# Flash Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/flash
> Markdown URL: https://aitinkerers.org/technologies/flash.md
> Technology record last updated: 2026-02-23T15:30:15Z
> Generated: 2026-09-21T04:43:03Z

Flash was a dominant multimedia software platform (Macromedia, then Adobe) used for creating vector graphics, animation, and Rich Internet Applications (RIAs), officially discontinued on December 31, 2020.

Adobe Flash, formerly Macromedia Flash, was the industry standard for delivering interactive web content: animations, browser games, and embedded video players. The platform used the proprietary SWF file format and ActionScript programming language, driving the early 2000s web experience. Despite its widespread adoption, Flash faced increasing criticism for performance issues and persistent security vulnerabilities, notably after Steve Jobs’ 2010 open letter. Open standards like HTML5, WebGL, and WebAssembly provided viable, secure alternatives. Adobe announced its End-of-Life (EOL) in 2017, ceasing support on December 31, 2020, with Flash content blocked from running by January 12, 2021.

- Official technology site: https://www.adobe.com/products/flashplayer/end-of-life.html
- Public AI Tinkerers demos and talks: 14
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Construindo Linr, um editor interativo de timelines com Deepseek rodando no Cursor](https://curitiba.aitinkerers.org/talks/rsvp_xqVhn6TV5MI)

O Linr é um editor online de timelines visuais - útil para checkpoints, milestones, diagramas, eventos historicos, registros de viagem, funil de vendas e afins. Configurei os modelos DeepSeek V4 dentro do Cursor usando o Cline como harness, porque o agente nativo do Cursor não aceita rotear para a API da DeepSeek. Com esse setup rodando, o Linr saiu do zero. Na demo ao vivo eu mostro, em três atos: O setup : por que o caminho obvio não funciona (o agente do Cursor recusa a chave externa) e como o Cline resolve isso dentro da mesma IDE. O coração: pego o Linr num estado inacabado e dou uma tarefa real ao agente rodando DeepSeek ao vivo - uma feature que falta ou um bug - e vocês veem o modelo construindo software de verdade, com o custo em dolar subindo na tela. O payoff: abro o app funcionando e mostro quanto custou, em tokens e em dólar, chegar ali.

- Event context: AI Tinkerers Curitiba: Encontro de Agosto (no EBANX) — 2026-08-26 — Curitiba
- Public talk page: https://curitiba.aitinkerers.org/talks/rsvp_xqVhn6TV5MI

### [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

### [EvoFit: Building a Cross-Cultural AI Fitness Coach That Bridges Eastern Wellness and Western Exercise Science](https://hong-kong.aitinkerers.org/talks/rsvp_dvIFWxBl4TM)

EvoFit is an AI-powered fitness system that acts as a personalized coach — combining Eastern wellness traditions like Tai Chi with Western exercise science, all within a single app. Demo:For the demo: we're showcasing three live modules. The Movement Module lets users upload a workout video (e.g. squats) and receive AI-generated form feedback and scoring. The Real-Time Coaching Module uses your device camera for live Tai Chi practice — the system tracks your pose frame-by-frame, coaches you through movements, and scores your form in real time. The Food Module lets you photograph a meal to get instant calorie and nutrition analysis, or input your available ingredients and fitness goals to have AI generate a personalized recipe complete with instructions, macros, and who it suits best.

- Event context: AI Tinkerers Hong Kong at AWS: Agentic AI in Action (April) — 2026-04-29 — Hong Kong
- Public talk page: https://hong-kong.aitinkerers.org/talks/rsvp_dvIFWxBl4TM

### [Argus - LLM Real-Time Certification Layer](https://ottawa.aitinkerers.org/talks/rsvp_JKQNRo6WKWY)

I built Argus, an infrastructure-level inference control layer for LLMs that classifies a prompt before the model call, applies a model-calibrated control envelope, certifies the generated behavior with observable metrics, and only releases the output after remediation and closure checks pass. In the demo, Argus shows the full chain end to end

- Event context: AI Tinkerers Ottawa Meetup — April 25th, 2026 — 2026-04-25 — Ottawa
- Public talk page: https://ottawa.aitinkerers.org/talks/rsvp_JKQNRo6WKWY

### [Argus - Real-Time LLM Certification Layer](https://montreal.aitinkerers.org/talks/rsvp_eUNEeswRKAg)

I built Argus, an infrastructure-level inference control layer for LLMs that classifies a prompt before the model call, applies a model-calibrated control envelope, certifies the generated behavior with observable metrics, and only releases the output after remediation and closure checks pass. In the demo, Argus shows the full chain end to end.

