A neural network for programming online: a browser chat with no install

A neural network for programming online is the shortest path to a result: open a chat in the browser, paste the code, get an explanation or a fix. You do not have to install anything, configure an editor or deal with a command line. This approach suits understanding an error, writing a function, making sense of someone else's snippet or sketching tests. Below: how to organize the work, which models exist as of October 18, 2026, which requests work better, and when to move to an agent in your editor.

How to start without installing anything

Access from Russia goes through the Zukko gateway: payment by SBP (the payment page adds an 8.5% fee on top), a MasterCard or cryptocurrency, top-up from $1. Then three steps:

1. Sign up in the Zukko dashboard and top up your balance.
2. Open the browser chat↗.
3. Pick a model and ask a question.

For the exact button names and where the model selector sits, look at the interface itself: it may change. If you later need a key for an editor or scripts, you can create one in the API keys↗ section. The general order of steps is in the Quick start↗.

Which model to choose

The choice depends on the task and budget. Catalog data as of October 18, 2026: prices in dollars per 1M tokens, "strength" is the Intelligence Index.

A sensible scheme: ask simple questions ("what does this error mean", "rewrite this loop") on a cheap model, and complex ones (architecture, a tangled bug, refactoring several functions) on a stronger one. Do not judge code quality from the table: the strength index is general and your task is specific. Take one real snippet, run it through two or three models and compare the answers. For choosing by the numbers, see the strength index explained↗.

How to paste code so the answer is useful

The chat sees only what you paste into it. A good request has five things:

1. Context: language, version, framework.
2. Code: the snippet itself in a fenced block, preferably with the file name on the first line.
3. What you expected and what happened: one sentence each.
4. The full error text: with the traceback, not a retelling.
5. Constraints: "do not change the function signature", "no new libraries".

A template for debugging an error:

Python 3.12, FastAPI. The endpoint returns 500 on an empty list.

File app/routes.py:
<paste the code>

Expected: an empty list in the response.
Got: the error below.
<paste the traceback>

Find the cause, suggest a minimal fix and show a diff.
Do not add libraries.

A few more templates that pay off:

• Code review: "Check the function for bugs, edge cases and security problems. Answer as a list: problem, line, fix."
• Tests: "Write pytest tests for this function: normal cases, empty input, invalid input. Test code only."
• Explanation: "Explain step by step what this snippet does, for someone who knows Python but not this library."
• Refactoring: "Simplify the code without changing behavior. Show what you changed and why."

More ready-made wordings are collected in 10 prompts for programmers↗.

What a chat cannot do

It is important to understand the limits of an online chat so as not to waste time:

• It does not see your project. If the code depends on a neighboring file, paste that too, or describe the interface.
• It does not run code on your machine. A suggested fix must be verified by running it and by tests. A model can get a function name or a library parameter wrong, and a confident tone does not guarantee correctness.
• It is not a safe place for secrets. Do not paste passwords, keys, tokens or personal data. Replace them with placeholders like API_KEY=xxx.
• History grows. The longer the conversation, the more tokens go into each new answer. Start a new conversation for a new task.

When to move to an agent

A chat is convenient while the task fits in a couple of snippets. Signs it is time for a tool in your editor or terminal:

• you keep copying files back and forth;
• a change touches more than three files;
• you want something to run the tests itself and fix the code based on the result;
• the project is large, and without searching it the answers come out generic.

Then an agent fits: for example Claude Code↗ in the terminal or an editor extension. The same key works in the chat and in agents, and the balance is shared.

What a conversation costs

Suppose you debugged a bug: over the conversation, 40,000 tokens went to input (counting the history being resent) and 5000 to output. These are assumptions for the calculation, not a measurement. The calculation ignores cache, at prices as of October 18, 2026:

• claude-opus-5-5: 40,000 × 0.8 / 1M + 5000 × 4 / 1M = 0.032 + 0.02 = $0.052;
• claude-sonnet-5-5: 40,000 × 0.4 / 1M + 5000 × 2 / 1M = 0.016 + 0.01 = $0.026;
• gpt-6.1-sol: 40,000 × 0.27 / 1M + 5000 × 1.32 / 1M = 0.0108 + 0.0066 = $0.0174;
• deepseek-v4.1-flash: 40,000 × 0.013 / 1M + 5000 × 0.038 / 1M = 0.00052 + 0.00019 = $0.00071.

Even on the strongest model in the table such a conversation costs about five cents. Current prices are at GET https://api.zukko.pro/v1/models or on the "Models" page in the dashboard.

FAQ

Can I program with a neural network without installing software?

Yes. For code questions, debugging and function sketches, a browser chat is enough. You will need an editor and an agent when the work goes beyond a couple of files.

Which neural network for programming is available in Russia?

Through the Zukko gateway you get Claude, GPT, Gemini, DeepSeek, Qwen and GLM models with a single key. Payment: SBP, a MasterCard or cryptocurrency. Choose by price, context and strength index, and test on your own code.

Can I trust the code a model produces?

As a draft, yes; as a finished solution, no. Run the code, execute the tests and read the diff. Ask the model to explain why the fix works, which helps you spot a mistake.

What should I paste into the chat for an accurate answer?

The language and version, the code itself, the expected and actual behavior, the full error text and your constraints. The more specific the request, the fewer guesses the model makes.