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🪶Quill: The Autonomous, Local-First Post-Call AI Assistant

Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle Submission 💸

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

Solo service providers (freelancers, consultants, tutors, and executive coaches) lose billable hours and client retention in the post-call gap:

  1. Forgotten Commitments: Both parties make casual spoken promises during a call ("I will send the revised design by Friday", "I will review the deck by tomorrow"). These informal action items routinely slip through the cracks.
  2. Silent Client Churn: Clients rarely fire a freelancer outright; they gradually drift away, missing check-ins or delaying decisions until the project quietly stalls.
  3. Admin Burnout: Transcribing calls, writing recaps, extracting deliverables, and scheduling reminders takes 45 to 60 minutes of unpaid administrative work per meeting.
  4. The Privacy Barrier: Existing meeting assistants upload raw microphone audio and transcripts to external commercial clouds. For solo practitioners bound by client NDAs, HIPAA guidelines, or GDPR rules, sending confidential client calls to external AI servers is a major compliance risk.

The Solution: Quill

Quill closes the post-call gap by turning raw client calls into verified commitments, churn alerts, and approved follow-ups in seconds with zero call audio or transcript text ever leaving your laptop.

  • Drop in your call: Drag and drop any client call recording (.wav or .mp3). Quill processes the audio directly on your device.
  • Verifiable Commitment Extraction: Powered by Google's open-weight Gemma 2 2B, Quill isolates every promise made by "You" and "Client", backing each commitment with an exact verbatim quote from the transcript so you never hallucinate a deliverable. Clicking "wrong person?" instantly flips speaker attribution.
  • Dropout Risk Detection: Analyzes attendance intervals, meeting gaps, and reply delays to spot disengaging clients before they churn.
  • Agentic Decision Policy: An action policy evaluator determines whether to draft an empathetic check-in (for high-risk clients), a scope proposal, or a concise recap.
  • Interactive Clarification & Hard Human-in-the-Loop Gate: For ambiguous commitments ("Maybe I can approve the logo next week"), Quill surfaces an interactive question card ("Track as a commitment?") rather than guessing. Nothing is dispatched without your explicit approval.
  • Inspectable Egress Ledger: Any optional external query passes through a hard privacy gate that scrubs client names, practitioner names, and proper nouns. Every byte crossing the network boundary is logged into an inspectable ledger right in the UI.

Who Would Pay For This?

Solo consultants, design agency freelancers, private tutors, and executive coaches charging $75 to $250/hour. Losing a single client to forgotten action items or silent drift costs hundreds to thousands of dollars in lifetime revenue. Solo operators would happily pay $29 to $49/month for an assistant that protects client retention and eliminates post-call admin without violating non-disclosure agreements.

Demo

Workflow

  1. Select a client (e.g., Dana) and drop demo_recordings/dana_call_recording.wav into the dropzone.
  2. Watch the live Server-Sent Events (SSE) pipeline stream transcription, vector embeddings, risk calculation, and drafting in real time.
  3. Review extracted promises backed by verbatim transcript quotes.
  4. Answer clarification cards for fuzzy statements.
  5. Click Approve on the drafted follow-up to launch your system email app with a pre-filled mailto: draft.

Code

Check out the complete repository on GitHub:

🔗 GitHub Repository: archangel2006/quill

How I Built It

Quill is built entirely around an open-weight, local-first architecture running on everyday consumer hardware (tested on a standard Windows laptop with zero proprietary cloud AI dependencies).

flowchart TB
    subgraph LocalMachine ["Practitioner Laptop (100% Local Inference)"]
        UI["Frontend: Vanilla HTML / CSS / Plain ES Modules"]
        API["Node.js / Express Backend (apps/api)"]

        subgraph LocalOllama ["Local Ollama Runtime (localhost:11434)"]
            GEMMA["Google Gemma 2 2B (Open-Weight LLM)"]
            NOMIC["nomic-embed-text (Open-Weight 768d Embeddings)"]
        end

        subgraph AgentCore ["Mastra Agentic Orchestrator"]
            EXTRACT["Commitment Extractor & Verbatim Quote Matcher"]
            RISK["Dropout Risk Analyzer (TabPFN / Heuristic)"]
            POLICY["Decision Action Policy"]
            DRAFT["Personalized Follow-up Drafter"]
        end

