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Lattice AI

Your model is the voice you use today. Your Brain is the asset you keep.

모델은 갈아타도, 내 지식은 내 컴퓨터에 남는 로컬 우선 AI 브레인.

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v10.8.0 Living Brain walkthrough

Chat, files, folders, notes, and web pages all flow into one durable knowledge graph on your computer. Any model — local MLX or cloud — can speak with that memory. Nothing leaves your machine without explicit consent.

대화·파일·폴더·웹페이지가 전부 내 컴퓨터 안의 지식 그래프로 쌓이고, 어떤 모델이든 그 기억을 이어받아 대화합니다.

What You Can Do

Chat with a Brain that remembers — every conversation grows durable, source-linked memory Brain Chat See how knowledge connects — a real relationship graph, not a file list Memory Graph
Capture anything — files, whole folders, notes, screenshots, web pages Capture Automate with review — agent changes become proposals you approve first Review Center
Pick a model in one click — recommended local models for your hardware Recommended Models Stay in control — audit, roles, retention in a separate admin surface Admin Console
Watch a file become memory — three named steps, not a pipeline diagram Material to memory Say how much it may do alone — one dial in plain words; dangerous actions stay blocked either way Settings

Why Lattice AI

  • Own your memory — knowledge lives in a local SQLite Brain you can back up, export, inspect, and restore (.latticebrain encrypted archive).
  • Model-independent — switch between local MLX models and cloud models without rebuilding context from zero.
  • Honest by design — the Brain tells you when retrieval context is limited, when captured pages extracted poorly, and when the vector index is catching up.
  • Safe automation — automations are consent-first drafts; edits to existing content always pass through a reviewable proposal with a diff.

매번 AI에게 프로젝트 맥락을 다시 설명하고 있다면, 지식이 여러 서비스에 흩어져 있다면, 그 기억을 특정 회사가 아니라 내가 소유하고 싶다면 — Lattice AI가 그 브레인입니다.

Quick Start

pip install ltcai        # or: npm install -g ltcai
LTCAI                    # then open http://127.0.0.1:4825/app

Apple Silicon local models: pip install "ltcai[local]". Desktop app (Tauri) ships as a dmg on each GitHub Release.

First-run flow — wake the Brain, pick the owner, load a recommended model:

Login Model install Model library

Screenshot index and capture notes: output/release/v10.8.0/SCREENSHOT_INDEX.md

Current Release

The current release is 10.8.0 — Within Reach:

10.7.0 rearranged all twelve screens. 10.8.0 is about the things that were already there but out of reach: a button below the fold, a message in a language you did not choose, a file a small model nearly produced.

  • The first three screens fit on the screen. Onboarding rendered a 390px decorative Brain above every step, so on a 1440×900 display the login form, the recommended model and the install progress all began below the fold — the first thing the product asked a new person to do was scroll. The organism is a hero on the welcome step and a 104px mark after it; the welcome screen itself went from 1175px of content to 770px, so its primary button is visible without scrolling.
  • One language per screen, including the ones the server writes. API messages were literals at the raise site, written in whichever language the endpoint's author was thinking in — Korean from auth.py, English from browser.py. latticeai/core/messages.py is a catalog with both, resolved from X-Lattice-Language (falling back to Accept-Language); the browser extension, which showed English and Korean in the same popup, has one too. tests/unit/test_server_messages.py fails on a message that exists in only one language, and on a router that reverts to a hardcoded literal.
  • A small model's near-miss is no longer thrown away. File generation picked the longest rejected reply to repair, so a 900-character apology beat a truncated-but-real HTML document; it now scores candidates by how close they are to being a file. A byte-identical repeated reply buys one extra attempt with a prompt that names the repetition. Prose file types (.md, .txt) had no validation at all and would save "Sure! Here is the document:" as the document.
  • Re-indexing costs what changed. The incremental vector rebuild materialised every node and chunk in memory and asked one SELECT per item whether it had changed. It streams now, with a single prefetched hash map — and a settled index performs zero embeddings, asserted rather than assumed.
  • More ground under the frontend. Statement coverage 47.35% → 52.26%, 424 → 504 tests, including the app shell (App.tsx, previously 0%), the admin console (0%), the folder picker (2.7%) and the API client.
  • Evidence bound to this build. output/release/v10.8.0/ holds the twelve capture screens, walkthrough gif/webm, and the asset-manifest / mock-server fingerprints that lint re-checks before merge.
  • Exact artifact names only. Publish paths list dist/ltcai-10.8.0-* and ltcai-10.8.0.tgz — never dist/*.

