# GPT-4o Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/gpt-4o
> Markdown URL: https://aitinkerers.org/technologies/gpt-4o.md
> Technology record last updated: 2026-02-22T16:41:33Z
> Generated: 2026-08-25T04:39:11Z

GPT-4o (omni) is OpenAI's flagship multimodal model: it delivers GPT-4 intelligence with native, real-time processing across text, audio, and vision.

This is GPT-4o, OpenAI’s 'omni' model: a single neural network natively handling text, audio, and image inputs and outputs. It matches GPT-4 performance on English text and code, but surpasses it on non-English language, vision, and audio benchmarks. The speed is a major upgrade: it achieves human-level responsiveness in voice, with an average response time of 0.32 seconds (a significant jump from GPT-4’s 5.4 seconds). Developers get a 128K token context window and a model that is more cost-efficient than its predecessor, making high-intelligence, real-time applications viable.

- Official technology site: https://openai.com/index/hello-gpt-4o
- Public AI Tinkerers demos and talks: 57
- Result page: 1 of 3

## Recent Public Talks and Demos

### [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

### [Words to World, AI learning strategies for building world models.](https://san-diego.aitinkerers.org/talks/rsvp_mnJaK0Bd4l8)

Can a text prompt (or reference art) build a physically real world, no splats, no diffusion, but full physics? Once you have this, can a robot or biocomputer navigate it? Full demo of the system running live (local / remote hybrid) How it is all plumbed and built. The system uses a variety of learning strategies including self-modifying code and rules to both iteratively improve a world model and, iteratively improve the generation of that model AND, iteratively improve the improvement of that model (3 level hierarchy). It uses a large range of optimising strategies to avoid uncanny valley effects and attempt to produce cinematically realistic images.

- 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_mnJaK0Bd4l8

### [From Benchmark to Agent: A2A‑Driven Werewolf Arena for Evaluating LLM Agents](https://bogota.aitinkerers.org/talks/rsvp_w6B43riADns)

Werewolf Arena is a multi‑agent social‑deduction benchmark built on the AgentX–AgentBeats stack, developed as part of the second cohort of TribuPapers and the 2025 Berkeley Agentic AI MOOC, and submitted to the AgentX–AgentBeats competition. It instantiates the Werewolf game as an 8‑player environment where a Green Agent orchestrator manages role assignment, day/night phases, interaction flow, and ELO‑style ratings, while multiple LLM‑driven Purple Agents connect via the A2A protocol to debate, vote, and execute actions under partial information. The project provides a research‑oriented evaluation pipeline that stress‑tests core agentic capabilities such as probabilistic reasoning, persuasion, deception and detection, role‑conditioned behavior, and narrative consistency in noisy multi‑agent settings. Each game produces both quantitative metrics (win‑rate by role, survival, vote accuracy, ELO adjusted by opponent strength) and qualitative scores via an LLM‑as‑a‑Judge component inspired by G‑Eval, which rates agents along multiple dimensions (reasoning quality, persuasive power, deceptive skill, adaptability, and consistency) and generates textual justifications. Technically, the system is implemented in Python with FastAPI for Green/Purple A2A endpoints, uses OpenAI LLMs (configurable models such as gpt‑4o‑mini) for both policies and judge, exposes a JavaScript/HTML/CSS leaderboard UI, and is fully containerized with Docker/Docker Compose and automated via GitHub Actions CI to run tournaments and update metrics reproducibly. The project was developed collaboratively by Daniel Santiago Sandoval Higuera, Sadid Alexis Romero Mahecha, Julian Anibal Henao Garcia, and Andres Felipe Garcia Sanchez.

- Event context: AI Tinkerers Bogotá: El Primer Meetup de 2026 — 2026-02-26 — Bogotá
- Public talk page: https://bogota.aitinkerers.org/talks/rsvp_w6B43riADns

