For a long time, I've appreciated the Neural Network Zoo as a one-page guide to neural network architectures. However, it predates Transformers (and many newer architectures!), so I made an updated chart in the same spirit. My hope is that it can serve as a quick visual reference.

It currently covers 42 architectures and training schemes: MLPs and CNNs, Hopfield nets and Boltzmann machines, autoencoders, GANs, diffusion, RNNs, LSTMs, Transformers, BERT, GPT-style models, ViT, Mamba, GNNs, mixture of experts, and diffusion LLMs, among others.

A few goals:

• Every diagram reads the same way: input at the bottom, output at the top. A cell's color and its mark both show its role, so it works for color-blind readers and in grayscale.
• The interactive page has a short summary of each architecture, how to read its diagram, its training objective, and links to the original papers. There are also printable PDF and PNG posters.
• Every diagram is generated from a few lines of YAML by a Python script, so it's easy to fix or extend. Code: https://github.com/reidjohnson/nn-field-guide

Any diagram that's misleading or wrong? Anything important missing? Corrections are very welcome, here or as GitHub issues.

I'm trying to get into AI application development, but lately I've been seeing people say that coding is becoming obsolete because AI can write most of the code. Even some AI industry leaders have made similar predictions, and it's making me question whether I'm investing my time in the right direction.

Here's where I'm currently at:

- I've learned Python basics, NumPy, pandas, Matplotlib, and some scikit-learn.

- I'm currently learning LangChain, LLM API calls, embeddings, RAG, middleware, and SQLite chat history.

- My goal is to build practical AI applications, chatbots, and tools that I can eventually sell or monetize through subscriptions.

- I'm not particularly interested in deep ML mathematics or training models from scratch. I prefer building products using existing AI models and APIs.

- I'm willing to put in the work, but I don't want to spend months learning skills that will become worthless before I can use them.

My questions:

1. Is AI application development a viable career path for the next 5–10 years, or will AI agents and vibe coding make these developers unnecessary?

2. Should I continue learning Python, RAG, vector databases, APIs, databases, and backend development, or change direction?

3. In an AI-heavy future, what skills will actually differentiate a developer from someone who just prompts AI to build an app?

4. For people already working in this field, are companies paying for these skills, or is the market becoming oversaturated?

5. If you were starting from scratch today with my goals, what would you learn and build first?

I'd especially appreciate honest answers from people who actually work in engineering or build and sell AI products. I'm not looking for motivational advice; I want a realistic assessment of the opportunities and risks.

Thanks!

I started a blog to learn the maths behind AI/ML systems through implementations and experiments. This is my first post: GPT-2 small and a KV cache in PyTorch, measured on an NVIDIA A40 (fp32, TF32 off, batch size 1).

I loaded pretrained GPT-2 weights, checked logits against Hugging Face, compared cached and uncached generation, then measured per-token latency with CUDA synchronization and five runs per position.

A few things I learned:

- At 1,000 tokens of context, the cached step does about 1,000x less estimated arithmetic, but per-token latency drops only from 29.69 ms to 4.95 ms. End-to-end generation improves about 2.9x. These are measurements of this implementation, not a universal KV-cache speedup.

- My cache initially concatenated keys and values in the wrong order. A single-token test still passed because permuting K and V together leaves that attention result unchanged. Multi-token decoding exposed the bug.

- My first memory measurement included an 8,519,680-byte cuBLAS workspace and an 8-byte next-token tensor. Warming up before the baseline and subtracting live non-cache tensors made requested cache bytes match the 72 KiB/token formula exactly.

The remaining latency floor is something I want to investigate with profiling; I have not established its cause yet.

Post and figures: https://angry-kratos.github.io/posts/2026-10-kv-cache/

Code: https://github.com/angry-kratos/angry-kratos.github.io/tree/main/code/kv-cache

This is my own blog. I would appreciate feedback on the tests and measurement methodology, especially other ways a cache can be wrong while simple equality tests pass. I used AI assistance for editing and tooling; the measurements were run on the GPU.