# programming-language Published articles for programming-language. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Adafruit's New CircuitPython 'Turbo' Brings Native Code To Tiny Boards DevFeed: [Adafruit's New CircuitPython 'Turbo' Brings Native Code To Tiny Boards]() Original publisher: [Read original article]() Author: EditorDavid Published: 2026-09-17T02:34:00Z Content type: news Language: en Sources: [Slashdot]() Topics: [CircuitPython](), [Microcontroller](), [MicroPython](), [Python](), [Compiler](), [Hardware](), [Open Source]() Tags: [benchmarks](), [circuitpython](), [code](), [compiler](), [examples](), [hardware](), [micropython](), [open-hardware](), [opensource](), [optimized](), [programming](), [programming-language](), [python]() ### AI overview Adafruit has published CircuitPython Turbo, a workflow that compiles selected Python functions into native machine code for compatible microcontroller boards while leaving the rest of an application in Python. In a documented Mandelbrot benchmark, the compiled computation ran 19.71 times faster than bytecode, though the result applies to the measured computation rather than the whole application. ### Source excerpt Targeting students and beginners, Adafruit released "CircuitPython" in 2017 (as a derivative of the MicroPython microcontroller-optimized programming language). Now Adafruit managing director Phillip Torrone (also long-time Slashdot reader ptorrone) brings this update: Adafruit has published CircuitPython Turbo, a workflow that compiles selected Python functions into native machine code on a computer, then loads them onto compatible microcontroller boards. It builds on MicroPython's Native and Viper emitters. In a documented Metro RP2040 fixed-point Mandelbrot test, Viper cut computation time from 8.335 seconds to 0.423 seconds, a 19.71x speedup over bytecode. The rest of the application stays in Python. The guide includes benchmarks, source code and hardware demos. The speedup is for the measured computation, not the whole application. "Turbo support is now included in the latest official CircuitPython builds for RP2040 and RP2350 boards..." explains Torrone's announcement at Adafruit.com. "The new Turbo in CircuitPython helps when the board spends time calculating: making neopixel effects, drawing fractals, processing audio, filtering sensor readings, or preparing lots of pixels. Those projects can get smoother animation, quicker responses, or room to do more things at once." With Turbo, it's easier, better, and now even faster to make LED light up costumes that also reacts to sound at the same time, a sensor dashboard with animated graphics, or a tiny game doing physics while drawing the screen. Turbo speeds up the busy Python parts. It won't make a slow sensor or display connection faster... Your computer turns selected functions into instructions the chip can run directly. Python still handles the rest. We have measured speedups, real display captures, and examples you can pull apart to see what happened. None of this arrived alone. CircuitPython, MicroPython, PyMCU, compiler tools, open hardware, and people sharing their work gave us pieces to connect. The Bao ## Ubuntu 26.10 Moves cp, mv & rm Over To Rust Coreutils For 100% Transition DevFeed: [Ubuntu 26.10 Moves cp, mv & rm Over To Rust Coreutils For 100% Transition]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-16T12:26:49Z Content type: news Language: en Sources: [Phoronix]() Topics: [Ubuntu](), [Bevy](), [Linux](), [Programming language]() Tags: [commands](), [desktop-linux](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [memory-safety](), [open-source-graphics](), [phoronix](), [phoronix-test-suite](), [programming-language](), [rust](), [ubuntu](), [ubuntu-benchmarks](), [ubuntu-hardware]() ### AI overview Ubuntu 26.10 completes its transition from GNU Coreutils to Rust Coreutils, with the cp, mv, and rm commands now provided by the Rust-based project. Earlier Ubuntu releases had delayed this change because of compatibility issues. ### Source excerpt While Ubuntu Linux has been making use of the Rust Coreutils project over the past year since Ubuntu 25.10 as an alternative to GNU Coreutils and part of their effort to get more system components implemented in the memory-safety-focused Rust programming language, Ubuntu 26.10 finally completes the transition with the cp, mv, and rm commands now coming from Rust Coreutils... ## Microsoft annoints Rust as a 'Tier 1' internal language DevFeed: [Microsoft annoints Rust as a 'Tier 1' internal language]() Original