### [Fluo aka we have Duolingo at home](https://toronto.aitinkerers.org/talks/rsvp_3TuvA7LAEd0)

My partner is Turkish, and I'm taking classes to learn his language. But there's a gap that no textbook or classroom can fill—I don't have a learning companion who actually knows what I've learned and can meet me where I am. So I built it. Architecture Overview I built a multi-agent conversational system using FastAPI, SQLAlchemy, and OpenAI's API (GPT-4o + Whisper + TTS). The system maintains persistent state of the learner's knowledge and dynamically adapts conversation difficulty in real-time. Core Components 1. Note Extraction Pipeline (NoteExtractorAgent) Accepts multiple image uploads or raw text input Uses GPT-4o's vision capabilities to parse handwritten/printed class notes Extracts structured data: vocabulary (target word, native translation, example sentences) and grammar concepts (name, description, usage examples) Supports configurable native/target language pairs 2. Knowledge Persistence Layer SQLAlchemy ORM with SQLite backend Per-language vocabulary and grammar tables with fluency tracking (low/medium/high) Tracks times_practiced and times_correct for spaced-repetition-style prioritization 3. Lesson Planning Agent (LessonPlannerAgent) Selects vocabulary/grammar items prioritizing low-fluency scores Generates a structured lesson plan constrained to CEFR levels (A1-C2) Passes both practice items and all known items to enable natural vocabulary reuse without exceeding learner's level 4. Dialogue Agent (DialogueAgent) Maintains conversation state and full transcript Bidirectional speech support: Whisper for STT, OpenAI TTS for audio responses Receives lesson plan + full knowledge context to stay within learner's vocabulary bounds Detects conversation end signals and handles graceful session termination 5. Analysis Agent (AnalysisAgent) Post-conversation transcript analysis Identifies: correctly used target vocabulary, grammatical errors, new words attempted Updates fluency scores in database based on performance

- Event context: AI Tinkerers Toronto - February 2026 @ Cohere! — 2026-02-26 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_3TuvA7LAEd0

### [Vibe Coding to Production: A PM’s LLM Pipeline for OE Data Cleaning](https://dhaka.aitinkerers.org/talks/rsvp_ov_SnvJW0s0)

A PM’s experiment in turning LLMs into a real OE data cleaning system using Claude, GPT-4 mini, and Apps Script. From prompts to production in the real world.

- Event context: AI Tinkerers Dhaka 2nd Meetup: Dhaka Builds with AI! — 2026-02-07 — Dhaka
- Public talk page: https://dhaka.aitinkerers.org/talks/rsvp_ov_SnvJW0s0

### [How I stopped worrying about API outages and learned to love failover chains](https://nashville.aitinkerers.org/talks/rsvp_IL5Oy-JzYNk)

A technical walkthrough of building a multi-LLM orchestration layer for my job application automation tool. I'll show the actual code that handles: Provider selection logic with automatic failover (Claude → OpenAI → Gemini) Circuit breaker pattern for serverless - how to persist failure state across Vercel cold starts using Redis Model fallback when newer models aren't accessible (handling the "Claude 4.5 dropped but my API key doesn't have access" problem) Cost optimization through provider ordering - no fancy algorithms, just "try the cheap provider first for cheap tasks" Live demo: triggering an AI generation and watching the provider selection happen in real-time via DevTools.

- Event context: AI Tinkerers Nashville – January 29th, 2026: Live Demos, Code, and Architecture — 2026-01-29 — Nashville
- Public talk page: https://nashville.aitinkerers.org/talks/rsvp_IL5Oy-JzYNk

### [AI-powered language quiz generator](https://waterloo.aitinkerers.org/talks/rsvp_dKoLwNdgNlA)

* intro: Chisme born out of curiosity to explore AI integrations + desire to hide answers when asking chatGPT to create a quiz * quick UI flow of Chisme, an AI-powered language quiz generator: https://learn.findingluisa.com/ * brief overview on how to instantiate an OpenAI Client in python: https://github.com/LuisaTu2/ai-quiz-generator/blob/main/api/openai_client.py * share some learning/reflections and pitfall examples: https://learn.findingluisa.com/learn-reflections/

- Event context: AI Tinkerers Waterloo – December Meetup — 2025-12-15 — Waterloo
- Public talk page: https://waterloo.aitinkerers.org/talks/rsvp_dKoLwNdgNlA

### [Vibe Scaffold - A wizard-style tool that guides you from a rough idea to a refined technical architecture that AI coding agents can execute](https://seattle.aitinkerers.org/talks/rsvp_aUibZaTvUtY)

I created vibescaffold.dev. It is a wizard-style AI tool that will guide you from idea → vision → tech spec → implementation plan. It will generate all the documents necessary for AI coding agents to understand & iteratively execute on your vision. How it works: - Step 1: Define your product vision and MVP - Step 2: AI helps create technical architecture and data models - Step 3: Generate a staged development plan - Step 4: Create an AGENTS.md for automated workflows I've used AI coding tools for awhile. Before this workflow (and now, this tool), I kept getting "close but not quite" results from AI coding tools. I learned that the more context & guidance I gave these tools up front, the better results I got. My presentation will be: 1) Here's the tool and how it works - quick walk-through 2) Here are how AI coding agents behave when given these documents 3) My soft launch & some metrics 4) My thoughts on the gaps in the marketplace of vibe coding & AI-assisted coding (if this is appropriate)

- Event context: AI Tinkerers Seattle Meetup: Dev Tools Track — December 8, 2025 — 2025-12-09 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_aUibZaTvUtY

### [Agentic AI at The Chefz: From LLMs to Business Impact](https://amman.aitinkerers.org/talks/rsvp_hE3nMHLm6To)

A practical look into how we're deploying AI agents using n8n and GPT-4o to reduce churn, optimize performance, and make restaurant engagement proactive and intelligent.

