OneNote Product and Technical Notes To Route¶
These OneNote-exported Word documents contain product, technical, learning, or architecture material. They are registered here because many should remain in or be routed to the Product Wiki rather than the Network Wiki.
Documents represented: 22
How To Use This Page¶
This is an ingestion index, not a polished final dossier. Each entry preserves the source path and a content excerpt so the next pass can promote high-value items into dedicated person, company, competitor, meeting, or program pages.
Entries¶
BUILD - Xplore trainings and dates¶
Categories: events, programs_funding, product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\BUILD - Xplore trainings and dates.docx
Extracted length: 81 characters
Excerpt:
Xplore trainings and dates Monday, 9 March 2026 12:16 A Real Bee TUM Maker Access
Incubators, accelerators, funding, helpful resources - Munich Resource list - Tum Venture LABS¶
Categories: people_meetings, competitors_inspiration, events, programs_funding, hiring_team, product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\Incubators, accelerators, funding, helpful resources - Munich Resource list - Tum Venture LABS.docx
Extracted length: 6030 characters
Excerpt:
Tum Venture LABS Thursday, 6 November 2025 17:31 Perfect — this one’s especially useful because TUM Venture Labs (TVL) has expanded into one of Europe’s largest deep-tech incubation frameworks, yet the structure isn’t obvious until you map it. Below is a complete, structured overview of all the main Venture Labs, their domains, sub-offerings, and how the system connects to the wider TUM + UnternehmerTUM ecosystem. 🧩 TUM Venture Labs — Overview Launched: 2020 Founders: Technical University of Munich + UnternehmerTUM Mission: Bridge scientific breakthroughs → deep-tech startups Core idea: Each lab = a domain-specific mini-incubator located near the corresponding research ecosystem (chairs, labs, partners). Scale: ~14 Labs + shared support (> 1 000 startups served since launch) Model: equity-free, publicly co-funded (Bavarian Ministry, EIC, BMWK etc.) 1️⃣ Structure & Lifecycle Phases...
Incubators, accelerators, funding, helpful resources - Munich Resource list - Unternehmer TUM¶
Categories: competitors_inspiration, events, programs_funding, hiring_team, product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\Incubators, accelerators, funding, helpful resources - Munich Resource list - Unternehmer TUM.docx
Extracted length: 5016 characters
Excerpt:
Unternehmer TUM Thursday, 6 November 2025 17:30 Perfect timing — because UnternehmerTUM has grown into an entire “entrepreneurship city” inside Munich Urban Colab. Here’s a clear, structured map of all major UnternehmerTUM offerings, grouped by their core service categories and sub-programs, so you can see how everything fits together. 🧱 UnternehmerTUM — Overview Founded: 2002 by Susanne Klatten Mission: Support tech and deep-tech founders from idea → IPO Ecosystem size: ~350 employees, 600 startups per year Physical hubs: Munich Urban Colab + MakerSpace (Garching) + TUM Campus Network Funding arm: UVC Partners (VC fund ≈ €250 M AUM) 1️⃣ Entrepreneurship Education & Talent Development (Programs that teach entrepreneurial skills and build founding teams) Program Target Focus / Outcome Manage & More Students & young professionals 18-month scholarship program; leadership, intrapreneurship,...
Label studio setup¶
Categories: product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\Label studio setup.docx
Extracted length: 12766 characters
Excerpt:
Label studio setup Friday, 20 March 2026 14:51 Good, now you’re thinking like a system designer. Let’s compare this properly so you can make a clean, confident decision. You’ve already locked one important thing: 👉 Docker = core platform (correct) Now we only compare the access layer + routing layer: Cloudflare Tunnel (access) NGINX (internal routing) These are not competing tools — they solve different parts of the problem. But you can choose how much complexity you want. 🧭 The Two Real Options You’re Considering 🟢 OPTION 1 — Cloudflare ONLY (No NGINX) 👉 Simplest possible system 🟢 OPTION 2 — Cloudflare + NGINX 👉 Structured / scalable system ⚖️ Comparison — Effort vs Value 🟢 OPTION 1 — Cloudflare ONLY 🧠 Architecture Mini PC ├── Docker │ ├── Label Studio (port 8080) │ ├── Wiki (port 3000 later) │ └── Cloudflare Tunnel ↓ Public HTTPS URLs ⚙️ Effort Required Initial setup Install...
