I built a local AI workout planner for my gym-obsessed friend

Ohm Anand — Sun, 04 Oct 2026 20:47:55 +0000

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

My friend Ritesh is a proper gym guy.

He already enjoys lifting and spends a lot of time training, so motivation isn't really his problem.

Structure is.

Some days he'd train chest, another day triceps, and there wasn't always a clear structure for how his muscle groups should fit together across the week.

Legs were another problem. He didn't know many leg exercises, so they were easier to neglect.

So I built LiftMate around that problem.

LiftMate asks for:

Training goal
Experience level
Training days
Session duration
Available equipment
Muscle priorities

It then builds a structured workout around those constraints.

But I didn't want it to be just another app that generates a workout and leaves you there.

The actual flow is:

Plan → Train → Log → Progress

You can start the workout, log every set, complete the session and get a recommendation for what to do next time.

I showed the working version to Ritesh, and the project was built around the way he actually trains rather than around a generic fitness-app idea.

Demo

Watch the LiftMate Demo

The demo shows the complete flow:

Onboarding → Personalized Workout → Local AI Personalization → Active Workout → Set Logging → Completion → Progression

A look inside LiftMate

The screenshots show the onboarding flow, personalized workout, AI explanation, active workout and completed session.

Code

GitHub: https://github.com/ohm-7643/LiftMate

The repository contains the complete LiftMate application, including the frontend, backend, database, workout planner, AI integration and validation logic.

How I Built It

The most important design decision I made was simple:

I didn't let the AI design the entire workout.

LiftMate has a deterministic workout planner that handles the parts that should be predictable:

Exercise selection
Equipment constraints
Muscle-group priorities
Sets and reps
Rest periods
Session duration
Weekly training structure
Progression

Then I added Qwen3-8B, running locally through Ollama.

Qwen has a much smaller job.

It personalizes the workout.

The model can prioritize a couple of exercises based on the user's goals and provide a short coaching note.

The AI response is then validated before it reaches the user.

LiftMate checks that:

The exercises are actually allowed
The user's equipment supports them
There are no duplicates
They fit the planned focus
The prescriptions stay within allowed limits
The response has the expected structure

If the local model fails, times out or returns something invalid, LiftMate falls back to the deterministic workout.

So the architecture is:

User Profile → Deterministic Planner → Qwen3-8B → Validation → Workout

That separation was intentional.

I wanted the model to make the experience more personal without making the whole application depend on whatever the model happens to generate.

Why Does Open Innovation Matter?

I could have called a closed AI API and asked it to generate a workout.

That would have been easier.

But for LiftMate, running an open-weight model locally made more sense.

With Qwen3-8B running through Ollama, the AI personalization can happen on the user's own machine. A fitness profile doesn't have to be sent to a third-party AI provider just to decide which exercises to prioritize.

It also gives me control.

I can inspect the prompts, change the rules, swap the model, or experiment with another open model without rebuilding the whole application around one provider.

More importantly, I don't have to blindly trust the model.

The deterministic planner remains responsible for the actual workout structure, while the model adds personalization on top.

That was probably the biggest thing I learned from this project:

Open-source AI doesn't have to mean letting the model do everything.

For LiftMate:

AI personalizes.

Rules keep it reliable.

My Agent Session

I didn't use DevRelay for this project, so I'm leaving this optional section out.

Prize Categories

No partner category claimed.

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Ohm Anand — Sat, 03 Oct 2026 08:56:06 +0000

DEV Community: Ohm Anand