# FastAPI Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/fastapi
> Markdown URL: https://aitinkerers.org/technologies/fastapi.md
> Technology record last updated: 2026-09-18T15:13:58Z
> Generated: 2026-09-22T23:34:11Z

FastAPI is a modern, high-performance Python web framework for building APIs with automatic OpenAPI documentation.

FastAPI is a robust, high-speed Python web framework: it is built on Starlette (for async capabilities) and Pydantic (for data validation and serialization). Leveraging standard Python 3.8+ type hints, the framework automatically generates interactive API documentation (Swagger UI/ReDoc) and enforces data validation, effectively reducing developer-induced errors by an estimated 40%. This architecture delivers performance on par with Node.js and Go, significantly increasing feature development speed (up to 300% faster). It is production-ready, fully supporting OpenAPI and JSON Schema standards for all API specifications.

- Official technology site: https://fastapi.tiangolo.com
- Public AI Tinkerers demos and talks: 181
- Result page: 1 of 8

## Recent Public Talks and Demos

### [Katalog — an AI app that makes post-surgery shoulder range of motion visible](https://tokyo.aitinkerers.org/talks/rsvp_BDzQthaV_gg)

Katalog is an AI app that supports rehabilitation after shoulder surgery by making range of motion visible. I broke my shoulder. Six months after surgery I found out my arm wasn't going up — I had thought it was. A mirror shows you where your hand reaches, not what your shoulder actually did. Prop a phone against something, stand 2 m back, raise your arm for 60 seconds. It reports the angle measured against your own body axis, plus how much your body compensated to get there. Pose estimation runs inside the browser; no video ever leaves the device. Live I'll show the working system end to end: - measuring (the screen says 135°, the record says 107° — the trunk was leaning) - mirror vs. measurement side by side, same instant, same hand height - the history, and the agent reading it and deciding one action As a stretch goal I'm rebuilding the attention layer in integer arithmetic (CIA) to run ViT on an FPGA and measure its power draw — if that lands in time, I'll show that version too.

- Event context: AI Tinkerers Tokyo — October 5 Meetup — 2026-10-05 — Tokyo
- Public talk page: https://tokyo.aitinkerers.org/talks/rsvp_BDzQthaV_gg

### [Owning Your AI Stack: Turning an LLM Proxy Into a Full-Fledged Control Plane](https://barcelona.aitinkerers.org/talks/rsvp_JwKfi-8o4K8)

Otari is Mozilla.ai's open-source LLM infrastructure platform. I will show the current state of the code in github, as well as a live deployment with the current working features.

- Event context: AI Tinkerers Barcelona - September Demo Night — 2026-09-17 — Barcelona
- Public talk page: https://barcelona.aitinkerers.org/talks/rsvp_JwKfi-8o4K8

### [Bias-Free RAG: Simulating Political Candidates with AI](https://saopaulo.aitinkerers.org/talks/rsvp_uTVDpnRMXT4)

Café com o Candidato is an open-source site where users pick a 2026 Brazilian presidential candidate and chat with an AI simulation powered by RAG over public data, replying with the candidate's speech patterns and mannerisms — always with a clear disclaimer that it's a simulation with no affiliation. In the live demo I'll show the chat working end-to-end and, behind it, the RAG pipeline: how a user's question pulls the most relevant chunks from public sources via pgvector before reaching the model.

- Event context: AI Tinkerers SP e Oracle - Meetup Agosto — 2026-08-27 — São Paulo
- Public talk page: https://saopaulo.aitinkerers.org/talks/rsvp_uTVDpnRMXT4

### [\[Case Study\] 90,000 rejections, 100 meetings: the AI outbound engine I optimized to not send](https://valencia.aitinkerers.org/talks/rsvp_9Hq5aZKj4fk)