- Event context: AI Tinkerers Amman Meetup — November 15, 2025 — 2025-11-15 — Amman
- Public talk page: https://amman.aitinkerers.org/talks/rsvp_hE3nMHLm6To

### [Evolving Contexts: Applying Agentic Context Engineering for Self-Improving LLM Agents](https://bogota.aitinkerers.org/talks/rsvp_hkMdJmEiStk)

In this talk, I’ll present a practical walkthrough of Agentic Context Engineering (ACE) — a novel framework introduced in the paper “Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models.” I will explain how ACE addresses challenges like brevity bias and context collapse by enabling agents to iteratively refine their own contextual understanding through three core components: the Generator, Reflector, and Curator. Using a simplified case study, I’ll demonstrate how a language-model-based agent can evolve its behavior and improve performance over time without parameter updates, simply by re-engineering its context and memory representations.

- Event context: AI Tinkerers Bogotá - Halloween Edition! — 2025-10-31 — Bogotá
- Public talk page: https://bogota.aitinkerers.org/talks/rsvp_hkMdJmEiStk

### [Using AI to Build a Smarter Tax Calculator for Canadian Millennials and Gen Z ⚡💻](https://toronto.aitinkerers.org/talks/rsvp_3yAln1-U3ZQ)

In this 6-minute demo, I’ll show how I used AI to code an interactive Canadian tax savings calculator from scratch 🛠️🤖. Using AI prompts, I generated everything—from web app code to scripts that scrape official government sites for the latest tax data 📊🇨🇦. You’ll get a live walkthrough of the tool itself as well as a peek behind the curtain at the AI-powered building process 👀✨. The calculator lets users add multiple income sources and visually breaks down taxes into provincial and federal brackets. It also shows before-and-after tax savings from RRSP, FHSA, and TFSA contributions, turning what usually feels like a nightmare of numbers into clear, colorful, digestible visuals 🌈📈. I’ll highlight the AI-generated code that handles scraping, tax logic, and chart rendering.

- Event context: AI Tinkerers Toronto - October 2025 Meetup at CIBC | Simplii — 2025-10-30 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_3yAln1-U3ZQ

### [How Not to Kill Anyone: Safety Layers in Medical Reasoning](https://poland.aitinkerers.org/talks/rsvp_Z32_cW6T-Nc)

We're building slimtwin: a platform that uses your data - from smart scales, wearables, and bloodwork - to build a digital twin of your health. A system that adapts in real time. That learns with you. That reflects your body today - not last month. I'll be presenting an early version of our product targeted at dietitians.

- Event context: AI Tinkerers Poland #5 - Meetup in Warsaw (September) — 2025-09-18 — Poland
- Public talk page: https://poland.aitinkerers.org/talks/rsvp_Z32_cW6T-Nc

### [Writing Books with CrewAI](https://boston.aitinkerers.org/talks/rsvp_O8flPxCMcNw)

Writing Books with CrewAI explores how autonomous AI agents can transform the way we write and publish books. Instead of treating writing as a solitary word-by-word endeavor by an author, CrewAI organizes multiple agents into a coordinated "crew" that handles everything from generating outlines and drafting chapters to compiling a polished manuscript. This talk will walk through the full pipeline, idea to finished PDF, showing how to harness CrewAI’s orchestration, scripts, and binding tools to create long-form works with structure, consistency, and efficiency. You’ll see how writers can step into more of a creative-director role while AI handles the heavy lifting of research, drafting, and formatting. We'll walk through the key agents and tasks so you may understand the code flows but also how it dovetails with the author's creative and editorial process to result in a high-quality publication. CrewAI encapsulates and simply handles many details of agent orchestration and we'll look at some of the challenges faced in employing this technology and how they we're overcome. You will see research agents feed the writer agents background for their work and in real time, you'll watch the system generate full, detailed chapters. Speaker Bio Dan Hermes is the author of Prompt Power: Learn to Create ChatGPT Prompts, released last month. He works at the intersection of transformative technology and empowered teams, advancing innovation in Generative AI, Large Language Models, and software development. With a portfolio of books and articles on AI, mobile, and architecture, Dan has contributed to the growth of AI knowledge and its application across industries. Over the past decade, he has led global software and data teams at companies such as Avanade, an Accenture and Microsoft joint venture, architecting advanced applications for airlines, banks, and municipalities. For more than 20 years, he ran Lexicon Systems, a consultancy serving Microsoft, Fidelity Investments, Thermo Fisher Scientific, DraftKings, and the FAA, delivering tailored AI and software solutions. His thought leadership has appeared in IBM Mobile Business Insights and Microsoft’s MSDN Magazine, and he has spoken at countless developer user groups and venues such as IBM Think and Microsoft Ignite. Today, Dan leads projects at the forefront of GenAI, LLMs, and AI-driven applications, helping businesses harness AI’s transformative potential to build exceptional products and experiences.

