lotti

Lotti

Come say hi on Discord — questions, support, ideas, or just what you are using Lotti for.

A distributed system of record for your memories — kept on your devices, and nowhere else.

Everything you capture in Lotti is a memory: a voice note, a screenshot, a conversation, a photo from a birthday party, an hour of focused work, the journal entry you spoke into your phone last night. Lotti keeps those memories on your own devices and syncs them between those devices with end-to-end encryption; no cloud ever holds a readable copy. On top of that record sit applications — tasks, relationships, goals, events, projects, a daily journal — most of them looked after by a personal AI agent that reads what you recorded and proposes what to do next. Agents propose; you decide. Your memories are not rewritten behind your back, not even by the agents that work for you.

macOS · Linux · Windows · iOS · Android. Flutter and Dart, GPL-3.0, in development since 2016.

I have tracked around 11,000 hours of my own work in it since 2022.

Read the manual · Install · Blog series

The vision: a system of record you own

Most personal software treats your life as content to be stored on someone else’s computer and mined. Lotti starts from the opposite premise: your memories are a record, and a record is only worth something if it is complete, unaltered, and yours. Four principles follow from that.

1. Your memories live only on your devices

There is no Lotti cloud, no Lotti account, and no server that stores your memories. Each device holds the full record in a local SQLite database, with audio, images and other attachments beside it on the filesystem. No telemetry, no analytics, and nothing uploaded to Lotti — you can confirm that by reading the source or watching the traffic.

The record is distributed, not centralised: every device you pair is a full peer. Pairing gives a new device everything written from then on; once you send it your history from a device that has it, a phone, a laptop and a desktop each carry the whole record, and losing one costs you nothing as long as another survives. Your data is a file you already have, with a documented schema, so there is no export wizard and nothing to unlock.

2. End-to-end encrypted sync through a zero-trust relay

Devices talk to each other through a relay rather than a storage service: a Matrix homeserver (Synapse). The relay is a zero-trust component. Whoever operates it — you, a friend, a hosting company — never holds a key, so they cannot read your memories, and they cannot slip changes into your record either. You do not have to trust the operator with your data; you only rely on them to keep the relay running.

How it works:

• Encryption. Each change is sent as an event in a private, non-federated sync room created with Matrix’s m.megolm.v1.aes-sha2 encryption. Room messages are encrypted with Megolm, and the Megolm session keys are exchanged between devices over Olm, using the Vodozemac implementation of both. Audio and images are uploaded as Matrix encrypted attachments: AES-256-CTR with a per-file key, IV and SHA-256 hash that travel only inside the encrypted event.
• Only verified devices get keys. Keys are shared only with devices you have verified yourself through an emoji comparison. A device that joins the account without completing verification receives ciphertext it cannot read, and incoming events from unverified devices — or anything sent in plaintext — are dropped rather than applied, so whoever operates the server cannot inject changes into your record.
• Trust comes from verification, not from the server. The emoji comparison is what stops a relay from posing as one of your devices: verify only devices you are holding, and the operator never gets a key.
• What zero trust does not cover. The operator never sees plaintext, but can see metadata: the account ID and room name, which device synced and when, IP addresses, device display names, and the size of events and files. They also control availability — they can delay, withhold or delete ciphertext, but not read or alter it, and your devices still hold the full record.
• The relay is not an archive. Sync rooms carry a 30-day m.room.retention policy. Synapse only enforces that when the operator turns retention on (and media retention separately); the hosted Lotti Sync service runs its own purge as well. Either way, the durable copy of your record is on your devices, not on the relay.
• Conflicts are resolved, not overwritten. Every entry carries a vector clock keyed by device, so a device that was offline for a week rejoins without losing writes, and genuinely concurrent edits surface as conflicts for you to resolve. Devices also track each other’s counters and ask a peer to re-send anything missing.

The full runtime picture — pairing, the sync log, backfill and failure handling — is in the sync concept and security and privacy.

