# Python Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/python?page=5
> Markdown URL: https://aitinkerers.org/technologies/python.md?page=5
> Technology record last updated: 2026-09-18T15:13:57Z
> Generated: 2026-09-21T06:38:46Z

Python: The high-level, general-purpose language built for readability, powering everything from web backends to advanced machine learning models.

Python is the high-level, general-purpose language prioritizing clear, readable syntax (via significant indentation), ensuring rapid development for any team . Its ecosystem is massive: use it for robust web development with frameworks like Django and Flask, or leverage its power in data science with libraries such as Pandas and NumPy . The Python Package Index (PyPI) provides thousands of community-contributed modules, offering immediate solutions for tasks from network programming to GUI creation . The language is actively maintained by the Python Software Foundation (PSF), with the stable release currently at Python 3.14.0 (as of November 2025) .

- Official technology site: https://python.org
- Public AI Tinkerers demos and talks: 662
- Result page: 5 of 28

## Recent Public Talks and Demos

### [A context graph system that allows autonomous agents to make a reliable decision](https://paris.aitinkerers.org/talks/rsvp_BhRPE_2HqtA)

In this session, Yann introduces a context graph system designed to enhance how autonomous agents reason and make decisions. By structuring information as interconnected context nodes rather than isolated inputs, this approach enables agents to maintain coherence, track dependencies, and act more reliably in complex environments. The talk will explore how context graphs bridge the gap between raw data and actionable intelligence, making agent behavior more predictable, interpretable, and robust.

- Event context: AI Builders Meetup Paris - OpenClaw (featuring 42 AI & HEC Vibe) — 2026-03-26 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_BhRPE_2HqtA

### [How AI Agents Work, from while to wow!](https://paris.aitinkerers.org/talks/rsvp_cVHew7whhkA)

A clone of OpenCLAW to learn how it works

- Event context: AI Builders Meetup Paris - OpenClaw (featuring 42 AI & HEC Vibe) — 2026-03-26 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_cVHew7whhkA

### [Miró: Synthetic Audience Analysis using LLM Agents and Graph Database](https://manizales.aitinkerers.org/talks/rsvp_QJ7Kot1_hP8)

In this demo, I will do a code deep-dive into "Miró", an engine I built to forecast the social and critical reception of upcoming books using synthetic readers. Instead of a product pitch, I will focus entirely on the technical architecture and the integration layer between LLMs and Graph Database. I'll walk through the code live, showing: Agent Generation Pipeline: How I parse static PDFs containing psychological profiles and translate them into "Synthetic Reader" nodes with their respective master prompts. The Predictive Engine: The orchestration code that drives how agents "read" the book, interact with each other, and how these interactions continuously update the graph database state. Graph-based Memory Management: A look into how I solved the challenge of persistent agent memory by dynamically creating complex relationships (such as CHATTED_ABOUT) and appending conversation histories as extendable edge properties using RAG. Analysis Dashboard: A quick look at how the Python backend consumes this dynamic graph network to feed an interactive react frontend, rendering resonance, friction, and abandonment connections.

- Event context: 🚀 ¡14vo Encuentro de AI Tinkerers Manizales! 🤖 — 2026-03-25 — Manizales
- Public talk page: https://manizales.aitinkerers.org/talks/rsvp_QJ7Kot1_hP8

### [Prediciendo el futuro con inteligencia de enjambre](https://manizales.aitinkerers.org/talks/rsvp_QR6Nx_VHh04)

MiroFish es un motor de predicción con IA de nueva generación basado en tecnología multi-agente. La inteligencia de enjambre lleva acompañándonos mucho tiempo, pero MiroFish lleva este concepto al siguiente nivel: mediante la extracción de información semilla del mundo real (noticias, señales financieras, borradores de políticas), construye automáticamente un mundo digital paralelo de alta fidelidad donde miles de agentes inteligentes con personalidades independientes, memoria a largo plazo y lógica conductual interactúan libremente. Puedes inyectar variables dinámicamente para deducir trayectorias futuras con precisión. En esta charla presentaremos MiroFish-ES, el fork en español del proyecto, y haremos una demo en vivo de sus capacidades de predicción.