An AI-run cold outbound engine for a link-building agency — a CRM I own end to end. It sources prospects, enriches them, finds their contact data automatically, scores them 0–5 against our ICP, then writes personalized multichannel sequences (email + LinkedIn) built from each prospect's website, LinkedIn profile, and recent activity — and hands them to a sender, while every reply flows back into one channel-agnostic thread model I can query in plain language. To date: ~257k prospects processed, 175k messages generated, 1,000+ replies, 100+ sales meetings, and six figures of revenue directly attributable. The counterintuitive part — and what I'll show live — is that the machine is optimized to reject, not to convert. Every send spends finite domain and sender reputation, so between each stage sits a gate whose job is to throw work away: the score gate permanently drops ~90k non-fit prospects before writing a word, and a send-time re-check has blocked 1,591 already-approved messages. Live I'll walk the pipeline dashboard and per-job run history, the score distribution and its cutoff, real generated messages (a good one and one that was embarrassingly wrong), and the per-prospect cost ledger — 1.64M rows — that prices every decision.

- Event context: GTM Teardown August — AI Tinkerers Valencia — 2026-08-11 — Valencia
- Public talk page: https://valencia.aitinkerers.org/talks/rsvp_9Hq5aZKj4fk

### [Your AI agent will lie to you: the "say-do gap" and a deterministic fix](https://dubai.aitinkerers.org/talks/rsvp_eS1r2yCCqzw)

ReceptionAI is a production AI receptionist for real-estate agencies — an LLM agent that qualifies leads, books viewings, and follows up over WhatsApp via function-calling. Live, I'll run a real after-hours enquiry through it end-to-end (message → qualification → booked viewing, with the tools firing in real time), then open the hood: the tool-calling loop, the deterministic "lead-capture safety net" that guarantees a lead is never lost even when the model skips a tool call, and how I verified it against the database. Real system, real code, real logs — not a canned video.

- Event context: AI Tinkerers Dubai — August Demo Day — 2026-08-08 — Dubai
- Public talk page: https://dubai.aitinkerers.org/talks/rsvp_eS1r2yCCqzw

### [EduSense: A Multi-Agent AI Platform for Emotion-Aware Education](https://dubai.aitinkerers.org/talks/rsvp_KyNKCO_MVFc)

EduSense is a multi-agent AI education platform built for the Kaggle AI Agents Capstone. It uses DeepFace for real-time facial emotion analysis via webcam, and 7 specialized agents — orchestrated by a central workflow — to generate lessons, quizzes, flashcards, and PDF reports for instructors. The backend runs on FastAPI with PostgreSQL. I'll demo the live agent pipeline: emotion capture, the sequential agent workflow, and the instructor dashboard.

- Event context: AI Tinkerers Dubai — August Demo Day — 2026-08-08 — Dubai
- Public talk page: https://dubai.aitinkerers.org/talks/rsvp_KyNKCO_MVFc

### [When the AI Judge Is Wrong: A Verifiable Floor for LLM Evaluations](https://dubai.aitinkerers.org/talks/rsvp_e3czbFAtNPA)

I am building Lithrim, an open-source eval harness that pairs a configurable LLM judge council with a deterministic grounding floor. The grounding floor changes an outcome only when it can prove the change against an external oracle. Live on the real UI, I'll grade a few AI-generated clinical notes (no real patient data) using a two-judge council. The judges catch the planted defects, but they also over-flag, so recall is high and precision is low. The floor then clears a provable false positive through a grounding check against a SNOMED CT terminology server. It also independently verifies a real upcoded diagnosis against the terminology graph, without another model call and with the same result on every run. The final verdict accuracy visibly improves, and the floor has never cleared a genuine defect; that counter is shown on every run. Everything shown runs against the live system with a per-run audit trail. The repo is public.

