# Python Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/python?page=2
> Markdown URL: https://aitinkerers.org/technologies/python.md?page=2
> Technology record last updated: 2026-09-18T15:13:57Z
> Generated: 2026-09-21T04:43:03Z

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: 2 of 28

## Recent Public Talks and Demos

### [Evaluating Multi-Agent Systems Beyond the Final Answer](https://seattle.aitinkerers.org/talks/rsvp_lgxuusz0aeg)

I am building an evaluation framework for multi-agent AI systems that compares not only whether the system gets the right final answer, but how the agents collaborate to get there. In the live demo, I will show a small multi-agent workflow with a planner agent, worker agents, and a critic/evaluator agent, then walk through the code, traces, tool calls, handoffs, failure cases, and scoring logic used to evaluate each run. The framework captures the full agent trajectory: which agent took control, what tools were called, where context was passed or lost, how much the run cost, how long it took, and whether the final outcome matched the task goal. I will demo how the same task behaves under different setups, such as a single-agent baseline, a manager-agent architecture, and a multi-agent handoff design.

- Event context: Women in AI - Builders Track: Seattle Inaugural Meetup — 2026-07-15 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_lgxuusz0aeg

### [Operating an Agentic Homelab with Agent Guides](https://seattle.aitinkerers.org/talks/rsvp_wvjI8VfiuMM)

I built a multi-node homelab with layered, enterprise-grade security (passwordless auth, secrets management, host hardening) and an OpenTofu plus Ansible control plane for infrastructure and deployment. AI agents operate it instead of me clicking through consoles. In order to facilitate rapid iteration and configuration I created a new open-standard data format called Agent Guides, which are open, Skill-compatible markdown runbooks. Provisioning a new LXC, rotating credentials, or updating firewall settings are repetitive over time, but the details drift and some of the APIs have undocumented behavior, so a single prompt is unreliable and a fixed script breaks. Plain wiki runbooks aren't ideal because they go stale as soon as the environment changes. What I wanted was documented procedures an agent can follow but still improvise when it hits something unexpected. The guides for my homelab are executed primarily by agents, whose harness leaves an audit log to review afterwards, so another agent may update the guide automatically instead of rediscovering the same problem the next time.

- Event context: AI Dev Tools Track - Seattle - July 13 — 2026-07-14 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_wvjI8VfiuMM

### [stress-testing autonomous research agents](https://zurich.aitinkerers.org/talks/rsvp_N7RI28XXj20)

I built a minimal autoresearch loop for coding agents, and I test the agent-environment behaviour during standard and broken evaluations. I will show how the system is structured as a research sandbox. I will walk through the live workflow, and I will show when and how the different evals I tested changed the agent-environment behaviour.

- Event context: AI Tinkerers Zürich: July Build Night with Swisscom Ventures — 2026-07-01 — Zürich
- Public talk page: https://zurich.aitinkerers.org/talks/rsvp_N7RI28XXj20

### [How We Put a Data Warehouse in the Browser and Let AI Agents Explore It](https://toronto.aitinkerers.org/talks/rsvp_j4OM0Xw7uFQ)

We built a customer-specific data warehouse that runs entirely in the browser. At Relay, every customer has a personalized financial dataset containing transactions, balances, and other banking data. Traditionally, exposing insights from that data required backend APIs, analytics services, and purpose-built dashboards. Instead, we export customer data into compressed Parquet files, load them into DuckDB-WASM, and run a full analytical SQL engine directly in the browser. Every customer effectively gets their own tiny data warehouse. The surprising part came next: once the warehouse existed locally, AI agents became dramatically simpler. Instead of building tool chains, APIs, or MCP servers, we gave agents direct SQL access to the customer's warehouse and let them investigate the data themselves. In the demo I'll show: - A customer-specific warehouse running entirely in the browser - Analytical queries executing locally with no backend round trips - AI agents exploring financial data and generating insights - The architecture powering this in production - The code behind the browser warehouse and agent workflows - The challenges we hit scaling hundreds of tiny warehouses

- Event context: AI Tinkerers Toronto - June 2026 - with Relay Financial x PostHog — 2026-06-30 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_j4OM0Xw7uFQ

### [Enhancing AI Agents with Durability](https://boston.aitinkerers.org/talks/rsvp_feJAeO6zZ-M)

We’ve combined AI agents with Durable Execution techniques to create agents that are durable and observable. The result is a pattern that can be used to continue execution in the event of outages, conserve LLM calls, record past run history, catch rare production failures and reproduce them in house. I’ll demo using a basic research agent and show what the code looks like.