- Event context: 🚀 ¡14vo Encuentro de AI Tinkerers Manizales! 🤖 — 2026-03-25 — Manizales
- Public talk page: https://manizales.aitinkerers.org/talks/rsvp_QR6Nx_VHh04

### [Reliable ML Predictions with Conformal Prediction](https://edmonton.aitinkerers.org/talks/rsvp_pjcUgYgII2c)

ML models make prediction but these predictions can be wrong. In high-stakes environments like healthcare, this has consequences. In normal everyday tasks like search or text generation, these wrong predictions are known as hallucinations. To integrate and augment ML models in society, we need them to be reliable! In this demo, I will show how to implement Conformal Prediction, a model-free framework that reasons how confident our model is. Ideally, if our model is not confident then, they should say "I don't know". However, current model deployments don't do this. Conformal Prediction wraps around any model to output a set instead of a single point. The sets are guaranteed to contain the true label with statistical validity. With this, we can statistically guarantee how reliable our predictions are instead of using adhoc unreliable methods that have no guarantees. I will be demo'ing how to convert your model to be more reliable with Conformal Prediction. This framework is general and useful in many applications. This will be a live demo that goes through the coding process and technical implementation of Conformal Prediction.

- Event context: AI Tinkerers Edmonton: March 2026 Meetup — 2026-03-25 — Edmonton
- Public talk page: https://edmonton.aitinkerers.org/talks/rsvp_pjcUgYgII2c

### [Accurate vibe analytics](https://nashville.aitinkerers.org/talks/rsvp_TEGj2gbUeIQ)

Dbt-skillz is a project I open sourced that "compiles" a dbt project into a claude code compatible skill. We use it in our CI/CD workflow to always stay fresh. We use it to (1) build accurate BI/reporting dashboards, and (2) review them with a review-agent workflow that uses the skill.

- Event context: AI Tinkerers Nashville – Software Development in the Age of AI — 2026-03-25 — Nashville
- Public talk page: https://nashville.aitinkerers.org/talks/rsvp_TEGj2gbUeIQ

### [CyberSentinel: Building a Threat Detection Pipeline](https://miami.aitinkerers.org/talks/rsvp_8u_zBunWIJ0)

CyberSentinel is a multi-stage security analysis pipeline. It ingests raw logs from firewalls, SSH, web servers, and intrusion detection systems, identifies threats, maps them to MITRE ATT&CK and the Cyber Kill Chain, and generates a report with severity scores and indicators of compromise. For the live demo, I'll run it against a simulated 5-phase attack (port scanning, SSH brute force, background noise, SQL injection, data exfiltration) and show how 458 log lines become 2 correlated threat clusters and 12 IOCs in under 2 seconds.

- Event context: AI Tinkerers Miami: Spring Demos at The Lab — 2026-03-25 — Miami
- Public talk page: https://miami.aitinkerers.org/talks/rsvp_8u_zBunWIJ0

### [The Layer Between You and the Model](https://nashville.aitinkerers.org/talks/rsvp_vfOwHHG2DJ4)

When you send a message to an LLM, what happens before the model sees it? Something has to load context, decide what tools are available, shape what the model can and can't do. That's the harness. Everyone building agents has one, most people just don't call it that. This is a code walkthrough of a harness I've been building. I'll show what's actually in that layer: context loading, command constraints, persona switching, conversation branching. Not polished or a product pitch. Just the guts of what sits between me and the model. If you've been building something similar and didn't know the name for it, find me afterwards.

- Event context: AI Tinkerers Nashville – Software Development in the Age of AI — 2026-03-25 — Nashville
- Public talk page: https://nashville.aitinkerers.org/talks/rsvp_vfOwHHG2DJ4

### [From ServiceNow Ticket to Terraform Pull Request using Skills, MCP with Coding Agents](https://nashville.aitinkerers.org/talks/rsvp_3hn_1rgR6k0)

