# code-completion Published articles for code-completion. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## The Rider 2026.3 Early Access Program Is Open DevFeed: [The Rider 2026.3 Early Access Program Is Open]() Original publisher: [Read original article]() Author: Alexander Kurakin Published: 2026-09-07T09:35:21Z Content type: release Language: en Sources: [The JetBrains Blog]() Topics: [debugging](), [C++]() Tags: [c-plus-plus](), [code-completion](), [debugger](), [debugging](), [eap](), [game-development](), [gamedev](), [net-tools](), [rider](), [test-coverage](), [unreal-engine]() ### AI overview Rider 2026.3's first Early Access build adds rainbow brackets, streamlined data-breakpoint setup, a consolidated game-development plugin category, and code-completion filters. ### Source excerpt The first Early Access build for Rider 2026.3 is now available! It includes rainbow brackets, an easier way to set data breakpoints, a new Game Development plugin category, and filters in code completion. You can get the build from our website or through the Toolbox App. Here's a look at what's new. Rainbow brackets: You [...] ## KotlinLLM is Going Open Source DevFeed: [KotlinLLM is Going Open Source]() Original publisher: [Read original article]() Author: Anastasia Birillo Published: 2026-07-28T07:50:41Z Content type: article Language: en Sources: [Kotlin : A concise multiplatform language developed by JetBrains | The JetBrains Blog]() Topics: [kotlinllm](), [Kotlin](), [Open Source](), [IntelliJ IDEA](), [Large Language Model](), [Code generation](), [AI-assisted coding](), [Software Engineering]() Tags: [code-completion](), [code-generation](), [engineering](), [intellij](), [intellij-idea](), [kotlin](), [kotlinllm](), [llm](), [llms](), [open-source](), [plugin](), [research](), [research-prototype](), [software](), [software-engineering]() ### AI overview KotlinLLM is an open-source research prototype and IntelliJ IDEA plugin for Kotlin/JVM projects. It introduces Smart macros whose generated Kotlin source evolves from runtime scenarios, allowing applications to delegate selected logic to an LLM without calling the model on every request or relying on a separate runtime agent. ### Source excerpt TL;DR KotlinLLM is now public. It's a research prototype for delegating runtime logic to an LLM from Kotlin code. Instead of calling an LLM on every request or running a separate agent, you can write an explicit Kotlin call. Its body is generated Kotlin source code, and that code is updated as your application hits [...] ## Rich Text And Code Editing: RichTextArea And A Syntax-Highlighting CodeEditor DevFeed: [Rich Text And Code Editing: RichTextArea And A Syntax-Highlighting CodeEditor]() Original publisher: [Read original article]() Author: Shai Almog Published: 2026-06-28T00:00:00Z Content type: article Language: en Sources: [CodeName One]() Topics: [Syntax Highlighting](), [Code](), [cross-platform](), [Web](), [JavaScript](), [Kotlin](), [Python](), [CSS](), [JSON](), [XML]() Tags: [code](), [code-completion](), [cross-platform](), [css](), [javascript](), [kotlin](), [python](), [syntax](), [web](), [xml]() ### AI overview The article describes RichTextArea and CodeEditor, two Codename One visual editors built on a shared semantic abstraction. A bundled cross-platform web-view engine is the default backend, while ports can provide optional native peers. The CodeEditor supports syntax highlighting for eight languages, themes, automatic bracket and quote closing, and asynchronous code completion. ### Source excerpt RichTextArea and a syntax-highlighting CodeEditor, both built on a shared editor abstraction with a cross-platform web-view engine and an optional native peer. ## What's new in DevTools (Chrome 149) DevFeed: [What's new in DevTools (Chrome 149)]() Original publisher: [Read original article]() Author: Matthias Rohmer X GitHub LinkedIn Bluesky Published: 2026-06-02T07:00:00Z Content type: release Language: en Sources: [developer.chrome.com: Blog]() Topics: [Developer Tools](), [AI-assisted coding](), [debugging](), [Web Development]() Tags: [agents](), [ai](), [ai-coding](), [announcement](), [chrome](), [cli](), [code-completion](), [css](), [debugging](), [gemini](), [http](), [javascript](), [mcp](), [mcp-server](), [release](), [tools]() ### AI overview Chrome 149 updates DevTools for agents and AI assistance, adding stable MCP server and CLI support, experimental WebMCP debugging, and richer page-analysis features. ### Source excerpt DevTools for agents is stable, AI assistance gets major upgrade with Lighthouse and widgets, new WebMCP debugging tools. ## NPM Install And Copilot Instructions DevFeed: [NPM Install And Copilot Instructions]() Original publisher: [Read original article]() Author: Gleb Bahmutov Published: 2025-12-30T05:00:00Z Content