# Cloud Run Gen2 Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/cloud-run-gen2
> Markdown URL: https://aitinkerers.org/technologies/cloud-run-gen2.md
> Technology record last updated: 2026-02-23T03:48:55Z
> Generated: 2026-09-21T01:35:28Z

Cloud Run Gen2 provides a full Linux-compatible environment with faster startup times and expanded system call support for demanding serverless workloads.

Gen2 moves beyond the gVisor sandbox to a micro-VM architecture (using a Linux 5.15+ kernel) that supports Network File System (NFS) mounts and Google Cloud Filestore. Performance is the priority: cold starts drop by up to 50% for large containers while resource limits scale to 32GB of RAM and 8 vCPUs. This environment enables specific use cases like mounting shared volumes, accessing /dev/shm for high-speed IPC, and running legacy binaries that require standard Linux syscalls. It is the definitive choice for data-heavy pipelines and applications needing deep OS integration.

- Official technology site: https://cloud.google.com/run/docs/configuring/execution-environments
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Building Document Consciousness: How I Taught Gemini to Think in 6 Dimensions](https://san-diego.aitinkerers.org/talks/rsvp_CuPjQckMXvM)

I'll demo Clasio, a document intelligence platform I built solo on a 100% Google Cloud stack, showing how I use Gemini 2.5 Flash (extensible to Gemini 3 in a few keystrokes) to extract what I call "6D Document Consciousness" - analyzing every uploaded document across What, Who, When, Where, Why, and How dimensions simultaneously. I received $25K in credits from Google Cloud for Startups for Clasio. The technical meat of the talk: - How I built an async AI queue that processes 25 documents in 75 seconds using 30 parallel Gemini workers on Cloud Run - The structured extraction prompt engineering that gets Gemini to reliably output 6D consciousness JSON (and what failed before it worked) - A 6-tier search waterfall that goes from exact consciousness match down to fuzzy vector similarity using pgvector on Cloud SQL - returning direct answers, not document lists - How I handle connection pool management when you have 30 concurrent Gemini API calls each taking 2-10 seconds (spoiler: release the DB connection before the API call, not after) - Live demo: upload a stack of immigration documents and watch the system classify, extract entities, detect deadlines, and answer natural language questions in real time This is a solo founder build - no team, no VC money, just a product person who learned TypeScript and shipped to production on GCP.

- Event context: AI Tinkerers San Diego: February Meetup at Google — 2026-02-27 — San Diego
- Public talk page: https://san-diego.aitinkerers.org/talks/rsvp_CuPjQckMXvM

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