Most coding agents are really good at generating infrastructure code from scratch. But how to steer them to follow existing Terraform conventions in the form of base modules: encoding security policies, tagging standards for cost allocation, naming conventions, and compliance guardrails into reusable building blocks. These modules are how we enforce consistency across environments, track cloud spend back to business units, and pass regulatory audits. In this talk, I'll show you how to use skills to define a better infrastructure provisioning workflow. One that steers coding agents to first discover your existing conventions, understand the risks, implement changes within those guardrails, and provide full visibility into every decision made along the way. This gives cloud operations teams the ability to orchestrate infrastructure requests with human-in-the-loop validation at every stage, see huge productivity gains The system uses coding agents designed to work directly on a filesystem—reading and modifying code, running bash commands and interacting with real repositories. Built using Agent Skills and the Claude Agents SDK, this talk walks through how the workflow is structured, what works today. I'll also cover how this approach is agnostic to agent runtimes and model providers - built on open standards (MCP + Skills), so you're not locked into any single vendor and how the same pattern extends to other tasks across the enterprise beyond infrastructure provisioning.

- Event context: AI Tinkerers Nashville – Software Development in the Age of AI — 2026-03-25 — Nashville
- Public talk page: https://nashville.aitinkerers.org/talks/rsvp_3hn_1rgR6k0

### [\[UofT\] PIIGuard: A Hybrid AI pipeline for detecting and redacting your text](https://toronto.aitinkerers.org/talks/rsvp_dbEwXZ4p79s)

Our project, PIIGuard, is a local privacy oriented tool designed to help users safely share text by removing sensitive personal information before it leaves their device. People often copy and paste content into emails, chat platforms, collaborative documents, or AI tools without realizing how much personally identifiable information it may contain. Additionally, many existing PII removal tools rely on cloud processing, which can expose the data they’re meant to protect. PIIGuard focuses on building a lightweight system that performs PII detection entirely on the user’s device using a hybrid approach that combines regular expressions, named entity recognition models, and optionally a small local language model. These methods identify sensitive information such as names, phone numbers, email addresses, identification numbers, and addresses. Detected items are highlighted with labels so users can clearly see what information may be sensitive. The system then generates a sanitized version of the text where detected items are replaced with consistent placeholders, allowing the text to be shared while preserving its structure and meaning. Users paste text into the interface, review detected PII, and copy a sanitized version through a simple and transparent workflow.

- Event context: AI Tinkerers Toronto - March - hosted by Mozilla! — 2026-03-25 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_dbEwXZ4p79s

### [Life of a Software Artist in the Age of AI](https://toronto.aitinkerers.org/talks/rsvp_EIVzdJmFGnM)

I'm building Togather (an events service for agents) and Covenant (a universal constitution between existing and emerging intelligences to be used as training data) using opencode and agents.

- Event context: AI Tinkerers Toronto - March - hosted by Mozilla! — 2026-03-25 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_EIVzdJmFGnM

### [\[UofT\] Beyond Baseline RAG: Building a Reliable and Transparent Policy Chatbot for Government Guidance](https://toronto.aitinkerers.org/talks/rsvp_oPuu6wo8x8Q)

Government procurement policies are often complex, lengthy, and distributed across multiple directives and guidance documents. Employees seeking clarification must manually search through these materials, which can be time-consuming and may lead to inconsistent interpretations. In this talk, we present the design of a retrieval-augmented policy chatbot that assists users in navigating procurement policies by answering questions directly from source documents while providing transparent citations. Our system uses a Retrieval-Augmented Generation (RAG) architecture to ground responses in official policy text. Documents are segmented and embedded into a retrieval index, allowing the system to surface relevant policy excerpts in response to natural language queries. The language model then generates answers strictly from the retrieved evidence and provides citations so users can verify the source material. Beyond a baseline RAG implementation, the project explores several mechanisms to improve reliability and transparency. These include structure-aware document chunking, detection of contradictory or overlapping policy statements, and a self-verification loop that checks generated answers for unsupported claims. The system also incorporates user feedback signals and an evaluation pipeline that measures retrieval accuracy, evidence grounding, and response quality. Together, these components aim to demonstrate how AI assistants can support policy interpretation while maintaining transparency, accountability, and user trust.