type: tutorial Language: en Sources: [Gleb Bahmutov]() Topics: [npm](), [GitHub Copilot](), [Cypress](), [Testing](), [AI-assisted coding]() Tags: [advice](), [ai](), [code-completion](), [copilot](), [cypress](), [dependency](), [github-copilot](), [npm](), [plugin](), [process](), [testing]() ### AI overview This tutorial describes updating Copilot instruction files when adding npm dependencies, using a Cypress test for sorted inventory prices as an example. It compares Copilot's suggested assertion with a chai-sorted plugin and shows how instructions can guide Copilot toward a shorter, preferred assertion. ### Source excerpt Here is something I have noticed about my projects and using Copilot lately. When I add a new NPM dependency, I also update the Copilot i ## Foundation vs. Instruct vs. Thinking Models DevFeed: [Foundation vs. Instruct vs. Thinking Models]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2025-12-24T07:07:00Z Content type: opinion Language: en Sources: [Alex Ewerlof Notes]() Topics: [foundation-models](), [Large Language Model](), [AI-assisted coding]() Tags: [ai](), [code-completion](), [foundation](), [llms](), [models]() ### AI overview This article explains the distinction between base and instruction-oriented language models using a software metaphor. It describes base models as pretrained systems that predict the next token from learned patterns, without an inherent concept of questions, answers, or instructions, and discusses code completion as one use case. ### Source excerpt A Senior Engineer's Mental Model for AI ## Laravel Idea Plugin is Free for PhpStorm DevFeed: [Laravel Idea Plugin is Free for PhpStorm]() Original publisher: [Read original article]() Author: Sam Published: 2025-09-19T08:40:12Z Content type: news Language: en Sources: [Laravel Blog]() Topics: [PHP](), [AI-assisted coding](), [Code generation](), [debugging]() Tags: [ai](), [ai-agents](), [code-completion](), [code-generation](), [debugging](), [ide](), [junie](), [laravel](), [laravel-boost](), [php](), [phpstorm](), [tooling]() ### AI overview Laravel Idea is now free for PhpStorm users. The article describes Laravel-aware IDE features and explains how Laravel Boost provides project context to AI agents. ### Source excerpt As an IDE we recommend, we're very happy that PhpStorm users can now use the Laravel Idea plugin for free. ## AI Programming Paradigms: A Timeline DevFeed: [AI Programming Paradigms: A Timeline]() Original publisher: [Read original article]() Author: Kaushik Gopal Published: 2025-07-08T00:00:00Z Content type: opinion Language: en Sources: [Kaushik Gopal's Site]() Topics: [Artificial Intelligence](), [agentic-coding](), [AI-assisted coding](), [Programming](), [cursor](), [ide](), [GitHub Copilot](), [Large Language Model](), [GitHub]() Tags: [agentic](), [agentic-coding](), [agents](), [ai](), [ai-for-autocomplete](), [autocomplete](), [code](), [code-completion](), [coding](), [context](), [copilot](), [cursor](), [llm](), [programming](), [visual-studio]() ### AI overview The article outlines a progression of AI programming paradigms: super autocomplete, conversational coding, agentic coding, and possible multi-agent workflows. It describes how each approach changes the way developers interact with code and development tools. ### Source excerpt A developer podcast host recently said they only use AI for autocomplete. This shocked me. That's two generations behind today's state of the art. This is how the field is evolving: I spoke about this topic in episode #301 of Fragmented. It was recorded more recently so reflects my more current thoughts on the topic. Download directly AI Programming Paradigms at a Glance Super autocomplete: AI predicts code, not just keywords. Conversational Coding: Chat with your IDE, direct the AI, iterate together. Agentic Coding: AI acts independently - runs commands, checks work, iterates. Simul Agentic? : Multiple sub-agents, parallel workflows, working together. I. Super autocomplete # This used to be one of the biggest selling points of certain IDEs like JetBrains & Visual Studio.1 The first wave of AI changed this. IDEs now predict entire classes and logic blocks, not just keywords. They excel because they feed surrounding code context to the LLM for relevant suggestions. GitHub deserves credit for kicking off the revolution with their Copilot offering. Here's an early YouTube video of mine from June 20222 showing it in action. Autocomplete keeps advancing. Cursor (today's most popular AI IDE) has "Tab". JetBrains, the pre-AI autocomplete champion, is building Mellum (an LLM built for code completion). This paradigm is alive and well, but it's become table stakes. Most developers use it, but it's far from the frontier. II - Conversational Coding # When ChatGPT took the world by storm, a new paradigm