publisher: [Read original article]() Author: Joab Jackson Published: 2026-09-11T06:26:00Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [Microsoft]() Tags: [bugs](), [devops](), [memory](), [microsoft](), [programming-language](), [rust](), [windows]() ### AI overview Microsoft has designated Rust a Tier 1 internal language, with the stated aim of helping developers address memory bugs in Windows. ### Source excerpt Redmond's coders get the tool to help them scrub memory bugs off Windows ## Julia 1.13 released DevFeed: [Julia 1.13 released]() Original publisher: [Read original article]() Author: jzb Published: 2026-09-10T13:23:50Z Content type: release Language: en Sources: [LWN.net]() Topics: [releases]() Tags: [programming](), [programming-language](), [release](), [release-notes]() ### AI overview Julia 1.13 is released with faster package precompilation, REPL improvements, and Juliaup graphical-interface updates. ### Source excerpt Version 1.13 of the Julia programming language has been released. Highlights include faster precompilation of packages, improvements to Julia's REPL, and Juliaup, a graphical interface for the Julia version manager. A full list of changes can be found in the release notes. LWN covered Julia 1.12 in November 2025. ## A quick overview of atomics in C DevFeed: [A quick overview of atomics in C]() Original publisher: [Read original article]() Author: Daniel Lemire Published: 2026-09-09T20:41:53Z Content type: tutorial Language: en Sources: [Daniel Lemire]() Topics: [C](), [Concurrency](), [Concurrent Programming](), [Programming]() Tags: [atomics](), [c](), [join](), [ordering](), [posix](), [programming-language](), [systems](), [thread](), [threads]() ### AI overview This tutorial introduces atomics in C, explaining data races on shared non-atomic variables, atomic accesses, instruction reordering, and memory-ordering models including relaxed, release, and acquire semantics. It also discusses C11 threads and platform support limitations. ### Source excerpt If you write in C, by default, you use a single thread. Extra cores do not help until you create more threads. However, if you include the header , you can pass a function to thrd_create, and wait for it with thrd_join. #include #include int worker(void *arg) { printf("hello from thread %d\n", *(int ... Continue reading A quick overview of atomics in C ## Client-Side SDK Generation with VMware Cloud Foundation OpenAPI Specs DevFeed: [Client-Side SDK Generation with VMware Cloud Foundation OpenAPI Specs]() Original publisher: [Read original article]() Author: vmwareblogs Published: 2026-09-07T09:38:44Z Content type: tutorial Language: en Sources: [VMware Blogs]() Topics: [OpenAPI Specification](), [SDKs](), [.NET](), [C#](), [developer tooling](), [Java](), [Python](), [PowerShell]() Tags: [apis](), [cli](), [cloud](), [cloud-infrastructure](), [csharp](), [developer](), [developer-tooling](), [docker](), [home-page](), [java](), [net](), [openapi](), [powershell](), [programming](), [programming-language](), [python](), [rest](), [rest-api](), [sdk](), [specifications](), [vcf-9-0](), [vcf-9-1](), [vcf-sdk]() ### AI overview This guide explains how to generate a strongly typed .NET C# client SDK from VMware Cloud Foundation and vSphere OpenAPI specifications. It covers OpenAPI Generator, installation options, large specification handling, API-surface scoping, and the REST and JSON-RPC interfaces targeted by the example. ### Source excerpt Starting with VMware Cloud Foundation (VCF) 9.0, VMware officially publishes OpenAPI specifications for VCF APIs. This is a major milestone for developers, enabling you to consume VCF APIs using your programming language of choice. VMware provides first-class developer tooling such as VCF PowerCLI for PowerShell users and the VCF SDK offering Java and Python bindings. ... Continued The post Client-Side SDK Generation with VMware Cloud Foundation OpenAPI Specs appeared first on VMware Blogs. ## How an MIT research project became a global programming language DevFeed: [How an MIT research project became a global programming language]() Original publisher: [Read original article]() Author: Zach Winn | MIT News Published: 2026-08-31T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [The Julia Language](), [Programming language](), [Data Science](), [Complex Systems](), [Data analysis](), [Open Source]() Tags: [ai](), [ai-platform](), [alan-edelman](), [analysis](), [applications](), [artificial-intelligence](), [chris-rackauckas](), [complex-systems](), [computer-science-and-artificial-intelligence-laboratory-csail](), [deshpande-center](), [jeff-bezanson](), [julia-programming-language](), [juliahub](), [language](), [mit-schwarzman-college-of-computing](), [open-source](), [programming](), [programming-language](), [software](), [startups](), [stefan-karpinski](), [viral-shah]() ### AI overview An MIT research project created Julia, a free and open-source programming language for scientific research, data analysis, and complex-systems modeling. The article describes Julia's adoption by researchers, engineers, companies, and universities, and introduces JuliaHub's Dyad 3.0 AI platform. ### Source excerpt With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more. ## When you keep AI Lean, you keep AI correct DevFeed: [When you keep AI Lean, you keep AI correct]() Original publisher: [Read original article]() Author: Phoebe Sajor Published: 2026-08-28T07:40:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [AI Bots](), [Functional programming]() Tags: [agentic-ai](), [agents](), [ai](), [ai-agents](), [ai-models](), [automated-reasoning](), [code](), [developers](), [functional-programming](), [optimization](), [podcast](), [programming](), [programming-language](), [reasoning](), [se-stackoverflow](), [se-tech]() ### AI overview A discussion of using Lean to prove AI-agent correctness, combine automated reasoning with probabilistic AI models, and continuously optimize code. ### Source excerpt Ryan chats with Leo de Moura, Senior Principal Applied Scientist at AWS and the creator of the Lean language, about proving correctness in AI agents with the Lean language, how automated reasoning complements probabilistic AI models, and the use of AI for continuous code optimization. ## The best workflow engine is a programming language DevFeed: [The best workflow engine is a programming language]() Original publisher: [Read original article]() Author: Pranay Prakash Published: 2026-08-27T07:00:00Z Content type: opinion Language: en Sources: [Vercel News]() Topics: [SDKs]() Tags: [airflow](), [encryption](), [infrastructure](), [kubernetes](), [programming-language](), [tls](), [typescript](), [workflow]() ### AI overview The article argues that programming languages can express workflow graphs more naturally than manually defined DAGs. It describes using TypeScript for durable execution and contrasts that approach with the operational setup required to run Temporal workers and infrastructure. ### Source excerpt The idea of orchestrating long-running, stateful logic on top of unreliable, stateless infrastructure isn't new. We've had message queues, job runners, microservice choreographies, and full-blown workflow engines for a long time. What we never had was a version of it that felt good to write. I'd spent about six months working on a fork of Temporal, mostly on weekends, trying to turn it into a serverless answer with DX that felt more Vercel-native. Eventually it dawned on me that to ship that experience, I'd need to own the execution environment too. So I dropped the fork, joined Vercel, and started hacking on a new framework from scratch alongside Nathan Rajlich. Code is already a DAG A workflow is a DAG, a directed acyclic graph. Before Temporal (and Cadence before it), nearly every workflow framework made you draw that DAG by hand. Apache Airflow is the canonical example. You describe your pipeline as an explicit graph of tasks and dependencies, and your actual logic gets buried inside the nodes." That always felt backwards to me. We already have a tool for expressing "do this, then that, and these in parallel, and branch here." It's called a programming language. An abstract syntax tree is a DAG. Software itself is a DAG. I knew that in the abstract, but it didn't truly hit me until I saw Temporal, where you write what looks like normal sequential code and the engine makes it durable underneath. It was the dream all along. What running Temporal taught me Temporal is great once your infrastructure exists. I was starting from scratch, and setting it up meant: Standing up Temporal Cloud (or self-hosting the server: Frontend, History, Matching, and Worker services, plus a Cassandra/Postgres/MySQL backend and sharding). Running your own worker fleet: Temporal never executes your code. Your workers poll the server and run your workflows and activities. In practice that's a Kubernetes cluster you own. Wiring it all together: task queues, activity registration, client co ## Java's String.indexOf can be slow (quadratic) DevFeed: [Java's String.indexOf can be slow (quadratic)]() Original