# Semantics Published articles for Semantics. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## A guide to Transformer architecture in modern language models DevFeed: [A guide to Transformer architecture in modern language models]() Original publisher: [Read original article]() Author: Ashish Pratap Singh Published: 2026-05-14T04:15:11Z Content type: tutorial Language: en Sources: [AlgoMaster Newsletter]() Topics: [Transformer architecture](), [LLMs](), [Transformer]() Tags: [architecture-pattern](), [better](), [deep-dive](), [layer](), [llms](), [model](), [parallel](), [performance](), [semantics](), [sequence](), [syntax](), [tokens](), [transformer-architecture](), [transformers]() ### AI overview This tutorial explains the Transformer architecture, including its original encoder-decoder design for translation and the decoder-only variant used for modern language generation. It describes decoder components such as masked multi-head self-attention, feed-forward networks, layer normalization, and residual connections, and introduces the Pre-LayerNorm pattern. ### Source excerpt A single 2017 research paper changed the future of AI forever and gave rise to multiple unicorn companies. ## Consistency Models in Azure Cosmos DB: From Strong to Eventual DevFeed: [Consistency Models in Azure Cosmos DB: From Strong to Eventual]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-16T00:00:00Z Content type: article Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [consistency](), [Databases](), [Availability](), [Latency](), [Replication](), [Azure]() Tags: [availability](), [cloud-architecture](), [consistency](), [cosmosdb](), [database-design](), [databases](), [distributed-systems](), [latency](), [linearizable](), [pacelc](), [replication](), [semantics](), [system-design]() ### AI overview This article explains how Azure Cosmos DB uses five consistency levels to expose PACELC trade-offs between consistency, availability, latency, and read freshness. It describes Strong consistency, bounded staleness, and Session consistency, including their operational trade-offs and suitable use cases. ### Source excerpt Cosmos DB exposes the PACELC trade-off as five explicit levels instead of forcing a strong-or-eventual choice. Strong gives linearizable reads at the cost of write latency and availability. Session, the practical default for user-facing apps, gives read-your-writes within a session via per-partition session tokens while staying highly available. ## Think About SQL MERGE in Terms of a RIGHT JOIN DevFeed: [Think About SQL MERGE in Terms of a RIGHT JOIN]() Original publisher: [Read original article]() Author: lukaseder Published: 2025-03-13T14:45:53Z Content type: tutorial Language: en Sources: [jOOQ]() Topics: [SQL](), [etl](), [rdbms]() Tags: [databricks](), [etl](), [firebird](), [merge](), [outer-join](), [postgresql](), [right-join](), [schema](), [semantics](), [source](), [sql](), [sql-server](), [statement](), [syntax](), [table](), [target-table](), [when-matched](), [when-not-matched](), [when-not-matched-by-source]() ### AI overview The article explains SQL MERGE by relating its target-and-source behavior to a RIGHT JOIN. It covers updating matching rows, inserting nonmatching rows, deletion behavior, and the restriction against duplicate matches. ### Source excerpt RIGHT JOIN is an esoteric feature in the SQL language, and hardly ever seen in the real world, because almost every RIGHT JOIN can just be expressed as an equivalent LEFT JOIN. The following two statements are equivalent: It's not unreasonable to expect these two statements to produce the same execution plan on most RDBMS, ... Continue reading Think About SQL MERGE in Terms of a RIGHT JOIN -> ## Accessibility Considerations with Stacked Cards Custom Layout DevFeed: [Accessibility Considerations with Stacked Cards Custom Layout]() Original publisher: [Read original article]() Author: Eevis Panula Published: 2024-07-25T03:26:13.958000Z Content type: tutorial Language: en Sources: [Eevis Blog]() Topics: [Accessibility](), [User interface design](), [Compose](), [Android]() Tags: [accessibility](), [assistive-technology](), [component](), [compose](), [semantics](), [user]() ### AI overview This article examines accessibility issues in a stacked-cards custom layout built with Compose. It focuses on Switch Access, where deleting a card causes focus to disappear, and discusses moving the click action to the stack component as a partial fix. It also considers improving content labels for Voice Access and notes that the coverage is not exhaustive. ### Source excerpt A couple of weeks ago, I published a blog post Stacked Cards Layout With Compose - And Cats. The layout has some accessibility issues, and I aim to fix some of them in this blog post. There are a couple of issues I'm not fixing here because of the scope of this blog post; instead, I'm discussing the problems they're causing and possible solutions. This time, I will also be pointing out some things that are good