Most people open Google Sheets to handle monthly budgets, invoices, or attendance logs. I wanted to see if spreadsheet cells could work as an actual computer chip. That led to SSX Silicon 1, a working 32-bit computer processor built inside a single Google Sheets workbook. It runs with 32 registers, a 16-kilobyte memory space, 36 instructions, and a 32 by 32 pixel color display. The sheet does not use pre-recorded animations, placeholder numbers, or external file links. Every calculation runs through a five-stage pipeline: fetch, decode, execute, memory access, and writeback.

You can run the processor in two ways. With Google Sheets iterative calculation turned on, toggling a manual clock cell advances the machine cycle by cycle using native circular formulas. For continuous execution, an embedded Google Apps Script V8 engine powers interactive buttons and a custom top menu, running programs at hundreds of instructions per second. The 32-bit arithmetic logic unit handles numbers up to 4.29 billion across 36 instructions, covering arithmetic, division, bit shifting, logic gates, and subroutine branching. It also tracks four hardware status flags on every cycle: Zero, Carry, Sign, and Overflow.

The memory layout organizes a self-contained 16-kilobyte address space across dedicated sheets. This includes 4,096 words of program ROM, 4,096 words of read-and-write data RAM, system I/O registers, and a stack region starting at address 0x3FFF. Thirty-two registers handle active computation: R0 stays fixed at zero, R1 through R27 store math and variables, and R28 through R31 serve as pointers for the frame, stack, return address, and program counter. A dedicated video RAM block at 0x2400 maps directly to a 32 by 32 grid of 1,024 cells, using conditional formatting and a 16-color RGB palette to draw borders, crosshairs, and pixel graphics. I also wrote a Python two-pass assembler that turns human-readable .ssx assembly code into spreadsheet-ready CSV tables, along with interactive HTML architecture diagrams built with Archify.

To make sure the CPU calculates accurately, I tested every instruction against a Python reference emulator across five benchmark programs. The hardware self-test verifies all 32 arithmetic operations, memory reads, and stack frames in 77 cycles, leaving the golden signature 0xCAFEBABE in register R1. A 32-bit Fibonacci program generates 47 terms in RAM up to 1,836,311,903 without rounding or overflow. A prime number sieve finds all 25 primes under 100 using memory arrays, an integer matrix program calculates a 3x3 matrix product, and a graphics routine draws a multicolored geometric crest across 232 lit pixels. The project includes five PDF documents: an academic research paper, an instruction set manual, a microarchitecture specification, a verification report, and a beginner guide. Everything runs in a standard web browser. For anyone who wants to open the processor in Google Sheets, check out the Python compiler, or read the manuals (including the step-by-step Beginner Guidebook and Research Paper), the full open-source release is available on my GitHub!

Inside the repo, you will find:

The ready-to-import Google Sheets CPU workbook (.xlsx)

The Python assembler & bit-exact reference emulator

Interactive HTML architecture & pipeline diagrams

All 5 PDF manuals and the 5 sample assembly programs

Your step by step guide are so helpful

Created a straight-forward @Framer plugin, something I myself would use. In short: finds all images with missing Alt text. In long: ✨ Alt text checks: Missing alt text: Images with no alt attribute at all. Empty alt text: Alt text is set to "". This is correct for decorative images, but worth checking when it is not. Generic alt text: Vague descriptions such as “image” or a filename instead of meaningful text. Duplicate alt text: Different images sharing the exact same alt text. ✨ Link text checks: Empty link text: A link with no visible text at all. Vague link text: Text such as “click here” or “read more” with no context. Duplicate link text: Different links sharing the exact same visible text. You can also choose to skip hidden layers, so your results reflect the content visitors can actually see. Try out here: https://lnkd.in/eF5M8DNq 🤘

Checking for vague and duplicate link text on top of alt text is a nice touch, Arta. The option to skip hidden layers should cut a lot of noise from the results.