Every few months the open-source community mints another AI framework. Most of them wrap Python bindings around heavy C++ kernels, or they stack abstraction layers so high that the runtime alone weighs more than the models they serve. CatAI moves in the opposite direction. It is a native AI engine written entirely in C++, built from the tensor math upward. The point is not to create yet another friendly skin over PyTorch. The point is to own every byte of memory and every cycle of compute, starting at the hardware boundary.
Why Another Engine?
If you have shipped anything to production, you already know the pain. Pull a standard deep-learning stack into a container and watch the image bloat to multiple gigabytes. Dependencies fight each other. The Python interpreter adds latency. The dispatcher that routes ops to CUDA or CPU introduces subtle overhead that becomes impossible to profile once it disappears into a dozen nested frameworks. For edge devices, embedded robotics, or latency-sensitive backends, that tax is real. A pure C++ engine eliminates the middleman. It talks to the operating system and the silicon directly, with no garbage collection, no global interpreter lock, and no serialization dance between languages.
CatAI treats this as a feature, not a compromise. The project is being written from scratch in C++ because the author wants to decide exactly how tensors live in RAM, how they move through cache hierarchies, and how kernels are scheduled across threads. That is not masochism. It is the only way to guarantee that behavior is predictable when you are squeezing performance out of limited hardware.
What “From Scratch” Actually Means
In most modern frameworks, tensor math is handled by opaque calls into vendor libraries like cuDNN, oneMKL, or MPS. That is perfectly sensible for shipping fast, but it hides the mechanics of the operation. CatAI is writing its own core tensor math and memory layouts. That means designing the fundamental data structures that hold multi-dimensional arrays, choosing how strides and offsets are calculated, and deciding whether to store data in row-major, column-major, or custom tiled formats depending on the access pattern.
This is deep systems work. When you write a matrix-multiply kernel by hand, you stop thinking in terms of torch.matmul and start thinking about L1 cache lines, register pressure, and loop tiling. You decide whether to block for 32x32 tiles or 64x64 based on the SIMD width of the target CPU. You align allocations to 64-byte boundaries so AVX-512 loads do not cross cache lines. You question whether std::vector is the right container for tensor storage, or whether a custom arena allocator gives you better locality and zero fragmentation across an entire inference graph.
Memory layout is equally critical. A naive naïve n-dimensional array can kill performance if the channels-last image data is accessed in a channels-first pattern. In CatAI, these layouts are first-class citizens, not afterthoughts handled by a graph optimizer running at export time.
The Optimization Mindset
Bare-metal optimization sounds like a buzzword until you start counting nanoseconds. It means fusing operations so intermediate results never leave the CPU registers or L1 cache. It means implementing a layer-norm followed by a GELU as a single kernel, saving an entire round-trip to DRAM. It means writing your own thread pool instead of leaning on OpenMP defaults, because you know your workload is bursty and you do not want the runtime spawning and joining threads every forward pass.
It also means understanding when not to write assembly. Sometimes the compiler vectorizes a loop better than hand-written intrinsics. The discipline ismeasurement: profile, hypothesize, change one variable, and profile again. This engine is being built by people who enjoy that grind. If you have ever spent an afternoon rewriting a convolution loop to shave two milliseconds off a batch, you already understand the culture.
Who We Need
This is not a one-person show. Building a backend from zero requires distinct skills that rarely overlap in a single brain. If you are reading this and considering whether to jump in, here is where you might fit:
C++ developers who know modern standards but also know when templates cause compilation bloat. You should be comfortable with raw pointers when necessary and smart pointers when appropriate, and you should care about binary size as much as syntax sugar.
Math experts who can derive backward-pass gradients for non-standard activations, reason about numerical stability in mixed-precision training, and optimize algorithms before they become code. If you can explain why a log-sum-exp trick matters, you are in the right mental space.
Low-level memory specialists who think about allocators, page faults, and NUMA topology. The engine needs memory pools for graph execution, scratch buffers for kernels, and strategies for reusing tensor storage across training steps without leaking or fragmenting.
Systems engineers who understand how a misplaced syscall can stall an entire training loop. Scheduling, I/O, and synchronization primitives are the glue that holds the math together.
You do not need to be a world-class specialist in all four areas. Most contributors will start by owning one kernel or one allocator and learning the rest as the architecture solidifies.
Architecture and Custom Math
The backend logic is being built collaboratively, and that starts with architecture debates. Will the engine use a static computation graph, where the entire model is defined and optimized before runtime? Or will it support eager execution with a tape for automatic differentiation? How will autodiff be represented—operator overloading, source transformation, or a graph IR? These decisions shape everything else.
Custom neural net math means more than reimplementing standard layers. It means the freedom to invent new ones. If you want a convolution variant with a non-standard sparse kernel or an activation function that has no name in the literature, you write the C++ forward and backward passes and plug them directly into the engine. There is no Python API to fight, no monkey-patching required. The math is the code, and the code is the interface.
How to Get Involved
If this resonates, the full project breakdown and current roadmap are documented in detail on the author’s Dev.to post. You can read the specifics, see what has been built so far, and understand exactly where help is needed.
Project details: https://dev.to/banana_cool/building-a-native-c-ai-engine-catai-from-scratch-looking-for-collaborators-l8m
There is also a Telegram group for anyone who wants to hang out, ask questions, or follow progress without committing to a pull request immediately.
Community: https://t.me/GyaanSetuAi
The Real Takeaway
The modern AI stack has become a black box. We treat frameworks like magic appliances: data goes in, model comes out, and we hope the opacity does not bite us at deployment. CatAI rejects that comfort. It is slower to build this way. You will write more code, debug more segfaults, and rethink assumptions that higher-level frameworks hide from you. But you will also understand why the machine behaves the way it does. In an industry where everyone is racing to abstract away the hardware, there is real value in going the other direction and touching the metal. That understanding is what separates someone who calls APIs from someone who builds systems.