# Read Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/read
> Markdown URL: https://aitinkerers.org/technologies/read.md
> Technology record last updated: 2026-05-27T20:41:49Z
> Generated: 2026-09-23T05:44:39Z

Read AI is an automated meeting assistant that delivers real-time transcription, summaries, and actionable insights across your communication channels.

Read AI acts as a central intelligence hub for your workspace by automatically joining meetings on Zoom, Google Meet, Microsoft Teams, and Webex to generate instant transcripts, summaries, and action items. Beyond basic note-taking, the platform analyzes participant engagement, tracks talk-time distribution, and measures sentiment to help teams run more efficient sessions. Through its Search Copilot feature, Read AI unifies data across meetings, emails, Slack threads, and CRMs, allowing users to query their entire communication history using natural language prompts to find exact decisions and context in seconds.

- Official technology site: https://www.read.ai
- Public AI Tinkerers demos and talks: 2
- Result page: 1 of 1

## Recent Public Talks and Demos

### [The loss curve lied: catching hidden safety drift inside the fine-tuning, automated with an agent!](https://zurich.aitinkerers.org/talks/rsvp_Vnt244xXFE0)

We built a fine-tuning where a Claude-Code based agent that helps founders and researchers fine-tune models on their datasets, and get more insights than just the training and eval losses. In particular, we also show the model’s regress on safety and general capability, metrics that otherwise would be overlooked. For example, we give it a medical instruction dataset; the agent asks a few multiple-choice questions and proposes concepts like "recommends unsafe or contraindicated actions," "downplays red-flag symptoms that need urgent care," and "implies clinical authority it doesn't have," then writes the run config. Then, for each concept, it fits a single direction in the model's activations (difference-of-means, following Persona Vectors, Chen et al. 2025, arXiv:2507.21509) that “encodes” that concept, and reads that direction off every checkpoint during LoRA training. Live we can show the agent conversation proposing the concepts, as how they drift alongside per-checkpoint evals. This is part of an ongoing research agenda with a collaboration of researchers from ETH Zurich, ETH AI Center and Agentic Systems Lab. We formulated our research into an agentic harness and live demo in an anthropic hackathon last week, and want to share more about it! Also, we imagine this as an open-source effort.

- Event context: AI Tinkerers Zürich: July Build Night with Swisscom Ventures — 2026-07-01 — Zürich
- Public talk page: https://zurich.aitinkerers.org/talks/rsvp_Vnt244xXFE0

### [Lazy Marketing v3: From Meeting to Content Pipeline in Zero Click](https://seattle.aitinkerers.org/talks/rsvp_TtXOReDjy68)

Most of us have 10+ meetings a week full of insights worth sharing — but turning those into content never happens. Marketing Machine fixes that by wiring together Claude, Gemini, Slack, Google Sheets, and the LinkedIn API into a fully automated content pipeline. A Read.ai transcript hits a webhook. Claude extracts the best marketing hooks and expands them into on-brand LinkedIn posts using a 100+ line brand-voice prompt. You pick and refine posts entirely inside Slack with buttons and modals — no separate app needed. Gemini generates an accompanying image. Approved posts queue up in Google Sheets and auto-publish to LinkedIn on a schedule. In this demo, I'll walk through the full loop live — from raw meeting transcript to a published LinkedIn post — and dig into the prompt engineering, multi-model orchestration, and Slack-as-a-UI patterns that make it work. Built with Node.js, Express, and APIs anyone can sign up for. No GPUs, no fine-tuning, no ML infra required.

- Event context: AI Tinkerers Seattle: GTM Track — March — 2026-03-26 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_TtXOReDjy68

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- [LinkedIn API](https://aitinkerers.org/technologies/linkedin-api) ([Markdown](https://aitinkerers.org/technologies/linkedin-api.md)) — 2 public demos
- [LoRA](https://aitinkerers.org/technologies/lora) ([Markdown](https://aitinkerers.org/technologies/lora.md)) — 17 public demos
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