Where can Kettering learners find the best vibe coding and AI agents classes?
The ONS counted 63,150 people in the Kettering built-up area at the 2021 census, with Desborough, Burton Latimer and Rothwell listed separately in North Northamptonshire and Barton Seagrave recorded as one of the town's suburbs. Any learner from 6 to 67 there can study vibe coding, AI agents, Python, coding and maths with us over live video, with a tutor in India all to themselves or in a same-level class of five to ten. Reasoning is taught before tools, so an agent's choices make sense to the person running it. A free first lesson closes with our recommendation. The Kettering project sets two game-playing agents against each other on the real streets of the town centre. Ongoing lessons are USD 100 a month in a class or USD 150 a month one-to-one.
Chess programs and many other game-playing and planning agents rest on one idea: before choosing a move, imagine the opponent's strongest reply, and theirs, and so on. That idea is called minimax. This project turns the streets of central Kettering, taken from OpenStreetMap, into a board for an old puzzle called cops and robbers. One agent chases, one escapes, and both look ahead. The learner discovers how a clever trick named alpha-beta pruning lets an agent see further with far less work, and something more surprising: on these streets, a lone cop usually cannot win at all, and an agent needs a way to find that out.
Facts last verified 29 September 2026. Teaching is online; no Kettering branch is claimed.
Age and enthusiasm are the guide. Lesson one on any course is live and free, and no payment card is needed to book it.

The how-to-think programme: strategy games, thinking two moves ahead and spotting a trap.
See the syllabus
Scratch games first, then apps built by describing them to an AI and playtesting them.
See the syllabus
Python and web projects with AI help, including the street-map chase game.
See the syllabus
Agents that plan, search, use tools and know when a goal cannot be reached.
See the syllabusBrowse the course atlas for more than one hundred options and use the coding roadmap to check prerequisites.
The four we are known for
These run underneath everything above. Every one is live and online, placed by ability rather than by age, and the first class is free.

Python, web and AI projects where the learner still owns the thinking.
See the syllabus
Automate the work you already do, then let AI carry part of it.
See the syllabus
Train a model, read what it learned, and be able to say why it is wrong.
See the syllabus
Run AI coding agents on real work without losing control of the codebase.
See the syllabusCensus counts published by the ONS for Kettering and three smaller built-up areas in the same unitary area.
| Built-up area | People (2021) |
|---|---|
| Kettering | 63,150 |
| Desborough | 11,900 |
| Burton Latimer | 10,445 |
| Rothwell | 8,620 |
The ONS publishes these as four separate figures, and we leave them that way rather than inventing a total. North Northamptonshire also contains Corby, Wellingborough, Rushden and many smaller places. Barton Seagrave is recorded as a suburban area and Pytchley as a village in the same unitary authority. Schools here teach the national curriculum for England, so tell us your holiday weeks and we will keep them clear.
Our Northamptonshire page covers the wider county and the East Midlands page the region. Why thinking comes before prompting is explained on learn to think, not just use AI tools.
Turn OpenStreetMap into a game board, let a minimax agent look ahead, and prove which chases can ever be won.
The learner downloads a small rectangle of central Kettering from the OpenStreetMap API and keeps the roads, paths, footways and steps. Each junction becomes a square on the board: 640 junctions joined by 756 links, including 101 dead ends. The rules are simple. The cop moves first; on each turn a player either walks along one link or stays put; the cop wins by landing on the robber's junction. Eight real places are pinned to their nearest junctions, among them Kettering Library, the Manor House Museum and Newlands Shopping Centre.
A minimax agent chooses a move by imagining every reply. The cop tries each of its moves; for each, it assumes the robber will answer with the move that is worst for the cop; then it looks another turn deeper, and so on, up to a limit called the horizon. At the horizon it scores the position by how many links still separate the two. Alpha-beta pruning is the clever part: as soon as a move is shown to be worse than one already found, the agent stops exploring it, because a sensible opponent would never allow it. The answer is identical; the work is not.
| Turns looked ahead | Plain minimax | With alpha-beta pruning | Work saved |
|---|---|---|---|
| 2 | 11 | 9 | 18.2% |
| 4 | 119 | 52 | 56.3% |
| 6 | 1,271 | 239 | 81.2% |
| 8 | 14,381 | 1,023 | 92.9% |
| 10 | 167,653 | 4,217 | 97.5% |
Every extra pair of turns multiplies plain minimax's work by roughly eleven, and at ten turns it examines 167,653 positions. With alpha-beta pruning the same search needs 4,217 and reaches exactly the same verdict. The savings grow the deeper the agent looks, which is why pruning matters so much to any game-playing program. The verdict itself is sobering, though: after ten turns the cop is still 24 links away. Looking ahead does not help if the chase cannot be won.
