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Card · concepts · updated 03.07.2026
concept · foundation of all AI
A program that learns from examples rather than following pre-written rules. Show it a million photos of cats - it learns to recognize cats; show it the entire internet - it learns to write text and code. ChatGPT, Claude, Midjourney, and all AI agents on this site are neural networks.
68 years
The term
2012
Rise
transformers
Today
Paths exist
From Russia
input layers output
x₁ ──┐ ┌─ ● ─ ● ─┐
x₂ --|--> | ● - ● | --> "cat: 0.97"
x₃ ──┘ └─ ● ─ ● ─┘
weights are adjusted
by learning from errors
# all the magic - fitting billions of numbers that minimize error▌What it gives you
What it is not
What we call it
We call it: neural network. Also known as: neural net, neural network.
Verdict vibecoding.tech · 2026-07-03 · full glossary
Four steps, and no mysticism: a neural network - a huge number of simple numbers tuned to minimize error.
Families · Types of neural networks - for different data types. One thing is useful to know: almost everything you use in 2026 - is transformers.
| Type | What it's for | Today |
|---|---|---|
| Convolutional (CNN) | vision: photo recognition, medical imaging | still alive in narrow tasks; integrated into multimodal LLMs |
| Recurrent (RNN) | sequences: speech, translation before 2017 | displaced by transformers |
| Transformers | language, code, images, sound - a universal architecture | the standard: GPT, Claude, Gemini, DeepSeek - all run on them |
| Diffusion | image and video generation from noise | Midjourney, Stable Diffusion, video models |
The concept is 68 years old, and for most of that time it was considered a dead end. The timeline explains why everything exploded right now.
1958
Rosenblatt's perceptron
the first trainable neuron; promises of "thinking machines" - and half a century of disappointment
1986
Backpropagation
Rumelhart, Hinton, Williams: a training algorithm that still works today
2012
AlexNet wins ImageNet
deep networks on GPUs crush the classics - the industry turns to deep learning
2017
Transformer
"Attention Is All You Need" (Google): the architecture behind all modern LLMs
2022
ChatGPT
on November 30, neural networks become a product for everyone; the word goes mainstream
2026
Agents
neural networks do not just answer, they work: write code, run tests, and take tasks to completion
"Which neural network is better" - is a meaningless question until the task category is named. Honest routing:
Neural networks and code (2026)
Flagship LLMs autonomously solve 60–70% of real engineering tasks
Our anchor benchmark DeepSWE, snapshot 10.07: GPT-5.6 Sol - 72.7%, Fable 5 - 69.9%. So “which neural network is best for code” is a measurable question, not a matter of taste.
vibecoding.tech index · 2026-06-05
From Russia
There are options for every budget: DeepSeek - free and no VPN required; GigaChat and YandexGPT - local
Western flagships (ChatGPT, Claude, Gemini) are not directly available from Russia - via OpenRouter or RU aggregators in rubles (markup ~1.5–2×).
Russia access (our guide) · 2026-07-02
This concept has maximum hype around it in both directions. A breakdown of the most persistent claims:
"A neural network thinks and understands"
predicts the next token from statistics. Whether scaling such a machine produces anything like understanding - an open scientific debate, not a fact
"It's just hype, it will burst soon"
on our anchor benchmark, flagship models autonomously solve 60–70% of real engineering tasks - that is measured work, not promises
"It will replace programmers"
replaces part of the work, not the profession: routine goes to the machine, while task-setting, verification, and architecture become more valuable
"A neural network always lies"
makes confident errors without warning - so the working pattern is: generation + verification (tests, review), not blind trust
"You cannot use it from Russia"
Western flagships - through intermediaries; DeepSeek and local models - directly
In everyday speech, these are all synonyms, and that is fine. For choosing a tool, the hierarchy is useful: each next term - is a special case of the previous one.
| Term | What it is | Example |
|---|---|---|
| ·AI | umbrella term: any "smart" programs | from a chess engine to ChatGPT |
| ⊃Machine learning | programs that learn from data | recommendations, anti-spam, forecasts |
| ⊃⊃Neural network | a machine-learning method: layers of weighted neurons | face recognition, translators |
| ⊃⊃⊃LLM | large language transformer neural network | GPT-5.6, Claude Opus, Gemini, DeepSeek |
| ⊃⊃⊃⊃GPT | a family of LLMs from one company (OpenAI) | "chatgpt" - a product based on GPT |
A program that learns from examples instead of following prewritten rules. Show it a million photos of cats - it will learn to recognize cats; show it the entire internet's text - it will learn to write text. ChatGPT, Claude, Midjourney - all of these are neural networks.
No. AI is an umbrella term for any “smart” programs. Neural networks are the most successful way to build it (they learn from data). LLMs like ChatGPT are a type of neural network for text. In everyday use, the terms have merged, and that is fine; the distinction helps when choosing a tool.
It depends on the task, and for code it is measurable: in the DeepSWE snapshot of 10.07, GPT-5.6 Sol - 72.7%, Fable 5 - 69.9%. Other leaders handle text and images. There is no universal “best” - there is a ranking for each task.
Free: DeepSeek (works from Russia without a VPN), free plans for ChatGPT and Gemini, GigaChat. Flagship subscriptions - $8–20/mo for basic access, $100–200 for heavy work. APIs are billed by token: a typical coding task on a flagship costs a few dollars.
Local (GigaChat, YandexGPT) and Chinese ones (DeepSeek - free, without a VPN) - yes, directly. Western flagships block Russia by IP and cards: working options - OpenRouter (crypto/foreign card) and RU aggregators in rubles with a ~1.5–2× markup.
They are not replacing the profession, but part of the work: flagships already write routine code on their own (60–70% of real tasks on benchmarks). Value is shifting to task definition, result verification, and architecture - that is what vibecoding is about. Our practical path is in the “Practice” section.
A regular program does what a programmer explicitly wrote; a neural network does what it learned from data. Hence its strength (it can handle things rules cannot express: language, vision, code) and weakness (it can be confidently wrong - “hallucinate,” and no one can say exactly why).
Related concepts
Vibecodingthe practice this site exists for: a neural network writes code
MCPa protocol that lets AI agents connect tools
GPT-5.6OpenAI's latest coding flagship - see what a model card looks like
More on the topic
Entry · neuroset · facts · 9 (source and date for each) · card edited 2026-07-03 · facts verified 2026-07-03
Where to go next
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