publisher: [Read original article]() Author: Daniel Lemire Published: 2026-08-22T14:56:16Z Content type: article Language: en Sources: [Daniel Lemire]() Topics: [Java](), [openjdk](), [Programming]() Tags: [algorithm](), [complexity](), [cpu](), [java](), [openjdk](), [programming-language]() ### AI overview Java's String.indexOf can exhibit O(n-m) behavior on adversarial inputs with long substrings. The article compares it with the Two-Way algorithm and explains why Java's implementation remains suitable for typical workloads. ### Source excerpt In Java, you find the location of a substring using indexOf. String haystack = "The quick brown fox jumps over the lazy dog"; String needle = "fox"; int index = haystack.indexOf(needle); Naively, you might implement indexOf by a loop inside a loop, like so. int naiveIndexOf(String haystack, String needle) { for (int i = 0; ... Continue reading Java's String.indexOf can be slow (quadratic) ## Concurrent Servers: Part 7 - Rust DevFeed: [Concurrent Servers: Part 7 - Rust]() Original publisher: [Read original article]() Author: Eli Bendersky Published: 2026-08-15T16:41:00Z Content type: tutorial Language: en Sources: [Eli Bendersky]() Topics: [Rust](), [Concurrent Programming](), [Network](), [Server](), [Programming]() Tags: [concurrency](), [concurrent](), [misc](), [network](), [network-programming](), [programming](), [programming-language](), [rust](), [servers](), [threads]() ### AI overview Part 7 of a series on concurrent network servers explains how to implement the series' state-machine protocol in Rust. It covers a sequential server, one thread per client, and a fixed thread pool. ### Source excerpt This is part 7 in a series of posts on writing concurrent network servers. In this part, we discuss how the challenges described in earlier parts are tackled in the Rust programming language. All posts in the series: Part 1 - Introduction Part 2 - Threads Part 3 - Event-driven Part 4 - libuv ... ## Leonardo de Moura on Lean, Formal Verification, and the Future of Mathematics DevFeed: [Leonardo de Moura on Lean, Formal Verification, and the Future of Mathematics]() Original publisher: [Read original article]() Author: Ryan Peterman Published: 2026-08-10T13:03:04Z Content type: article Language: en Sources: [The Developing Dev]() Topics: [Lean](), [Formal verification](), [math](), [Large Language Model]() Tags: [code](), [formal-verification](), [google](), [language](), [llms](), [math](), [podcasts](), [programming](), [programming-language](), [software](), [verification]() ### AI overview An interview with Leonardo de Moura, creator of Lean, about using the programming language for machine-checkable proofs, software verification, and mathematical reasoning. The discussion also covers how LLMs can work with Lean to generate and verify proofs. ### Source excerpt In 2024, AlphaProof from Google Deepmind broke through in competition math achieving a silver-medal in Interational Mathematical Olympiad (IMO). ## Create Python GUI Apps on Raspberry Pi With PyQt DevFeed: [Create Python GUI Apps on Raspberry Pi With PyQt]() Original publisher: [Read original article]() Author: Patrick Fromaget Published: 2026-07-30T01:34:44Z Content type: tutorial Language: en Sources: [RaspberryTips]() Topics: [Python](), [GUI](), [Qt](), [apt](), [Terminal](), [Programming](), [Linux](), [C++]() Tags: [apt](), [c-plus-plus](), [cheat-sheet](), [gui](), [how-to](), [linux](), [programming](), [programming-language](), [python](), [raspberry-pi](), [raspberry-pi-os](), [systems](), [user-interface]() ### AI overview A practical tutorial on creating Python GUI applications with PyQt on Raspberry Pi. It introduces PyQt and Qt, then covers installation through APT or pip on Raspberry Pi OS. ### Source excerpt I use Python for many Raspberry Pi projects, especially when working with GPIO pins or automating simple tasks. But running a script in the terminal is one thing; giving it a clean, user-friendly interface is another. PyQt is one of the easiest ways to build this. PyQt is a library that can be used to... ## Amazon is investing in the Lean Focused Research Organization DevFeed: [Amazon is investing in the Lean Focused Research Organization]() Original publisher: [Read original article]() Author: Byron Cook; Shawn Bice Published: 2026-07-26T08:00:00Z Content type: news Language: en Sources: [Amazon Science homepage]() Topics: [AI Bots](), [AI Chat]() Tags: [agents](), [ai-agents](), [amazon](), [amazon-aurora](), [amazon-bedrock](), [amazon-bedrock-agentcore](), [amazon-web-services-aws](), [automated-reasoning](), [aws](), [developer](), [formal-verification](), [generative-ai](), [large-language-models-llms](), [llm](), [programming](), [programming-language](), [research](), [responsible-ai](), [safety](), [software-testing](), [testing](), [trustworthy-ai]() ### AI overview Amazon is making a long-term investment in the Lean Focused Research Organization to advance Lean, a programming language for mathematical correctness proofs. The article highlights Lean-based verification for safer AI agents and AWS systems. ### Source excerpt As AI agents take on higher-stakes decisions, Lean programming language makes it possible to mathematically prove they will behave safely. ## Dependent Types and the Challenge of Automating Proofs DevFeed: [Dependent Types and the Challenge of Automating Proofs]() Original publisher: [Read original article]() Author: Adam Langley Published: 2026-07-26T00:00:00Z Content type: opinion Language: en Sources: [ImperialViolet]() Topics: [Programming language](), [Lean](), [Programming](), [Coq]() Tags: [interactive](), [language](), [machine](), [programming](), [programming-language]() ### AI overview The article discusses dependently typed languages such as Coq, Rocq, and Lean, which can encode subtle invariants in a type system. It explains that writing and checking proofs can require substantial effort, contributing to the niche status of these languages and motivating attempts to automate proof obligations. ### Source excerpt I've long had a soft spot for dependently-typed languages like Coq Rocq and Lean. They offer the possibility of a type system capable of encoding and enforcing arbitrarily subtle invariants. The sort of thing that, in regular languages, ends up (at best) as a comment, and which quickly gets lost as the size of the team grows. Then you get subtle misunderstandings and components that don't quite fit together. It's often the case that those components have grown to a sufficient size that, when the problem is noticed, aligning either of them is a wearying prospect. Perhaps, say dependent types seductively, you could write those invariants formally and have a machine check them. (p.s. Coq changed its name! I remember many years ago at a Coq conference in Princeton, I tried suggesting that, in an English-speaking world, having a programming language called Coq was an impediment. I don't think the audience agreed at the time. I also joked that many of the talks there sounded like a speech by Tyrion Lannister, there being so many Coqs and Hoares. A joke that was hilarious and timely, even though it fell completely flat, coming as it did before the final season of that show and our collective memory-holing of it.) The problem has always been that with great type-system power comes great proof effort. I can certainly attest to entire days spent proving really quite simple things. Doing proofs is actually quite fun: it's challenging, interactive, and there's a clear goal. But gosh, does it take a lot of time, especially if, like me, you don't know what you're doing. There's also the periodic, galling experience, at the end of many hours of effort, where you realise that the goal that you're trying to prove is, in fact, false. The classic result here is the retrospective from the seL4 effort that found that, even though the project was large enough for the engineers to develop considerable experience, they spent about 10 times as much time proving as they did designing and imp ## Arithmetic interpreter: Write yourself a compiler, Part II DevFeed: [Arithmetic interpreter: Write yourself a compiler, Part II]() Original publisher: [Read original article]() Published: 2026-07-21T22:00:00Z Content type: tutorial Language: en Sources: [Tomasz Nurkiewicz around Java and concurrency]() Topics: [Compiler](), [Programming language](), [Programming](), [Regular expression]() Tags: [arithmetic](), [compiler](), [go](), [intermediate](), [interpreter](), [operations](), [programming-language](), [writing-compiler]() ### AI overview This tutorial extends a simple arithmetic interpreter to support addition, subtraction, multiplication, and division. It updates the regular-expression parsing and interpreter logic, with a later installment planned to compile the language into an intermediate representation. ### Source excerpt In the previous article, we created the most naive interpreter, which can basically execute number + number expressions. A logical extension is obviously to handle all basic operations: addition, subtraction, multiplication and