To settle that, the learner stops searching forwards and works backwards through every possible position, a method called retrograde analysis. Of all 408,960 starting pairs, the cop can force a capture from just 5,781, or 1.41%. In 77.4% of those, the robber starts on one of the 186 junctions that sit on branches leading to dead ends rather than on a loop; the wins take a median of 13 turns and at most 43. From Kettering Library the cop cannot force a capture against a robber starting at any of the seven other landmarks, not even the Manor House Museum six links away. Wherever streets form a loop around a block, the robber can keep the block between them forever.
Play cops and robbers on a hand-drawn street map and find the loop that saves the robber.
Load a small street map into Python and let a two-turn look-ahead agent choose its moves.
Code minimax with alpha-beta, count the pruned positions and run the backwards analysis.
Street data is from OpenStreetMap and its contributors under the Open Database Licence. The game, the agents, the position counts and the retrograde analysis are our own work.
Planning well includes knowing when a goal is out of reach.
| On the Kettering streets | For AI agents in general |
|---|---|
| Assume the opponent replies well | Plan for the world pushing back, not the easy case |
| Pruning saved 97.5% of the work | Smart search beats brute force |
| Ten turns ahead was still not enough | A longer horizon cannot fix an impossible goal |
| Working backwards proved it | Check whether success is even possible |
| A lone cop fails around a loop | Sometimes the answer is more resources, not more effort |
In our vibe coding lessons the learner describes a game and an AI writes the first version, and a chase like this one is a favourite. It is also a good test of whether the learner understands the code: an AI will happily produce a minimax function that runs forever, or one that never notices the robber can always escape. AI agents built on language models face the same trap, trying again and again at a task that cannot succeed. A well-built agent checks whether a goal is achievable and says so. Learners move on to agents after Python, usually as older teenagers or adults, and Copilot Studio agents are covered in one-to-one lessons only. For the full path, read the agents course for students in the UK and understand the code, don't copy-paste.
Modern Age Coders has no connection with OpenStreetMap, the ONS or any venue named on this page. Their maps and figures made the project possible; the agents, and any mistakes in them, are ours.
We treat the school year as a starting hint, and the free session fixes the real level.
Strategy, thinking ahead and explaining a choice.
Problem Solving and Computational Thinking for KidsScratch Coding for KidsGames and small apps made with AI help and playtested by the learner.
Vibe Coding for KidsPython and AI for KidsGraphs, search and adversarial agents beside GCSE and A level.
Vibe Coding for TeensAI and Machine Learning for TeensSearch, planning, tools and language-model agents in Python.
Generative AI CoursePython MasterclassIt imagines the opponent's strongest reply to each move, then picks the move whose worst case is least bad.
That is minimax, and in Kettering it needed 167,653 positions to look ten turns ahead until alpha-beta pruning cut the work to 4,217. Many game-playing programs combine this kind of search with learned judgement about which positions are good.
Learners who build it themselves also learn its limits: no amount of looking ahead helps when, as on most Kettering starting squares, the game cannot be won.
Understanding how agents plan against an opponent gives Kettering teenagers a real head start with AI, a strong reason to learn coding in 2026. The longer argument is in why teenagers should still learn to code in 2026.
A home computer and a broadband connection that manages video calls are enough.
Typing, prompting and running are all done by the student, while the tutor follows along on their screen and keeps asking why.
Year groups are only a clue; the opening lesson picks the first topic, and we note any exam board.
Session one is free of charge and ends with a suggested course.
Five to ten UK learners at a similar stage share every group.
None during school holidays.
Tutors move with the UK clocks in spring and autumn, so the hour you chose stays put.
Why lessons happen online
Finding five learners at one level, all free on one evening, in a single town is rare. Online, geography stops mattering.
In Kettering, as everywhere outside India, our international rate applies.
One full lesson free, rounded off with a course suggestion.
Around eight live lessons a month in a small class.
Around eight live private lessons a month.
Fees are quoted in US dollars rather than pounds. The first invoice comes only after the trial has fixed a course and a regular slot, and the pricing page explains breaks, absences and moving between class and private tuition.
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We only need an age or year group and something the learner loves. A trial might be a strategy puzzle, a Scratch chase game built with an AI, a first Python program, or a two-move look-ahead of their own.
Minimax, the chase project, vibe coding, agents and the practical details.
The ONS gives 63,150 for the Kettering built-up area at the 2021 census.
Yes, over live video for learners aged 6 to 67 across Kettering and North Northamptonshire.
A way for a game-playing agent to choose a move by assuming the opponent always replies as well as possible, then picking the move with the least bad worst case.
Two agents play cops and robbers on the real streets of Kettering town centre; learners code minimax with alpha-beta pruning and prove which chases the cop can ever win.
Once they have some Python, usually from the later teens; Copilot Studio agents are taught one-to-one only.
No, everything is taught live online.
Yes, in computer science and maths, with understanding as the aim rather than promised grades.
From 6 up to 67.
The first lesson is free, then USD 100 a month in a group or USD 150 a month privately.
Lessons pause. Send us the dates.
Northampton has a page and project of its own, as do Mansfield and Leicester. Every town and county we cover is linked from the UK hub.