- Event context: AI Tinkerers Boston: GTM Agentic AI Launch — 2026-06-29 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_feJAeO6zZ-M

### [Building an ML Decision Lab for Agriculture: Turning Predictions into Learning](https://lausanne.aitinkerers.org/talks/rsvp_4vwa4V9v2ek)

FarmBuddy is an interactive machine learning decision-support system built with Streamlit and a Random Forest regression pipeline. Users can modify agricultural inputs such as crop type, fertilizer usage, land area, and season, then observe how model predictions change in real time. Beyond prediction, the system includes decision logging, before-and-after scenario comparison, input validation, and a learning summary layer designed to help users understand how machine learning models respond to changing conditions. During the demo, I will show the live application, model inference workflow, session-state architecture, and the decision comparison engine.

- Event context: AI Tinkerers Lausanne June 2026 Meetup — 2026-06-25 — Lausanne
- Public talk page: https://lausanne.aitinkerers.org/talks/rsvp_4vwa4V9v2ek

### [From Local Agent to Emergent Data Scientist](https://nurnberg.aitinkerers.org/talks/rsvp_T0ZyJGZ5BE0)

A local agent gets a stateful Jupyter kernel and a DuckDB database connection instead of fixed tools, giving it a full Python code execution environment. It has to predict outcomes for head and neck cancer patients from cohort data alone, with the current patient's outcome hidden. What I didn't design: the agent trained its own ML models and weighted their outputs based on the patient's individual features, unprompted. Live: I'll show agent traces and behavior in Logfire, walk through the system design, and explain the sandboxed code execution setup.

- Event context: AI Tinkerers Nürnberg: June Meetup (Community Hub) — 2026-06-24 — Nürnberg
- Public talk page: https://nurnberg.aitinkerers.org/talks/rsvp_T0ZyJGZ5BE0

### [What you can do with a used GPU](https://nurnberg.aitinkerers.org/talks/rsvp_YlYa6jJt-us)

A private Telegram-based personal knowledge assistant that lets me save notes from my phone, store and retrieve them with ChromaDB on a VPS accessible anytime, and ask questions answered by a local llama.cpp model running on my home GPU over a private network i setup. Will show probably everything, or as much as i can in the time i have, but demo's are fun and I want to show how the information propagates through what parts of the code to what parts of my telegram UX and use it on stage with suggestions from the crowd for notes. and maybe also show how we can change the prompt handling this assistant on the fly without touching the code using the agenta prompt management platform. maybe also show how i setup lamacpp for maximum inference time even for low vram machines with big beefy models.

- Event context: AI Tinkerers Nürnberg: June Meetup (Community Hub) — 2026-06-24 — Nürnberg
- Public talk page: https://nurnberg.aitinkerers.org/talks/rsvp_YlYa6jJt-us

### [Geospatial perspective on business data](https://poland.aitinkerers.org/talks/rsvp_5LV47mbsR8o)

A geospatial data pipeline that ingests and enriches US business location data from Overture Maps, combining with H3 and additional data sources to generate actionable business intelligence.

- Event context: AI Tinkerers Warsaw: GTM Engineering Track — 2026-06-24 — Poland
- Public talk page: https://poland.aitinkerers.org/talks/rsvp_5LV47mbsR8o

### [Smart flascard](https://nurnberg.aitinkerers.org/talks/rsvp_qt8PZZzucz8)

Smart Flashcard is a vocabulary learning app that helps users turn new words into guided practice and daily study habits. I will demonstrate the live product, including word capture, review sessions, personalized practice, and the learning workflow.

- Event context: AI Tinkerers Nürnberg: June Meetup (Community Hub) — 2026-06-24 — Nürnberg
- Public talk page: https://nurnberg.aitinkerers.org/talks/rsvp_qt8PZZzucz8

### [WebRTC Powered Agents](https://montreal.aitinkerers.org/talks/rsvp_wR1CDxmD5UI)

A simple live-video WebRTC pipeline you can hook LLM agents and ML models into. I will show code and basic architecture for: - Setting up a WebRTC pipeline. - Conversational agent interactions. (Query against video footage, manipulating app state, etc...) - Promptable DINO-based realtime object detection. - Realtime SAM2 segmentation. - Realtime depth detection.