3. Memories stay unaltered, with clear provenance

A system of record is worthless if it can be quietly edited. Lotti keeps what you said and did and what an agent thinks in separate databases on disk, and agent-authored changes reach your record through a code path that requires your approval: an agent suggests, and nothing happens until you confirm. A confirmed checklist change keeps an approval receipt, and the writes an agent makes without asking are logged in its audit trail.

A small, named class of writes is pre-approved rather than proposed, because asking every time would add friction without telling you anything new — for example transcribing a recording, saving an AI summary as a new entry beside your own, or titling a task that has none. The exact set is defined in ADR 0102 and can only grow by another decision record. Some things are human-only by construction: an event agent has no tool that can set an event’s rating or cover photo.

Record provenance is work in progress. The goal is that every entry carries a verifiable record of who authored it — you, an agent acting under a consent you gave, or an agent proposal you approved — signed and chained per device so that the record can prove it was not altered. The cryptographic building blocks are in the codebase and modelled in TLA+, but entries are not signed yet, and the design will keep moving as this lands.

See Two databases for how the separation is enforced.

4. Inference that keeps nothing — and eventually runs on your own devices

Agents need a model, and today the most capable models mostly run in a datacenter. Lotti has no inference backend of its own: every request goes to a provider you configured, under your own key, routed per category. Ideally that is a provider with a zero-data-retention policy, one that processes the request and keeps neither the prompt nor the response, so that inference leaves no copy of your memories behind. Lotti cannot verify a provider’s retention terms; choosing one is your due diligence, covered under Your inference is a routing decision.

The long-term direction is inference on your own devices, as they become powerful enough to run capable models. The record and the reasoning over it would then both stay at home. Part of that is already here: speech recognition runs fully offline with Whisper or Voxtral, and lighter jobs run on a local model. For the agents, today’s answer is the cloud with zero data retention, because they need a larger class of model than personal hardware runs fast enough yet — see Local models and the sweet spot for agents.

Applications on top of the record

The storage layer is the product’s foundation; the applications are what you use every day. Each one reads from the same record, and all of them except the journal can be given a persistent AI agent with its own report, memory, wake schedule and proposal history. Tasks and the journal are on by default; the other applications are Sections you switch on under Settings → Sections. Turning a section off only hides it — nothing you recorded is deleted.

Tasks, with task agents and pull request tracking

Every screenshot and audio recording is a memory, attached to the work it belongs to. A rambling voice note comes back as a task with a checklist.

A task agent reads everything linked to a task and keeps a living report. It proposes changes rather than making them: a title, an estimate, a due date, priority or status, new or ticked-off checklist items, labels, follow-up tasks, links to related tasks, time entries. Each proposal waits for you to confirm or dismiss it. Automatic updates decide when the agent wakes, not what it may change.

Pull request tracking closes the loop between what you planned and what actually shipped. Turn it on for a task from its “+” menu, link one or more GitHub pull requests, and Lotti keeps a summary of each — a one-liner and a TL;DR — refreshed when you open the task or ask it to. The pull requests become part of the task agent’s context, so it can propose ticking off the checklist items a merged PR actually covers, naming the PR as its evidence. You still confirm each one. A personal access token, kept in the device keystore and synced end-to-end encrypted, is only ever sent to api.github.com.

Coding prompts and other briefs are a skill you trigger: they turn a task plus your notes into a coding, design, image or research prompt you can paste into the tool of your choice.

Relationships, with a relationship agent

The People section is for the relationships you want to look after. Record a conversation, or dictate afterwards who you spoke to and what you talked about; a check-in holds the recordings, transcripts, notes and photos.

Mark someone as important and they get a relationship agent. Before you meet again it gives you a briefing: what was discussed last time, how things have been going, suggested talking points, and what to pay attention to or steer clear of. It reminds you when it has been too long, and it can propose up to three tasks arising from a conversation — each waiting for your confirmation.

