Shreyas Lad
Software / Product Engineer, Founder & CTO @ Intual.ai
Reach out to me via or LinkedIn.
Reading Recs
- Working Backwards
- Crossing the Chasm
- Blue Ocean Strategy
I credit Working Backwards with forming my measured, problem-focused approach to leadership. It's the reason we have a strong writing culture at Intual.
Who??
This was many years ago, but I got my start doing Python and Java. That quickly changed to C and x86 assembly when I started building operating systems. How are binaries executed? How does your graphics card talk to your computer? How does your screen work? If you've taken an operating systems class, you already know the answer. Bear with me, I was 14.
I built my first 32-bit operating system and tested it directly on my laptop. Groundbreaking. Learned about boot sequences, chainloading, protection rings, interrupts, BIOS & UEFI, and much more. My bootloader at the time was 512MB, the exact size of the x86 bootsector. Nothing fancy, it loaded a small C kernel which used a VESA framebuffer to display a terminal to the screen. I built a rudimentary shell with some basic commands (like my own neofetch). I built my first kernel memory allocator and ironically, it leaked memory. Don't ask.
At the time, I was dead set on being an embedded engineer. I built Slate, my first 64-bit operating system. This time I learned about virtual memory, scheduling algorithms, multi-core processing (SMP), power management (ACPI), device comms (PCI, PCIe), and attempted to learn the mess of the USB stack (xHCI). Around the same time, I wrote an ext2 FS implementation for the limine bootloader.
Although I've moved on from operating systems, my interests still lie in distributed systems and design. Instead of C, I mostly write in Go, Python, and Typescript. At Salestable.ai, I onboarded as an AI engineer being #2 on the AI team. I led the creation of 5+ products in just a couple months. I work across the stack and focus on product + design.
I now serve as the CTO of intual.ai. Our platform reduces time to incident resolution by creating a semantic layer on top of your data. Analysts use us to perform deep research on multi-modal data from substations, planes, trucks, buildings, etc. It's an interesting problem at the intersection of UI/UX, LLMs, graphs, workflows, and big data. I look for people who take pride in their work, shoot me an email if that's you.
Shreyas Lad