- Event context: AI Tinkerers Montreal - April Demo Night — 2026-04-22 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_eUNEeswRKAg

### [Building NousyBooks - Orchestrating Low-Latency Multimodal Voice Agents with Gemini Live](https://atlanta.aitinkerers.org/talks/rsvp_La-nqq5lOEo)

I built NousyBooks, an AI-powered storytelling platform where children become the heroes of their own books. I built this project as part of Gemini Live Agent Hackathon Challenge. The core of the experience is "Nousy," a floating multimodal voice assistant that uses the Gemini Live API to brainstorm story themes, collect character details, and select art styles through natural, bidirectional conversation.

- Event context: AI Tinkerers Atlanta: Community Demos & Technical Deep Dives — 2026-04-21 — Atlanta
- Public talk page: https://atlanta.aitinkerers.org/talks/rsvp_La-nqq5lOEo

### [From Abacus to AI: Small Potatoes, Big Results](https://hong-kong.aitinkerers.org/talks/rsvp_3XIaboEtPiM)

I'll share my real, month-long journey as a complete non-coder who used today's AI tools (like Grok, Copilot, Claude Haiku, and especially Claude Sonnet) to build actual working things: two World of Warcraft in-game addons for my uncles to track character stats and items automatically, a "Next Bus" app for our island's private bus routes (pulling schedules from PDFs and images), and a simple dinner-bell phone app that rings my phone when someone hits bell button their phone. The talk walks through the messy reality — starting with total confusion (SQL commas feeling like ancient abacus work, AWS looking like a blurry PS2 game, pasting code line-by-line and debugging parentheses I didn't understand), failing a lot, switching models when one got stuck, and eventually getting functional apps and addons. I'll show how I "failed faster" by iterating quickly, using my own low-tech version control (100+ numbered folders), and leaning on different AIs for different strengths. It's not about becoming a pro developer overnight — it's about an ordinary person getting useful results with zero prior experience.

- Event context: AI Tinkerers Hong Kong GBA at the Hive: Creative AI Demos & Technical Show-and-Tell — 2026-03-26 — Hong Kong
- Public talk page: https://hong-kong.aitinkerers.org/talks/rsvp_3XIaboEtPiM

### [AI-to-USD: An LLM Agent That Self-Corrects Industrial Scene Generation for Isaac Sim](https://nyc.aitinkerers.org/talks/rsvp_Y4sGAt1oy-A)

I built an end-to-end pipeline that turns natural language descriptions into validated USD industrial scenes and synthetic training data for computer vision. The pipeline has two stages: Stage 1: Spec-to-Sim Copilot: You describe a battery module assembly task ("assemble a 2x3 grid of LG E63 cells with a UR10e robot"). An LLM (Gemini 2.0 Flash with structured output) generates a Pydantic-validated ModuleTask JSON. Then, 5 industrial safety rules are checked against the spec: thermal spacing between cells, robot arm reachability, cell rotation alignment for busbar welding, module tray bounds, and cell count limits. If validation fails, the system feeds structured error reports back to the LLM for self-correction (up to 3 repair attempts). This is the interesting part: pure LLM generation fails silently for spatial tasks — cells overlap by 5-10mm, coordinates fall outside robot reach — so the validation loop catches what the LLM can't reason about. Stage 2: Industrial SDG Lab: The validated scene gets domain randomization across 4 axes (lighting intensity, material color shift, camera pose, object placement jitter) to produce variant USDA scenes with COCO-format annotations — ready for training object detection models. An Omniverse-style 3D viewport built with Three.js lets you interactively inspect the scene with PBR materials, bloom post-processing, and a property inspector panel. Live demo: I'll type a prompt, show the LLM generating a spec, trigger a validation failure, watch the AI self-correct, then generate randomized variants with the 3D viewport.

- Event context: March Demo Day, hosted by Flowglad — 2026-03-18 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_Y4sGAt1oy-A

### [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

### [Building a Production-Ready ESG KPI Extraction Pipeline with Gemini API](https://vienna.aitinkerers.org/talks/rsvp_LPKQctsj3Mw)

A practical deep-dive into building an AI-powered system that extracts 170+ structured KPIs from ESG/financial PDF documents. I'll walk through the real engineering decisions behind our two-stage extraction pipeline: how we use Gemini's Files API with explicit caching to control costs, parallel structured outputs processing to speed up extraction, LLM-based conflict resolution for multi-document scenarios and how we evaluate the pipeline. Expect code snippets, architecture diagrams, and honest lessons learned from development.