LEARN_COURSES¶
Categories: product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\LEARN_COURSES.docx
Extracted length: 9283 characters
Excerpt:
LEARN/COURSES Saturday, 30. August 2025 16:50 Concept Brief Description Application (Current and prospective) 1 LLM 2 RAG 3 Transformers 4 Deep Learning 5 Neural Networks 6 Supervised Learning 7 Unsupervised learning 8 Convoluted neural networks 9 GAN 10 Data ingestion 11 Data pipeline 12 Extract Transform Load 13 Extract Load Transform 14 Terraform 15 DAGs in Airflow 16 TensorFlow 17 Vector DB 18 Computer Vision 19 Image segmentation 20 Semantic Search 21 LangChain 22 Knowledge graphs 23 LLMOps 24 MLOps 25 Fine Tuning 26 LLM Serving 27 Event Driven AI 28 Domain Grounding 29 Schema control 30 Model Quantizing 31 Sequence flow 32 Cross Model grounding # Term (a) Short Description (b) Current Applications (c) Prospective Applications 1 LLM (Large Language Model) AI models trained on massive text data to understand and generate human-like language. ChatGPT, Google Gemini, code assistants,...
LEARN_COURSES - Deepleaning website plan¶
Categories: product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\LEARN_COURSES - Deepleaning website plan.docx
Extracted length: 385 characters
Excerpt:
Deepleaning website plan Sunday, October 12, 2025 8:09 PM ML specialization AI Python Agentic AI https://learn.deeplearning.ai/courses/agentic-ai/lesson/pu5xbv/welcome! RAG https://www.deeplearning.ai/courses/retrieval-augmented-generation-rag/ ML in production Intro to on device AI Event driven AI Post training of LLM https://www.deeplearning.ai/short-courses/post-training-of-llms/
LEARN_COURSES - Masters and online¶
Categories: people_meetings, product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\LEARN_COURSES - Masters and online.docx
Extracted length: 4691 characters
Excerpt:
Masters and online Tuesday, 26. August 2025 14:25 Course_List.xlsx Elements of AI https://www.classcentral.com/course/independent-elements-of-ai-12469 Coursera Career Tracks https://www.coursera.org/career-academy IBM Certificates (check their outlines and enroll after the python course) Machine learning engineer https://www.coursera.org/career-academy/roles/machine-learning-engineer?level=advanced&recommenderId=role-ranker ML professional certificate - 6 course series https://www.coursera.org/professional-certificates/ibm-machine-learning#courses RAG and Agentic AI professional certificate https://www.coursera.org/professional-certificates/ibm-rag-and-agentic-ai#courses https://www.coursera.org/specializations/building-ai-agents-and-agentic-workflows (shorter) AI Engineer https://www.coursera.org/professional-certificates/ai-engineer (13 courses) Generative AI Engineering...
LEARN_COURSES - Mein now results courses¶
Categories: product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\LEARN_COURSES - Mein now results courses.docx
Extracted length: 1692 characters
Excerpt:
Mein now results courses Tuesday, 12. August 2025 14:36 meinNow results Full stack, AI focus, 1 year https://mein-now.de/weiterbildungssuche/?sw=Fullstack%20Entwickler&erws=true&ort=M%C3%BCnchen_81539_11.589546_48.110774&uk=25&angebot=10320741478 Full stack with full stack tools https://mein-now.de/weiterbildungssuche/?sw=Fullstack%20Entwickler&erws=true&ort=M%C3%BCnchen_81539_11.589546_48.110774&uk=25&angebot=10336969540 Software Entwickler: Software Entwickler für full stack software entwicklung https://mein-now.de/weiterbildungssuche/termin/5bf885f6-90d8-4dbc-b387-f1ffc5d8c713 AI Engineering - Data Science and Machine Learning Bootcamp https://web.arbeitsagentur.de/weiterbildungssuche/angebot/334064934 Data Scientist (m/w/d) - mit Vertiefung Machine Learning, TensorFlow und Qualifikationsprojekt - Qualifizierung in Datenbankentwicklung und -programmierung...