- Event context: AI Tinkerers Montreal - June Demo Meetup — 2026-06-17 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_wR1CDxmD5UI

### [From One Startup a Month to Firing Yourself](https://valencia.aitinkerers.org/talks/rsvp_lyo5PIPqYP4)

Sage Router: Locally-hosted open source AI model routing for serious agents. One endpoint. Any provider subscription. The router figures out the rest.

- Event context: AI Tinkerers Valencia June Meetup ft. PostHog — 2026-06-16 — Valencia
- Public talk page: https://valencia.aitinkerers.org/talks/rsvp_lyo5PIPqYP4

### [Why I gave up on agent voting: hard escalation in a 3-reviewer LLM pipeline](https://nyc.aitinkerers.org/talks/rsvp_gIVCOg1tRfI)

Redline is a production-readiness review tool for AI agents. Three specialized LLM reviewers (Engineer, Risk, Business) read an agent transcript against a company's pasted rulebook, mark up the dossier in three colors, and a synthesizer returns a Deploy, Hold, or Kill verdict. Live demo: pasting a real agent dossier into sanjitkangovi.com/redline, watching the three reviewers annotate in parallel, then walking through the deterministic synthesis layer in the backend that decides the final verdict. I'll show the prompt structure for each reviewer role, the JSON contract between reviewer and synthesizer, and the server-side escalation logic firing when a critical Risk finding contradicts the other two reviewers.

- Event context: NY Tech Week Demo Day ft PostHog, Convex, Veris, & HPE — 2026-06-03 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_gIVCOg1tRfI

### [What Is Your Coding Agent Actually Searching?](https://columbus.aitinkerers.org/talks/rsvp_t4IMPq7lJnc)

I built SearchBench, a harness for running controlled evaluation rounds over coding-agent search behavior. A SearchBench round takes real bug-localization tasks, gives an agent access to a repository, and checks whether it found the files that were actually changed in the human fix. The harness compares an incumbent search strategy against one or more challengers, then writes a static evidence bundle with exact-hit, hop-distance, token-usage, failure, and report artifacts. For the demo, I’ll run a small live ablation round over three cases. I’ll reuse cached Bash/native-search results as the incumbent, then run a few IC challenger policies in parallel. The question is: when IC succeeds or fails, was the important factor anchor quality or graph lookahead? I’ll show the actual workflow: preflight cost prediction, run planning, parallel execution, actual spend, cost-prediction error, generated bundle artifacts, and a short report explaining what changed. One thing I want to show explicitly is how much information the harness can derive from a small, bounded run: exact hits, hop distance, token usage, failure modes, and the next optimization target.

- Event context: AI Tinkerers - Columbus June Meetup — 2026-06-01 — Columbus
- Public talk page: https://columbus.aitinkerers.org/talks/rsvp_t4IMPq7lJnc

### [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

### [Agentic video editing pipeline](https://montreal.aitinkerers.org/talks/rsvp_5bqTjWicF8s)

After interviewing 50+ video editors, head of marketing, film directors, we found the boring part of the professional video editing job that can be automated. I will demo the main video ingestion workflow: transcode to 480p using ffmpeg, upload that video to GCS, takes some frames and use it for classification, ask gemini to look at the video for video understanding, piping the audio to assemblyai for the transcript, use ffmpeg to segment the video, use gemini embedding on each segment so it is searchable.

- Event context: AI Tinkerers Montreal - May Demo Meetup @ Ubisoft — 2026-05-26 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_5bqTjWicF8s

### [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

### [Self-Healing Infrastructure as Code with AI Agents](https://montreal.aitinkerers.org/talks/rsvp_HF74ilFl-U8)

The env-healing-agents project is an autonomous self-healing agent that monitors any running environment, detects problems in real time, diagnoses their root cause using AI, and automatically applies fixes. Here's what it does end-to-end: 1. Monitor; It tails log streams from any source; Kubernetes pods, files, AWS CloudWatch, systemd journald, stdin pipes, or process stdout and matches every line against known issue patterns . 2. Diagnose; When a pattern matches, it extracts the relevant error windows (±10 lines of context around each error line) and sends them to an AI model for root-cause analysis. Models in use: - Claude via Anthropic Vertex AI - Gemini The AI returns a structured diagnosis: root cause, severity, confidence score, and the recommended fix to apply. 3. Remediate; If the confidence score meets the threshold (default 0.7) and remediation is enabled, it executes the fix from fix strategies. 4. Learn; Every outcome is recorded. After each run the learning agent adjusts the confidence scores for each issue pattern based on whether fixes succeeded or failed, so the agent gets more accurate over time.