Release notes: RELEASE.md · Full history: docs/CHANGELOG.md

Expected artifacts for 10.8.0 release must use exact filenames:

  • dist/ltcai-10.8.0-py3-none-any.whl
  • dist/ltcai-10.8.0.tar.gz
  • ltcai-10.8.0.tgz
  • dist/ltcai-10.8.0.vsix
  • src-tauri/target/release/bundle/dmg/Lattice AI_10.8.0_aarch64.dmg

Do not use wildcard artifact uploads. Package registry publishing remains owner-run.

Architecture At A Glance

FastAPI on localhost is the source of truth; the React/Vite frontend and the Tauri desktop shell sit on top; the independent lattice_brain package owns the graph, memory, ingestion, and portability. Local-first by default — cloud calls, downloads, Telegram, and update checks are opt-in.

See ARCHITECTURE.md for details and docs/DEVELOPMENT.md for the developer workflow (npm install && npm run dev, validation via npm run lint, npm run test:unit, npm run test:visual).

Known Limitations

  • External package registries are owner-published and can lag behind GitHub.
  • PostgreSQL/pgvector is optional scale/migration tooling. SQLite remains the live local Brain store in 10.3.0.
  • Docker, model downloads, cloud model calls, Telegram, Brain Network, and update checks require explicit user action.
  • Conversation does not fabricate answers when no model is loaded. Agent and workflow simulation without a loaded LLM is deterministic and LLM-free (it does not call a model) — labeled as such, never presented as autonomous model success.
  • Some backend-generated messages (for example the Postgres DSN notice) are produced server-side in English and are shown as-is; server-side i18n is not part of 10.3.0.

Release History

Version Theme
10.8.0 Within Reach
10.7.0 Plain Surface
10.6.4 Loud Limits
10.6.3 Loud Limits
10.6.2 Ask First
10.6.1 First Things
10.6.0 Promoted Panels
10.5.0 Everyday Words
10.4.0 Named Ground
10.3.0 Measured Ground
10.2.0 Load-Bearing Fixes
10.1.1 Reachable Boundary
10.1.0 Hybrid Brain
10.0.1 One Source of Truth
10.0.0 Plain Language
9.9.9 Lean Shell
9.9.8 Autonomy Dial
9.9.7 No Gaps Left
9.9.6 Same Brain Everywhere
9.9.5 Closed Gaps
9.9.4 Durable Loops
9.9.3 Closed Loops
9.9.2 Artifact Trust
9.9.1 Clean Foundations
9.9.0 Fail-Closed Trust
9.8.0 Honest Knowledge Pipeline
9.7.0 Proactive Hybrid Brain
9.6.0 Trusted Agent Loop
9.5.0 Command Center
9.4.0 Question-Driven Everyday Automation
9.3.0 Proactive Brain Intelligence
9.2.0 Model-Agnostic File Generation
9.1.0 Code Review Completion & Fail-Closed Runtime
9.0.0 Code Review Closure & Runtime Cleanup
8.9.0 Scoped Memory & Tool Policy Hardening
8.8.0 Brain Core Extraction & Recall Proof Hardening
8.7.0 Runtime State Hygiene & Release Evidence Refresh
8.6.0 Desktop Capture & Navigation Reliability
8.5.0 Tool Registry Readiness & Config DI
8.4.0 Action-Aware Brain Chat
8.3.0 Orchestrated Brain Readiness
8.2.0 Brain Brief
8.1.0 Intuitive Brain Home
8.0.0 Runtime Architecture Contract

Per-release details: RELEASE_NOTES.md

Documentation

License

MIT. See LICENSE.

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