# circuit An interconnected network of electrical or electronic components through which current flows to perform a defined function. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Name that Ware, August 2026 DevFeed: [Name that Ware, August 2026]() Original publisher: [Read original article]() Author: bunnie Published: 2026-08-30T06:29:56Z Content type: article Language: en Sources: [bunnie's blog]() Topics: [circuit]() Tags: [2026](), [circuit](), [name-that-ware]() ### AI overview The August 2026 "Name that Ware" entry identifies the featured object as an integrated circuit from a different era than the previous month's item. ### Source excerpt The Ware for August 2026 is shown below. Technically, this is an integrated circuit - just from a very different era than last month's ware! ## Winner: Identifying a Bandgap Voltage Reference Circuit in a Chip Micrograph DevFeed: [Winner: Identifying a Bandgap Voltage Reference Circuit in a Chip Micrograph]() Original publisher: [Read original article]() Author: bunnie Published: 2026-08-30T06:27:57Z Content type: article Language: en Sources: [bunnie's blog]() Topics: [circuit](), [standard]() Tags: [2026](), [baochip](), [circuit](), [name-that-ware](), [process]() ### AI overview The article identifies the featured chip structure as a bandgap voltage reference built around paired NPN transistors. It explains how the circuit maintains a near-constant voltage and why dummy transistors surround the active devices. ### Source excerpt The structure in question for last month's ware are a pair of NPN transistors that form the core of a bandgap voltage reference circuit. As its name implies, a bandgap voltage reference generates a near-constant voltage for use inside chips. The voltage is intended to be fairly constant over process variations, temperature variations, and power [...] ## Top Resiliency Patterns for Developers DevFeed: [Top Resiliency Patterns for Developers]() Original publisher: [Read original article]() Author: Saurabh Dashora Published: 2026-04-21T06:08:56Z Content type: tutorial Language: en Sources: [System Design Codex]() Topics: [resiliency](), [distributed-systems](), [circuit](), [Availability](), [Latency](), [Scalability](), [Architecture & Design]() Tags: [availability](), [circuit](), [distributed-systems](), [latency](), [monitoring](), [resiliency](), [scalability](), [timeout]() ### AI overview A quick guide to resiliency patterns for distributed systems. It introduces downstream and upstream patterns, then explains timeouts, circuit breakers, and retries with exponential backoff as ways to limit cascading failures and help downstream services recover. ### Source excerpt Quick Guide ## Halloween Spooktacular: Fog Machine Madness DevFeed: [Halloween Spooktacular: Fog Machine Madness]() Original publisher: [Read original article]() Author: James Heller Published: 2023-09-26T21:50:49Z Content type: article Language: en Sources: [Apartment 304]() Topics: [circuit](), [Microcontroller](), [Reverse Engineering](), [Hardware](), [MIDI](), [Requirements]() Tags: [apartment-304](), [circuit](), [custom-software-solutions](), [devops](), [devops-engineer](), [electronics](), [hardware](), [requirements](), [reverse-engineering](), [software-architecture](), [software-development](), [software-engineer]() ### AI overview A Halloween automation project uses reverse engineering and circuit design to control a fog machine when trick-or-treaters are nearby. The article describes investigating the machine's 5-pin MIDI remote, designing a circuit with a microcontroller and LEDs, and isolating the connections to protect the controller from potentially damaging voltages. ### Source excerpt Go, k3s, NATS, circuit design, and fog. Oh the madness. A tale of over engineering. ## Hello, Perceptron: An introduction to artificial neural networks DevFeed: [Hello, Perceptron: An introduction to artificial neural networks]() Original publisher: [Read original article]() Published: 2023-05-05T14:05:31Z Content type: tutorial Language: en Sources: [Matt Might]() Topics: [Neural Network](), [Artificial Intelligence](), [Generative AI](), [Algorithm](), [circuit]() Tags: [ai](), [algorithm](), [circuit](), [generative-ai](), [introduction](), [neural-networks]() ### AI overview This tutorial introduces artificial neural networks through Frank Rosenblatt's perceptron. It explains how a single perceptron can be trained to mimic the AND, OR, and NOT logical functions, then examines its inability to learn XOR as motivation for multilayer neural networks. ### Source excerpt Generative AI tools like ChatGPT and Midjournery are able to replicate (and often exceed) human-like performance on tasks like taking exams, generating text and making art. Even to seasoned programmers, their abilities can seem magical. But, obviously, there is no magic. These things are "just" artificial neural networks - circuits inspired by the architecture of biological brains. An AI-imagined image of a neural network (Midjourney) In fact, much like real brains, when broken down to their building blocks, these systems can seem "impossibly simple" relative to what they achieve. (Modern computing is also magical in that sense, in that all of what computers are able to do reduces to simple logical building blocks - gates that calculate basic operations with truth values, such as AND, OR and NOT.) The purpose of this article is to give programmers without much exposure to machine learning an understanding of the key building block powering generative AI: the artificial neuron. Toward that end, this article has three goals: to implement a perceptron - the simplest artificial neuron; to train perceptrons how to mimic AND, OR and NOT; and to describe the leap to full-fledged neural networks. Click here to read the rest of the article ## Taylor Series and Accelerometers DevFeed: [Taylor Series and Accelerometers]() Original publisher: [Read original article]() Published: 2020-07-26T11:11:41Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Mathematics](), [circuit]() Tags: [accelerometers](), [capacitors](), [engineering](), [mathematics](), [physics](), [smartphone](), [taylor-series]() ### AI overview An explanation of how Taylor series relate to the design of modern smartphone accelerometers, including the capacitor principles used in these devices. ### Source excerpt In my book, A Programmer's Introduction to Mathematics, I