- Event context: AI Tinkerers Toronto - March - hosted by Mozilla! — 2026-03-25 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_oPuu6wo8x8Q

### [AgentChatBus: A Persistent Multi-Agent Debate Room for AI Coding Workflows](https://toronto.aitinkerers.org/talks/rsvp_0rs9k8evwXA)

I will demo AgentChatBus, a 100% free and open-source project (with zero commercial elements) featuring an MCP server, a browser-based Web UI, and an experimental VS Code extension. It turns a normal IDE coding task into a persistent multi-agent review-and-debate workflow. Instead of relying on a single assistant response, a task is sent into a shared thread where multiple role-specific agents, such as security, performance, and architecture reviewers, can inspect the same context, challenge each other, and return a concrete repair checklist. Humans can monitor agent activities, view thread states via Server-Sent Events (SSE), and jump into the debates in real time using the built-in browser UI, our VS Code extension, or through full compatibility with other popular MCP-enabled IDEs (like Cursor, Antigravity, etc). The demo will be shown live from VS Code (though it fully supports other IDEs), leveraging both the standard MCP tools for AI agents and our human-facing interfaces for orchestration. The core idea is editor-agnostic: using MCP and REST/SSE as open infrastructure for reproducible agent-human collaboration. I will show the workflow end to end, then briefly open the hood on the protocol mechanics, including strict message synchronization, persistent thread state, and dynamic agent joins.

- Event context: AI Tinkerers Toronto - March - hosted by Mozilla! — 2026-03-25 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_0rs9k8evwXA

### [\[UofT\] Give Your Local File System Memory - Intelligent Document Reference](https://toronto.aitinkerers.org/talks/rsvp_xdm4yT8hgkU)

Our application is an intelligent document search and question-answering system designed to help users quickly find information within their personal files. Instead of relying on file names or exact keyword matches, the system analyzes the actual content of documents and allows users to search using natural language queries. The application automatically indexes files from the user’s file system, extracts their content, and organizes the information in a way that makes it easy to retrieve later. When a user asks a question or searches for a topic, the system identifies the most relevant files and sections of text, then returns either the file paths or a summarized answer supported with citations to the original documents. The system supports multiple file formats, including documents, spreadsheets, images, and text files, enabling users to search across different types of data in one place. By combining semantic search with AI-powered reasoning, the application helps users navigate large collections of files more efficiently and quickly locate the information they need.

- Event context: AI Tinkerers Toronto - March - hosted by Mozilla! — 2026-03-25 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_xdm4yT8hgkU

### [Encoding Engineering Judgment into AI Coding Agents](https://toronto.aitinkerers.org/talks/rsvp_d8pBUS_N4jA)

LLMs are great at writing code, but they mostly follow patterns they learned from random internet repos. That’s fun until those patterns start leaking into your production codebase. In this talk I’ll demo Mystro, a system that lets you encode your own coding practices and use them to guide an AI coding agent. Instead of hoping the model does the right thing, you can test, evaluate, and retry automatically until it starts following your rules. It's using the same underlying algo (GEPA) as autoresearch from Karparthy that was recently released. Think less vibes, more engineering policy. A quick look at what it might take to actually run coding agents inside real codebases.

- Event context: AI Tinkerers Toronto - March - hosted by Mozilla! — 2026-03-25 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_d8pBUS_N4jA

### [Plug-in hybrid: deterministic solving engine to combine with LLMs](https://bremen.aitinkerers.org/talks/rsvp_Dk-hX27J8AE)

Python ply Parsers convert quantitative models into graphs, which are used as a basis for dynamic decision solving.

- Event context: AI Tinkerers Bremen — 2026-03-25 — Bremen
- Public talk page: https://bremen.aitinkerers.org/talks/rsvp_Dk-hX27J8AE

### [Niuwn AI: Building an AI Twin That Speaks As Me to Strangers](https://bremen.aitinkerers.org/talks/rsvp_BIo4d4pBN0I)