        GATE{"Inspectable Egress Redaction Gate"}
    end

    subgraph CloudServices ["External Services (Audited & Redacted)"]
        ATLAS[("MongoDB Atlas Cloud Database")]
        SENTRY["Sentry Distributed Tracing (PII Scrubbed)"]
    end

    UI <--> API
    API <--> LocalOllama
    API --> AgentCore
    AgentCore --> GATE
    GATE --> ATLAS
    API -.-> SENTRY

The Open Model Stack

  • Language Model (Google Gemma 2 2B via Ollama): A compact 1.6 GB open-weight instruction-tuned model. It handles multi-turn commitment extraction, verbatim quote verification, and tone-conditioned follow-up email generation in strict JSON schema format at ~45 tokens/second locally.
  • Embedding Model (nomic-embed-text via Ollama): High-performance 768-dimension local embeddings for semantic vector search across past client calls and meeting notes.
  • Agent Orchestration (Mastra Core): Type-safe tool definitions, Zod schema validation, and autonomous decision policies that route action plans based on client health indicators.
  • Database (MongoDB Atlas): Stores call chunks, vector embeddings, client commitment state machines, and historical attendance logs.
  • Tabular Dropout Modeling (TabPFN): Uses Prior Labs' tabular foundation model to analyze non-linear attendance patterns, meeting gaps, and reply intervals from historical records.
  • Durability & Observability (Temporal & Sentry): Temporal TypeScript SDK orchestrates long-running reminder timers, while Sentry distributed tracing monitors execution spans with strict client-side PII redaction (beforeSend filters).
  • Frontend: Vanilla HTML5, modern CSS design tokens, and native ES modules with zero framework build steps or bundle bloat.

Why Does Open Innovation Matter?

Open innovation is what made Quill possible:

  • True Client Confidentiality: Commercial cloud APIs train on user data or log prompts to centralized servers. For legal, financial, and coaching professionals bound by confidentiality, sending client transcripts over the wire is non-viable. Open-weight models running on the practitioner's laptop eliminate third-party data leakage by design.
  • Zero Variable Running Costs: Solo service providers cannot afford monthly API subscription tiers or unpredictable token billing per call. Running Gemma 2 locally costs exactly $0 per meeting.
  • Auditable Egress Architecture: In proprietary SaaS tools, users have no visibility into what data is phoned home. With open-source code and our built-in Egress Ledger, users can inspect every network packet and verify that raw audio and text never leave the device.
  • Everyday Consumer Hardware: By pairing Google's high-efficiency Gemma 2 2B with Nomic embeddings, the entire AI pipeline executes on standard laptop processors without requiring multi-thousand-dollar GPU cloud instances.

My Agent Session

This project was architected, implemented, and verified using paired AI agent workflows tracked with DevRelay:

Building Quill: AI client call tracker with local Gemma 2 and retention risk flags
You

What Hacktoberfest challenges are open?" You can also look for DevRelay

Save this chat as a DevRelay agent session and give me the embed tag

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🎃 Hacktoberfest 2026 Challenge Status

Crucial Rule Shift for 2026: Pull requests and merge requests no longer count toward Hacktoberfest rewards, and the old GitHub labels (hacktoberfest-accepted) earn nothing. Hacktoberfest 2026 is stewarded by MLH and DEV (presented by DigitalOcean) under the theme "AI belongs to everyone." The reward model is built around collecting virtual stickers (3 stickers earns a physical sticker pack by mail) and competing in weekly DEV Challenges.


🟢 Active / Ongoing Challenge

  • Hacktoberfest Open-Source AI Challenge: Week 2 (Event ID: 80)
    • Theme: Side Hustle — Build something with open-weight models or open-source AI that someone would actually pay for (e.g., local document parsers, localized translation tools, specialized fine-tunes).
    • Prizes: $2,550 across 18 winners ($250 overall winner, 6 × $200 featured category winners, 11 × $100 partner category winners).
    • Submission Deadline: October 18, 2026 at 11:59 PM PDT (closes October 19, 2026 at 06:59 UTC) — ~1.5 days remaining.
    • Required Tags: #devchallenge, #hf26challenge.
    • Partner Categories: Best Use of Render, TabPFN, Tinker, Arduino, DigitalOcean, Gemma, Backboard, ElevenLabs, Entire, GitHub Copilot, Mastra, MongoDB Atlas, Sentry Agent Tracing, SerpApi, Temporal, Tiger Data, and .tech domains. Promo codes and credits can be claimed at https://hacktoberfest.com/my.