Goals, with goal agents

Define a goal — hours on a category or label, a measurement you track — and a goal agent watches your actual record against it. A deterministic evaluation runs every day at no inference cost. When you drift off track, the agent reminds you, with an OS notification and a banner explaining what slipped; when you are on track, it stays out of your way, its report records the progress, and you can ask it for an encouraging nudge. If the goal itself no longer fits, it can propose a revision, which you approve.

Events

Track the events that matter — a trip, a birthday party, a gathering — with the photos, recordings and notes that belong to them. An event agent narrates what you captured into a short living recap. The star rating and cover photo stay yours alone: the agent has no tool that can set them.

Projects

A project groups the tasks that serve one outcome. A project agent keeps a report on what is going on: current status, a health verdict, what is still missing, and recommended next steps. It can propose a status change or a new task for your approval. The project’s summary also flows into every task agent’s context, so work on a single task is informed by the project it belongs to.

Daily journal

Speak or type a journal entry, attach photos, or let a day’s recordings stand as they are. Transcription runs locally with Whisper or Voxtral if you want it to, and your journal can be routed to a local model — or to no model at all. The journal is the purest form of the record: your words, kept as you said them, on your devices — read by no model unless you route it to one.

You choose the brain, and you can see what it cost

Lotti has no inference backend. Route each category of your life to the compute you are willing to stand behind: a cloud provider with zero data retention, the European option Lotti recommends, or a local model as personal hardware catches up. The usage view reports tokens and requests for every cloud call, and spend, energy and CO₂e for the providers that report them — today that means Melious. Local inference is not measured at all, because the cost moves onto your own hardware and grid.

The sync protocol and the agent runtime are formally verified

Keeping several devices consistent without a server in charge is a distributed-systems problem, and it is treated as one. The parts where concurrency and crashes could lose or duplicate your data — the sync log, how agents are woken and recover after a crash, how a confirmed suggestion is applied when two devices race, how synced agent state converges, the agents’ message log, pull request tracking, and the Daily OS jobs — are written down as 50 TLA+ models and model-checked with TLC every night across 177 configurations, under crashes, injected failures, clock skew and messages arriving in any order (the ledger keeps the running totals). No saved change is ever declared lost, and as long as devices keep reconnecting and a failed send is retried, every saved change reaches every device. A double tap never applies a confirmed change twice. Checking the models found and closed dozens of real bugs. These are statements about the design, not a proof that every line of code matches it; the specs say exactly what is covered and which cases remain open, and generated traces drive the real code through the same scenarios. The code is held to them the ordinary way, too: over 35,000 tests at 99.9% line coverage, including more than 900 property-based tests.

Install

AppImage (Linux, no app store): download Lotti-<version>-x86_64.AppImage from Releases, mark the downloaded file executable (chmod +x on it, or your file manager’s permissions dialog) and start it. It brings its own media, keychain and recording libraries and runs on distributions from 2022 on (glibc 2.35 or newer: Ubuntu 22.04, Debian 12, Fedora 36 and later) that have GTK 3 and Mesa’s OpenGL ES, as any GNOME or KDE desktop does. It mounts itself with FUSE, which desktops ship by default; where FUSE is missing, run it with --appimage-extract-and-run. Unlike Flathub, it does not update itself.

Features at a glance

Capture

• Audio recording anywhere in the app, transcribed locally with Whisper (99 languages) or Voxtral, or through a cloud provider with audio support.
• Entries: notes, images, screenshots, measurements, and surveys, attached to the work, person, event or day they belong to.

Organize and reflect

• Tasks with full lifecycle (open, groomed, in progress, blocked, on hold, done, rejected), checklists, estimates, priorities, due dates, labels, linked context, optional generated cover art, and linked GitHub pull requests.
• Time tracking recorded against the plan rather than instead of it, with focus ratings. A task describes the outcome you want; a time record describes what actually happened. Lotti keeps them as separate facts, so the record stays honest when the week gets noisy.
• Time analysis over categories, habits, and measurements.

