Polish Developer Launches Windows Monitor App on Microsoft Store After Warehouse Job Loss

·1 views

Marcin Firmuga, a Polish developer, has launched PC Workman — a Windows system monitoring application — on the Microsoft Store after building it during nights following a warehouse job loss in the Netherlands days before Christmas. Working 15-kilometre daily shifts for roughly 19 złoty an hour, Firmuga coded on an overheating 2014 laptop between shifts to develop the tool. PC Workman version 1.8.0 aims to explain not just that a PC is slow, but why, using an on-device AI assistant called hck_GPT that requires no internet connection or API key. The assistant reads live hardware data and responds in plain language, processing queries through a local intent-parsing system built entirely by Firmuga. The project began as a means of survival and personal purpose during financial hardship, ultimately resulting in a publicly available Windows application.

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)

ADA: Open-Source Data Analyst Tool That Separates LLM Reasoning from Math

A developer has released ADA (Automated Data Analyst), a free, open-source tool designed to address the unreliability of using large language models for numerical data analysis. Users can upload CSV or Excel files and receive dashboards, anomaly detection, forecasts, and plain-English insights, all computed locally using the Python library pandas. Unlike typical LLM-based tools, ADA's optional AI layer only accesses column names and never the underlying row data, ensuring privacy and computational accuracy. The tool requires no API key for its core functionality and is licensed under MIT, meaning anyone can use or contribute to it. The solo developer behind ADA has stated a goal of making the tool increasingly LLM-free to ensure fully deterministic, reproducible results.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

FounderOS Gives Solo Founders an AI Board of Directors Built on Qwen

Developers Steven and Vincent built FounderOS, an AI-powered board of directors designed to help solo founders and small teams make high-stakes business decisions without access to real advisors. The system uses eight specialist AI agents — including analysts, a Skeptic, and a Chair — that debate a submitted decision and produce a structured board memo. Built on LangGraph and powered by Qwen models via DashScope, a full board deliberation takes roughly two minutes and costs about two cents. Unlike typical multi-agent demos, FounderOS preserves disagreement in the final output, attributing dissent to specific agents when consensus is not reached. The tool also maintains a markdown-based memory vault per company, allowing the board to reference past decisions when forming new recommendations.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

FROST Framework Introduces Hierarchical 'Family Lineage' Governance for AI Agents

FROST is an open-source AI agent framework that organizes agents into a three-tier hierarchy — Elders, Parents, and Children — modeled on biological lineage and governance structures. Each tier has defined roles: Elders set immutable constitutional rules, Parents coordinate domain tasks and approve outputs, while Children execute specific tasks and are discarded after use. The framework addresses a key limitation in most existing agent systems — the lack of persistent, inherited memory — by implementing a layered memory model where ancestral data is read-only and cannot be overridden by descendant agents. In July 2026, FROST was upgraded to V5.0, introducing a five-dimensional meta-model covering capability management, task orchestration, event cataloging, platform integration, and versioned governance rules. Available under the Apache 2.0 license on Gitee, FROST is positioned as a meta-framework suited for enterprise-grade, compliance-sensitive multi-agent systems rather than simple LLM call workflows.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

How to Strip GPS Metadata From Both Parts of an iOS Live Photo Before Upload

iOS Live Photos consist of two paired resources — a JPEG still image and a video clip — both of which can carry location metadata that must be scrubbed before upload. A common privacy gap occurs when the still image is cleaned of GPS EXIF data but the paired video retains location metadata, especially during interrupted or retried uploads. Developers are advised to use Apple's PHAssetResource API to enumerate all asset resources and apply privacy checks to each output individually, not just the visible image. Lifecycle edge cases, such as app backgrounding or crashes mid-upload, must be handled so that a scrubbed still is never paired with an unprocessed original video. Server-side verification is also recommended, as device-only inspection can miss queued originals or metadata embedded in multipart upload manifests.

0 comments Read more at DEV Community