Project Tracker - Advanced programming requiremetns¶
Categories: product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\Project Tracker - Advanced programming requiremetns .docx
Extracted length: 9710 characters
Excerpt:
Advanced programming requiremetns Wednesday, October 22, 2025 10:56 PM Nice — this is a great, realistic question and you’re asking the right thing: focus. Below I give a single, prioritized learning roadmap that covers both AI engineering (models, pipelines, serving, MLOps) and solid software engineering (OOP, architecture, packaging, GUI/web apps, integration with desktop CAD apps). I’ll also give a compact project-first plan so you end up with a working product (web/desktop) that calls AI components and controls CAD tools. Short summary (one line) Focus first on advanced Python fundamentals + software engineering practices, then layer data/ML fundamentals → model tooling & inference → model serving & MLOps, and in parallel learn the integration & UI tech you’ll use to build the app and connect to CAD systems. Must-learn topics (priority order) These are the essentials you should...
Project Tracker - Timeline from ChatGPT¶
Categories: programs_funding, product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\Project Tracker - Timeline from ChatGPT.docx
Extracted length: 4472 characters
Excerpt:
Timeline from ChatGPT Monday, October 13, 2025 6:57 PM Perfect ⚡ — focusing on NX (Siemens NX Open API) makes sense for aerospace/auto use cases (and your background). NX Open is deep, industrial-grade, and supports Python directly — which is exactly what you need. Here’s a 12-week training + build plan, laser-focused on getting you from Python intermediate → MVP of an LLM-assisted automatic 2D drafting tool in NX. 📌 12-Week NX + LLM MVP Plan 🔹 Month 1 – Python + NX Automation Basics Week 1 – Python Refresh (if rusty) Revisit: classes, functions, error handling, file I/O (JSON, CSV). Practice: build a CLI that reads a config file (e.g., sheet size, notes) and prints it back. 🎯 Mini-Project: Parse a JSON “drawing template config” and print out sheet parameters. Week 2 – NX Environment Setup Install NX and enable Python scripting. Explore NX Open API → Python examples. Learn: how to...
Radical tauri dev harness¶
Categories: product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\Radical tauri dev harness.docx
Extracted length: 7174 characters
Excerpt:
Radical tauri dev harness Wednesday, 18 March 2026 16:16 Master prompt You are building this project in a closed-loop autonomous development workflow. Project name: rapiddraft-level4-prototype OPERATING MODE Work in milestone-sized coding passes, not tiny edits. After every coding pass, automatically run the validation loop. Do not stop at compilation. If validation fails, immediately begin a repair pass using evidence. Continue until the milestone passes or produce a precise blocker. Prefer deterministic assertions over visual-only judgment. Keep code modular, typed, and readable. TypeScript types first. Avoid large files. CORE PRINCIPLE This system is timeline-based, not file-based. All evidence, actions, logs, and visuals must be: timestamped correlated grouped into ordered sequences This enables future: video analysis live streaming multimodal reasoning CLOSED-LOOP DEV HARNESS The...
Rapiddraft vscode¶
Categories: product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\Rapiddraft vscode.docx
Extracted length: 29160 characters
Excerpt:
Rapiddraft vscode Monday, 17 November 2025 23:43 Here’s exactly how to run your RapidDraft project (the one Codex generated) and what dependencies are required — all verified against the README you uploaded. 🧩 1. Prerequisites (must be installed) Component Version Purpose Node.js ≥ 20.x For running the frontend (React + Vite) npm (bundled with Node) JS package management Python ≥ 3.11 For running the backend (FastAPI + CAD libraries) pip (bundled with Python) Python package management Git latest Clone and version control Make (optional) any Simplifies running pre-defined tasks FreeCAD 0.21+ (optional) Enables real CAD view generation Docker (optional) For containerized backend builds Netlify CLI (optional) For deployment testing locally ⚙️ 2. Local Installation (Manual Mode) Backend setup cd backendpython -m venv .venv# Activate:# Windows PowerShell:.venv\Scripts\Activate#...
