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Technical debt (also known as design debt or code debt, but can be also related to other technical endeavors) is a concept in software development that reflects the implied cost of additional rework caused by choosing an easy (limited) solution now instead of using a better approach that would take longer.

As with monetary debt, if technical debt is not repaid, it can accumulate 'interest', making it harder to implement changes. Unaddressed technical debt increases software entropy. Technical debt is not necessarily a bad thing, and sometimes (e.g., as a proof-of-concept) is required to move projects forward. On the other hand, some experts claim that the "technical debt" metaphor tends to minimize the impact, which results in insufficient prioritization of the necessary work to correct it.

As a change is started on a codebase, there is often the need to make other coordinated changes in other parts of the codebase or documentation. Changes required that are not completed are considered debt, and until paid, will incur interest on top of interest, making it cumbersome to build a project. Although the term is used in software development primarily, it can also be applied to other professions.

Unums (universal numbers) are a family of number formats and arithmetic for implementing real numbers on a computer, proposed by John L. Gustafson in 2015. They are designed as an alternative to the ubiquitous IEEE 754 floating-point standard. The latest version is known as posits.

The Zope Object Database (ZODB) is an object-oriented database for transparently and persistently storing Python objects. It is included as part of the Zope web application server, but can also be used independently of Zope.

Features of the ZODB include: transactions, history/undo, transparently pluggable storage, built-in caching, multiversion concurrency control (MVCC), and scalability across a network (using ZEO).

FLOW-MATIC, originally known as B-0 (Business Language version 0), was the first English-like data processing language. It was developed for the UNIVAC I at Remington Rand under Grace Hopper from 1955 to 1959, and helped shape the development of COBOL.

Crash-only software refers to computer programs that handle failures by simply restarting, without attempting any sophisticated recovery. Correctly written components of crash-only software can microreboot to a known-good state without the help of a user. Since failure-handling and normal startup use the same methods, this can increase the chance that bugs in failure-handling code will be noticed, except when there are leftover artifacts, such as data corruption from a severe failure, that don't occur during normal startup.

Crash-only software also has benefits for end-users. All too often, applications do not save their data and settings while running, only at the end of their use. For example, word processors usually save settings when they are closed. A crash-only application is designed to save all changed user settings soon after they are changed, so that the persistent state matches that of the running machine. No matter how an application terminates (be it a clean close or the sudden failure of a laptop battery), the state will persist.

The VIC-20 (known as the VC-20 in Germany and the VIC-1001 in Japan) is an 8-bit entry level home computer that was sold by Commodore Business Machines. The VIC-20 was announced in 1980, roughly three years after Commodore's first personal computer, the PET. The VIC-20 was the first computer of any description to sell one million units, eventually reaching 2.5 million. It was described as "one of the first anti-spectatorial, non-esoteric computers by design...no longer relegated to hobbyist/enthusiasts or those with money, the computer Commodore developed was the computer of the future."

In computer science, the ski rental problem is a name given to a class of problems in which there is a choice between continuing to pay a repeating cost or paying a one-time cost which eliminates or reduces the repeating cost.

SQRL (pronounced "squirrel") or Secure, Quick, Reliable Login (formerly Secure QR Login) is a draft open standard for secure website login and authentication. The software typically uses a link of the scheme sqrl:// or optionally a QR code, where a user identifies via a pseudonymous zero-knowledge proof rather than providing a user ID and password. This method is thought to be impervious to a brute force password attack or data breach. It shifts the burden of security away from the party requesting the authentication and closer to the operating system implementation of what is possible on the hardware, as well as to the user. SQRL was proposed by Steve Gibson of Gibson Research Corporation in October 2013 as a way to simplify the process of authentication without the risk of revelation of information about the transaction to a third party.

In 3D computer graphics, anisotropic filtering (abbreviated AF) is a method of enhancing the image quality of textures on surfaces of computer graphics that are at oblique viewing angles with respect to the camera where the projection of the texture (not the polygon or other primitive on which it is rendered) appears to be non-orthogonal (thus the origin of the word: "an" for not, "iso" for same, and "tropic" from tropism, relating to direction; anisotropic filtering does not filter the same in every direction).

Like bilinear and trilinear filtering, anisotropic filtering eliminates aliasing effects, but improves on these other techniques by reducing blur and preserving detail at extreme viewing angles.

Anisotropic filtering is relatively intensive (primarily memory bandwidth and to some degree computationally, though the standard space–time tradeoff rules apply) and only became a standard feature of consumer-level graphics cards in the late 1990s. Anisotropic filtering is now common in modern graphics hardware (and video driver software) and is enabled either by users through driver settings or by graphics applications and video games through programming interfaces.

Electrochemical Random-Access Memory (ECRAM) is a type of non-volatile memory (NVM) with multiple levels per cell (MLC) designed for deep learning analog acceleration. An ECRAM cell is a three-terminal device composed of a conductive channel, an insulating electrolyte, an ionic reservoir, and metal contacts. The resistance of the channel is modulated by ionic exchange at the interface between the channel and the electrolyte upon application of an electric field. The charge-transfer process allows both for state retention in the absence of applied power, and for programming of multiple distinct levels, both differentiating ECRAM operation from that of a field-effect transistor (FET). The write operation is deterministic and can result in symmetrical potentiation and depression, making ECRAM arrays attractive for acting as artificial synaptic weights in physical implementations of artificial neural networks (ANN). The technological challenges include open circuit potential (OCP) and semiconductor foundry compatibility associated with energy materials. Universities, government laboratories, and corporate research teams have contributed to the development of ECRAM for analog computing. Notably, Sandia National Laboratories designed a lithium-based cell inspired by solid-state battery materials, Stanford University built an organic proton-based cell, and International Business Machines (IBM) demonstrated in-memory selector-free parallel programming for a logistic regression task in an array of metal-oxide ECRAM designed for insertion in the back end of line (BEOL). In 2022, researchers at Massachusetts Institute of Technology built an inorganic, CMOS-compatible protonic technology that achieved near-ideal modulation characteristics using nanosecond fast pulses