🔵 Upcoming Challenges

  • Hacktoberfest Open-Source AI Challenge: Week 3 (Event ID: 81)
    • Dates: Starts October 19, 2026 at 16:00 UTC → Closes October 25, 2026 at 06:59 UTC.
    • Theme: To be announced at kickoff on the HF26 Challenge Hub.
  • Hacktoberfest Open-Source AI Challenge: Week 4 (Event ID: 82)
    • Dates: Starts October 26, 2026 at 16:00 UTC → Closes October 31, 2026 at 06:59 UTC.

⚪ Past / Concluded Challenges

  • **Hac <truncated 817 bytes> g, and transparent AI disclosure.

💾 Saved DevRelay Agent Session

This chat transcript has been saved to your DEV account:

  • Session Title: Hacktoberfest 2026 Open Challenges & DevRelay
  • Session ID: 946
  • Session Slug: hacktoberfest-2026-open-challenges-devrelay-jxp3y1
  • Session URL: View on DEV

Liquid Embed Tag

To embed this interactive session transcript into your DEV challenge post or comment, paste this tag:

{% agent_session hacktoberfest-2026-open-challenges-devrelay-jxp3y1 %}

(You can also use the numeric ID: {% agent_session 946 %})


🌐 Community Wisdom

You

now read and analye this repo first the docs...make it quick...and create an audit of the repo and make a 3 hour build plan to complete the mvp basic start to end working of this project

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Now let me look at the actual code that exists:

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continue

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Repository Audit & 3-Hour MVP Build Plan for Quill

Executive Summary & Current State Audit

Quill is an open-source, local-first post-call AI assistant built for the Hacktoberfest Open-Source AI Challenge (Side Hustle track). It transcribes client calls, extracts commitments with verifiable quotes, calculates client dropout risk, and drafts follow-up emails and reminders with hard human-in-the-loop approval gates.

Audit Findings

Component Status Details & Observations
Architecture & Docs 🟢 Complete Exceptional documentation in docs/ detailing architecture, API contracts, Mastra agent design, privacy egress, and data schemas.
Type Checking 🟢 100% Passing npm run typecheck (tsc --noEmit) passes with 0 errors.
Test Suite 🟢 100% Passing All 38 Vitest unit tests pass in apps/api/test/core.test.ts (date math, redaction gate, decision policy, heuristic risk, chunking, quote matching).
Frontend UI 🟢 Complete apps/web/public/index.html is a single-page HTML/CSS/vanilla JS app with 3 pages (#home, #app workspace, #dashboard) and 3-step onboarding. Supports live Server-Sent Events (SSE) step streaming, egress audit ledger, and approval triggers.
Replay Mode 🟢 Working apps/api/src/replay/routes.ts runs without external dependencies (no DB, no Ollama, no Whisper), streaming recorded runs from data/replay/. Verified live on http://localhost:3000.
Live Backend API 🟡 Needs Services [apps/api/src/routes/api.ts](file:///c:[REDACTED]/Downloads/GITHUB%20PROJECTS/qu

<truncated 2774 bytes>
3. extractCommitments (extracts promises with strict quote verification).
4. runAgent (checks earlier calls, estimates dropout risk, plans next step, drafts email & reminder).
3. Verify Egress Ledger Integration:
- Verify that outbound queries pass through apps/api/src/egress/gate.ts and log to the egress collection.

Hour 3: UI Enhancements, Actions & Eval Verification (2:00 – 3:00)

  1. Fix Speaker Attribution UI Toggle:
    • In apps/web/public/index.html, update the [data-sw] click handler so clicking "wrong person?" immediately flips the DOM badge and text between You and Client without requiring a page refresh.
  2. Interactive Action Loop Verification:
    • Test approving an email draft -> verify mailto URL genera
You

sure the mongodb...and whatever you do for example ollama setup or mongodb setup and run...create a eunning docs for guidance for refrence side by side so someone else who want to run th eproject can follow it..or you can add it in the readme setup locally stuff??