- Event context: AI Tinkerers Dubai — August Demo Day — 2026-08-08 — Dubai
- Public talk page: https://dubai.aitinkerers.org/talks/rsvp_e3czbFAtNPA

### ["QA a la velocidad de la luz (y sin fuga de datos): potenciando testing exploratorio con modelos de IA ejecutados 100% en local](https://panama.aitinkerers.org/talks/rsvp_RO5JPx08rOc)

FullQA.ai es una plataforma de QA asistida por IA que genera, documenta y automatiza casos de prueba a partir de la exploración de una aplicación web, ejecutando los modelos de lenguaje y visión completamente en local — sin depender de APIs externas ni exponer datos sensibles. En la demo mostraré en vivo un flujo completo de punta a punta: El sistema funcionando en tiempo real: una extensión de navegador captura la sesión exploratoria (clics, navegación, formularios) y el backend la convierte en documentación de QA estructurada con capturas por paso. Arquitectura: cómo se conectan la extensión, la app de escritorio, el servicio API y los modelos locales servidos vía Ollama, con fallback a disco y generación de imágenes deterministas por paso. Generación de código: cómo se produce automáticamente un script de Playwright listo para ejecutar a partir de la sesión grabada. Repo y logs: enseñaré el código real, la estructura de proyectos con contexto, y los logs de inferencia local corriendo sobre la GPU.

- Event context: AI Tinkerers Panamá: Construyendo el futuro con inteligencia artificial — 2026-07-31 — Panama
- Public talk page: https://panama.aitinkerers.org/talks/rsvp_RO5JPx08rOc

### [The Goldilocks zone between autopilot and a blank file](https://minneapolis-saint-paul.aitinkerers.org/talks/rsvp_T02lAwAfSBo)

Most ML tooling makes you leave the tool to get to the next layer. Templates are on Hugging Face, helpers are scattered across GitHub, compute is somewhere else again, and the first week goes to stitching instead of modeling. The usual fix is to point a code model at the gaps, which works badly here for a specific reason: bad ML code does not crash, it runs. You watch the loss curve descend for four hours, pay for the GPUs, and find out the helper was quietly wrong the whole time. So ACI sits between full autonomy and a rigid template. Sub-autonomous, in the sense that the assistance is retrieval and ranking rather than generation. Two ranking models I trained order verified project templates and PyTorch helper functions out of a library curated by hand, currently 178 core blocks and 7,719 across 16 extension packs. A language model does write the one line plain English summary on each block and powers the help chat. It never writes code and never touches the ranking. The converter is what I actually want to show. Block graph to real PyTorch and back, fully deterministic, surviving nested blocks across two serialization formats. Hand edit the emitted script and it parses back into blocks. It is a compiler, not a template you cannot touch. Deployed at aimlse.org, so I will just run it. Real login, open a project, go template to blocks to script, then edit the script and watch the blocks change to match. Then throw a cell at a sandboxed PyTorch kernel container through a Redis queue and watch the output stream back with the colors intact. Three processes on one EC2 box under Docker Compose. run_api on 8000 serving the frontends, auth on 8001, job processor eating the queue. App code baked into the image, static assets bind mounted, which I did for reasons I am still not sure were correct. The editor is open on the site with no account needed, so anyone can pull it up during the demo and try to break the compiler while I am talking.

- Event context: AI Tinkerers Minneapolis-Saint Paul — July Meetup — 2026-07-29 — Minneapolis Saint Paul
- Public talk page: https://minneapolis-saint-paul.aitinkerers.org/talks/rsvp_T02lAwAfSBo

### [Job Seeker](https://paris.aitinkerers.org/talks/rsvp_rUKvMBG0Ezg)

I build a software to help to find a job

- Event context: Offstage w/ Docker feat ClickHouse — 2026-07-07 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_rUKvMBG0Ezg

### [The Prompt That Worked in Isolation and Died in Production: Eval-Driven Prompt Engineering for a Real-Time Interview Copilot](https://seoul.aitinkerers.org/talks/rsvp_S4LRW_RGb3U)