describe the Taylor Series as a "hammer for every nail." I learned about another nail in the design of modern smartphone accelerometers from "Eight Amazing Engineering Stories" by Hammack, Ryan, and Ziech, which I'll share here. These accelerometers are designed using a system involving three plates, which correspond to two capacitors. A quick recap on my (limited) understanding of how capacitors work. ## Concrete Examples of Quantum Gates DevFeed: [Concrete Examples of Quantum Gates]() Original publisher: [Read original article]() Published: 2016-01-11T14:05:22Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [circuit]() Tags: [circuit](), [circuits](), [linear-maps](), [mathematics](), [matrices](), [python](), [quantum](), [quantum-computing]() ### AI overview A quantum-computing tutorial explains how local quantum gates affect the full state vector of a multi-qubit register, using a three-qubit swap-gate example. ### Source excerpt So far in this series we've seen a lot of motivation and defined basic ideas of what a quantum circuit is. But on rereading my posts, I think we would all benefit from some concreteness. "Local" operations So by now we've understood that quantum circuits consist of a sequence of gates $ A_1, \dots, A_k$, where each $ A_i$ is an 8-by-8 matrix that operates "locally" on some choice of three (or fewer) qubits. ## Multiple Qubits and the Quantum Circuit DevFeed: [Multiple Qubits and the Quantum Circuit]() Original publisher: [Read original article]() Published: 2015-01-26T09:00:00Z Content type: article Language: en Sources: [Jeremy Kun]() Topics: [circuit]() Tags: [2](), [bits](), [circuits](), [entanglement](), [linear-algebra](), [multiple](), [physics](), [quantum](), [quantum-computing](), [tensor-product](), [tensors]() ### AI overview This article explains why the tensor product is the natural mathematical representation of the joint state of multiple qubits. It also introduces basic quantum gates and the definition of a quantum circuit. ### Source excerpt Last time we left off with the tantalizing question: how do you do a quantum "AND" operation on two qubits? In this post we'll see why the tensor product is the natural mathematical way to represent the joint state of multiple qubits. Then we'll define some basic quantum gates, and present the definition of a quantum circuit. Working with Multiple Qubits In a classical system, if you have two bits with values $ b_1, b_2$, then the "joint state" of the two bits is given by the concatenated string $ b_1b_2$. ## The Quantum Bit DevFeed: [The Quantum Bit]() Original publisher: [Read original article]() Published: 2014-12-15T10:00:52Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Quantum Computing](), [Computing](), [circuit]() Tags: [bits](), [circuit](), [circuits](), [complex-numbers](), [computing](), [linear-algebra](), [mathematics](), [measurement](), [quantum](), [quantum-computing](), [quantum-mechanics](), [qubit](), [unitary-matrices]() ### AI overview An introduction to quantum computing that extends classical circuit concepts to qubits. It defines a qubit as a unit vector in the complex plane of two dimensions and explains why extracting information from qubits differs from reading classical bits. ### Source excerpt The best place to start our journey through quantum computing is to recall how classical computing works and try to extend it. Since our final quantum computing model will be a circuit model, we should informally discuss circuits first. A circuit has three parts: the "inputs," which are bits (either zero or one); the "gates," which represent the lowest-level computations we perform on bits; and the "wires," which connect the outputs of gates to the inputs of other gates. ## A Motivation for Quantum Computing DevFeed: [A Motivation for Quantum Computing]() Original publisher: [Read original article]() Published: 2014-12-08T10:00:48Z Content type: article Language: en Sources: [Jeremy Kun]() Topics: [Quantum Computing](), [circuit](), [Computing](), [quantum mechanics]() Tags: [circuit](), [computing](), [experiments](), [mathematics](), [physics](), [quantum](), [quantum-computing](), [quantum-mechanics](), [unitary-matrices]() ### AI overview This article motivates the study of quantum computing by connecting quantum mechanics with the quantum circuit, a computing model that extends the classical Turing-machine perspective. It argues for teaching quantum mechanics from its conceptual core, including amplitudes and generalized probability. ### Source excerpt Quantum mechanics is one of the leading scientific theories describing the rules that govern the universe. It's discovery and formulation was one of the most important revolutions in the history of mankind, contributing in no small part to the invention of the transistor and the laser. Here at Math ∩ Programming we don't put too much emphasis on physics or engineering, so it might seem curious to study quantum physics. But as the reader is likely aware, quantum mechanics forms the basis of one of the most interesting models of computing since the Turing machine: the quantum circuit. ## The Complexity of Communication DevFeed: [The Complexity of Communication]() Original publisher: [Read original article]() Published: 2014-11-10T09:00:25Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Computer science](), [Algorithms, Complexity](), [Streaming](), [circuit]() Tags: [communication](), [communication-complexity](), [computational-complexity](), [fourier-analysis](), [information-theory](), [log-rank-conjecture](), [lower-bounds](), [matrices](), [streaming-algorithms](), [theory]() ### AI overview This tutorial introduces communication complexity: how much information two parties must exchange to jointly compute a function of separate inputs. It presents the basic two-player model and explains the subject's use in proving lower bounds, including applications to circuit design and streaming algorithms. ### Source excerpt satellite One of the most interesting questions posed in the last thirty years of computer science is to ask how much "information" must be communicated between two parties in order for them to jointly compute something. One can imagine these two parties living on distant planets, so that the cost of communicating any amount of information is very expensive, but each person has an integral component of the answer that the other does not.