emerged. Conversational coding, where you chat with the AI and pair program together. Unlike the autocomplete era, where you trust the AI's suggestions, here you direct the AI, give it context, and nudge it toward better solutions. This is arguably what most devs use today and envision when they hear "AI programming". It feels magical, productivity jumps are real, and it's hard to think of every going backwards. Cursor leads this charge: chat with your IDE and have it make code ## Codestral Embed DevFeed: [Codestral Embed]() Original publisher: [Read original article]() Published: 2025-05-28T12:00:00Z Content type: article Language: en Sources: [Mistral AI Blog]() Topics: [Embeddings](), [Retrieval-Augmented Generation](), [AI-assisted coding](), [code-completion](), [AI search](), [coding]() Tags: [code-completion](), [code-search](), [coding](), [cost](), [developer](), [embeddings](), [performance](), [retrieval-augmented-generation]() ### AI overview Mistral releases Codestral Embed, an embedding model specialized for code retrieval. The article presents benchmark results, configurable embedding dimensions and precisions, and trade-offs between retrieval quality and storage cost. It highlights applications including retrieval-augmented generation, semantic code search, similarity search, duplicate detection, clustering, and code analytics. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## Meet Cursor Tab and Never Look Back: The Future of AI-Assisted Coding DevFeed: [Meet Cursor Tab and Never Look Back: The Future of AI-Assisted Coding]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2025-04-04T00:13:42Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [AI-assisted coding](), [cursor](), [Vibe coding](), [code-completion](), [Code]() Tags: [ai](), [ai-assisted-coding](), [autocomplete](), [code-completion](), [cursor](), [developer](), [learning](), [product](), [side-projects](), [vibe-coding](), [vs-code]() ### AI overview The article introduces Cursor Tab, an AI-assisted coding feature designed to keep developers in control while writing code. It contrasts "tab coding" with more autonomous vibe coding and describes predictive coding and zero-entropy edits, where Cursor anticipates larger code changes and handles predictable actions after the developer's intent is clear. ### Source excerpt It's crazy to think that "vibe coding" started just weeks ago, with this tweet from Andrej Karpathy: Now, you can get a job as a vibe coder, and Stanford professors are giving lectures on it. That escalated quickly. In its short life, vibe coding has come to mean all kinds of AI-... ## HarmonyOS App Development 101 DevFeed: [HarmonyOS App Development 101]() Original publisher: [Read original article]() Author: Jasper Morgan Published: 2025-01-18T23:23:13Z Content type: tutorial Language: en Sources: [Snapp Mobile - Medium]() Topics: [App](), [Development](), [ide](), [huawei](), [SDK](), [Tooling](), [jetbrains](), [Package Management](), [Testing](), [Open Source]() Tags: [android](), [app](), [app-development](), [apps](), [code-completion](), [debug](), [deployment](), [development](), [harmony-os](), [huawei](), [ide](), [jetbrains](), [open-source](), [package-management](), [programming-languages](), [refactoring](), [sdk](), [testing](), [tools](), [ui](), [ui-framework]() ### AI overview An overview of HarmonyOS Next app development, covering the Huawei-based DevEco Studio IDE, project configuration, SDK versions, modules, and common development features. The article also outlines programming languages, UI frameworks, build tools, package management, execution, debugging, testing, and profiling. ### Source excerpt Huawei have taken a big step in moving away from the Android platform with the launch of new devices running HarmonyOS Next. This is a brand new operating system that had been design specifically for devices. This means it is lightweight, supports a range of device types, is modular and extensible. In this article we aim to give you an overview of what is available to developers wishing to build apps on this new platform. To that end, we will be looking at the following topics: The IDE Programming Languages UI Framework Build Tools Package Management Running code (devices/emulators) Debugging Testing Profiling Other cool stuff If you are curious about the Harmony platform, its Open Source foundations (OpenHarmony) and its global reach (Oniro), we have written about it here. The IDE The development environment for Harmony apps is currently a Huawei-based IDE which is based on the JetBrains IDE platform. The good news is that JetBrains offers a world-class IDE platform and it makes for a very solid development experience for Harmony apps. For those, wanting to keep to the Open Source side of Harmony (OpenHarmony and Oniro), you are out of