InterviewMate (interviewmate.tech) is a real-time interview copilot I built and run in production: it listens to a live interview, transcribes it as it happens, and streams back answer suggestions grounded in the candidate's own experience — in under a second. The pipeline is Deepgram streaming STT into a FastAPI WebSocket Q&A pipeline, RAG retrieval over my own past answers, and a Claude streaming response. I'll run the working system live. Then I'll show the part that actually mattered: the eval harness behind the system prompt. It started as a bug — my copilot answered the viral "car wash" question correctly ("drive") while the same model, called with no prompt around it, said "walk," and I couldn't explain which prompt layer was responsible. So I turned the production prompt into a controlled experiment: 6 prompt conditions, 20 runs each, showing STAR-style structured reasoning beating context injection by 2.83x — and a follow-up where that same winning prompt collapsed to 0–30% once I dropped it into the real 60-line production prompt. Code, eval logs, and the pipeline architecture will all be on screen.

- Event context: AI Tinkerers Seoul: Inaugoral Demo Night — 2026-07-02 — Seoul
- Public talk page: https://seoul.aitinkerers.org/talks/rsvp_S4LRW_RGb3U

### [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

### [Scrape, sense, snipe: local LLMs reading Twitter to trade Polymarket](https://zurich.aitinkerers.org/talks/rsvp_jO-ELvIgcaQ)

A self-hosted pipeline that scrapes Twitter, runs local LLMs to score sentiment, cross-references on-chain Polymarket activity, and surfaces ranked trade suggestions on a dashboard. I review the suggestions and place the trade myself.

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

### [Persistent Semantic Memory for AI Agents — SQLite-vec Instead of a Vector DB](https://zurich.aitinkerers.org/talks/rsvp_DuHc2MBp1uo)

An open-source MCP server that gives any AI agent persistent semantic memory — no separate vector database required. SQLite-vec handles KNN search directly inside SQLite; a local ONNX model (all-MiniLM-L6-v2) generates embeddings with zero API cost and 80–150ms latency. The service runs as both an MCP server (for Claude Desktop, Codex, etc.) and a REST API simultaneously, serving 14+ AI clients from a single process. Live demo: I'll connect Claude to the running service, store context across tool calls, and show semantic retrieval in real time including what happens when two processes fight over the same SQLite file. Live Demo: https://mcpmemory.services/

- Event context: AI Tinkerers Zürich: July Build Night with Swisscom Ventures — 2026-07-01 — Zürich
- Public talk page: https://zurich.aitinkerers.org/talks/rsvp_DuHc2MBp1uo

### [Protecting Secrets from Long-running Agents](https://boston.aitinkerers.org/talks/rsvp_qu4vU02kztE)

I built Sesame which is an auth broker proxy for AI agents that protects your secrets from never getting exposed to the agents. Sesame brokers the request and protects you from any unintended actions. Sesame offers full monitoring and alerting mechanisms so human operators can make safe agentic mutation especially in production environments.

- Event context: AI Tinkerers Boston: GTM Agentic AI Launch — 2026-06-29 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_qu4vU02kztE

### [Building a Narrative Intelligence Graph and a narrative App from 200+ News Sources Using LLM Agents](https://austin.aitinkerers.org/talks/rsvp_vxPl4WEYVfg)

I built a narrative intelligence system that continuously ingests articles from 200+ news sources and automatically extracts events, actors, narratives, affiliations, and stance changes over time. The system uses multiple LLM-powered agents to transform raw news into a structured intelligence graph that can answer questions such as: • Who is pushing a specific narrative? • Which actors changed their position? • Which organizations consistently align on issues? • How did a narrative evolve over time? During the demo I'll show the live pipeline, extraction workflow, graph construction process, and how the system generates narrative intelligence from real-world news events.