• Categories and labels to make the boundaries that your decisions actually use.
• Habits, measurables, and health data imported from Apple Health and other sources.
• Sections you switch on under Settings → Sections: Daily OS, Projects, Goals, Habits, Dashboards, People and Events. They are in daily use and ship switched off, so you choose which parts of Lotti you use.

AI and automation

• Agents for every application: task, project, goal, relationship and event agents, plus a day planner. Each keeps its own report, memory and proposal history.
• Providers, models, and inference profiles as three separate layers, so routing is explicit and the blast radius of a change is visible before you make it.
• A European route, offered rather than imposed. Onboarding highlights Melious.ai, an OpenAI-compatible EU endpoint serving open-weight models. You bring your own key and hold your own contract with whoever you pick.
• Works with everything else too: OpenAI, Anthropic, Mistral, Google, Alibaba, Nebius, OpenRouter, Ollama, or any OpenAI-compatible endpoint, including a custom base URL pointed at your own gateway.
• Agent templates and souls: a template defines a responsibility, a soul defines voice, tone bounds, coaching style, and an anti-sycophancy policy. Both are versioned, both are improved through reviewable 1-on-1 sessions, and both can be rolled back.
• Grievances and 1-on-1s. When an agent annoys you, say so — no form and no magic phrase required. It records the grievance and brings it up in the next 1-on-1, where the two of you reconcile what changes. The result is an assistant that evolves rather than one you configure once.
• Contextual skills for work that does not need a durable agent, including the prompt generators described above.
• Usage and impact: tokens and requests by period, category and model for every cloud call, plus cost, energy and CO₂e wherever the provider reports them — currently Melious. Other providers return token counts only, so those columns stay empty rather than being estimated.
• Turn all of it off. Lotti is useful without any inference configured.

Sync and data

• End-to-end encrypted sync across your devices, relayed by a Synapse homeserver whose operator never holds a key — see the vision for how it works.
• The first device is set up against a homeserver with the included tools/matrix_provisioner CLI, which creates the sync account and encrypted room and produces a single-use pairing bundle — or, in builds that offer it, by subscribing to the hosted Lotti Sync service from the app. On Linux you can also enter an existing Matrix account directly.
• Every device after that pairs from one you already use: scan a QR code, compare a check code shown on both screens, then complete emoji verification. The pairing QR carries the sync account’s credentials, so treat it like a password. Until verification finishes, the new device receives ciphertext it cannot read.
• Your data is a file you already have. Local SQLite with a documented schema, plus attachments on the filesystem.

Two databases, human-in-the-loop by construction

Lotti keeps what you said and did and what an agent thinks in different databases on disk.

The user database is the system of record: tasks, notes, audio, photos, time recordings, journal entries, people, events. It is treated with care, and agents do not write into it freely.

The agentic database is agent working memory: definitions, memories, wake history, reasoning traces, reports, proposals. It may grow and it may be pruned. It can be thrown away and rebuilt without losing anything that matters about you.

The rule matters because of how it is enforced. Agent-authored content sits in a different file on disk and reaches the user database only through a code path that requires your approval, or through one of the pre-approved paths described above and defined by ADR 0102. Every other change an agent wants to make to your record is a proposal you confirm or dismiss. Both databases sync between your devices the same way.

Privacy, sovereignty, and where your compute happens

Three different claims get flattened into the word “private.” Lotti makes all three. They are worth separating, because they fail in different ways.

Your record is not collected

Lotti collects nothing — the record stays on your devices, as described in the vision. Two things do leave, both because you configured them: ciphertext to the homeserver you chose, and inference requests to the provider you chose (plus, if you turn on pull request tracking, requests to the GitHub API).