division. The time has come! ## Creator of OCaml: Functional Programming, Formal Verification, Programming Languages | Xavier Leroy DevFeed: [Creator of OCaml: Functional Programming, Formal Verification, Programming Languages | Xavier Leroy]() Original publisher: [Read original article]() Author: Ryan Peterman Published: 2026-07-20T13:05:11Z Content type: article Language: en Sources: [The Developing Dev]() Topics: [OCaml](), [Functional programming](), [Programming](), [Formal verification](), [Programming language]() Tags: [formal-verification](), [functional](), [functional-programming](), [ocaml](), [programming](), [programming-language](), [verification]() ### AI overview A podcast transcript featuring Xavier Leroy discusses what distinguishes OCaml, including its functional and systems programming capabilities, type inference, control structures, predictable execution costs, and low-latency garbage collection. The conversation also covers formal verification through the CompCert project and comparisons with languages such as Rust and JavaScript. ### Source excerpt Professor Leroy (Creator of OCaml) is incredibly knowledgeable. ## Profunctor Optics DevFeed: [Profunctor Optics]() Original publisher: [Read original article]() Author: Bartosz Milewski Published: 2026-07-19T11:39:02Z Content type: article Language: en Sources: [Bartosz Milewski's Programming Cafe]() Topics: [Haskell](), [Programming language](), [Programming](), [modules]() Tags: [category-theory](), [haskell](), [language](), [lens](), [mapping](), [modules](), [optics](), [profunctors](), [programming-language](), [tambara-modules]() ### AI overview This article explains profunctor optics through Tannakian reconstruction. It presents optics as a category, describes lenses and their composition in Haskell, and introduces Tambara modules as a representation that simplifies optic composition. ### Source excerpt You may think of Tannakian Reconstruction as an example of redundant encoding. It lets you replace a simple hom-set with a much more complex end that is taken over an entire functor category. Why would anyone want to do it? The answer is simple: composition! Morphisms on the left compose according to the rules of [...] ## Announcing Rust 1.97.1 DevFeed: [Announcing Rust 1.97.1]() Original publisher: [Read original article]() Author: The Rust Release Team Published: 2026-07-16T00:00:00Z Content type: release Language: en Sources: [Rust Blog]() Topics: [Rust](), [releases](), [LLVM](), [Optimization](), [Programming language](), [Beta channel](), [ci]() Tags: [beta-channel](), [bugs](), [ci](), [llvm](), [optimization](), [programming-language](), [release](), [releases](), [rust](), [update]() ### AI overview Rust 1.97.1 is a point release that fixes a miscompilation related to an LLVM optimization. The release backports an LLVM fix and disables the underlying change in Rust 1.97.0 that increased the likelihood of the issue. ### Source excerpt The Rust team has published a new point release of Rust, 1.97.1. Rust is a programming language that is empowering everyone to build reliable and efficient software. If you have a previous version of Rust installed via rustup, getting Rust 1.97.1 is as easy as: rustup update stable If you don't have it already, you can get rustup from the appropriate page on our website. What's in 1.97.1 Rust 1.97.1 fixes a miscompilation in an LLVM optimization. We have backported both an LLVM fix and a disable of the underlying change in Rust 1.97.0 of Rust's generated IR that increased the likelihood of this happening. However, note that the underlying miscompilation has been present since at least Rust 1.87. If you'd like to help us out by testing future releases, you might consider running your code's CI or locally using the beta channel (rustup default beta) or the nightly channel (rustup default nightly). Please report any bugs you might come across! Contributors to 1.97.1 Many people came together to create Rust 1.97.1. We couldn't have done it without all of you. Thanks! ## Using OxCaml to implement type-safe reference counting between OCaml and Python DevFeed: [Using OxCaml to implement type-safe reference counting between OCaml and Python]() Original publisher: [Read original article]() Author: Nicolas Trangez Published: 2026-06-15T00:00:00Z Content type: tutorial Language: en Sources: [Jane Street]() Topics: [OCaml](), [Python](), [Programming language](), [Programming](), [Data analysis](), [Machine learning]() Tags: [compiler](), [gc](), [memory](), [memory-management](), [ocaml](), [parallelism](), [programming-language](), [python]() ### AI