- Event context: AI Tinkerers Boston: Langchain & Beyond Meetup — 2025-08-25 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_O8flPxCMcNw

### [Vox Machina](https://sf.aitinkerers.org/talks/rsvp_uT0AlmivIzE)

`vox-machina` is a bot that sits alongside you while you play TTRPGs, automating bookkeeping tasks. It can keep track of player health, status effects, inventories, etc., answer questions that come up over the course of play (e.g. "do I roll this with advantage?"), and (optionally) help enforce game rules. I'll briefly cover how this agent works: processing audio streams from multiple users, creating a single game state from them, and having the agent respond when asked questions (or interjecting to remind players of a rule).

- Event context: AI Tinkerers - Voice Agents Science Fair — 2025-06-26 — San Francisco
- Public talk page: https://sf.aitinkerers.org/talks/rsvp_uT0AlmivIzE

### [Patch Party: Live-Fixing LLM Agents](https://london.aitinkerers.org/talks/rsvp_-7V7yhBZXMQ)

This is a live demo of a feedback loop that patches autonomous agents in real time. We’ll show how we built a minimal actor-critic framework that catches step-level agent failures—like hallucinated facts, logic errors, or missed tool calls—and injects precise critiques to recover mid-task. We'll walk through: How we used τ‑Bench and DA-Code to build a taxonomy of agent failure types How we tagged real traces and analyzed which errors matter most (e.g., reasoning failures > tool bugs) How we implemented the critic loop using FastAPI + GPT-4o or Claude as the agent, and optional models/humans as critics How a 1-2 sentence critique can improve success rates by 30%, without replanning or retraining How to plug this loop into your own stack using just a trace, a tagger, and a hook We’ll live-debug an agent solving a DA-Code task, show failure in action, and patch it with a model-generated critique. It’s messy, practical, and shows how real-time judgment can boost reliability with minimal overhead.

- Event context: AI Tinkerers London - June Meetup — 2025-06-25 — London
- Public talk page: https://london.aitinkerers.org/talks/rsvp_-7V7yhBZXMQ

### [Vibecoding an AOAI Real-time Veo 3 Prompt Generator](https://seattle.aitinkerers.org/talks/rsvp_RWN3Gp0Va4s)

A NextJS web app for humans to describe the video they’d like Veo3 to generate. The app leverages Azure OpenAI’s Real-Time API as an agent to walk users through the experience. The agent has two tool calls. Tool 1 is a call to AOAI gpt-4o to create the prompt. Tool 2 is a call to Gemini Veo3 API. (Uses Veo2 until allowlisted for Veo3).

- Event context: Friday - AI Tinkerers Seattle - May Meetup — 2025-05-31 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_RWN3Gp0Va4s

### [AI that edits your resume](https://seattle.aitinkerers.org/talks/rsvp_HGAE1liMtbc)

I'll be presenting a resume builder with human-in-the-loop editing that works like Cursor for your resume. The demo walks through the complete workflow from job scraping and resume import to targeted edits and user approval. I'll show how conflict-free AI suggestions get generated with streaming tool-calls and updates the UI. The interface gives users close control with "preview before apply" edits so you can finalize your draft exactly how you want.

- Event context: Friday - AI Tinkerers Seattle - May Meetup — 2025-05-31 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_HGAE1liMtbc

### [Text to Circuit board (art)](https://toronto.aitinkerers.org/talks/rsvp_Sl6w14hL9C8)

Circuit boards are actually a really cool medium for art. Hackathon badges, functional business cards or just circuit boards with a cool picture on them. I wanted to explore ways to make circuit board art a lot more accessible to create, so I made an app that let's you upload any image and convert it to a circuit board with that design in less than 60 seconds. This circuit board will be production ready, so you can order it on the spot and get your customized circuit board to your house in less than a week.