The other side of that: there is no server-side backup and no account recovery, because no server holds a readable or lasting copy of your data. Sync is the redundancy story — but it is not automatic history. Pairing a device gives it everything written from then on; your existing settings and back catalogue arrive only when you run Send settings and Send message history from the device that already has them. Until you do, the new device is a live peer, not a backup. Do that early, and a second device means losing one costs you nothing.

Keep an ordinary backup as well, since replication covers hardware loss and a backup covers everything else. Your record is a SQLite database sitting on your own disk with a documented schema, so reading it takes a query rather than permission. Take a consistent copy with VACUUM INTO while the app is running, and bring the attachments directory along with it, because audio and images live on the filesystem rather than in the database.

On a phone the database sits inside the app sandbox, so the practical route there is to pair a desktop and take the copy from that peer.

Deleting works the other way round, and the order matters: delete entries in the app rather than deleting the database files. Purging removes an attachment by walking the deleted rows that still point at it, so a database removed first leaves its audio and images orphaned on disk. Paired devices and ordinary backups keep their own copies either way, and anything already sent to a provider is subject to that provider’s retention, not yours.

What this does not protect against: a compromised device. The on-device SQLite files live inside your OS user account and are not separately encrypted by Lotti today. That includes the sync session: its access and refresh tokens, and its Olm identity and Megolm session keys, which the Matrix SDK stores pickled under a key derived from the account ID rather than from a secret. Anyone who can read your files can read your record and impersonate that device. The sync password, AI provider keys and GitHub token are the exception: they sit in the OS keystore. App-level at-rest encryption is a candidate for the roadmap; until it lands, rely on your operating system’s full-disk encryption, give Lotti’s on-device data the same care you would give any personal app on the same machine, and weigh that before putting your most sensitive categories in it.

Your sync relay does not need your trust

Sync runs over Matrix with Vodozemac for end-to-end encryption, through a Synapse homeserver you can run yourself or rent from anyone. Its operator does not need to be trusted with your data: the relay only ever handles ciphertext, keys go only to devices you verified, and events from anything else are dropped. Both databases sync this way. The details are in the vision.

What this does not protect against: metadata and availability. Whoever runs the homeserver can observe your account, which devices synced and under what device names, from which IP addresses, when, and roughly how much. Not what. They can also stop relaying, and how long the relay keeps ciphertext depends on whether its operator enables retention.

Your inference is a routing decision

Lotti has no inference backend. Every AI call goes to a provider you configured, under your own account and API key, and you choose per category which provider that is. Work can go to one provider while your journal goes to another, or to none at all. That granularity is the entire point.

Prefer zero data retention. The provider you route a category to sees what that category sends it. The safest cloud choice is a provider whose terms commit to zero data retention — no prompts or responses kept after the request, no training on them. Many providers offer that only on request or on specific plans, so read the terms rather than the marketing page.

Onboarding highlights a European route. Melious.ai is a German company routing open-weight models across a network of EU infrastructure providers. They state that requests are not used for training and that data stays under European jurisdiction. Those are their claims, on their terms, and worth reading in full before you rely on them. Directories such as staysin.eu list Melious, but they are compiled from providers’ own published material, not from an audit.

Mistral, Google, Alibaba, OpenAI, Anthropic, OpenRouter, and any other OpenAI-compatible endpoint work equally well. Lotti does not rank them, does not summarise their terms, and cannot verify anyone’s claims about jurisdiction, retention, or training. Picking a provider is your decision and the due diligence is yours.

What Lotti does instead is refuse to let the decision be invisible:

• Routing is configured per category, ahead of time, rather than negotiated in the moment — so which provider a given piece of work goes to is a setting you chose, not a prompt you clicked past. The flip side is that there is no just-in-time notice at the point an agent or a transcription fires; the onboarding and settings screens are where that decision is made and shown.
• A friendly display name is not the security boundary. The provider detail view shows the actual base URL, the models attached to it, and every profile that depends on them.
• Usage & Impact logs every request that left the machine and which model served it, with token counts throughout and cost, energy and CO₂e wherever the provider reports them.

