diff --git a/README.md b/README.md
index d28deb2..55295f3 100644
--- a/README.md
+++ b/README.md
@@ -1,22 +1,90 @@
 ![Icon](https://user-images.githubusercontent.com/29038605/222264516-e8132d83-e9dc-4436-a1fd-7bdf046c0034.png)
+
 # Alligator.Compact
-Alligator = Algorithm library for game theory
+
+**Algorithm Library for Game Theory** — a generic, high-performance solver for two-player zero-sum games.
 
 [![.NET](https://github.com/boraaros/Alligator.Compact/actions/workflows/build.yml/badge.svg?branch=main)](https://github.com/boraaros/Alligator.Compact/actions/workflows/build.yml)
 
-**Sonar**
+## Overview
+
+Alligator.Compact provides an **abstract AI solver** that can play any two-player zero-sum game optimally — you only need to define the rules and the board representation. The solver handles the rest: search, pruning, caching, and move ordering.
+
+The core idea is simple: implement three interfaces (`IRules`, `IPosition`, `IConfiguration`), and get a fully working game AI out of the box.
+
+## Quick Start
+
+```csharp
+// 1. Implement IRules<TPosition, TStep> and IPosition<TStep> for your game
+
+// 2. Create a solver
+var rules = new MyGameRules();
+var config = new MyGameConfiguration();
+var provider = new SolverProvider<MyPosition, MyStep>(rules, config);
+ISolver<MyStep> solver = provider.Create();
+
+// 3. Get the optimal move
+IList<MyStep> moveHistory = new List<MyStep>();
+MyStep bestMove = solver.OptimizeNextStep(moveHistory);
+```
+
+## Interfaces to Implement
+
+### `IPosition<TStep>` — the game board
+
+| Member | Description |
+|---|---|
+| `ulong Identifier` | Unique hash of the position (e.g. [Zobrist hashing](https://en.wikipedia.org/wiki/Zobrist_hashing)) |
+| `sbyte Value` | [Static evaluation](https://en.wikipedia.org/wiki/Evaluation_function) from the current player's perspective |
+| `void Take(TStep step)` | Apply a move to the board |
+| `void TakeBack()` | Undo the last move |
+
+### `IRules<TPosition, TStep>` — the game logic
+
+| Member | Description |
+|---|---|
+| `TPosition InitialPosition()` | Create the starting board |
+| `IEnumerable<TStep> LegalStepsAt(TPosition)` | Enumerate all legal moves at a position |
+| `bool IsGoal(TPosition)` | `true` if the game ended with a decisive result (not a draw) |
+
+## How the Solver Works
+
+The solver uses **iterative deepening** with **MTD(f)** (Memory-enhanced Test Driver with null-window alpha-beta), also known as *Best Node Search*. At each iteration depth (2, 4, 6, …), it performs a series of null-window alpha-beta searches to converge on the minimax value, then narrows the set of candidate moves.
+
+### Algorithm Features
 
-[![Lines of Code](https://sonarcloud.io/api/project_badges/measure?project=boraaros_Alligator.Compact&metric=ncloc)](https://sonarcloud.io/summary/new_code?id=boraaros_Alligator.Compact)
+| Feature | Status | Notes |
+|---|---|---|
+| Negamax alpha-beta pruning | ✅ Implemented | Core search framework |
+| Iterative deepening | ✅ Implemented | Even depths: 2, 4, 6 |
+| MTD(f) / Best Node Search | ✅ Implemented | Null-window search with candidate narrowing |
+| Transposition table | ✅ Implemented | Zobrist hash, exact/lower/upper bound entries |
+| Killer move heuristic | ✅ Implemented | 2 killer moves stored per depth |
+| Static evaluation cache | ✅ Implemented | Avoids redundant `IPosition.Value` calls |
+| Principal Variation move ordering | ✅ Implemented | Best move from TT tried first |
+| Quiescence search | ❌ Not yet | Would reduce horizon effect |
+| History heuristic ordering | ❌ Not yet | Move ordering by historical beta-cutoff frequency (scores already recorded) |
+| Late move reductions (LMR) | ❌ Not yet | Reduce depth for moves unlikely to be good |
+| Null move pruning | ❌ Not yet | Skip a turn to get quick upper bound |
+| Parallel search | ❌ Not yet | e.g. Lazy SMP or YBWC |
+| Opening book support | ❌ Not yet | Pre-computed opening moves |
+| Configurable search depth | ❌ Not yet | Currently hardcoded max depth |
 
-[![Bugs](https://sonarcloud.io/api/project_badges/measure?project=boraaros_Alligator.Compact&metric=bugs)](https://sonarcloud.io/summary/new_code?id=boraaros_Alligator.Compact)
+## Solution Structure
 
-[![Vulnerabilities](https://sonarcloud.io/api/project_badges/measure?project=boraaros_Alligator.Compact&metric=vulnerabilities)](https://sonarcloud.io/summary/new_code?id=boraaros_Alligator.Compact)
+| Project | Description |
+|---|---|
+| **Alligator.Solver** | The abstract solver — game-agnostic core library |
+| **Alligator.SixMaking** | [Six Making](https://www.youtube.com/watch?v=FHdltzwaAJg) game implementation |
+| **Alligator.TicTacToe** | Tic-tac-toe implementation (minimal example) |
+| **Alligator.Demo** | Interactive console demo (human vs AI tic-tac-toe) |
+| **Alligator.Benchmark** | Performance benchmarks for the solver |
+| **Alligator.Test** | Unit tests |
 
-[![Code Smells](https://sonarcloud.io/api/project_badges/measure?project=boraaros_Alligator.Compact&metric=code_smells)](https://sonarcloud.io/summary/new_code?id=boraaros_Alligator.Compact)
+## Requirements
 
-[![Duplicated Lines (%)](https://sonarcloud.io/api/project_badges/measure?project=boraaros_Alligator.Compact&metric=duplicated_lines_density)](https://sonarcloud.io/summary/new_code?id=boraaros_Alligator.Compact)
+- .NET 10
 
-This is the new and compact version.
+## License
 
-### Projects:
-Alligator.Solver: The abstract core library of artificial intelligence for different two-player zero-sum games.
+See [LICENSE](LICENSE) for details.