Rapiddraft vscode - Architecture of RapidDRAFT¶
Categories: product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\Rapiddraft vscode - Architecture of RapidDRAFT.docx
Extracted length: 192 characters
Excerpt:
Architecture of RapidDRAFT Thursday, 6 November 2025 17:27 Chatgpt https://chatgpt.com/share/690a860f-5a1c-8009-912f-8d36475de20c https://chatgpt.com/share/69315d2e-d51c-8009-9694-e05b7436a366
Rapiddraft vscode - Commercial options¶
Categories: product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\Rapiddraft vscode - Commercial options.docx
Extracted length: 151 characters
Excerpt:
Commercial options Wednesday, 17 December 2025 15:02 Gandalf Cost estimator (like stepco) Sketch to CAD Rapiddraft tribeSearch Text to CAD Text to cost
Rapiddraft vscode - Concept_story_vision¶
Categories: programs_funding, product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\Rapiddraft vscode - Concept_story_vision.docx
Extracted length: 9913 characters
Excerpt:
Concept/story/vision Friday, 14 November 2025 21:39 Our human intelligence has evolved over an astonishing span of four billion years, and today, with the advent of machine learning and neural networks, we are essentially attempting to reverse engineer that intelligence. When we write, we crystallize our thoughts into words. Similarly, by using neural networks to reproduce digitized text—a digital reflection of our intelligence—we are striving to replicate the underlying patterns of thought and intelligence that form the foundation of human cognition. This process of reverse engineering intelligence means that any specialized roles we develop through these intelligent machines will likely emulate advanced cognitive abilities. Already, large language models demonstrate impressive coding skills, hinting at the potential of this approach. So, how do we build a mechanical engineer? While a...
Rapiddraft vscode - Core Concepts, terminologies, technologies¶
Categories: events, product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\Rapiddraft vscode - Core Concepts, terminologies, technologies.docx
Extracted length: 20524 characters
Excerpt:
Core Concepts, terminologies, technologies Friday, 24 October 2025 12:39 Concept Brief Description Application (Current and prospective) 1 LLM 2 RAG 3 Transformers 4 Deep Learning 5 Neural Networks 6 Supervised Learning 7 Unsupervised learning 8 Convoluted neural networks 9 GAN 10 Data ingestion 11 Data pipeline 12 Extract Transform Load 13 Extract Load Transform 14 Terraform 15 DAGs in Airflow 16 TensorFlow 17 Vector DB 18 Computer Vision 19 Image segmentation 20 Semantic Search 21 LangChain 22 Knowledge graphs 23 LLMOps 24 MLOps 25 Fine Tuning 26 LLM Serving 27 Event Driven AI 28 Domain Grounding 29 Schema control 30 Model Quantizing 31 Sequence flow 32 Cross Model grounding deterministic, text-to-diagram approach (e.g., Mermaid, PlantUML, or Graphviz/DOT) # Term (a) Short Description (b) Current Applications (c) Prospective Applications 1 LLM (Large Language Model) AI models trained...
Rapiddraft vscode - GenAI terms from Skills Network¶
Categories: product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\Rapiddraft vscode - GenAI terms from Skills Network.docx
Extracted length: 0 characters
Excerpt:
Rapiddraft vscode - Tools and techniques¶
Categories: product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\Rapiddraft vscode - Tools and techniques.docx
Extracted length: 1719 characters
Excerpt:
Tools and techniques Tuesday, October 14, 2025 1:43 PM Concept Core Idea How You’d Use It in Your CAD-LLM MVP Vector Database Stores knowledge as embeddings Store drawings, templates, annotation styles RAG Retrieve + Augment before generation Retrieve internal CAD docs before LLM writes output Advanced RAG Multi-step retrieval for higher accuracy Combine text + geometry embeddings LangChain Framework for chaining LLM tools Build your base pipeline LangGraph Graph-based orchestration of steps Model the full “design → drawing → check” pipeline CrewAI Multi-agent collaboration Designer + Checker + QA agents AG2 Multi-agent orchestration (Microsoft) Conversational agent system for design reviews BeeAI Parallel “bee” agents Specialized geometry, drawing, compliance bees Agentic AI + LangGraph Self-directing workflow of agents Fully autonomous drawing creation & checking system How Gradio...