A LAN endpoint is worth the same scrutiny. “Local” means it did not go to a hosted provider, not that no network hop occurred.

What this does not protect against: a provider that does not do what its terms say. Nothing in Lotti can verify that, and neither can anyone else from the outside. What Lotti can do is show you every request it made, where it went, and what it cost.

Local models and the sweet spot for agents

Some of Lotti runs well on your own machine today. Speech is fully offline via Whisper or Voxtral, and a local inference server handles lighter jobs. Lotti connects to:

• Ollama and oMLX (MLX on Apple Silicon), which have their own provider types in Lotti’s settings;
• anything else that serves an OpenAI-compatible API, through the OpenAI Compatible provider: Python-based servers such as vLLM, SGLang and mlx-lm, as well as llama.cpp’s llama-server and LM Studio.

The agents are a different matter. Their sweet spot right now is models of the size and capability of DeepSeek V4.1 Flash and GLM 5.3 Flash, both of which perform particularly well on Lotti’s agent workloads. The models people typically run locally are not that far behind on correctness, but they are slow: Qwen 3.8 27B, for example, lands only a few points below them, yet is far slower even on cloud inference, occasionally stalls, and misses the day planner’s 30-second budget (the evaluation). On a personal machine that gap only widens: even an M4 Max with 128 GB of RAM does not yet run the agents fast enough to keep up with daily use. Personal hardware is improving quickly, and closing that gap is the long-term direction.

LottiGym is how new models are judged. Models enter the market every few weeks, and a benchmark score says little about whether one can run a task agent. LottiGym runs a candidate through Lotti’s real agent harnesses — task, goal, relationship, Daily OS planning and query exercises, several samples each — grades the results, and records correctness, cost and latency side by side (recent results). A new model earns a recommendation by passing it:

python3 tool/lotti_gym.py assess --model MODEL_ID

The practical answer today is the cloud, with zero data retention: run the agents on a sweet-spot-class model from a provider whose terms commit to keeping nothing, and keep speech fully offline with Whisper or Voxtral if you like. Image generation has no local path yet — cover art goes through Gemini or Alibaba. As personal hardware gets faster, models of that class are expected to move onto your own devices, until a cloud provider is a choice rather than a necessity.

Input on running Lotti locally is very welcome. If you have a model, quantisation, runtime or hardware setup that keeps up with the agents, please share it in a GitHub issue or on Discord.

Energy is a routing decision too

Inference runs in a physical building on a specific grid. The Usage & Impact view reports tokens and requests for every cloud call, broken down by category and by model, so “what did my thinking cost” has an answer rather than a vibe.

How complete that answer is depends on the provider. Cost, energy, CO₂e and water are recorded when the provider returns them with the response, which today means Melious; every other provider reports token counts only, and those columns stay empty rather than being filled with an estimate. That is a real limit on the dashboard: route your work through a provider that discloses nothing, and the impact of that work is not something Lotti can show you.

The route highlighted in onboarding publishes power usage effectiveness per datacenter and carries Green Web Foundation verification, which is more than most disclose. Running a model locally moves the cost onto your own hardware and your own grid, which is a different trade rather than a free one, and the dashboard does not measure it.

Choosing a renewable-powered route avoids outsourcing the health and climate cost of fossil-powered compute, gas turbine generation in particular, to communities with less power to refuse it. That is a reason to care where your tokens get processed even if you do not care who reads them.

If none of that satisfies you

Turn AI off. Configure no provider at all, or point every category at Ollama with local Whisper for speech. Lotti is a complete task manager, time tracker, and journal without a single inference call leaving the machine.

In development

Built and documented, but not enabled in a default build. Turn it on under Settings → Sections, and expect it to change.

