I like this and I feel it would be a great part of my plan to someday really understand spreadsheets, except I am not wanting to learn anything in MS Office. But definitely seems like a good market to be in, and a nice tool.
I guess you could do stuff like find lots of common VBA, formulas etc. written in the past, from MSDN magazine articles and similar, and package them up as a library of add-ins that you would get as part of your membership automatically.
xllify takes your existing Excel formulas or VBA and produces clean, reusable custom functions packaged as proper add-ins. You can also just describe what you need in plain English.
Yes, this part is an LLM wrapper. LLMs can reliably write code that targets xllify, given adequate guardrails.
The engine is written in C++ and uses Luau (Roblox's Lua fork) as the sandboxed scripting runtime. Luau might seem an odd choice, but it's genuinely well-suited here, it's fast, sandboxed, and compiles easily for both native and WASM targets. And you don't really need to know Luau to use xllify: the AI writes it for you. We'll see if Luau is a barrier.
Anyway, the same Luau code runs two ways:
.. As a native XLL add-in on Windows, talking directly to Excel's C API. This gives you in-process, multi-threaded function execution with low overhead. If you've ever wrestled with the C API directly, you know why this matters. It's extremely fast but utterly brutal to work with. xllify tames.
.. As a web-based add-in via WebAssembly, using the Office JS platform. Same code, same functions, but now deployable on Mac and Web with all the discoverability and security benefits that come with the modern add-in model.
Luau coroutines are supported, so functions can do non-blocking work (API calls, streaming data) without locking up the calculation chain. The runtime ships with a comprehensive standard library so you're not starting from nothing.
DuckDB is available to do heavy lifting on data analytics problems on data that might be too big to fit into Excel.
LLMs can hallucinate, so xllify doesn't rely purely on generation. The AI will select from a library of verified, tested functions where they exist rather than generating from scratch every time. Industry-specific function packs (finance, healthcare) are on the way.
There are two ways to build:
.. xllify Assistant runs inside Excel itself. You have a conversation with the assistant, build and test functions live, and when you're done you can export a .zip with a ready-to-deploy add-in setup including the package.json Office JS packages.
.. xllify agent tooling for local development if you prefer working in your own editor/terminal.
The add-ins are fully self-contained. No runtime dependencies, no phoning home, no ongoing model costs. Once built, it's yours. Works in air-gapped environments.
Try the online demo and sign up for early access: https://xllify.com - I would love your thoughts on this niche little thing.
I like this and I feel it would be a great part of my plan to someday really understand spreadsheets, except I am not wanting to learn anything in MS Office. But definitely seems like a good market to be in, and a nice tool.
I guess you could do stuff like find lots of common VBA, formulas etc. written in the past, from MSDN magazine articles and similar, and package them up as a library of add-ins that you would get as part of your membership automatically.
xllify takes your existing Excel formulas or VBA and produces clean, reusable custom functions packaged as proper add-ins. You can also just describe what you need in plain English.
Yes, this part is an LLM wrapper. LLMs can reliably write code that targets xllify, given adequate guardrails.
The engine is written in C++ and uses Luau (Roblox's Lua fork) as the sandboxed scripting runtime. Luau might seem an odd choice, but it's genuinely well-suited here, it's fast, sandboxed, and compiles easily for both native and WASM targets. And you don't really need to know Luau to use xllify: the AI writes it for you. We'll see if Luau is a barrier.
Anyway, the same Luau code runs two ways:
.. As a native XLL add-in on Windows, talking directly to Excel's C API. This gives you in-process, multi-threaded function execution with low overhead. If you've ever wrestled with the C API directly, you know why this matters. It's extremely fast but utterly brutal to work with. xllify tames.
.. As a web-based add-in via WebAssembly, using the Office JS platform. Same code, same functions, but now deployable on Mac and Web with all the discoverability and security benefits that come with the modern add-in model.
Luau coroutines are supported, so functions can do non-blocking work (API calls, streaming data) without locking up the calculation chain. The runtime ships with a comprehensive standard library so you're not starting from nothing.
DuckDB is available to do heavy lifting on data analytics problems on data that might be too big to fit into Excel.
LLMs can hallucinate, so xllify doesn't rely purely on generation. The AI will select from a library of verified, tested functions where they exist rather than generating from scratch every time. Industry-specific function packs (finance, healthcare) are on the way.
There are two ways to build:
.. xllify Assistant runs inside Excel itself. You have a conversation with the assistant, build and test functions live, and when you're done you can export a .zip with a ready-to-deploy add-in setup including the package.json Office JS packages.
.. xllify agent tooling for local development if you prefer working in your own editor/terminal.
The add-ins are fully self-contained. No runtime dependencies, no phoning home, no ongoing model costs. Once built, it's yours. Works in air-gapped environments.
Try the online demo and sign up for early access: https://xllify.com - I would love your thoughts on this niche little thing.