SShortSingh.
Back to feed

Lessons from Building Motus, an iPhone Fitness App That Tracks Reps via Camera

0
·1 views

Motus is an iOS fitness app developed by LoudOwls that uses the smartphone camera to automatically track body movement and count exercise repetitions, removing the need for manual logging. The app was built natively with Swift and SwiftUI, using GuruSDK for camera-based body tracking and Supabase for user accounts and social data. Rather than functioning as a standalone feature, the camera tracking is embedded within a structured workout flow that includes exercises, sets, rest periods, and rep targets. A key design challenge was ensuring the tracking worked reliably during real movement while keeping the user interface minimal, since exercisers rarely have hands free to interact with their phones. Motus also incorporates social features such as a feed, stories, and reactions, reflecting the insight that technology can capture workout data while community engagement drives long-term user retention.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

Mojo: A High-Performance Programming Language Built for AI Systems

Mojo is a new programming language designed specifically for writing systems-level code optimized for AI workloads. It aims to combine high performance with usability for AI and machine learning development. One of its notable quirks is its unconventional file extension, which uses a fire emoji (.🔥). The language has drawn attention in developer communities for its focus on bridging the gap between AI research and low-level system performance. Developer Ekemini Samuel published an introductory overview of Mojo on DEV Community in August.

0
ProgrammingDEV Community ·

Databricks LTAP Architecture Proposed for Asia Real-Time Financial Markets

A private, invitation-only event called Hong Kong Databricks FSI Community Day 2026 is set to take place aboard a boat on Hong Kong Island waters, bringing together financial data professionals independent of Databricks corporation. The gathering will feature over thirty technical proposals addressing real-world financial architectures, including cross-border liquidity management and real-time streaming across Hong Kong and Singapore. One key session proposes an institutional LTAP (Lake Transactional Analytical Processing) architecture using Databricks Lakebase and Lakehouse to unify transactional applications with large-scale analytics under a single governed storage layer. Lakebase would handle low-latency transactional workflows such as trader watchlists and regulatory approvals, while Lakehouse would serve live market data queries to concurrent dashboards, APIs, and AI agents. Unity Catalog is central to the design, providing unified access control, data lineage, and audit capabilities across all workloads and jurisdictions.

0
ProgrammingDEV Community ·

Microsoft Agent Framework Supports File, Class, and Code-Based Skills in C#

A developer and speaker at Build.AI 2026 has published a detailed walkthrough on implementing Skills within the Microsoft Agent Framework (MAF) using C#. Skills in MAF are focused capability sets defined through concise prompts and supporting scripts, enabling large language models to invoke specific functions. The framework supports multiple skill types, including file-based, class-based, code-defined, and MCP-based skills, with most scripts currently written in Python due to C# scripting limitations. MAF selects skills through a four-step process — advertise, load, execute, and respond — injecting skill descriptions into the system prompt for the LLM to reference. The post also contrasts Skills with Workflows, noting that Skills suit flexible, single-domain tasks while Workflows are better for deterministic, multi-step processes with real-world side effects.

0
ProgrammingDEV Community ·

New Benchmark Tests If AI Models Know When to Admit They Can't Answer

A developer has created a 200-item benchmark called ESCALATE to measure whether AI models can recognize the limits of their own knowledge, not just whether they answer correctly. Each task includes a deliberate "unanswerable" scenario in roughly one in five items, where the only correct response is to escalate rather than guess. The benchmark spans four task types — routing, classification, judgment, and grounded question-answering — and scores models on both accuracy and false-confidence rate. The project compares frontier models hosted on Kaggle against small open-source models ranging from 1B to 8B parameters running locally on a single laptop. The author has pre-registered predictions before results are in, including the hypothesis that some small local models may show lower false-confidence rates than certain frontier models.