ChessIQ

Strong players don’t just spot tactics - they evaluate positions at a glance. ChessIQ helps you practice that skill in a fun, puzzle-like format.

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Inspiration

Tactical puzzles have always dominated chess training platforms, and for good reason—they help players calculate forcing lines and spot tactics quickly. However, in real games, a huge part of chess strength comes from positional understanding: evaluating space, piece activity, king safety, pawn structure, and long-term initiative.

While tactical training is widely available online, there is very little accessible, structured training for positional evaluation skills. Most players only develop this through experience, analysis, or watching stronger players explain positions.

ChessIQ was built to close that gap by turning positional evaluation into a repeatable, puzzle-like training system.

What it does

ChessIQ is a positional training platform where players are shown real chess positions and asked to evaluate which side is better (White, Black, or Equal).

Instead of solving tactics, players must rely on strategic concepts such as:

  • Piece activity
  • King safety
  • Space advantage
  • Pawn structure
  • Initiative

After submitting an answer, players receive Stockfish-based evaluation feedback, allowing them to compare their intuition with objective engine evaluation.

The goal is to build faster, more accurate positional judgment over time.

How I built it

ChessIQ was built using a web-based stack focused on performance and scalability.

  • Frontend: HTML, CSS, JavaScript (SVG-based chessboard rendering)
  • Backend / Database: Firebase Realtime Database for storing positions and evaluations
  • Chess data: Lichess games processed using the Berserk API in Python
  • Engine evaluation: Stockfish used to precompute position evaluations
  • Hosting: Deployed on Render

Our overall data relied on Python preprocessing, and then the positions were uploaded onto a realtime database, for frontend-driven architecture that doesn't rely on a model real-time but just pulling evaluations from a database.

Challenges I ran into

One of the biggest challenges was building a system that reliably converts real Lichess games into clean training positions. We approached this by using Lichess's API in order to source our Lichess games from top players, then using python's chess library to clean and validate positions before adding them to our database.

Another challenge was designing a difficulty system that could meaningfully separate positional complexity without relying purely on engine evaluation differences.

On the technical side, integrating Firebase with a fast-loading frontend and maintaining efficient data flow across sessions required multiple iterations.

Accomplishments that I'm proud of

  • Built a fully functional positional training system based on real chess games
  • Created a Positional Rating (PR) system to track improvement over time
  • Scaled the dataset to 500+ real positions
  • Implemented accounts, leaderboards, and progress tracking
  • Designed a fast, puzzle-like experience focused purely on positional understanding
  • Turned an abstract idea (“positional puzzles”) into a working product

What I learned

This project helped me understand how much complexity exists behind seemingly simple training tools.

I learned:

  • How to structure real-world chess data into usable training formats
  • How Stockfish can be used for educational feedback, not just analysis
  • How UX design directly affects engagement in puzzle-based systems
  • How difficult it is to quantify “positional difficulty” compared to tactics
  • How iterative development (v0.1 → v1.0) is necessary for refining core mechanics

What's next for ChessIQ

ChessIQ is still actively being developed, with plans to expand beyond the current version.

Upcoming directions include:

  • Casual and Blitz-style timed puzzle modes (Puzzle Rush-style gameplay)
  • Friends system and social leaderboards
  • Daily positional challenges
  • More detailed post-puzzle feedback and explanations
  • AI-generated explanations for stored positions (v1.5 goal)
  • Improved statistics and training analytics
  • Continued UI/UX refinement and mobile optimization

The long-term goal is to make positional training as structured, engaging, and accessible as tactical training already is.

Read more: https://lichess.org/@/Vyom_Joshi/blog/chessiq-v10-is-here-train-your-positional-chess-like-never-before/w3Tu84CQ

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