An AI reading companion for academic surveys and technical books. It doesn't just chat next to your PDF — it reads the same book you do, prepares lessons from the papers the book cites, remembers what you understood and where you got stuck, and teaches with citations you can click to jump back into the text.
Local-first and backend-free: sign in with your Claude or ChatGPT subscription, or use a DeepSeek API key. Books, annotations, notes, memory, and credentials never leave your machine.
The app opens to a Today home: one card to continue the book you were reading, one for the day's briefing from the sources you follow. The briefing itself is a finite document you read top to bottom, sorted into four tiers, with a clear end.
The AI reads every item in full and sorts each into exactly one tier against your reading profile:
- Worth your time — two to four items that earn opening, each with a one-line reason written to you and referencing your interests, not generic praise.
- In one line — the items worth knowing but not opening; the line carries the actual news, what happened and the number that matters, so reading it is the whole point.
- Out of your lane — at most one deliberate anti-echo-chamber pick: something important you would not normally follow, labeled and set apart. On a day with no honest candidate there is none.
- Filtered — everything else, collapsed to a tally ("vendor PR ×8, conference recap ×6") that expands to the dropped titles, each with a Show anyway.
A one-line overview opens the page and is allowed to say the day is mostly noise. When the same story runs in more than one outlet — including a Chinese source and an English one reporting the same event — triage keeps one entry, names both outlets, and files the rest as duplicate coverage.
Read any item in the app: a clean typographic page with its images, opened from the day's cache. Your reactions feed back — opening, dismissing, and appealing a filtered item are logged and shown to the next day's triage, so it learns your taste over time. The overview, reasons, and one-liners are written in your configured AI output language, even when the source article is in another.
There are no built-in sources. On first run the AI introduces itself and asks what you follow — one or two questions at a time, digging before it proposes anything, never dumping a list. It suggests outlets from its own knowledge only once a concrete interest has surfaced.
Adding a source is a conversation. Name an outlet or paste a link, and the AI scouts the site — reading a homepage's navigation to find the real URL of the channel you meant — then probes it for a usable feed: trying the common paths, detecting RSS/Atom/RDF/JSON, judging whether the feed carries full text or only summaries, and telling a server-rendered list page from a browser-only app when there is no feed. It then fetches three sample articles as a trial. A confirm card shows the three titles with their character counts and whether the full body came back, and the AI subscribes only after you say yes.
Each source is a small declarative JSON descriptor — how to discover its items, where the body text comes from — that a generic engine runs. The AI writes or adapts it (a new URL, a tweaked link pattern, a same-site shape cloned) and proves it by actually fetching, so a wrong draft simply fails at the trial and the AI tells you. This connects sites that have no obvious feed.
The Sources page is the account of what you subscribe to: one row per source with its lane and pipe type, an on/off toggle, a health dot (green when the last run succeeded, amber when it failed — click for the last-success time and the error), and a delete. A box at the top takes a pasted site or RSS URL and probes, trials, and adds it in place, without going through the chat. There is no ordering or grouping — ranking is triage's job.
Every briefing carries a chat, and every item has its own Ask that anchors a thread to that article, with its full text and the day's overview in context. The companion sees the whole document, including the full filtered list with each item's category, so it can tell you what came in today, break it down per source, or defend why something was dropped.
Voice a standing preference — "be harsher on vendor PR", "keep the paper explainers", "I care more about robotics now" — and the AI drafts a complete revised profile and shows it on a confirm card. Nothing is saved until you Apply; when today's briefing already exists, applying offers to re-triage it on the spot against the new profile.
Ask it to redo the briefing and it runs one of two depths: a re-triage that re-sorts today's already-collected items with the current profile (no fetching — for after a profile change or a bad sort), or a full re-collection that fetches every source again, including any you just added, and re-triages, replacing today's briefing. It starts a background job and returns at once; a progress card tracks the run — sources collected, then triage liveness — and settles into the finished briefing. The AI only regenerates when you ask, never on its own.
One profile — four short sections (interests, taste, background, what you're reading now) — steers both the briefing's triage and the reading companion, and syncs across devices. Nothing is preset: the AI drafts and revises it only from preferences you actually voice, always through a confirm card you Apply. What you have open and the questions you got stuck on feed into how relevant the briefing judges each item.
Companion mode — you drive. Mark a passage with the AI pen and it explains it in place, like a video call with the book: the reply opens in a bubble you can expand, and the thread stays anchored to your highlight forever. The AI can turn pages on its own, run full-text search across the books in your topic, and read your existing highlights and notes when the conversation needs them. A button in the top bar opens a book-level thread for questions that belong to no particular passage ("what is this chapter about?").
Classroom mode — the AI drives. Toggle it inside any conversation and the AI switches from companion to teacher: the entire survey stays resident in its context, together with lesson notes it prepared for the papers the survey actually leans on. The toggle is remembered per book.
When you open a survey, the AI reads it, picks the 15–20 load-bearing citations, and prepares them in the background:
- Full texts come from arXiv first, then OpenAlex (no key needed), then Semantic Scholar (optional free API key in Settings avoids the shared rate pool).
- Each paper is digested by an agent loop into a lesson note — short papers in one pass, long ones by turning pages with the same tools you see in chat.
- The prep panel in the sidebar shows every paper's status; you can skip, retry, replan, or add papers by title, arXiv id, or URL.
- Preparation is lazy and chapter-driven: papers cited by the chapter you are reading get prepared first. Everything is resumable across restarts.