about the layout. I feel like I'm often just talking about problems and trying to find them, but this time, I'll share something that's working well, too. Before we dive into the different aspects I chose for this blog post, here is a fair warning: This list is not extensive and doesn't contain all possible accessibility problems that this kind of layout might have. I didn't test with every possible assistive technology or setting. My goal was progress over perfection, and I believe that the modifications I'll list are already significant improvements to the accessibility of this layout. Switch Access The first assistive technology I'm going to talk about and improve the app for is Switch Access. It's a service that lets the user navigate their phone with one or more switches. If you want to learn more, the Android Accessibility Checklist I've created has a section about Switch Access: Test with Switch Access. When testing with Switch Access, there was one problem I could find: When removing a card from the stack, the focus didn't go anywhere. It just disappeared. The reason for that is that the component on which the focus was had been deleted, and the accessibility service lost the focus. One way to fix this issue is by moving the click event to the stack-component instead of the card itself. We can do this by adding a semantics-modifier with an onClick-lambda to the CardStack's modifier-property. Let's also move the lastItem outside the CardStack so that we can use it in the function we add: // MainScreen.kt val lastItem = catIds.value.last() CardStack( Mo ## The Verse Calculus: a Core Calculus for Functional Logic Programming DevFeed: [The Verse Calculus: a Core Calculus for Functional Logic Programming]() Original publisher: [Read original article]() Published: 2022-12-12T16:23:17Z Content type: article Language: en Sources: [Lambda the Ultimate]() Topics: [Programming](), [Math and Logic]() Tags: [draft](), [functional](), [lambda-calculus](), [languages](), [logic](), [paper](), [programming](), [rewrite](), [semantics]() ### AI overview The draft paper introduces the Verse calculus (VC), a core calculus for functional logic programming. Its main contribution is a small-step rewrite semantics that enables reasoning about VC programs through successive rewrites, as with lambda calculus. The authors note that the design is still a work in progress and that the current rewrite rules may not be confluent. ### Source excerpt The Verse Calculus: a Core Calculus for Functional Logic Programming https://simon.peytonjones.org/assets/pdfs/verse-conf.pdf LENNART AUGUSTSSON, Epic Games, Sweden JOACHIM BREITNER KOEN CLAESSEN, Epic Games, Sweden RANJIT JHALA, Epic Games, USA SIMON PEYTON JONES, Epic Games, United Kingdom OLIN SHIVERS, Epic Games, USA/li> TIM SWEENEY, Epic Games, USA Functional logic languages have a rich literature, but it is tricky to give them a satisfying semantics. In this paper we describe the Verse calculus, VC, a new core calculus for functional logical programming. Our main contribution is to equip VC with a small-step rewrite semantics, so that we can reason about a VC program in the same way as one does with lambda calculus; that is, by applying successive rewrites to it. This draft paper describes our current thinking about Verse. It is very much a work in progress, not a finished product. The broad outlines of the design are stable. However, the details of the rewrite rules may well change; we think that the current rules are not confluent, in tiresome ways. (If you are knowledgeable about confluence proofs, please talk to us!)We are eager to enagage in a dialogue with the community. Please do write to us. ## Reasoning about asyncio.Semaphore DevFeed: [Reasoning about asyncio.Semaphore]() Original publisher: [Read original article]() Author: Guido van Rossum (noreply@blogger.com) Published: 2022-10-05T06:39:00Z Content type: article Language: en Sources: [Guido van Rossum]() Topics: [Concurrency](), [Concurrent Programming](), [await]() Tags: [await](), [fairness](), [implementing](), [performance](), [reasoning](), [semantics](), [synchronization]() ### AI overview The article explains asyncio synchronization primitives through a restaurant queuing analogy. It maps exclusive access and cancellation to a Lock, then explains why multiple concurrently seated guests require a Semaphore. It also discusses challenges involving fairness, correctness, semantics, and performance. ### Source excerpt In Silicon Valley is a very exclusive fast-food restaurant, which is always open. There is one table, where one guest at a time is served an absolutely fabulous hamburger. When you arrive, you wait in line until the table is available. Then the host takes you to the table and, this being America, you are asked a seemingly endless series of questions about how you would like your hamburger to be cooked and served. But today we're not talking about culinary delights. We're talking about the queuing system used by the restaurant. If you are