- Event context: AI Tinkerers Vienna: 2026 — 2026-02-19 — Vienna
- Public talk page: https://vienna.aitinkerers.org/talks/rsvp_LPKQctsj3Mw

### [LLM vs Classical Vision Models for Real-World Object Detection](https://dublin.aitinkerers.org/talks/rsvp_Gc378dJzNpk)

In this demo, I will show how I use an LLM for object detection in a Telegram Mini App, and compare this approach with classical vision models like YOLO. In the app, users need to take a photo of a physical gift before putting it into a public box. The photo is sent to the backend, where an LLM (Gemini 2.5 Flash-Lite) looks at the image and returns a simple result: object category, confidence, and a short description. The LLM does not make final decisions. Its output is checked by simple rules in the backend, which decide if the user can continue, if the gift should be blocked, or if an admin needs to review it. I will explain why I chose an LLM instead of a classical CV pipeline, how much it costs per request, how I handle failed model responses, and in which cases this approach works worse than models like YOLO.

- Event context: AI Tinkerers Dublin Meetup — Baseline, January 26, 2026 — 2026-01-26 — Dublin
- Public talk page: https://dublin.aitinkerers.org/talks/rsvp_Gc378dJzNpk

### [The Secret to Stunning UI: How AI Helped Me Design and Ship InkyCards in 7 Days](https://cologne.aitinkerers.org/talks/rsvp_EmnVHJCgoK0)

In just one week, I took InkyCard, a conversational language learning app from an idea in my head to a production-ready product. This session isn’t just about generating code; it’s about using AI to solve the "Developer Design Gap" and build a brand with a soul. I will demo the InkyCards workflow, focusing on: The Design Extraction Hack: How to feed UI inspiration (from sites like Dribbble) into LLMs to generate custom design systems, ensuring your app doesn’t look "AI-generated." Building the "Soul": Using Gemini Nano and Higgsfield to create a unique app mascot and custom iconography, overcoming creative blocks and building an emotional connection with users. Production Speed-running: A look at the "Plan-First" prompting strategy and context management that allowed me to build complex features—like real-time AI conversations (Firebase Vertex AI) and "Tap-to-Learn" flashcard generation—without losing code quality. Technical Deep-Dive: State Management & AI: How to use Claude and AntiGravity to scaffold architecture that stays clean as the project grows. The Fresh Convo Rule: My framework for managing LLM context to prevent "code rot" and quality degradation.

- Event context: AI Tinkerers Cologne #2: Let's Build. — 2026-01-21 — Cologne
- Public talk page: https://cologne.aitinkerers.org/talks/rsvp_EmnVHJCgoK0

### [Evolving GPU kernels](https://singapore.aitinkerers.org/talks/rsvp_JdX3E3Z__hA)

I entered the AMD Developer Challenge - which required writing specific GPU kernels for AMD chips. However, I'm not a kernel wizard, so my idea was to use Gemini Pro to do the kernel writing. But, taking it further, I made the process into loop, where the system dynamically decides what experiments to run to make the best kernel.

- Event context: AI Tinkerers Singapore: 8th Meetup - Antler x Sambanova - June 20th, 2025 — 2025-06-20 — Singapore
- Public talk page: https://singapore.aitinkerers.org/talks/rsvp_JdX3E3Z__hA

### [Build Conversational AI Agents that speak ALL of Singapore's languages!](https://singapore.aitinkerers.org/talks/rsvp__o3bhqKErzI)

Singapore is a vibrant tapestry of languages and cultures—and now your AI agents can be too. In this session, discover how to build multilingual conversational agents using ElevenLabs' powerful conversational AI platform. Learn how to seamlessly integrate automatic language detection and real-time switching capabilities to create agents that effortlessly understand and respond in English, Mandarin, Malay, Tamil, and more.

- Event context: AI Tinkerers Singapore: 6th Meetup - April 25th, 2025 — 2025-04-25 — Singapore
- Public talk page: https://singapore.aitinkerers.org/talks/rsvp__o3bhqKErzI

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