- Event context: AI Tinkerers Toronto - May 2025 Meetup: AGENTS at Ada — 2025-05-22 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_Sl6w14hL9C8

### [Breaking Down Legal Clauses: Giant Context Windows to the Rescue](https://paris.aitinkerers.org/talks/rsvp_XxJVM2hkS-c)

Problem: Splitting OCR-processed legal documents into relevant clauses is challenging due to inconsistent formatting, clause semantic between clause ... What I’ll Cover: I’ll walk through the iterative process that led me to an agentic pattern for chunking legal documents efficiently and cost-effectively using LLMs. Instead of asking the model to rewrite entire clauses — which is slow and expensive — I optimized the approach by leveraging differential capabilities (e.g., asking the LLM to output only line numbers or minimal edits). This significantly reduced inference time and cost.

- Event context: AI Tinkerers - Paris Meetup on May 15th — 2025-05-15 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_XxJVM2hkS-c

### ["Bidimensional testing in the LLM era"](https://milan.aitinkerers.org/talks/rsvp_WcQIYIdU3EQ)

Automated testing is a fundamental part of software development; let's look at how the test pyramid and parameter-exploration tests apply in the age of GenAI workflow development, by analyzing why we can't recognize that a Rolex watch is fake

- Event context: AI Tinkerers Milan - May 8, 2025 — 2025-05-08 — Milan
- Public talk page: https://milan.aitinkerers.org/talks/rsvp_WcQIYIdU3EQ

### [PromptPilot](https://rio-de-janeiro.aitinkerers.org/talks/rsvp_QYgrXmNM1rM)

PromptPilot is an experimental application that uses artificial intelligence to interpret screenshots and perform automated actions based on natural language commands. Integrated with ChatGPT, the system identifies visual elements in real time, calculates coordinates relative to the screen’s original resolution, and performs automated clicks on the operating system. The talk will showcase its current architecture, visual precision challenges, debugging tools, and the two-stage inference logic (macro/micro targeting).

- Event context: AI Tinkerers x Groq - Rio de Janeiro Inaugural Meetup (April) — 2025-04-26 — Rio de Janeiro
- Public talk page: https://rio-de-janeiro.aitinkerers.org/talks/rsvp_QYgrXmNM1rM

### [Podcast Localization: LLMs for Context-Aware Adaptation](https://singapore.aitinkerers.org/talks/rsvp_XtMxwGZv5pk)

I’ll demo a prototype that ingests an English podcast (multi-host), accurately transcribes and diarizes speakers, contextually translates to Mandarin with cultural nuance, synthesizes matching host voices, and reassembles a polished, time-synced audio output—all behind a simple Streamlit UI.

- Event context: AI Tinkerers Singapore: 6th Meetup - April 25th, 2025 — 2025-04-25 — Singapore
- Public talk page: https://singapore.aitinkerers.org/talks/rsvp_XtMxwGZv5pk

### [The Death of Agents: Building AI Workflows That Actually Scale](https://toronto.aitinkerers.org/talks/rsvp_zz0Coi-4yZM)

In this live walkthrough, I’ll demo how I built an LLM-powered pipeline system that replaces brittle agent frameworks with modular, composable reasoning stages. The system processes unstructured inputs (like emails and legal docs), extracts structured data, embeds it, retrieves semantically relevant context from a vector DB, and then makes decisions like: “Is this part of an existing deal or a new one?” No agents. No RAG gimmicks. Just pipelines, embeddings, and judgment calls from a model with traceability. I’ll walk through: Canonicalization (turning messy inputs into structured data) Vector-powered context graph retrieval LLM reranking and decision logic Real-time deal creation or attachment via reasoning How I kept it all observable, debuggable, and scalable across domains Everything will be live—from the code to the reasoning decisions. No slides, no fluff, just raw execution.

- Event context: AI Tinkerers Toronto - April 2025 Meetup at Shopify — 2025-04-24 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_zz0Coi-4yZM

### [Building educational content with AI workflows](https://toronto.aitinkerers.org/talks/rsvp_gNhQXjEyDJg)

We are working on AI assisted workflows to enable human content creators to be able to rapidly build educational courses that teach software development principles through a mobile application focused on optimizing for retention through learning science principles like spaced repetition (think Duolingo for software development). I will live demo as much of a full course creation workflow as I can fit in 6 minutes highlighting the various stages of our workflow, how the human can interact with the output and provide feedback at various stages, how we assess quality and validate correctness of the material. I will show how we organized the code for our workflows, which are all accessible currently as CLI tools, but we have future plans to productize the workflows for course creators.

- Event context: AI Tinkerers Toronto - April 2025 Meetup at Shopify — 2025-04-24 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_gNhQXjEyDJg

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