TensorFlow Robot Perception Quick Start Guide

I recently revisited a teaching example and realized how well it highlights a workflow I strongly believe in: combining #Simba with Python notebooks for control design. Analytical tuning, visualization, and high-fidelity switching simulations all live in the same loop — fast, transparent, and highly reusable. Full example here: https://lnkd.in/euUDGmEw

I turned Stephen Hawking’s event-horizon thermodynamics into a single-file, offline-ready HTML mini-lab. It computes: Hawking temperature (T ∝ 1/M) Bekenstein–Hawking entropy (S ∝ A) Evaporation time (approx., t ∝ M³) Engineering angle: reproducibility — the same calculations are mirrored in Python with venv/Docker notes. Repo: https://lnkd.in/dJHsVAmK #SoftwareEngineering #Python #Docker #DevOps #MLOps #ComputationalPhysics #Physics #BlackHoles

🚦SpeedShield – Operating system Semester Project: (Grouped Project) Built using Python & VS Code, SpeedShield is a real-time speed monitoring and road simulation system that detects unsafe driving and generates instant voice alerts. 📊 Live dashboard 🗒 GUI Implementation 🔊 Voice warning system 🚗 Speed & distance tracking Proud to apply Operating System concepts to real-world traffic safety 🚀 #OperatingSystem #Python #VSCode #SemesterProject #RealTimeSystem #WomenInTech #AI #TechInnovation

🚀 𝗗𝗮𝘆 𝟭𝟬/𝟯𝟬 — 𝗗𝗦𝗔 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 Day 10 already — and this journey is starting to feel less about solving individual problems and more about recognizing patterns and choosing the right approach faster. Today’s focus was on sliding window techniques — understanding how adjusting a window dynamically can help track subarrays or substrings efficiently without rechecking everything from scratch. 🔎 𝗗𝗮𝘆 𝟭𝟬 𝗙𝗼𝗰𝘂𝘀 • Learning sliding window logic • Practicing dynamic range tracking • Solved: ✅ Maximum Average Subarray I ✅ Longest Substring Without Repeating Characters ✅ Permutation in String Still learning, still improving — one problem at a time. On to Day 11 💪 #DSA #Python #LeetCode #LearningInPublic #Consistency #SoftwareEngineering #ProblemSolving

Rerun 0.29.0 is out! We're continuing to improve Rerun for roboticists. The release includes: 🤖 URDF loader improvements 🌳 UrdfTree utility in Python 📸 Experimental screenshot API from Python 💾 Improved memory panel 🦀 Updated ROS 2 example & documentation Check out the full release notes (link in the comments 👇)

🧠 Python Study Timer: Beat Procrastination Forever! Students built AI timers that force focus—phone silenced, robot guards desk, progress gamified! The no-escape code: python if distraction_detected(): robot.blast_warning() study_streak.reset() Lab results: 2x longer focus sessions. Zero phone checks. Real confession: "Robot shamed me back to books!" 😳 #GlobalRoboticsAI #StudyTimerBot #PythonFocus #ExamWarrior #STEMIndia #NoDistractions

What if your forecasting tool could access 200+ additional models out of the box? Forecasting often requires testing multiple models, and the best one for your data may not be in your tool's default set. Integrating models from external libraries like sktime usually means writing custom wrappers and running evaluation separately. TimeCopilot now integrates with sktime, giving you access to 200+ additional forecasters. Same cross-validation pipeline. Same interface as the defaults. This article by Khuyen Tran will walk you through: • How to add any sktime model to TimeCopilot with one line • How to run cross-validation across models from different libraries in one pipeline 🚀 Full article: https://lnkd.in/gqKy3tcY #TimeSeries #Forecasting #Python #DataScience

Every forecast I've posted recently (OpenClaw stars, US demographics, China demographics, etc.) was tested against multiple models before TimeCopilot picked one. The sktime integration makes this better with over 200 models to pick from right out of the box. Example: OpenClaw forecast tested Prophet, AutoETS, Theta, AutoARIMA, SeasonalNaive. AutoETS won. But I wouldn't have known that without testing all five... I now can do that with +200 models 🤩 Model selection rigor matters.

I’m currently training in Python for market finance, and I’m practicing through small personal projects. Recently, I built a simple put–call parity check to validate no-arbitrage consistency between call and put prices. The screenshot shows the script output: the parity holds (OK = True) and the difference is near zero, which confirms the prices are coherent within a numerical tolerance. I recently created a GitHub to share these projects: https://lnkd.in/dNxa46cm Next step: pricing a simple autocall product using Monte Carlo simulation. #Python #Derivatives #Options #BlackScholes #Pricing

I built SpiderTrace to help visualize how Pauli errors (X and Z) propagate through quantum stabilizer circuits using ZX diagrams. What it does: - Traces error propagation through Hadamard and CNOT gates - Generates step-by-step ZX diagrams showing how errors move - Includes an interactive circuit builder to experiment with custom circuits Why I built it: While learning quantum error correction and ZX calculus, I realized textbook equations were not enough. I needed to see how errors spread through circuits. SpiderTrace lets me do just that. What I learned: - Pauli error propagation rules in stabilizer circuits - ZX calculus and diagrammatic reasoning - How to structure a clean Python package with interactive visualization This tool is useful for anyone studying quantum error correction who wants to see error propagation in action. I’m planning to expand it to include Y errors and more Clifford gates. GitHub: https://lnkd.in/dTsW7K9c #QuantumComputing #ErrorCorrection #ZXCalculus #OpenSource #Python