🤖 Germany’s diving robot tackles ocean waste

ADVANCEMENTS IN ROBOTICS

1. 🌱 Reimagining weeding with AI & Lasers:

What if farming could be made both easier for farmers and safer for the planet? Two 22-year-old engineers from Hyderabad, Rahul Arepaka and George Mathew, are doing just that. While still in college, they founded Harvested Labs, the startup behind Rakshak, India’s first AI-powered laser weeding robot.

Instead of relying on pesticides or expensive manual labor, Rakshak uses cameras and high-power lasers to detect and burn weeds with pinpoint precision without harming crops. The result? Lower costs, healthier produce, and farming that’s more sustainable for future generations.

The journey wasn’t simple. From working out of their hostel rooms to raising a ₹5 crore pre-seed round led by Arali Ventures, plus angel backing from Anand Mahindra, they’ve shown how bold ideas can attract powerful support. Today, Harvested Labs has 8 engineers developing the tech full-time, with farmers already signing up for pre-orders.

Their mission is bigger than profits, it’s about empowering farmers with tech that’s environmentally conscious and scalable. As Rahul says, “Feeding the world should be beautiful.”

This is deeptech innovation at its best where AI, robotics, and purpose intersect.

2. 🤖 Surge: Morgan Stanley predicts China will widen its lead in humanoid AI robots over the next 3-5 years, thanks to aggressive government backing and rapid industrial adoption. With robotics seen as the next global tech battleground, China’s early dominance could reshape everything from manufacturing floors to elder care.

3. 🐜 Swarm: Researchers at NJIT have cracked the code on simulating ant swarms and the payoff could be huge. Their model mimics how ant clusters flow like fluids yet bounce back like elastic materials, unlocking possibilities for self-healing concrete, smarter robot navigation, and even traffic control. Nature’s tiniest engineers may end up shaping the future of cities and machines alike.

4. 👟 Spray: On has just revealed LightSpray™, a robotic process that builds a racing shoe’s upper in just three minutes, using a continuous filament instead of stitching or glue. The result? A seamless, ultralight upper for the new Cloudboom Strike LS designed for speed, reduced waste, and peak performance.

5. 🌊 Dive: Germany’s Technical University of Munich (TUM) has revealed a diving robot that’s part detective, part trash collector, armed with sonar, cameras, and a 4,000-Newton gripper to identify and retrieve underwater waste. What makes this more than just cool tech is its AI vision: it can spot trash on the seafloor and grab it like a deep-sea garbage picker. If deployed at scale, this could be a game-changer for ocean cleanup, saving marine life and restoring fragile ecosystems.

ROBOTICS ON SOCIAL MEDIA THIS WEEK

1. 🚀 Optimus: Elon Musk unveils Tesla Optimus Gen-3; faster, smarter, and closer to real-world humanoid use.

2. 🚁 DIY: Step-by-step guide to design, build, and fly your own drone perfect for makers, hobbyists, and first-time builders.

3. 🤖 Titans: A breakdown of the world’s top industrial robotics companies by market share from ABB and Epson to Fanuc and beyond.

4. ⚡ Breakthrough: A new AI leap is making humanoid robots more capable and more terrifyingly real.

5. 🧬 Nanotech: Tiny medical robots may one day travel inside your body to detect disease and even save lives.

6. 🤖 Pivot: Software alone won’t cut it, future belongs to those who merge coding with robotics.

7. ⚡ Revolution: NVIDIA is driving the UK’s next industrial revolution where AI powers robotics, quantum breakthroughs, and life sciences.

8. 🔧 Breakthrough: Spherical gears enter mass production, paving the way for more lifelike and efficient robotic limbs.

9. 🤖 Maker’s Guide: Step-by-step book that takes you from parts and sensors to building your own working robot.

10. 🚀 Breakthrough Day: From humanoid hands to AI reasoning, Meta AR glasses, and anti-aging AI, yesterday marked game-changing leaps in tech.

SUNDAY SCHOOL

21 AI tools to 10x your income before 2026!

SPECIAL

🤖 Introducing HMND-01 Alpha: The UK’s first industrial Humanoid Robot

Humanoid, a UK-based robotics & AI startup, pulled back the curtain on HMND-01 Alpha, a dual-armed, wheeled humanoid designed not just as a prototype, but for real industrial deployment. In just seven months they’ve built a machine that stands 220 cm tall, moves at 7.2 km/h, and can carry ~15 kg payloads, all while navigating warehouses, logistics centres, and fulfillment hubs.

What sets Alpha apart is its practical mission: tackling repetitive, physically demanding tasks so human workers can focus on what matters most. Think: picking, sorting, machine feeding, packaging, roles often overshadowed by manual labour fatigue.

Humanoid isn’t just pointing to big numbers, they’re backing performance. With $50M in founder-led capital, a team drawn from Apple, Tesla, Google, NVIDIA & more, and a business model built on Robots-as-a-Service (RaaS), their ambition is clear: improve productivity, reduce errors, and improve working conditions without expensive structural overhauls.

As HMND-01 Alpha goes through industrial facility testing, the learnings will shape its Beta version, due in Q3 2026.

What are the implications when humanoid robots can be rolled into your warehouse floor tomorrow? The future of work is catching up and fast.

PROMPT OF THE WEEK

Create an inventory turnover analysis model.

Build a complete Inventory Turnover Analysis Model in Excel or Python.

The model should:

1. Input Section

Accept the following raw data as inputs:

▫️ Opening Inventory

▫️ Closing Inventory

▫️ Cost of Goods Sold (COGS)

▫️ Optionally: Monthly or quarterly inventory and COGS data for trend analysis

▫️ Dates (to calculate period durations)

2. Core Calculations

▫️ Average Inventory = (Opening Inventory + Closing Inventory) / 2

▫️ Inventory Turnover Ratio = COGS / Average Inventory

▫️ Days Inventory Outstanding (DIO) = 365 / Inventory Turnover Ratio

3. Trend Analysis

▫️ Show Inventory Turnover Ratio over multiple periods (e.g., last 12 months or 4 quarters)

▫️ Show DIO over the same periods

▫️ Include % Change from Previous Period

▫️ Optional: Forecast future turnover using simple linear regression or moving averages

4. Benchmarking

▫️ Include an input section to manually enter industry benchmark ratios

▫️ Automatically compare company performance to benchmark

▫️ Highlight areas of concern (e.g., turnover is slowing down, DIO increasing)

5. Visualization

Use charts for:

▫️ Inventory Turnover Ratio trend

▫️ DIO trend

▫️ COGS vs. Inventory over time

▫️ Use conditional formatting to flag high or low turnover

6. Outputs/Insights

Generate automated commentary on:

▫️ Whether turnover is improving or worsening

▫️ Possible reasons for changes (e.g., overstocking, seasonality, drop in sales)

▫️ Suggested actions (e.g., reduce purchase orders, discount slow-moving items)

7. Optional Features

▫️ A dropdown to choose between monthly, quarterly, or annual analysis

▫️ Sensitivity analysis: What if inventory increases/decreases by X%?

▫️ Dashboard summary (for presentation)

Deliverables

▫️ Excel Model (with input, calculation, and dashboard sheets)

OR

▫️ Python Notebook (using pandas, matplotlib/plotly, and optionally Streamlit for dashboard)"

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Thanks for reading.

Until tomorrow!

Shinky & the Hanoomaan AI team