lucky to arrive at the restaurant when the table is available and there are no other guests waiting, you are seated right away. Otherwise, the host gives you a buzzer (from an infinite stack of buzzers!) and you are free to roam the neighborhood until your buzzer goes off. It is the host's job to ensure that guests are seated in order of arrival. When it is your turn, the host will cause your buzzer go off and you make your way back to the restaurant, where you will be seated. If you change your mind, you can return the buzzer to the host, who will take it back without lifting an eyebrow. If your buzzer has already gone off, the host will buzz the next guest, if any. Guests are always polite and don't abscond with their buzzers. The host is always fair and doesn't seat another guest ahead of you even if you take your time making it back. The above description fits that of a Lock. A guest arriving corresponds to the acquire() call; leaving is a release() call. Changing your mind is like getting cancelled while waiting in acquire(). You can change your mind before or after your buzzer goes off, i.e., you can be cancelled before or after the lock has awakened your call (but before you return from acquire()). One day the restaurant expands, hiring extra sous-chefs and opening several new tables. There is still only one host, whose job is not really changed. However, since multiple guests can be seated concurrently, a Semaphore must now ## Latent Effects for Reusable Language Components DevFeed: [Latent Effects for Reusable Language Components]() Original publisher: [Read original article]() Published: 2021-10-14T14:02:48Z Content type: article Language: en Sources: [Lambda the Ultimate]() Topics: [Haskell](), [Programming](), [Development](), [implementation]() Tags: [abstraction](), [deferred](), [effects](), [examples](), [functional](), [haskell](), [modular](), [monad](), [programming-languages](), [semantics](), [theory]() ### AI overview The article discusses latent effects, a generic class of control-flow mechanisms for modular language definitions. It describes how function abstractions, lazy computations, and MetaML-like staging can be expressed and combined, with a full Haskell implementation and examples. ### Source excerpt Latent Effects for Reusable Language Components, by Birthe van den Berg, Tom Schrijvers, Casper Bach Poulsen, Nicolas Wu: The development of programming languages can be quite complicated and costly. Hence, much effort has been devoted to the modular definition of language features that can be reused in various combinations to define new languages and experiment with their semantics. A notable outcome of these efforts is the algebra-based "datatypes "a la carte" (DTC) approach. When combined with algebraic effects, DTC can model a wide range of common language features. Unfortunately, the current state of the art does not cover modular definitions of advanced control-flow mechanisms that defer execution to an appropriate point, such as call-by-name and call-by-need evaluation, as well as (multi-)staging. This paper defines latent effects, a generic class of such control-flow mechanisms. We demonstrate how function abstractions, lazy computations and a MetaML-like staging can all be expressed in a modular fashion using latent effects, and how they can be combined in various ways to obtain complex semantics. We provide a full Haskell implementation of our effects and handlers with a range of examples. Looks like a nice generalization of the basic approach taken by algebraic effects to more subtle contexts. Algebraic effects have been discussed here on LtU many times. I think this description from section 2.3 is a pretty good overview of their approach: LE&H is based on a different, more sophisticated structure than AE&H's free monad. This structure supports non-atomic operations (e.g., function abstraction, thunking, quoting) that contain or delimit computations whose execution may be deferred. Also, the layered handling is different. The idea is still the same, to replace bit by bit the structure of the tree by its meaning. Yet, while AE&H grows the meaning around the shrinking tree, LE&H grows little "pockets of meaning" around the individual nodes remaining in the ## Introducing PathQuery, Google's Graph Query Language DevFeed: [Introducing PathQuery, Google's Graph Query Language]() Original publisher: [Read original article]() Published: 2021-06-30T13:23:37Z Content type: article Language: en Sources: [Lambda the Ultimate]() Topics: [Graphs](), [Query (disambiguation)](), [Google](), [data](), [Code](), [Latency](), [Optimization](), [modules](), [Object-relational mapping]() Tags: [code](), [data](), [declarative](), [dsl](), [google](), [graph](), [latency](), [mapping](), [module](), [optimization](), [semantics]() ### AI overview The article introduces PathQuery, a graph query language developed at Google to handle large query and data volumes and support its internal developer community. It describes declarative