luck right now. DevEco Studio is proprietary, albeit free to use software. Let's look at a couple of IDE features -- these should not surprise you but hopefully demonstrates that the platform is already in a good place (as far as IDEs go). Project Creation Creating a project will look familiar already for many. There are a few project templates to choose from to bootstrap your project. New Project Templates You will also then need to configure a couple of options -- a couple of notes on these (which could probably be expanded into a separate article). Firstly, the bundle name is used for app signing and identity. Secondly, an app supports a single SDK version. (The latest SDK version is 13, however version 11 is still widely used.) Thirdly we can specify the module name. The default ('entry') isn't very descriptive. If you have a singl ## Codestral 25.01 DevFeed: [Codestral 25.01]() Original publisher: [Read original article]() Published: 2025-01-13T08:00:00Z Content type: release Language: en Sources: [Mistral AI Blog]() Topics: [AI-assisted coding](), [developer tooling]() Tags: [ai](), [api](), [benchmarks](), [code-completion](), [code-generation](), [ide](), [latency](), [models]() ### AI overview Codestral 25.01 updates Mistral AI's coding model with a more efficient architecture and tokenizer, claiming roughly twice the code-generation and completion speed. It emphasizes fill-in-the-middle tasks, IDE-plugin availability, local or VPC deployment for enterprise use, and API integration. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## Keycloak Realm Configuration Management Tools Survey Results DevFeed: [Keycloak Realm Configuration Management Tools Survey Results]() Original publisher: [Read original article]() Author: Thomas Darimont Published: 2024-09-11T00:00:00Z Content type: article Language: en Sources: [Keycloak Blog]() Topics: [Keycloak](), [configuration-management](), [terraform provider](), [API](), [Terraform]() Tags: [code-completion](), [configuration-management](), [idm](), [kerberos](), [keycloak](), [ldap](), [management](), [openid-connect](), [realm](), [saml](), [sso](), [terraform](), [terraform-provider](), [validation]() ### AI overview The Keycloak project reports results from a community survey on realm configuration management tools, based on 433 responses. The Terraform Keycloak Provider received about 51% of votes, and the five most-used tools accounted for 84% of responses. Respondents also identified challenges involving Admin API usability, configuration change detection, release compatibility, automatically created components, linting, validation, and code completion. ### Source excerpt Three months ago, the Keycloak project conducted a survey to gather insights on realm configuration tooling within our community. The number of responses overwhelmed us! With a total of 433 (!) submissions, it highlighted the diverse range of options our community uses for configuring realms. Thank You for your valuable feedback! Popular Tools in Use The survey revealed a variety of tools employed by the community for realm configuration, including: Terraform Provider for Keycloak Keycloak-Config-CLI Self-developed Realm Configuration Management Keycloak JSON Import/Export Keycloak Admin CLI kcadm.sh EPAM Keycloak Operator Keycloak Ansible Keycloak Pulumi Custom Operator for Realm Import/Update and Client Provisioning Keycloak Operator Realm Import via Custom Resources Crossplane Provider for Keycloak KeycloakMigration keycloak-configurator Keycloak Groovy Helpers Tool Usage Distribution From the submissions, we observed the following distribution of tool usage among respondents: Terraform Keycloak Provider ~51% of the votes Keycloak-Config-CLI ~16% of the votes Self-developed Realm Configuration Management ~7% of the votes Keycloak JSON Realm Import/Export ~6% of the votes Keycloak Admin CLI ~4% of the votes These top five tools accounted for 84% of all responses. Areas for Improvement While each tool has its strengths and weaknesses, the survey highlighted several common challenges: Using the Admin API can be awkward and inconsistent, for example, with references using IDs versus aliases. Recognizing changes in the configuration, such as when new roles are added to service accounts via the Admin UI, can be challenging or impossible. Many tools depend heavily on the Keycloak version used and are often not compatible with new releases. Managing components that are automatically created by Keycloak, like service accounts, is challenging with existing configuration tools. Lack of support for configuration linting, validation and code completion What's Next? Based on t ## BigCodeBench: The Next Generation of HumanEval DevFeed: [BigCodeBench: The Next Generation of HumanEval]() Original publisher: [Read original article]() Author: Terry