Why BLD Coach exists

A blindfolded solve is two distinct skills sharing a single stopwatch. First you memorise the cube's state — a letter scheme, a route, a story. Then the blindfold goes on and you execute what you memorised, with no feedback until you take it off and find out whether you were right.

Those two halves fail for different reasons and improve at different rates. A solver whose memo is slow needs different practice from one whose execution is slow. But general speedcubing timers were designed for sighted solving, where there is only one phase, so they record one number — and that number cannot answer the only question a BLD solver actually has.

The workaround most serious solvers end up with is a general timer, a spreadsheet to reconstruct the split, and a browser tab full of commutators. It works, badly.

BLD Coach is that workaround built properly, as one app: a timer that separates memo from execution, statistics that treat DNFs as meaningful data rather than missing rows, and the algorithm reference for your method within reach while you are still holding the cube.

It is deliberately not a general cube timer with a blindfold mode bolted on. Every default assumes blindfolded solving — that success rate matters as much as average, that a session might be about learning M2 rather than setting a personal best, that you will want to know your memo trend over the last fifty attempts.

BLD Coach is developed by Embarc Information Technology, an Indian software company that has been building enterprise systems since 1994. Its author holds a Guinness World Record in blindfolded speedcubing, having solved 48 of 48 cubes blindfolded.

That matters less as a credential than as a design constraint: the app was specified by someone who needed it, from years of practice sessions where the existing tools got in the way.

Version 1.2 is feature-complete for daily practice. The timer, history, statistics, algorithm library, training sessions, theming and data import/export are all built and in use.

The app is in closed testing rather than general release. Android and iOS are both planned — it is a single Flutter codebase — and public availability will be announced once the feature set has settled.

The next priorities are an algorithm trainer that turns the reference library into spaced-repetition drilling, and an AI Coach that recommends what to practise based on your own solve history. AI is an addition to a working training app, not the product itself — a recommendation is only useful once there is a body of honest data behind it.

Beyond that: cloud sync across devices, a memo palace builder for planning routes, MBLD as a distinct solving mode, and more themes. None of it exists yet, and the roadmap labels each item accordingly.

Undecided. The current thinking is a free tier covering the timer, scramble generator and core statistics, with a paid unlock for the deeper training features — but nothing is fixed, and testers will hear before anything changes.