• Daily OS turns a spoken or typed check-in into a plan for one day. It reconciles what you said against your real tasks, drafts an agenda against your actual capacity, and presents every proposed change with its old time, its new time, and the planner’s reason. Nothing is committed until you hold the confirm control. Wrap-up at the end of the day separates what you finished from what carries forward. Documentation

Also under way, with no switch to flip yet: record provenance, so that every entry is signed by its author and the record can prove who wrote what — see the vision.

Next agent types on the roadmap: week planners, long-term commitment monitors, and effort-against-goals balancers — same building blocks, different jobs. Designed but not yet built: learning quizzes. See the roadmap.

Pricing and sustainability

Linux is and will always be free, with maximum functionality. No paywalls, no upsells. The same promise extends to any future fully open-source mobile operating system. The one thing it does not include is hosted sync infrastructure — running a homeserver is a real cost, so you either self-host Matrix or bring your own.

On other platforms, the basics stay free too. Task and journal capture, the task agent, voice transcription, the everyday loop.

More advanced agent features may eventually be in-app purchases on platforms where IAP is the norm (iOS/macOS, Android, Windows). The candidates: day- and week-planner agents, overarching project-management agents, longer-horizon commitment monitors. The exact split is not set yet.

Documentation

• Manual — the full guide, available in 11 languages. Start with the mental model if you want to understand the shape of the thing before installing it.
• Connect an AI provider — local Ollama or cloud Gemini, and the models and profiles around them
• Task management and voice capture — the everyday voice-to-checklist workflow
• Architecture — the two databases, vector clock sync, on-device inference
• Knowledge bundle — how the app actually works at runtime, subsystem by subsystem, written for contributors and coding agents alike; start with security and privacy and sync for the details behind this page
• Background — why this exists
• Privacy policy · Security policy
• Roadmap

Building it yourself: install Flutter via FVM (the repo includes .fvmrc), then make deps, make analyze, make test, fvm flutter run -d <device>. Full setup, including the Linux audio-codec and emoji-font packages, is in docs/DEVELOPMENT.md.

Status

The application is in active daily use and the agentic layer is real, working, and shipping. Development happens in the open, and the changelog is the honest version of what changed.

The agentic layer moves fastest: soul and template ergonomics, grievance handling, and pruning strategies are all under active development, and feedback there is especially useful. Local image generation, at-rest database encryption, and signed provenance do not exist yet.

The manual, including every screenshot in all 11 languages, is generated from a deterministic fixture workspace, so the documentation cannot quietly drift from the build it documents.

Stack: Flutter and Dart across five platforms, local SQLite via Drift with ObjectBox for embeddings, Whisper and Voxtral for on-device speech recognition, Matrix and Vodozemac (Olm/Megolm) for the encrypted sync transport, Glados for property-based tests, TLA+ and TLC for the formally verified sync protocol and agent runtime.

Contributing

The easiest way in is to say hi on Discord. Beyond that, two things genuinely help, and one thing to know up front.

Welcome:

• Issues and bug reports — the best place to start. Tell me what broke and how to reproduce it.
• Translations — new languages and corrections to existing ones. AI-assisted translation is fine provided you contribute from a real-name, established GitHub profile. Translation PRs are the one kind that gets merged.

Not accepted: code pull requests. Review capacity is the binding constraint, Lotti holds people’s personal data on their own devices, and AI-generated code now arrives far faster than it can be reviewed carefully. An unsolicited code PR will be closed unmerged, with thanks and without review — so please open an issue instead of writing the patch. It is more useful to me and cheaper for you.

CONTRIBUTING.md has the details, including what makes a translation PR mergeable.

License

GPL-3.0. See LICENSE.

Acknowledgments

Thanks to the Flutter team, the Qwen and Mistral teams for their open-weights models, OpenAI for the Whisper weights, the Ollama project, the Matrix.org community, the Vodozemac authors, and everyone contributing translations, issues, and ideas.

Built in public. GitHub · Substack