Electrification isn’t easy—and most people don’t see the chaos beneath the solar panels, batteries, EV chargers, and heat pumps going onto the grid. Coperniq cofounder and CTO Max Kazakov breaks down the hidden workflows behind distributed energy: legacy tools installers still rely on, hardware that doesn’t want to integrate, and why the next generation of “utilities” will look nothing like the last.

Coperniq is the workflow platform for contractors and energy companies to move from post-its and spreadsheets to a system that sells, permits, installs, and maintains distributed energy assets over decades.

Max also shares what it takes to build vertical SaaS for the physical world: curbside Figma demos during COVID, rebuilding their mobile app for 120°F rooftops with no cell service, designing a workflow engine that matches real-world permitting and interconnection, integrating a wild west of OEM hardware, and how AI is already reshaping their product and engineering culture.

Content:

(00:00) The Invisible Glue of the New Grid

(01:05) The Second Electrification Wave

(02:51) Cofounder Origins: Russia, Yemen, Berkeley

(06:12) Humans + Hardware Coordination Challenge

(08:10) Anti-MVP: Mini ERP on Day One

(11:56) Curbside Figma Demos during COVID

(14:47) Field Reality: 120° Rooftops, Zero Cell Service

(20:03) Stateful Workflows (Permits, Interconnection, Construction)

(24:56) Integrating OEM Hardware (Hitting Walls)

(28:41) The Dongle Question: Do we need software afterall?

(31:25) Rebuilding the Mobile App for an Offline-First World

(36:40) Hire Tinkerers, Not Pedigrees

(43:00) How AI Is Reshaping Coperniq

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Follow @Coperniq_AI for more.

Robots built like humans.

On High Bit, Dhanush Radhakrishnan, cofounder & CEO of Clone, explains how they’re letting biology set the blueprint for musculoskeletal, super-intelligent androids — synthetic humans straight out of sci-fi.

Powered by artificial muscles instead of motors and attached to anatomically accurate skeletons, Clone is building robots designed for human-level motion, durability, and full-body control.

Dhanush explains the early engineering choices that helped them move fast, their data strategy (motion capture, teleoperation, egocentric video), and his excitement about a future untethered biped.

Hosted by Brett Gibson, managing partner at Initialized.

Content:

(00:00) From Fragile to Durable

(00:37) Musculoskeletal Androids

(02:03) Why Artificial Muscles

(04:10) Starting Clone in Poland

(06:29) Removing Early Sensors

(10:14) The Durability Challenge

(14:10) Anatomy as Blueprint

(16:22) Building Custom Valves

(18:17) Hand to Full Body

(24:40) Prototyping with Pneumatics

(29:26) Delaying Tactile Skin

(32:39) Data: MOCAP + Teleop

(45:20) Toward an Untethered Biped

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In this episode, Brett Gibson talks with Lucas Young, cofounder and CEO of Deepnight about how they’re building AI-powered night vision that helps the military, law enforcement, and first responders see in near-total darkness. Deepnight combines AI with commodity digital sensors — the same kind used in smartphones — to replace expensive analog night-vision hardware that costs over $30,000 per unit and hasn’t kept pace with modern imaging technology.

Lucas explains how night vision has worked since World War II, why analog image intensifiers hit a ceiling, how smartphone photography paved the way for this breakthrough, and what it takes to bring military-grade low-light imaging into the field.

Chapters

(00:00) Why Night Vision Is Still Mostly Analog

(00:39) Deepnight’s Breakthrough: AI That Sees in the Dark

(01:44) How Their AI Reconstructs *Real* Scenes

(03:58) Lucas’s Path: Google Pixel → YC Founder

(05:26) Why Modern Cameras Rely on Software

(09:12) The Rise of AI-Enhanced Photography

(11:45) The Insight: AI Could Beat $30K Night-Vision Goggles

(13:03) How Traditional Night-Vision Tubes Work

(14:10) Starting Deepnight Without Knowing If It Would Work

(15:11) Early Prototypes: Offline → Real-Time Night Vision

(16:15) The Physics Challenge: Seeing in Moonless Starlight

(19:12) Running This on Smartphone-Class Chips

(22:27) Building a Custom Neural Network for Night Vision

(28:43) Can Cheap $50 Sensors Match Military Gear?

(48:06) What’s Next: Real Soldiers Using AI Night Vision

Subscribe to High Bit for more conversations with technical founders building what’s next, hosted by Brett Gibson of Initialized Capital.