Technology

Time Between Disengagements is a concept I came across in a recent article from Gitpod, and it offered an interesting new way to think about AI’s role in software development. It compares the evolution of AI in engineering to the progression of self-driving cars—where the key metric is how long an autonomous system can operate before a human needs to step in. That simple but powerful analogy really clicked with me. It reframes how we should think about the future of AI-assisted development—not just in terms of raw capability, but in how independently and safely these systems can work. ...

Startups are often drawn to microservices with the promise of scalability and flexibility, but adopting them too early can backfire. Microservices introduce overhead—more infrastructure to manage, more complexity in deployments, and more effort in monitoring and debugging. For small teams moving quickly, these challenges can slow progress rather than support it. Early on, simplicity is a major advantage. Keeping your architecture lean—whether that’s a monolith or a tightly scoped service—allows you to move faster, iterate quickly, and focus on building the product. The key is not to avoid microservices altogether, but to recognize when the benefits truly outweigh the costs. Premature optimization can lead to a fragile setup that’s hard to maintain without delivering real value. ...

The power grid is a vast and intricate system composed of generation units (like hydroelectric, thermal, and renewable energy plants), transmission lines, substations, transformers, and distribution networks that deliver electricity to homes and businesses. Orchestrating a black start across this interconnected web is a highly complex task—each component must be reactivated in a precise sequence to avoid overloads, instability, or cascading failures. Communication between grid operators, synchronization of frequency and voltage levels, and the gradual reintroduction of demand are all crucial elements. It’s a delicate dance that requires meticulous planning, real-time coordination, and robust contingency protocols. ...

It was introduced in 1979 as a hybrid 16/32-bit CISC microprocessor, featuring a 24-bit flat address bus and a 16-bit data path. This design achieved over 1 MIPS of performance and outclassed contemporaries like the Intel 8086 in both speed and programmability. It rapidly became the engine behind landmark personal computers, such as the Apple Lisa and Macintosh, the Commodore Amiga, and the Atari ST, as well as gaming consoles like the Sega Genesis, due to its orthogonal instruction set, ample register file, and large unified memory space. Even after its discontinuation in 1996, the 68000’s architecture endured in embedded controllers, printers, and industrial systems, cementing a legacy that shaped subsequent CPU designs across the industry. ...

Biometric identity verification has deep historical, philosophical, and cultural roots that go beyond mere technology. From ancient philosophical inquiries into the nature of self to modern discussions on ethics and privacy, the concept of identity has been a subject of ongoing debate. Understanding these dimensions is crucial as biometrics become increasingly integrated into our daily lives, influencing societal norms, practices, and public perception. Contemporary perspectives on identity, influenced by thinkers like Foucault and Derrida, challenge fixed notions and emphasize its fluid and dynamic nature. As we navigate this evolving landscape, it’s essential to consider the ethical, legal, and social implications of biometric technologies, ensuring they align with our values and respect individual autonomy. ...

China’s Zuchongzhi-3 quantum computer and Google’s Willow processor represent two contrasting approaches in the race for quantum supremacy. Both 105-qubit superconducting systems push computational boundaries but prioritize different technical milestones. Zuchongzhi-3, developed by Chinese researchers, demonstrates remarkable raw computational power. In a demanding random circuit sampling task, it processed one million samples using 83 qubits over 32 cycles in just a few hundred seconds—a feat that would take the world’s fastest classical supercomputer billions of years to replicate. This achievement highlights China’s rapid progress in scaling up quantum hardware and achieving unprecedented quantum speedup. ...