Guide Shows How to Build Offline-First Robot Control Apps Using Kotlin

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A new tutorial published on DEV Community walks developers through building an offline-first Android robot control application using Kotlin and Jetpack Compose. The guide focuses on asynchronous processing, lifecycle-aware state management, and safe separation between the Android interface and physical robot hardware. It covers practical concerns such as queuing commands while disconnected, preventing duplicate command execution on reconnect, and keeping emergency controls independent of high-bandwidth data streams. The architecture is designed to integrate with backends including ROS 2, NVIDIA Jetson, and multimodal AI systems. Developers are also advised on testing strategies covering scenarios like network loss, high-rate telemetry, and emergency-stop behavior.

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How a Jumia Product Dataset Was Cleaned and Turned Into an Excel Dashboard

A data analyst built an interactive Excel dashboard to examine product trends on Jumia, one of Africa's largest e-commerce platforms, using a scraped dataset of 115 products. The raw data required significant cleaning, including stripping currency symbols from price fields and extracting numeric ratings from descriptive text strings. Derived columns for price, discount, and rating categories were created using nested IF formulas to make the data analysis-ready. A key finding during cleaning was that roughly half the product listings lacked review counts or ratings, flagged as a data-quality gap affecting pricing and merchandising decisions. The project aimed to answer practical business questions around the relationship between discounts, ratings, prices, and customer engagement.

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ProgrammingDEV Community ·

GRPO Explained: A Lightweight RL Method for Fine-Tuning Large Language Models

Group Relative Policy Optimization (GRPO) is a reinforcement learning technique designed to fine-tune and align large language models more efficiently than traditional methods. Unlike PPO, which requires both a reward model and a critic model, GRPO eliminates the need for a separate critic by generating a group of responses and comparing them using a rule-based reward function. The method optimizes the model's policy by increasing the likelihood of better responses and reducing that of weaker ones, making it computationally lighter. GRPO differs from Supervised Fine-Tuning (SFT), which relies on curated prompt-response datasets and cross-entropy loss to train models. SFT is prone to issues like overfitting and catastrophic forgetting when training data is small, low-quality, or lacks diversity, making GRPO a compelling alternative in such scenarios.

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ProgrammingHacker News ·

Too AI; Didn't Read

Article URL: https://www.tai-dr.com/ Comments URL: https://news.ycombinator.com/item?id=49849625 Points: 5 # Comments: 1

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ProgrammingDEV Community ·

PowerSifu 0.3.5 drops polling in favour of D-Bus event-driven power management

PowerSifu, a free GTK tray application for Ubuntu and Debian, manages laptop power profiles by switching settings when AC power status changes and adjusting brightness accordingly. Early versions polled system state every five seconds, waking the CPU unnecessarily even when nothing had changed — a counterproductive design for a tool meant to reduce power consumption. Version 0.3.5 replaces this with persistent D-Bus proxies that subscribe to real-time property-change notifications from UPower and power-profiles-daemon, triggering callbacks only when the power source or active profile actually changes. A lightweight 60-second reconciliation check is retained as a safety net to catch missed signals or service restarts, but it is not the primary monitoring mechanism. Weekly schedule checks were also decoupled from the main loop, now sleeping until the next minute boundary and firing once, with the timer disabled entirely if no schedules are configured.

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