- Event context: AI Tinkerers Montreal - May Demo Meetup @ Ubisoft — 2026-05-26 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_HF74ilFl-U8

### [Easy self-serve Ray Clusters on Kubernetes, no YAML.](https://montreal.aitinkerers.org/talks/rsvp_8YtgDaZMryo)

Krayne, a lightweight CLI, Python SDK, and interactive TUI that provisions self-serve Ray clusters for AI researchers who dislike wrestling with Kubernetes manifests. For the demo, I will show how to bypass K8s YAML entirely to instantly bootstrap a Ray cluster using a simple command or through our interactive terminal interface. Then, I’ll peek under the hood to show the actual technical implementation: how the tool translates these simple inputs into the complex KubeRay Custom Resources and API calls required to orchestrate the pods behind the scenes. Lastly, I will demo a simple GPU workload running on Ray, in the cloud.

- Event context: AI Tinkerers Montreal - May Demo Meetup @ Ubisoft — 2026-05-26 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_8YtgDaZMryo

### [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

### [Conversation as a Signal Query Engine - HQIQ Maestro](https://dubai.aitinkerers.org/talks/rsvp_2-90lqKKdGc)

Maestro / Hadi is a voice - first generative UI agent platform — no chat bubble, no text input. A full-screen avatar composes the interface in real time as the user speaks: scenes transition, listings render, comparison views compose themselves, contextual sheets open. The agent IS the frontend. We have shipped Translator Mode — a conversational capability where the same agent enters bilingual interpreter persona mid-session when asked. A realtor speaks English, a guest speaks Arabic (or any of 30+ languages Gemini Live supports), the avatar mediates the exchange in real-time bidirectional audio, then exits gracefully when the realtor asks to return to normal — with all prior property context preserved. Architecture: two function tools (enable_translator_mode / disable_translator_mode) inject interpreter instructions into the agent's chat context via update_chat_ctx, and emit TRANSLATOR_MODE data packets that surface a UI status indicator on the frontend. The LLM detects the trigger phrase naturally — no regex, no state machine. Provider-agnostic: runs on Google DeepMind's Gemini Live API for native audio, or OpenAI Realtime as a fallback. Litmus test: "Would this have been impossible with a chat interface?" Yes. Voice translation IS the conversational cadence chat removes. The translator pattern generalizes — same hook supports accessibility narrators, interview coaches, any role-shaped persona switch. The agent's persona is its capability, not its costume.

- Event context: AI Tinkerers Dubai - May Demo Day — 2026-05-23 — Dubai
- Public talk page: https://dubai.aitinkerers.org/talks/rsvp_2-90lqKKdGc

### [Dev tools are all you need](https://seattle.aitinkerers.org/talks/rsvp_FBwC1XbQpYE)

Maibook is a desktop app - a network of you and AI agents personalized based on your file system activity. Demo will show: - Maibook briefly - Customized dev tools built for the project - strings_web - centrally manage all strings AI agents see - system and user prompts, tool schemas, tool docstrings, tool field descriptions and constraints - edit in tool, updates source files - dev_web - see sessions and debug what works, what doesn't - inspired by cc viewer - in the works - ccdbug - Others - threadviewer, benchmarking models, etc.

- Event context: AI Tinkerers Seattle: May Demo Night (Ft. Oracle & NVIDIA) — 2026-05-22 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_FBwC1XbQpYE

### [Clawrium - Fleet management for autonomous agents](https://seattle.aitinkerers.org/talks/rsvp_qKRfRvjwmm8)

CLI tool to manage multiple Openclaw (and its variants like Zeroclaw, nanoclaw etc) on the local network. I'll show a one command installation of Openclaw on a local machine, talk about security best practices and show how local models can be used for this Openclaw assistant. The demo will include a command center to manage all the agents running on local network on different machines.

- Event context: AI Tinkerers Seattle: May Demo Night (Ft. Oracle & NVIDIA) — 2026-05-22 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_qKRfRvjwmm8

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