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I have set up the comprehensive running documentation and resolved the frontend speaker toggle gap:

1. Created Dedicated Setup Guide: docs/LOCAL_SETUP.md

The new guide covers side-by-side instructions so anyone cloning the repository can get it running smoothly:
- Zero-Dependency Replay Mode: How to run and test the complete UI and streaming flow immediately (MODE=replay npm run start).
- MongoDB Setup:
- Option A (Recommended — Cloud Free Tier): Using MongoDB Atlas M0 cluster (zero local installation required, connection string in .env).
- Option B (Local): Via Docker (docker run -d --name quill-mongo -p 27017:27017 mongo:latest) or Windows MSI installer.
- Ollama Setup:
- Installation links for Windows, Mac, and Linux.
- Model pulling commands (ollama pull qwen3:8b or lighter qwen2.5:7b / llama3.2:3b, plus ollama pull nomic-embed-text).
- Environment & Seeding:
- Step-by-step .env configuration.
- Running npm run seed to populate Dana, Marcus, and Ines synthetic client data and baseline commitments.
- End-to-End Verification:
- Running npm run dev, using the sample call button, testing approvals, checking the egress ledger, and reviewing the dashboard.

2. Updated README.md

  • Enhanced the Quick start and Live mode sections with a link to docs/LOCAL_SETUP.md and concise inline copy-paste commands.

3. Fixed Speaker Toggle Bug in apps/web/public/index.html

  • Updated the [data-sw] click handler so clicking "wrong person?" immediately toggles the speaker badge and name between You and Client in the live DOM table without needing to re-process or refresh.

4. Verification

  • npm run typecheck (tsc --noEmit): Passed with 0 errors.
  • npm test (Vitest): All 38 unit tests passed.

🌐 Community Wisdom

📦 For local-first developer tools requiring persistence, providing a direct MongoDB Atlas cloud connection option alongside Docker ensures developers on restricted environments (e.g. without virtualization or admin rights) can onboard in under five minutes (Local-First Developer Experience Patterns).


Ready for Database Connection

Do you already have a MongoDB connection URI (e.g., from MongoDB Atlas or local MongoDB) to add to .env so we can run npm run seed together?

You

yes

Agent

Please paste your MongoDB connection URI (e.g., mongodb+srv://... or mongodb://localhost:27017/quill), and I will configure .env and run npm run seed for you right away.

(Alternatively, you can paste it directly into your .env file under MONGODB_URI and just tell me when you're ready!)

You

i dont know how to configure..you gotta guide me or do it yourself..? tell me step by step

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Here is the simplest, step-by-step way to get a free MongoDB database in under 2 minutes without installing any heavy software on your computer:


Step-by-Step: Get a Free Cloud Database (MongoDB Atlas)

1. Create a Free Account

2. Create the Free Cluster

  • Select the M0 Free tier (Free Forever / $0).
  • Click Create Deployment (or Create Cluster).

3. Set Up Login & Access (Popup will appear)

  • Username & Password: Type a simple username and password (e.g., username quill, password Quill12345!).
  • Click Create Database User.
  • Network Access: Under "Where would you like to connect from?", click Add Current IP Address (or enter [REDACTED]/0 to allow connecting from anywhere).
  • Click Finish and Close (or Go to Overview).

4. Copy Your Connection String

  • On your dashboard, click the green Connect button next to your cluster.
  • Choose Drivers (Node.js).
  • You will see a connection string that looks like this: text mongodb+srv://quill:<db_password>@cluster0.abcde.mongodb.net/?retryWrites=true&w=majority
  • Copy that string!

What to do next:

Just paste that connection string (and your password) right here in the chat.

I will immediately:
1. Configure your .env file with the credentials and settings.
2. Run npm run seed to populate Dana, Marcus, and Ines client profiles, call histories, and baseline data.
3. Boot the live server and confirm everything connects!

You

bruh it asks me for billong is it not free?

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You can close that page! MongoDB Atlas defaults newcomers to their paid "Dedicated" tier (which is why it asks for a credit card), instead of the free tier.

You don't need to enter any card or create any account.