I built a system where anyone can create a conversational AI version of themselves, not a chatbot that answers questions about you, but one that talks as you in first person to anyone who visits a shareable link. Think of it as replacing your static CV or LinkedIn profile with something people can actually have a conversation with. The core idea is a two-agent architecture. One agent (the Curator) works with you privately; you talk to it, upload your CV, share your blog posts, import your LinkedIn export and GitHub profile. It interviews you like a thoughtful journalist, asks follow-ups, spots gaps, and organizes everything into a structured knowledge base. The second agent (the Twin) is what visitors see. It reads from that knowledge base, speaks in your voice, respects boundaries you've defined, and gracefully handles things it doesn't know instead of hallucinating. What makes the system interesting technically: the entire knowledge base is plain Markdown files, transparent, editable, and version-controllable. Retrieval uses memsearch for hybrid BM25 + vector search over those markdown files, which turns out to work really well for professional data where you need both semantic understanding and exact matching on names, dates, and technologies. The two agents have strict asymmetric access, the Curator writes, the Twin only reads, which keeps the trust boundary clean and makes prompt injection defense on the public-facing side much more manageable. The system is built with GDPR in mind, owners can view, edit, or delete anything their Twin knows, and visitors are informed upfront that conversations may be reviewed by the owner. During the Live demo, I'll teach the Twin something new on stage and let the audience interrogate it.

- Event context: AI Tinkerers Bremen — 2026-03-25 — Bremen
- Public talk page: https://bremen.aitinkerers.org/talks/rsvp_BIo4d4pBN0I

### [What If Your OS Was Built for Agents, Not Humans?](https://la.aitinkerers.org/talks/rsvp_7OKp4tR0VHs)

AitherOS is a full operating system kernel for autonomous AI agents. Where traditional frameworks treat agents as stateless function calls, AitherOS gives every agent a persistent identity, a five-tier memory hierarchy, cryptographically signed capability tokens, and native inter-agent communication — the same primitives an OS kernel provides to processes, elevated to the agent abstraction. The system orchestrates 29 specialized agents across 12 architectural layers, from bare infrastructure through cognition, perception, memory, security, and training. Every request passes through intent classification and effort-based model routing, dynamically selecting the right LLM weight class for the task — from a 1B parameter model for simple lookups to a 14B reasoning model for complex planning — reducing inference costs by up to 90% without sacrificing capability. When a problem demands it, the swarm coding engine marshals 11 agents through a four-phase pipeline — architect, build, review, judge — producing tested, reviewed code autonomously. Security is not bolted on. Every agent capability is gated by HMAC-SHA256 signed tokens under a default-deny policy. Agents cannot acquire permissions they were not explicitly granted. The system continuously improves itself through a pain-driven learning loop: execution failures are captured, analyzed, and converted into corrective training examples that feed back into model fine-tuning automatically. AitherOS ships with an open Agent Development Kit supporting the MCP protocol, enabling third-party agents to plug into the full OS stack. The system running at demo.aitherium.com is not a prototype — it is the production deployment.

- Event context: AI Tinkerers LA: March Builder Meetup & Live Demos — 2026-03-20 — Los Angeles
- Public talk page: https://la.aitinkerers.org/talks/rsvp_7OKp4tR0VHs

### [Prompt Stretching: Applying Linear Transformations and Recursive Decomposition to Prompts](https://la.aitinkerers.org/talks/rsvp_nkNEynHMFvs)

I'll walk through an open source Python library I'm working on where LLMs generate other LLMs as reusable JSON configs. The core demo is "prompt stretching" — a pipeline that takes a single user prompt and fans it out through a family tree of transformations (inverse, abstract up/down, reflection, rotation, shear, scaling, recursion, dimension shift). Then I'll also demo subprompting — recursively decomposing a prompt into a tree of subprompts at configurable depth and branching factor (e.g. n=[4,3,2]), with concurrent execution, so anyone can build their own deep research agent. Finally, all the transformed/decomposed prompt variants and their responses can be pairwise evaluated.

- Event context: AI Tinkerers LA: March Builder Meetup & Live Demos — 2026-03-20 — Los Angeles
- Public talk page: https://la.aitinkerers.org/talks/rsvp_nkNEynHMFvs

### [Evaluation-Driven Skill Development](https://nyc.aitinkerers.org/talks/rsvp_kRiiAXuZymg)

Live demo of skillet, an open-source framework for developing Claude Code skills through evals. I'll walk the full loop: capture a failure as a YAML eval, run an evaluation baseline, generate a skill, then auto-tune it until it passes.

