DeepSeek Harness Remote
DeepSeek Harness Remote 可將手機或另一台電腦與你運行 DeepSeek Harness 的電腦安全連接,隨時隨地繼續你的工作,方便隨時查看進度並繼續工作。
AI 翻譯
個人 GitHub 帳號下公開儲存庫的擁有者可以登入並驗證所有權,自行管理介紹,無需管理員核准。
登入並產生驗證檔案,在 30 分鐘內將其提交至此儲存庫的預設分支。其他分支或分叉儲存庫中的檔案無效。然後在下方檢查檔案,即可無需管理員核准直接管理專案。請保留檔案以維持管理權限。
如需登入,將前往登入頁面。登入後請回到此處,再次點擊所選的驗證按鈕。
在專案資料夾中執行 /nomorevibe verify,即可驗證所有權並自行管理介紹。
一分鐘完成驗證Psych Copilot — post-session notes and reflective practice for therapists (working name)
翻譯準備中
取自儲存庫 README
正在載入完整 README。
檢視完整 README ↗A calm assistant for therapists, used after a session. It:
It deliberately does not give live advice, suggest diagnoses or score risk (see docs/decisions.md, ADR-4).
Demo mode. Until the EU AI provider is connected and the privacy review (DPIA) is done, use role-play sessions only. The app shows a banner to remind you.
You need two free tools, installed once:
| Tool | Why | Check it's installed |
|---|---|---|
| Node.js 24 | runs the website | node --version |
| uv | runs the Python worker (speech-to-text and AI) | ~/.local/bin/uv --version |
(Both are already installed on Shurik's Mac. To install uv elsewhere: curl -LsSf https://astral.sh/uv/install.sh | sh.)
Open Terminal in the project folder and run:
npm run setup
Then create your settings file (skip this if .env already exists — it does on Shurik's Mac):
cp .env.example .env
Open .env and fill in BETTER_AUTH_SECRET (any long random text; openssl rand -base64 32 makes one) and the two database passwords.
npm run up
This starts the database, the website and the background worker. Then open http://localhost:3210.
The first time, also load the demo data (a demo therapist with five role-play clients):
npm run db:seed
Emails aren't sent on your laptop. The quickest way in is http://localhost:3210/dev/login: one click signs you in as the demo therapist (admin), a therapist waiting for approval, or a brand-new user. To try the real sign-in screen instead, ask for a code and find it at http://localhost:3210/dev/mailbox. Both pages exist only on your laptop, never on the live site.
This turns a written role-play into audio with the Mac's built-in voices, then feeds it through the whole pipeline (transcription → speaker labels → audio deletion → note and reflection):
cd services/worker
~/.local/bin/uv run python tools/make_roleplay_audio.py ../../evals/sessions/02-mi-alcohol.json
~/.local/bin/uv run python tools/simulate_session.py --email [email protected] --client "Client B" --audio .data/roleplay/02-mi-alcohol --mode in_person --calibrate
Then open Today in the app — the session appears under "Being prepared", and is ready within a couple of minutes on a laptop.
To try real recording, add a client, record consent, and choose Start session → In the room (any browser) or Online call (Chrome or Edge: share the meeting tab with "Share tab audio" ticked).
Out of the box the AI is in demo mode: notes and reflections are clearly marked placeholders, so everything else can be tried without any account.
To use Claude for development (role-play data only):
.env, set LLM_PROVIDER=anthropic and paste the key into ANTHROPIC_API_KEY=.Ctrl+C, then npm run up).Before any real client data: switch to LLM_PROVIDER=bedrock (Claude on AWS in Frankfurt) — see ADR-3 — and complete the checklist at the end of docs/privacy-architecture.md.
An experiment for Shurik and the Clinical Lead only (see docs/decisions.md, ADR-10). Role-play sessions only.
.env, list the sign-in emails allowed to use it: [email protected],[email protected]. Restart (npm run up).Without an AI key the reads are [Demo] placeholders. To try it without a microphone:
cd services/worker
~/.local/bin/uv run python tools/simulate_live.py --email [email protected] --client "Client A" --audio .data/roleplay/01-cbt-sleep-worry --mode online_call --seconds 90
| Command | What it does |
|---|---|
npm run up | Start everything (database, website, worker) |
npm run db:seed | Reset the demo account with five role-play sessions |
npm test | Run all automated checks (security, consent, deletion, evidence, speaker labels) |
npm run eval | Run the five role-play sessions through the AI and create a rating report in evals/reports/ for the Clinical Lead |
npm run signups | Save demo requests and waiting-list sign-ups to .data/signups.csv |
apps/web/ the website and app (Next.js)
messages/ all wording, English (en.json) and Russian (ru.json)
src/app/(marketing) public pages: landing, privacy, consent template, legal
src/app/(app) the signed-in app: Today, Clients, Sessions, Supervision, Settings
services/worker/ background worker (Python): speech-to-text, speaker labels, AI steps
prompts/ AI instructions (versioned; clinical review needed)
packages/shared/ modality rubrics (CBT, MI, person-centred, ...) — clinical review needed
db/migrations/ database structure, security rules and the consent gate
evals/ role-play sessions, forbidden terms, rating reports
docs/ decisions (ADRs), privacy architecture, sub-processors, clinical review list
reference/prototype/ place the Clinical Lead's original prototype here (not published)
docs/decisions.md — every significant choice and why (ADR-1 to ADR-9)docs/privacy-architecture.md — data flows and protections (input for the DPIA)docs/subprocessors.md — companies that would process datadocs/clinical-review.md — everything the Clinical Lead needs to review