- Event context: AI Tinkerers Austin: GTM Engineering & Agentic Sales Systems — 2026-06-26 — Austin
- Public talk page: https://austin.aitinkerers.org/talks/rsvp_vxPl4WEYVfg

### [Prism: Privacy-First Multi-Model Orchestration](https://dubai.aitinkerers.org/talks/rsvp_gu_mOBH3NEM)

Prism is a privacy-aware AI orchestration layer that decomposes any query into parallel sub-tasks, routes each to the right model (local Qwen3, Groq, or Gemini) based on sensitivity, executes them concurrently via a dependency DAG, then synthesizes a single coherent response — all in one WebSocket-driven interface. Live demo will show: a real query decomposing into 3 parallel tasks, a DAG rendering the execution graph in real-time, the privacy gate redacting secrets before they leave the machine, and a local Qwen3.6-35B running on a laptop GPU via llama.cpp with MoE CPU offload.

- Event context: AI Tinkerers Dubai - June Demo Day — 2026-06-13 — Dubai
- Public talk page: https://dubai.aitinkerers.org/talks/rsvp_gu_mOBH3NEM

### [Cognitive Cell: Building a Route-Select-Render Control Layer for Workflow AI](https://columbus.aitinkerers.org/talks/rsvp_o1YZyZ3Z09I)

I built Cognitive Cell, a public Python package and HTTP sidecar that sits in front of an LLM and decides whether an input should be recorded, clarified, analyzed, planned, answered directly, or escalated. The accepted v9 stack is router-v4 → selector-v5 → finalizer-v9. I will show the live package, CLI, HTTP sidecar, architecture, traces, evaluation files, and the messy journey from a research prototype to a PyPI package. Live demo elements: - Python package: pip install "cognitive-cell[server]" - CLI: cognitive-cell --event-json ... - HTTP sidecar: cognitive_cell.server.app - /health endpoint - /v1/sidecar endpoint - trace output - evaluation CSV/JSONL artifacts - ablation results

- Event context: AI Tinkerers - Columbus June Meetup — 2026-06-01 — Columbus
- Public talk page: https://columbus.aitinkerers.org/talks/rsvp_o1YZyZ3Z09I

### [Facilitating content moderation with human-AI research](https://montreal.aitinkerers.org/talks/rsvp_5HwapIfgdRU)

We sought to better understand how to build a collaborative tool that harmonizes strengths and weaknesses between human and AI in the goal to facilitate and improve content moderation for online video games. In this talk, we will demo the research conducted behind ToxiSight, an internal tool developed at Ubisoft La Forge, that leverages human nuance and AI scale to improve judgements on toxicity in online game chat. We will present a demo that showcases how a human and AI model will interact to provide all available context and knowledge gaps as well as simultaneously re-train and fine tune a model. We also introduce how we can integrate a multi-disciplinary approach in this endeavour by leaveraging key theory in psychology to learn more about the support humans need and the shortcomings of the AI model.

- Event context: AI Tinkerers Montreal - May Demo Meetup @ Ubisoft — 2026-05-26 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_5HwapIfgdRU

### [Six Claude Agents and a Trust Boundary: A Clinical Co-Pilot](https://montreal.aitinkerers.org/talks/rsvp_4qzrWmd9MiM)

A multi-agent clinical co-pilot that runs six specialist Claude agents in parallel on top of a PHI-safe pipeline : every LLM call sees only de-identified text, and re-identification happens server-side after the model returns. Live, I'll load a synthetic 68F new-AFib case where the cardiology plan proposes amiodarone for a patient already on warfarin. The Triage, Differential, Pharmacy, Guidelines, Bias-Check, and Communication agents stream into six panels via asyncio.gather. The Pharmacy agent flags the warfarin–amiodarone CYP2C9/3A4 interaction; Bias-Check independently flags anchoring on rhythm control; the orchestrator detects the cross-agent convergence and elevates it as a high-severity flag. I'll then toggle the Safety Gate off so the audience sees raw PHI hit the LLM and get echoed back — then flip it on and show the audit log assertion (zero raw PHI, only counts + SHA-256 hashes). I'll show the FastAPI/WebSocket code that fans out the agents, the trust-boundary diagram, the structured-field redactor, the live audit log tail, and the keystone pytest (test_pharmacy_catches_warfarin_amiodarone). Repo, logs, and 35/35 passing tests are all open.