overview Jane Street describes using OxCaml extensions to implement type-safe reference counting between OCaml and Python. The approach addresses delayed deallocation when objects cross the language boundary, including for large data frames and scarce resources such as GPU memory. ### Source excerpt Jane Street is known for being an OCaml shop, but for years now Python has been our second major programming language, acting as the primary tool for data analysis and (especially importantly these days) machine learning. Most of our traders and researchers think and write in Python, even as the majority of our infrastructure is written in OCaml. ## Coding Challenge #124 - Du DevFeed: [Coding Challenge #124 - Du]() Original publisher: [Read original article]() Author: John Crickett Published: 2026-06-13T08:01:14Z Content type: tutorial Language: en Sources: [Coding Challenges]() Topics: [Code Challenge](), [POSIX](), [Unix](), [Utility Software](), [Programming]() Tags: [coding](), [exercise](), [filesystem](), [posix](), [programming](), [programming-language](), [run](), [unix](), [utilities]() ### AI overview A coding challenge asks readers to build their own POSIX-compatible du utility. It covers recursively traversing directories, calculating disk usage, handling hard links and symbolic links, and presenting results in multiple formats. ### Source excerpt This challenge is to build your own du. ## Simon Peyton Jones on Haskell and Why Functional Programming Matters DevFeed: [Simon Peyton Jones on Haskell and Why Functional Programming Matters]() Original publisher: [Read original article]() Author: Ryan Peterman Published: 2026-06-08T10:02:31Z Content type: article Language: en Sources: [The Developing Dev]() Topics: [Functional programming](), [Haskell](), [Programming](), [Declarative programming](), [Programming language]() Tags: [declarative](), [functional-programming](), [languages](), [programming](), [programming-language](), [types]() ### AI overview An interview with Simon Peyton Jones, co-creator of Haskell, about functional programming, its emphasis on values rather than mutation, and how it differs from imperative programming. ### Source excerpt Simon Peyton Jones is the co-creator of Haskell (pure functional programming language) and I interviewed him about functional programming, why it matters, and his thoughts on other programming languages. ## Genie Lessons from Genie Sessions: Prose as a Programming Language DevFeed: [Genie Lessons from Genie Sessions: Prose as a Programming Language]() Original publisher: [Read original article]() Author: Kent Beck Published: 2026-05-26T12:47:53Z Content type: opinion Language: en Sources: [Software Design: Tidy First?]() Topics: [Programming language](), [Programming](), [Code](), [AI Agent](), [Claude Code](), [Framework](), [Filesystems]() Tags: [ai-agent](), [claude-code](), [code](), [files](), [framework](), [language](), [programming](), [programming-language]() ### AI overview Kent Beck discusses OpenProse with one of its founders and examines how structured English can be used to define programs executed by an AI agent such as Claude Code. The article describes component requirements and guarantees, service-like composition, and file-based pointers for passing large outputs between sub-agents. ### Source excerpt Kent sits down with one of the founders of OpenProse to talk through their method of prose as a programming language. ## Monotonic Indentation DevFeed: [Monotonic Indentation]() Original publisher: [Read original article]() Author: Yegor Bugayenko (yegor256@gmail.com) Published: 2026-05-23T21:00:00Z Content type: opinion Language: en Sources: [Yegor Bugayenko]() Topics: [Code](), [formatting](), [Programming](), [Programming language](), [Python]() Tags: [code](), [code-formatting-style](), [code-indentation-rule](), [formatting](), [indentation-in-java](), [monotonic-indentation](), [off-side-rule](), [programming](), [programming-language](), [python](), [style]() ### AI overview The article proposes monotonic indentation as a source-code formatting rule: indentation may increase by exactly one unit between adjacent lines but may decrease by any number of units. It presents the rule as an optional check for existing style checkers or as a standalone tool, and compares it with Python's off-side rule. ### Source excerpt Between two adjacent lines, indentation may grow by one unit only, but may shrink by any number of units -- a rule that resolves most code formatting disputes. [Next page]()