This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
What I Built
Let’s be completely honest: nobody genuinely enjoys taking meeting notes or writing post-interview scorecards.
Scribbling down who said what, trying to track whether Priya promised the onboarding copy by Wednesday, and deciphering messy notes after a 60-minute technical interview is tedious, mentally draining, and stressful.
In honor of International Sloth Day and this week's "Slow and Steady" theme, I built MeetMind: a privacy-first, zero-effort meeting memory and interview coaching copilot powered by open-weight AI.
MeetMind is designed to let you channel your inner sloth:
- Zero manual note-taking: Sit back, sip your tea, and stay fully engaged in the conversation.
- Pure tab capture: Captures the meeting tab’s audio or Google Meet captions directly from Chrome—excluding your own background microphone noise.
- Evidence-grounded synthesis: Automatically structures meeting briefs and candidate reviews with strict separation between verbatim facts and AI interpretation.
- Cross-session memory: Ask questions across weeks of past meetings ("What did we decide about analytics launch?") without ever opening a doc.
Demo
Here is how MeetMind operates right from your browser:
- One-Click Capture: Click Start Recording on any Google Meet or web call tab. MeetMind captures tab audio cleanly using Chrome’s MV3 Offscreen API.
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Audio Preservation & Recovery: If your network stumbles, audio is automatically preserved in memory with 1-click retry and
.webmdownload backup—no lost meetings. -
Structured Briefings: In seconds, MeetMind generates clean, executive-ready markdown:
- Executive Summary: Core highlights.
-
Decisions Made: Explicit agreements tagged with
[Direct Evidence]. -
Action Items: Checklist with assignees and deadlines (
- [ ]). - Interview Coach Mode: Objective technical evaluation separating verbatim candidate quotes from AI coaching recommendations.
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Interactive Local Q&A: A built-in "Ask" panel that performs deterministic keyword retrieval across saved sessions with citation excerpts (
[S1]).
Code
đź’» GitHub Repository: https://github.com/aashutoshkumarbhardwaj/meetmind
The project consists of two clean, decoupled components:
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Chrome MV3 Extension:
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tabCapture+Offscreen Documentaudio capture pipeline - Visible Google Meet caption scraper with progressive speech de-duplication
- Built-in, zero-dependency client-side Markdown rendering engine with custom visual evidence badges
- Local session storage (up to 30 meetings preserved in-browser)
-
-
FastAPI Python Backend:
- Open-weight transcription via local
faster-whisper(or Groq Whisper) - Open-weight analysis via Ollama (
qwen2.5:7b,llama3.2, etc.) or hosted open-weights (openai/gpt-oss-120b) - Sliding-window rate limiting, secret redaction, and dual auth (
X-API-Token/Bearer) - Deterministic lexical retrieval engine for cross-meeting Q&A
- Open-weight transcription via local
How I Built It
Building an assistant that works reliably on open-source AI required solving four practical engineering challenges:
1. The Progressive Speech De-duplication Problem
Google Meet captions and live speech engines emit continuous progressive updates (e.g. YouTo., YouTo choose., YouTo choose our distributed database...). If fed directly to an LLM, these fragments clutter the context window and trigger hallucinated stuttering critiques.
I implemented a space-aware recursive node walker in the extension content script and a backend clean_progressive_transcript() filter that collapses intermediate prefixes into single coherent sentences.
2. Strict Evidence Grounding Protocol
Most AI meeting summaries hallucinate commitments or invent quotes. MeetMind uses a strict 3-tier evidence classification prompt:
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[Direct Evidence]: Verbatim quotes directly attested in the transcript. -
[AI Inference / Analysis]: Deductions, trade-off evaluations, and coaching suggestions. -
[Insufficient Evidence in Transcript]: Explicitly signals when information was not discussed, preventing the model from assuming proficiency or making up deadlines.
In the extension popup, a custom client-side renderer transforms these tags into sleek emerald, indigo, and amber visual pills.
3. Decoupled Local Open-Weight Pipeline
The backend is completely provider-agnostic. By setting two environment variables:
TRANSCRIPTION_PROVIDER=local
AI_PROVIDER=ollama
OLLAMA_CHAT_MODEL=qwen2.5:7b
The entire transcription and reasoning pipeline executes on the user's laptop using faster-whisper and Ollama, keeping 100% of the conversation offline.
Why Does Open Innovation Matter?
Open-weight models and local inference aren't just technical novelties for MeetMind—they are what make the product viable:
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Uncompromising Privacy & Compliance: Business meetings, 1-on-1s, and job interviews contain highly confidential information—salaries, IP, strategy, and personal stories. Strict NDA and GDPR compliance prevent many professionals from piping conversations through third-party proprietary APIs. Running open-weight models (
Qwen 2.5,Whisper) locally ensures confidential data never leaves the host machine. - Zero Metered Costs: Proprietary speech and LLM APIs charge by the minute and token. With open-weight models running on existing hardware, MeetMind costs $0.00 to run indefinitely.
- Offline Autonomy: Whether you're on a spotty train Wi-Fi or behind a secure corporate air-gap, local open models continue transcribing and generating notes without an active internet connection.
- Freedom from Vendor Lock-In: You are never locked into a single provider's deprecation cycle, pricing hike, or content policy shifts.
My Agent Session
This project was built and debugged pair-programming with Antigravity / Gemini CLI. The interactive session transcript has been preserved on DEV:
We need to build MeetMind: a privacy-first meeting memory and interview coaching extension + FastAPI backend that runs with open-weight models (Whisper + Ollama/Qwen) so users never have to take manual meeting notes again.
I will design the architecture: Chrome MV3 extension with tabCapture and offscreen audio recording, paired with a FastAPI backend supporting both local faster-whisper and Ollama open-weight LLMs with strict evidence grounding.
How do we prevent hallucinations and ensure meeting action items and interview feedback are strictly evidence-grounded?
We implement strict evidence separation rules: [Direct Evidence] for verbatim quotes, [AI Inference / Analysis] for synthesis, and [Insufficient Evidence in Transcript] for gaps. We also added progressive caption de-duplication and a zero-dependency client-side Markdown engine with custom visual status pills.
đź”— Session Link: https://dev.to/agent_sessions/building-meetmind-privacy-first-meeting-memory-with-open-weight-ai-ldtxlj
The Lazy Payoff
Here is the sloth math on what MeetMind saves:
- 30 to 45 minutes saved per meeting: No more organizing raw bullet points, tracking down action item owners, or drafting follow-up emails manually.
- 1 hour saved per interview round: Rather than staring at a blank scorecard wondering how the candidate explained concurrency, the interview coach extracts exact quotes and structured feedback instantly.
- Total mental energy conserved: You can lean back, listen with full attention, and let open-source AI do all the heavy lifting.
Prize Categories
- Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady (Primary Challenge Entry)
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