.
• Actions become context — every performed action lands in the Recent Actions log, same as your manual clicks, so follow-up questions know what's currently on screen.

The complete action reference

Everything above runs on a fixed vocabulary of 38 typed chat commands — each with a strict name, parameters and allowed values; a request that doesn't map onto one of them is answered in plain text instead of guessed at. You never type these names yourself — plain English is the interface — but the list below is the authoritative "what can it actually do", and it is how a skill references an action: a skill's text can name one by its action name ("on app start, use the show_card action with card allocation") and the assistant carries it out. Parameters marked ? are optional.

Actions: navigate & reveal

Actions: chart, holdings filter & the tutorial

Actions: search, live market data & this manual

These fetch data for the assistant's answer and leave your view where it is:

Actions: favourites & watchlist

Actions: simulation

Actions: trades & taxes

The tax actions answer with illustrative orientation, never tax advice:

Actions: adding data & notes

Actions: social

These need a signed-in account with Social switched on; a friend's figures are always relative (percentages and ratios, never money amounts):

Actions: settings & appearance

Actions that fetch data (market_lookup, docs_lookup, docs_search, search_assets, friend_portfolio, social_feed, social_requests, tax_free_summary, sell_plan, sync_connector, …) hand their result back to the assistant, which answers grounded in those numbers; when an action fails, the assistant reports the actual reason and answers textually — the guarantees above apply to every verb on this page.

One-click reports ✨

Suggestion cards sit right below the assistant's opening message (dimmed with a "Connect the Assistant" note while disconnected). Clicking one posts the exact server-side prompt as your side of the conversation — a compact bubble with a "show full prompt" expander, so what you read is literally what the model receives — and streams the report into the chat, where follow-up questions build on it. Clicking again regenerates; a grey timestamp stub on the card jumps back to the report.

Proactive tips

When connected, the assistant can comment as you work — star a stock and it might flag your existing concentration in that sector. Tune it with the Off / Minimal / Active slider in ⚙ Settings → Assistant (default: Active). Tips run on a compact context (~5× smaller than the chat's) plus a condensed digest of the conversation so far, so they fire fast (~1.5 s after your action, at most one attempt per 15 s) and never repeat what was already discussed.

Investing notes 📝

Short, timestamped reminders you write for the assistant — "sell X before earnings", "when the FED raises rates, reallocate more into bonds". Edit them behind the always-visible 📝 pill next to the asset search in the topbar (it pins to the screen as you scroll, on every tab), or under ⚙ Settings → Strategy, where they live alongside your investing thesis: up to 20 notes of 500 characters each, newest first. You can also just ask the assistant — "add a note: watch my cash level this month" — and it saves the note itself. They travel with every chat message, proactive tip and ✨ report — a tip can point back to "your note from 12 Jul", Buy advice weighs them when drafting the monthly plan, and Trades-tab candidates your notes mention get a small 📝 hint (the rule-based scores stay untouched). The assistant treats notes as your standing intentions: it weighs and references them, pushes back when your data argues otherwise, and never executes one on its own.

Skills — reusable custom instructions

Skills are Markdown files with standing instructions the assistant follows — output formats, focus areas, opening routines. Manage them under ⚙ Settings → Assistant → Skills: upload a .md file, click a skill's name to edit its text in a larger editor dialog, untick it to keep it without applying it, or ✕ to delete. Changes apply immediately (no Save needed), and skills are stored with your other session data — they survive restarts, ride the browser backup, and move into your account when you sign up. Limits: up to 10 skills, 16 KB each, 40 KB of enabled skill text in total. The chat's Context window lists your skills as their own section with a token estimate. A skill can also tell the assistant when to act, naming any of its dashboard actions from the complete action reference by name — show_card, sort_table, docs_lookup and the rest. Skills customize behavior — they can never override the assistant's safety and grounding rules, and never make it present financial advice.

Targeting surfaces with applies-to

The first line of a skill may name which assistant surfaces it applies to — for example applies-to: chat, tips. Parsing is lenient (Applies To =, an optional --- fence around the line, any comma or space separation all work); the line itself is stripped before the text reaches the model. Without a header, a skill applies to the chat only.

Example skills

An output-format skill for the Portfolio brief (save as brief-format.md):

applies-to: reports
When generating the Portfolio brief, always format the output like this:
- open with a single-line verdict (calm / watch / act), then the sections
- every number in bold, every percentage with one decimal
- close with "Next review:" and a suggested date

A tips-behavior skill:

applies-to: tips
When generating tips, include the concrete EUR amount at stake whenever
you mention a position, and prefer tips about concentration risk over
tips about daily price moves.

An app-start skill — the assistant's first reply of each session honors it:

applies-to: appStart
On app start, list my top 5 holdings by value with their last daily moves,
one line each, before anything else.

A skill that references actions by name — the assistant performs them when the skill's condition is met:

applies-to: appStart
On app start, use the show_card action with card "allocation" and comment
on the two largest weights. Then sort the holdings by this year's return
(the sort_table action on the holdings table, column returnPct, descending).

Four ready-made investing skills sit directly in ⚙ Settings → Assistant → Skills behind "Need inspiration?" — dividend-focus (income-first advice), value-discipline (valuation guardrails on every buy), risk-guardrails (concentration warnings) and opening-checkin (a recap when the app starts). One click adds them; edit the text afterwards to make them yours.

Where else the assistant shows up

• ✨ AI insights in the Portfolio tab's insight strip — up to four model-found patterns.
• ✨ Smart search — quoted prompts in the topbar finder and Explore.
• ✨ Semantic holdings filter — quoted text in the holdings table's filter box.
• ✨ News & filing summaries — on asset detail panels and the Events card's filed reports.

All of these run on the fast model, are marked with ✨ throughout the app, and stay visibly disabled (with a red "not connected" note) until you connect.