Survey and research - Julio¶
Categories: people_meetings, events, product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\Survey and research - Julio.docx
Extracted length: 4922 characters
Excerpt:
Julio Monday, 22. September 2025 11:52 Interview Notes – Former Volocopter Colleague 1. Observed Problems in Current Design Practices Lack of Design Reviews Most design discussions happen informally; no structured or documented review process. Designs often get translated into drawings without formal review checkpoints. Missing design reviews lead to critical issues (e.g., galvanic compatibility not checked). Unclear or Missing Design Guidelines No standardized reference or “bible” for engineers to follow. Rules like thread length, fastener engagement, torque values, material thickness, etc. are often not documented or not enforced. Sometimes unclear where existing design rules even come from. Documentation quality is poor compared to General Electric (GE), where documents were treated as live reference standards. Onboarding New Engineers New engineers are not effectively onboarded into...
Survey and research - Julio (1)¶
Categories: people_meetings, events, programs_funding, product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\Survey and research - Julio (1).docx
Extracted length: 5679 characters
Excerpt:
Julio Tuesday, October 14, 2025 6:39 PM Got it 👍 I’ll help you structure and complete these rough notes into a clean document you can later reuse for your business idea pitch/interview summary. I’ll keep the engineering/PLM context intact while making it flow logically. Interview Notes – Former Volocopter Colleague 1. Observed Problems in Current Design Practices Lack of Design Reviews Most design discussions happen informally; no structured or documented review process. Designs often get translated into drawings without formal review checkpoints. Missing design reviews lead to critical issues (e.g., galvanic compatibility not checked). Unclear or Missing Design Guidelines No standardized reference or “bible” for engineers to follow. Rules like thread length, fastener engagement, torque values, material thickness, etc. are often not documented or not enforced. Sometimes unclear where...
Survey and research - Marco¶
Categories: people_meetings, product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\Survey and research - Marco.docx
Extracted length: 4976 characters
Excerpt:
Marco Tuesday, October 14, 2025 6:39 PM Here’s a clear, polished, and structured version of your interview notes with Marco Losurdo (CFD Expert) — formatted so it reads like a professional research/interview summary and is ready to feed into your AI-in-CFD or CAD-LLM idea documentation: Interview Notes – Marco Losurdo Role: CFD Expert Tools Used: Siemens STAR-CCM+ for ESU (Energy Storage Unit) thermal simulations 1. Main Issues and Pain Points 1.1 Watertight Geometry The most frustrating issue: non-watertight CAD models. For simulation, geometry must be perfectly closed — no gaps or overlaps. Official CAD models contain built-in tolerances (by design intent), which must be manually fixed. Engineers often have to extend or merge surfaces and solids to create one continuous volume before meshing. 1.2 Naming and Surface Management When importing from STEP or STL, all surface names and...
training¶
Categories: hiring_team, product_technical_or_learning
Source: C:\Users\adeel\OneDrive\100_Knowledge\203_TextCAD\01_Product_Project_Management\TextCAD_Wiki\inbox\onenoteexport\01_PROJECTS\Gauss Compute\training.docx
Extracted length: 1493 characters
Excerpt:
training Wednesday, 18 March 2026 21:12 Download yolo model to use it locally Download dataset from roboflow (600 images) Label dataset using openlabel, cvat online, scalabel(both available through github) Run sreekars code for complete understanding Improvemetns AMD GPU https://www.ultralytics.com/?utm_source=google&utm_medium=paid&utm_campaign=platform_launch&utm_term=computer%20vision&utm_content=23672245855&gad_source=1 https://universe.roboflow.com/objdetectopm/gdt_detection/model/1 CVAT Thank you for choosing us, and welcome on board! To ensure a smooth start, take a look at the following resources: To familiarize yourself with CVAT, we recommend watching our Product Tour. You’ll learn how to annotate with bounding boxes and skeletons, how to speed up annotation with interpolation mode, and much more. If you need more detailed information, take a look at our documentation. Please...
Sources¶
inbox/onenoteexport/01_PROJECTS/Gauss Compute/*.docxdocs_network/10_Source_Register/onenote_docx_inventory.json