Out of curiosity, I tried to replicate this same CSS Grid pattern in QML using GridLayout and responsive layouts using States
Out of curiosity, I tried to replicate this same CSS Grid pattern in QML using GridLayout and responsive layouts using States
What’s coming up in KDAB’s training lineup? These upcoming in-person sessions in Berlin are designed to help you advance your C++ or QML knowledge and build stronger debugging skills for real-world Qt development.
Explore all available courses and get the details: https://training.kdab.com/scheduled-training/
Synchronize properties across dynamically instantiated QML elements, using a C++ singleton that acts as a message broker and recursive signal-slot connections. This design enables flexible and scalable value synchronization across components, with minimal coupling between UI and logic.
Read on: https://www.kdab.com/bind-qml-values-across-an-arbitrary-number-of-elements/
Escrevi meu primeiro plugin para o niri + DankMaterialShell — e documentei o caminho até funcionar 🐧
A ideia era simples: exibir a versão do kernel e o uptime do sistema direto na barra, sem abrir terminal. Resultado: `6.19.6-arch1-1 ⏱ 7h 54m`.
O que parecia meia hora virou um aprendizado sobre:
→ QML e a API de plugins do Quickshell
→ Qual sinal usar (`onStreamFinished`, não `onStreamEnded`)
→ Cache agressivo do DMS que ignora alterações em arquivo
→ `/proc/uptime` + awk como alternativa mais confiável ao `uptime -p`
O atalho mais útil do processo: usar o próprio código-fonte do DMS como documentação. `grep -r` em `/usr/share/quickshell/dms/` resolve mais rápido do que qualquer busca externa.
👉 https://www.riverfount.dev.br/posts/primeiro_plugin_niri_danklinux/
#Linux #Wayland #Niri #Quickshell #QML #DankLinux #OpenSource

O blog tem bastante conteúdo sobre software, Python e hardware embarcado. Mas há um lado que nunca apareceu por aqui: o ambiente de trabalho em si. Este é o primeiro relato sobre o setup com o compositor Wayland niri e o DankMaterialShell — e começa pelo primeiro plugin criado do zero para esse ambiente. A motivação foi simples: queria ver no painel a versão do kernel em uso e o tempo de uptime do sistema. Sem abrir terminal, sem script externo. Só um widget discreto na barra mostrando 6.19.6-arch1-1 ⏱ 7h 54m.
Quantum Machine Learning (QML) is moving from theory to reality.
Singh et al. maps out how QML is tackling "complex systems" from correlated matter to agro-climate modeling.
Key highlights:
- Surveys VQAs, Quantum Kernels, and Neural-Network Quantum States.
- Tackles "barren plateaus" and scalability issues.
- Real-world apps: Drug discovery, Cancer biology, and Climate data.
-Introduces Federated QML for privacy-preserving AI.

Quantum machine learning (QML) is rapidly transitioning from theoretical promise to practical relevance across data-intensive scientific domains. In this Review, we provide a structured overview of recent advances that bridge foundational quantum learning principles with real-world applications. We survey foundational QML paradigms, including variational quantum algorithms, quantum kernel methods, and neural-network quantum states, with emphasis on their applicability to complex quantum systems. We examine neural-network quantum states as expressive variational models for correlated matter, non-equilibrium dynamics, and open quantum systems, and discuss fundamental challenges associated with training and sampling. Recent advances in quantum-enhanced sampling and diagnostics of learning dynamics, including information-theoretic tools, are reviewed as mechanisms for improving scalability and trainability. The Review further highlights application-driven QML frameworks in drug discovery, cancer biology, and agro-climate modeling, where data complexity and constraints motivate hybrid quantum-classical approaches. We conclude with a discussion of federated quantum machine learning as a route to distributed, privacy-preserving quantum intelligence. Overall, this Review presents a unified perspective on the opportunities and limitations of QML for complex systems.