The AI cites what it teaches. Page references render as chips — click one and the reader jumps to the page, with the exact quoted sentence flashed as a transient violet highlight so you see precisely what was referenced. Figures render as inline cards cropped from the actual page (vector diagrams included); click to jump, or ask about a figure and the AI will look at the image itself through its vision tool.
When you hang up a conversation, the AI silently distills it: where you are in the book, what you now understand, where you were stuck, what you corrected it about. One fact per file, on your disk, inspectable in the sidebar's Memory tab. The next conversation opens with a snapshot — the AI knows you read to section 4.2, struggled with the KV cache last week, and resolved it. Corrections happen in conversation ("you remembered that wrong") rather than by editing files.
Paste a URL into the chat — an arXiv or OpenReview PDF, or a web article — and the AI ingests it: downloads, extracts, files it into the prep list, and can discuss it against the survey in the same turn. The survey is static; the field is not.
The sidebar has a Notes tab: one click generates chapter-by-chapter lecture notes for the whole book. The chapter plan comes from the PDF outline, or the model reads the table of contents when there is none. Notes carry [p.N] and [fig:N] anchors that jump into the book, just like citations in chat. Regenerate any chapter on its own, with an optional instruction to steer it. Your highlights and conversations in a chapter shape how deep its note goes, and explanations you explicitly endorsed in chat get absorbed into the note.
Notes also accrue as you read: a chapter distills into its note once your highlights move past it, chapters you never marked are skipped, and a final pass runs when you close the book. The notes overview is part of the context each conversation opens with, so the AI knows what the book has already covered.
Turn your book notes into a self-contained HTML slide deck for a talk — multiple books at once, with optional AI-drawn illustrations. It opens in any browser with everything inlined, nothing to serve.
Every chat composer has a push-to-talk mic. Hold to record, release to transcribe: recording runs in Rust (WebKitGTK's getUserMedia is unreliable on Linux), speech-to-text goes through any OpenAI-compatible endpoint, and an LLM pass cleans up the transcript. It defaults to SiliconFlow's free SenseVoice tier — add a SiliconFlow key in Settings.
Sign in with Google in Settings and everything syncs — books, reading positions, marks and highlights, conversations, and notes — through a visible "Reading Partner" folder in your own Google Drive. No accounts, no server: your data stays in your Drive, and you can open the folder and see the files. Sync runs automatically after sign-in, with a manual toggle and a Sync now button in Settings. Books are content-addressed, so the same PDF opened on two devices lines up. AI provider credentials are the one thing that never leaves the device.
Adaptive reasoning is on by default: low effort for conversation (fast answers, the model thinks only when the question demands it), medium for lesson prep (background work, quality first). Both are adjustable in Settings.
Prebuilt binaries for Linux, macOS and Windows are on the releases page. They are unsigned: macOS will refuse the first launch until you right-click the app and choose Open, and Windows SmartScreen will warn once.
First run: open Settings and connect one provider — Sign in with ChatGPT or Sign in with Claude uses your subscription through an OAuth flow in the browser (no API key), or paste a DeepSeek API key. Only one provider is active at a time; connecting one signs the others out. Optionally add a Semantic Scholar API key for lesson prep and a SiliconFlow key for voice input. The AI's output language is set here too and governs chat, notes, slides, and the briefing — nine languages, or auto to follow the language you write in. With no sources yet, the AI starts a guided conversation to help you subscribe to your first few.
Prerequisites: Bun, Rust stable, and the Tauri 2 prerequisites.
git clone git@github.com:Einstellung/Reading-Partner.git
cd Reading-Partner
bun install
bun run wasm # stage the self-hosted PDFium wasm (from the @embedpdf/pdfium package, offline)
bun run tauri devDrive sync needs your own Google OAuth Desktop client: copy .env.example to .env and fill in VITE_GOOGLE_CLIENT_ID / VITE_GOOGLE_CLIENT_SECRET. Without it the app runs fine, with sync disabled.
bun test runs the suite (no network, no AI tokens). An iOS/TestFlight pipeline is prepared in .github/workflows/ios-testflight.yml.
src/reading/engine/— the engine adapter: assembles EmbedPDF's headless core + plugins, renders from in-memory bytes, and converts annotations at the boundary (the shell persists its own JSON schema). All UI around it (toolbar, annotations list, AI) is the shell's.public/pdfium/pdfium.wasm— the PDFium engine binary, self-hosted (gitignored; staged bybun run wasmfrom the npm package, no CDN at build or runtime).src/ai/— provider streaming and the agent tool loop.src/reading/prep/— the lesson-prep pipeline.src/reading/sources/— fetching and extracting a pasted link.src/reading/papers/— academic literature search and the citation graph.src/memory/— the per-topic memory store.src/reading/figures/— figure extraction and rendering.src-tauri/— Tauri 2 app.- Design consensus documents (in Chinese) live in
docs/; hard-won engine/Tauri surprises are indexed indocs/pitfall/.
Early development, PDF only, moving fast. The screenshots above come from real reading sessions and may lag behind the current interface.
Copyright (c) 2026 Xinyuan (Einstellung). Source-available under the PolyForm Noncommercial License 1.0.0: free for personal use and academic research; any commercial use needs a separate commercial license — contact einstellungsu@gmail.com. The PDF engine is EmbedPDF (MIT), which renders through PDFium compiled to WebAssembly (Apache-2.0).