- Event context: AI Tinkerers Montreal - May Demo Meetup @ Ubisoft — 2026-05-26 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_4qzrWmd9MiM

### [GitFlix](https://dubai.aitinkerers.org/talks/rsvp_NOm-XVCUWNY)

GitFlix is an AI-powered developer tool that transforms any GitHub repository's commit history into a cinematic documentary-style video narration. For the demo, I'll show a working system, you paste a GitHub repo URL, the backend ingests the full commit history via the GitHub API, an analytics engine detects contribution eras, character arcs, and hero commits, a LangChain agent generates a structured script and Remotion renders it into an actual video with scenes, narration and transitions. I'll walk through the live app, the LangChain agent reasoning, and the architecture end to end.

- Event context: AI Tinkerers Dubai - May Demo Day — 2026-05-23 — Dubai
- Public talk page: https://dubai.aitinkerers.org/talks/rsvp_NOm-XVCUWNY

### [Talking to Your Infrastructure: Building a Conversational DevOps Agent with Rasa](https://paris.aitinkerers.org/talks/rsvp_IjdWIc5gQv4)

In this talk, I’ll share how I built a conversational AI agent using Rasa and the Scalingo API to manage real applications through natural language. Create apps, scale services, check logs, or update environment variables all with a single sentence. If infrastructure became conversational, what you will told her?

- Event context: AI Tinkerers Paris feat Scalingo: Conversational DevOps & AI Infrastructure — 2026-05-21 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_IjdWIc5gQv4

### [JEN-R8 Discovery Engine: An AI That Tries to Prove Itself Wrong](https://manchester-nh.aitinkerers.org/talks/rsvp_1xgFaiUwGiA)

JEN-R8 is an AI research engine that tries to prove itself wrong. It synthesizes hypotheses across scientific domains, pre-registers the exact conditions under which it will declare itself defeated, runs the analysis on public data, and auto-generates a post-mortem when its own gates fail. Those post-mortems then feed back into the next round of hypothesis generation; the engine learns from its own failures. You'll see the full loop live: cross-domain bridge -> drafted hypothesis spec -> pre-registered falsification gates -> real-time execution on public data -> a meaningful failure -> the engine articulating, on stage, exactly why its own claim was wrong. Then I'll walk through the operator console. Current state (as of demo time): - 60 hypotheses across 15 scientific domains - 5 hypotheses confirmed through full gate passage - 6 hypotheses abandoned with documented post-mortems - 5 provisional patents filed in cancer biomarker discovery - 3 pre-print papers in preparation - 2 developing collaborations with university research institutes JEN-R8 was designed and built by agents running on the Wisdom Layer SDK (the subject of my AI Tinkerers demo last month, featured in the April global newsletter.)

- Event context: AI Tinkerers Manchester (Bedford), NH - May 2026 Meetup — 2026-05-20 — Manchester NH
- Public talk page: https://manchester-nh.aitinkerers.org/talks/rsvp_1xgFaiUwGiA

### [Patent Mining for Engineers: Building an Agentic RAG System for Inventive Problem Solving using TRIZ & AI](https://poland.aitinkerers.org/talks/rsvp_oYsjqZvaY7E)

A pipeline that parses patent PDFs, extracts Technical Contradictions, classifies solutions into TRIZ Inventive Principles, and indexes everything into a vector database. This collection is then feeding an AI Agent that helps engineers solve real inventive problems. The demo starts with a raw patent PDF, submits it live to the processing endpoint, and walks through what gets extracted and indexed. Then, given a real mechanical engineering problem, the agent frames it as a TRIZ contradiction, retrieves relevant patents, and proposes concrete solution ideas, powered by domain knowledge, not just plain LLM generation.

- Event context: AI Tinkerers Poland #3 - Meetup in Wrocław — 2026-05-06 — Poland
- Public talk page: https://poland.aitinkerers.org/talks/rsvp_oYsjqZvaY7E

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