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

### [Fuse: Collaborative Brainstorming Intelligence](https://nyc.aitinkerers.org/talks/rsvp_23W_XDTXWSo)

I'll be demonstrating a new app that I built that uses Google's Gemini live agents for real-time interaction with the end user to help them during brainstorm sessions

- Event context: March Demo Day, hosted by Flowglad — 2026-03-18 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_23W_XDTXWSo

### [From Idea to Deployed App in 15 Minutes — The PM-as-Director Workflow for Non-Coders](https://chicago.aitinkerers.org/talks/rsvp_L5Pnf9N6o1k)

A fully functional web app starting from nothing but a product idea. I'll walk through the complete pipeline I teach creative professionals who've never written code: generating a production-grade PRD with Claude → prototyping in Google AI Studio → scaffolding and building in Firebase Studio/Lovable → running automated security checks with Vibe Audit (my open-source CLI with 39 security rules across 8 attack surfaces) → pushing to GitHub → deploying to Vercel. Every step is live code and terminal — no slides. The key insight: the PRD is the code direction. AI builds, you think.

- Event context: AI Tinkerers Chicago: March Meetup ft. Programmers Inc. — 2026-03-17 — Chicago
- Public talk page: https://chicago.aitinkerers.org/talks/rsvp_L5Pnf9N6o1k

### [Your Brand Translator](https://paris.aitinkerers.org/talks/rsvp__5FOk0bFKFc)

A personal branch is a must for today's professionals and entrepreneurs. But these same people don't have time to do it themselves and services cost thousands. So we build an agent, - based on OpenClaw for easy interaction via WhatsApp, Telegram and Signal - with a ton of image and video editing tool: IG filters, automatic clipping, deep image generation, caption generation, video from image generation, video modifications, music generation, application of any HF-based model - and the ability to schedule the posts via buffer Share your expertise and experience effortlessly.

- Event context: High-Performance Local AI Development: Kick-off ThinkStation PGX — 2026-03-17 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp__5FOk0bFKFc

### [Benchmarking Small Language Models Where It Actually Matters](https://paris.aitinkerers.org/talks/rsvp_ow7csxTS5J0)

Most SLM benchmarks answer the wrong question. They tell you how a model scores — not whether it works. This platform is designed for teams who care about real execution, not paper metrics. It lets you benchmark Small Language Models on Python and Polars code generation, under strictly controlled hardware conditions, with full visibility on performance, cost, and failure modes. Participants connect to the platform through a web interface and run benchmarks on large datasets and realistic workloads. The backend runs inside a Docker environment and can execute jobs directly on GPUs, whether locally or on dedicated infrastructure. Each run is configurable: quantization, decoding parameters, and runtime settings are part of the experiment, not hidden defaults. The focus on Polars is intentional. Most language models are very good at generating code for older, widely used libraries like pandas or NumPy. These libraries have been present in training data for years and appear in millions of examples online. Polars is different. It is a newer, high-performance data processing library designed for large-scale workloads, built around vectorized execution, query planning, and expression-based transformations. While it offers major performance advantages for large datasets, its programming model is significantly different from traditional Python data tools. As a result, many models struggle with it. They may generate code that looks correct but fails to run, produces incorrect results, or uses inefficient patterns that defeat the performance benefits of the library. This makes Polars an ideal stress test for evaluating whether a model truly understands modern data-processing workflows. Beyond model metrics (tokens/sec, VRAM usage, GPU utilization), the platform evaluates the generated code itself: Does it run? Does it produce the correct result? Is it efficient, or just “technically correct but slow”? To make progress measurable and engaging, the platform also supports a hackathon-style workflow: Real-time leaderboards Full attempt history per team and per benchmark Analytics dashboards highlighting where models systematically fail The goal is simple: give teams a new language to talk about model quality — not “good vs bad,” but correct, efficient, reliable, and production-ready. If you don’t measure this, you might end up shipping models that look good in demos… and quietly break in real workloads.

- Event context: High-Performance Local AI Development: Kick-off ThinkStation PGX — 2026-03-17 — Paris
- Public talk page: https://paris.aitinkerers.org/talks/rsvp_ow7csxTS5J0

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