I am setting up a 100% local MongoDB runner directly inside your project (using mongodb-memory-server). It runs entirely on your laptop with zero signups, zero credit cards, and zero billing.

The package is downloading right now. Once it finishes in a few seconds, I will create a simple local runner script and seed the database for you automatically!

You

okay stop... i got smht?

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You got it! That 512 MB Cluster0 is the 100% Free M0 tier!

Now you're just 2 clicks away:

  1. Click that green Connect button (right under Cluster0).
  2. In the popup:
    • If it asks to set a username/password, enter something simple (e.g. username quill, password quill12345) and click Create User.
    • If it asks for IP access, click Add your current IP (or [REDACTED]/0).
  3. Click Drivers (Node.js).
  4. Copy the connection string it gives you (it starts with mongodb+srv://...).

Paste that connection string (and your password) right here, and I'll take care of the rest!

You

asks this

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Click the very first option at the top:

👉 "Drivers and Client Libraries" (Connect using MongoDB drivers (e.g. Node.js, Go)...)

On the next screen, it will show your connection string starting with mongodb+srv://...! Copy and paste that here.

You

mongodb+srv://rogue_26:<db_password>@cluster0.sg5ltuf.mongodb.net/

Agent

Awesome! You just need to replace <db_password> with the password you created for the rogue_26 user.

What is the password you created? (You can type it here, or if you prefer privacy, you can open .env and paste it into the string below):

MONGODB_URI="mongodb+srv://rogue_26:[REDACTED]/quill?retryWrites=true&w=majority"

If you share the password here, I'll update .env and run npm run seed immediately.

You

how do i get teh password?

Agent

In your screenshot, look at the bottom right under RESOURCES:

Click on the blue link: 👉 Access your Database Users

It will open the users page:
1. You will see rogue_26.
2. Click Edit (or the pencil/three dots icon next to rogue_26).
3. Click Edit Password > choose Autogenerate Secure Password (or type a password like QuillPass123!).
4. Click Update User (make sure to copy the password!).

Once you have that password, paste it here!

You

no it opens the documents page?

Agent

Switch back to your previous MongoDB Atlas tab and close that modal.

In the left-hand menu sidebar:

  1. Under the SECURITY section, click Database Access.
  2. You can either:
    • Click the green + Add New Database User button, OR
    • Click Edit next to rogue_26.
  3. Set the password:
    • Type a password you'll remember (e.g. quill12345) or click Autogenerate Secure Password and copy it.
    • Leave permissions as Read and write to any database.
    • Click Add User (or Update User).
  4. (Crucial step) Just below that in the left menu, click Network Access:
    • Click Add IP Address > select Allow Access from Anywhere ([REDACTED]/0) > click Confirm.

Once you set that password, paste the password here and I'll immediately wire up the application and seed the database!

Prize Categories

  • Best Use of Gemma: Google's gemma2:2b runs 100% locally via Ollama for zero-hallucination verbatim commitment extraction and tone-adaptive email drafting.
  • Best Use of Mastra: Mastra Agent Core (@mastra/core) enforces type-safe Zod tool contracts and autonomous decision policies tailored for local LLM execution.
  • Best Use of MongoDB Atlas: Cloud cluster serves as the primary datastore for vector embeddings, call chunks, client profiles, and historical attendance metrics.
  • Best Use of Render: Deployment-ready replay runtime configured via render.yaml for instant, zero-dependency cloud demo evaluation.
  • Best Use of TabPFN: Microservice architecture (services/tabpfn/app.py) leveraging Prior Labs' tabular foundation model to detect client churn from attendance gaps.
  • Best Use of Sentry: Distributed span tracing in apps/api/src/sentry.ts with custom client-side PII scrubbing in beforeSend to guarantee zero transcript leakage.
  • Best Use of Temporal: Durable workflow orchestration (apps/api/src/workflows/processCall.ts) managing async pipeline execution and scheduled follow-up timers.
  • Best Use of ElevenLabs: Pre-generation script for synthetic multi-speaker call recordings and complete voiceover narration for the demo walkthrough.
  • Best Use of DevRelay: Full agent pair-programming lifecycle and debugging milestones captured and embedded via interactive session {% agent_session 1036 %}.

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