graph-traversal syntax, a foundation in a custom relational algebra variant, optimization opportunities, and a module and compilation system for reuse across projects. ### Source excerpt Introducing PathQuery, Google's Graph Query Language We introduce PathQuery, a graph query language developed to scale with Google's query and data volumes as well as its internal developer community. PathQuery supports flexible and declarative semantics. We have found that this enables query developers to think in a naturally "graphy" design space and to avoid the additional cognitive effort of coordinating numerous joins and subqueries often required to express an equivalent query in a relational space. Despite its traversal-oriented syntactic style, PathQuery has a foundation on a custom variant of relational algebra -- the exposition of which we presently defer -- allowing for the application of both common and novel optimizations. We believe that PathQuery has withstood a "test of time" at Google, under both large scale and low latency requirements. We thus share herein a language design that admits a rigorous declarative semantics, has scaled well in practice, and provides a natural syntax for graph traversals while also admitting complex graph patterns. Things that are somewhat interesting to me, from an engineering standpoint: 1. PathQuery has a module/compilation system, enabling re-use of PathQuery modules across projects. (Someone had mentioned that Google has around 40,000 PathQuery modules already, internally...) 2. PathQuery supports native functions so that some query pieces can be evaluated procedurally (peephole optimization) 3. Use of relational algebra to enable a lot of known optimizations, plus future optimizations Also, from a socio-linguistic perspective, Graph Languages are effectively the new Object-Relational Mapping layer, but they solve an interesting organizational problem of allowing multiple teams to code in different languages, without needing to re-write / re-implement entities and mapping configurations in each language. It's the Old New Thing again... ## Applications of Blockchain to Programming Language Theory DevFeed: [Applications of Blockchain to Programming Language Theory]() Original publisher: [Read original article]() Published: 2020-04-13T14:38:12Z Content type: opinion Language: en Sources: [Lambda the Ultimate]() Topics: [Blockchain](), [Programming](), [Programming language](), [smart contract](), [distributed-systems]() Tags: [blockchain](), [bug](), [distributed-computing](), [ethereum](), [fun](), [implementation](), [programming](), [programming-language](), [semantics](), [smart-contract](), [transactions](), [verification]() ### AI overview A forum discussion explores how blockchain could contribute to programming language theory, practice, and developer productivity. It considers mutual and common knowledge, distributed computing fabrics, third-party transactions, and the verification and security of smart contracts, including an Ethereum smart-contract logic bug. ### Source excerpt Let's talk about Blockchain. Goal is to use this forum topic to highlight its usefulness to programming language theory and practice. If you're familiar with existing research efforts, please share them here. In addition, feel free to generate ideas for how Blockchain could improve languages and developer productivity. As one tasty example: Blockchain helps to formalize thinking about mutual knowledge and common knowledge, and potentially think about sharing intergalactic computing power through vast distributed computing fabrics. If we can design contracts in such a way that maximizes the usage of mutual knowledge while minimizing common knowledge to situations where you have to "prove your collateral", third-party transactions could eliminate a lot of back office burden. But, there might be benefits in other areas of computer science from such research, as well. Some language researchers, like Mark S. Miller, have always dreamed of Agoric and the Decades-Long Quest for Secure Smart Contracts. Some may also be aware that verification of smart contracts is an important research area, because of the notorious theft of purse via logic bug in an Ethereum smart contract. ## (How to Write a (Lisp) Interpreter (in Python)) DevFeed: [(How to Write a (Lisp) Interpreter (in Python))]() Original publisher: [Read original article]() Published: 2010-09-30T00:00:00Z Content type: tutorial Language: en Sources: [Peter Norvig]() Topics: [Lisp](), [Python](), [Programming](), [scheme](), [compilers]() Tags: [compilers](), [how-to](), [interpreter](), [lisp](), [programming-languages](), [python](), [semantics](), [syntax]() ### AI overview This tutorial explains how to implement computer-language interpreters in general and presents Lispy, an interpreter for most of Scheme written in Python 3. It introduces Scheme syntax and semantics and compares its simplicity with Java and Python. ### Source excerpt A Lisp interpreter in 90 lines of Python