Yue Zhuo; Jiawei Liu; Qian Liu; Binyuan Hui; Niklas Muennighoff; Daniel Fried; Harm de Vries; Leandro von Werra; Clémentine Fourrier Published: 2024-06-18T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [LLMs](), [Benchmark](), [Code](), [code-completion](), [test-coverage]() Tags: [benchmark](), [code](), [code-completion](), [collaboration](), [community](), [complexity](), [evaluation](), [leaderboard](), [llms](), [performance](), [research]() ### AI overview The article announces BigCodeBench, a benchmark for evaluating the programming capabilities of large language models on practical, challenging tasks. It contains 1,140 function-level tasks using 139 libraries, with detailed instructions, interactive examples, runtime test harnesses, and extensive branch coverage. The benchmark measures whether models correctly generate code and compares its complexity and tool-use diversity with existing programming benchmarks. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Codestral DevFeed: [Codestral]() Original publisher: [Read original article]() Published: 2024-05-29T09:00:00Z Content type: article Language: en Sources: [Mistral AI Blog]() Topics: [Artificial Intelligence](), [Generative AI](), [Code generation](), [coding](), [code-completion](), [Benchmark](), [context window](), [Python](), [Java](), [C](), [C++](), [JavaScript]() Tags: [ai](), [bash](), [benchmark](), [c](), [c-plus-plus](), [code](), [code-completion](), [code-generation](), [coding](), [context-window](), [errors](), [eval](), [java](), [javascript](), [latency](), [models](), [open](), [python]() ### AI overview Mistral introduces Codestral, a 22B open-weight generative AI model designed for code generation. It supports code completion, fill-in-the-middle generation, function writing, and test writing across more than 80 programming languages. The article describes its 32k context window, benchmark performance, latency characteristics, and research/testing license. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## CodeGemma - an official Google release for code LLMs DevFeed: [CodeGemma - an official Google release for code LLMs]() Original publisher: [Read original article]() Author: Pedro Cuenca; Omar Sanseviero; Vaibhav Srivastav; Philipp Schmid; Mishig ᠮᠢᠰᠾᠢᠭ; Loubna Ben Allal Published: 2024-04-09T00:00:00Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [gemma](), [Large Language Model](), [code-completion](), [Google](), [hugging face](), [AI Development](), [Transformers](), [AI Chat](), [Benchmark](), [inference-endpoints](), [Cloud](), [Java]() Tags: [artificial-intelligence](), [c-plus-plus](), [cloud](), [code-completion](), [community](), [evaluation](), [gcp](), [gemma](), [google](), [hugging-face](), [inference-endpoints](), [java](), [javascript](), [llm](), [llms](), [nlp](), [open-source](), [research](), [transformers]() ### AI overview Google's CodeGemma release introduces open-access 2B and 7B code-specialist LLMs, including base and instruction-tuned variants. The models support code infilling, completion, generation, code understanding, conversational use, and mathematical reasoning, with integrations across the Hugging Face ecosystem and Google Cloud. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## 5 security best practices for adopting generative AI code assistants like GitHub Copilot DevFeed: [5 security best practices for adopting generative AI code assistants like GitHub Copilot]() Original publisher: [Read original article]() Author: Liqian Lim (林利蒨) Published: 2024-03-05T12:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Generative AI](), [AI-assisted coding](), [GitHub Copilot](), [code security](), [Code](), [snyk]() Tags: [acquisition](), [ai](), [blog](), [code-completion](), [code-reviews](), [code-security](), [developer](), [errors](), [generative-ai](), [github-copilot](), [pmm](), [security-best-practices](), [security-tools](), [snyk](), [snyk-code](), [validation]() ### AI overview This article presents five security best practices for adopting generative AI code assistants such as GitHub Copilot. It emphasizes keeping humans in the loop, validating and reviewing AI-generated code, educating teams about risks, and using security tools and guardrails. ### Source excerpt Learn how you can safely adopt AI code completion tools (like Copilot) by applying these 5 best practices and see how Snyk can make it easy to stay secure. ## Essential AI Tools to Boost Developer Productivity and Security DevFeed: [Essential AI Tools to Boost Developer Productivity and Security]() Original publisher: [Read original article]() Author: Liqian Lim (林利蒨) Published: 2024-03-01T05:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [dev-tools](), [AI-assisted coding](), [Vulnerabilities](), [Language models]() Tags: [ai](), [ai-coding](), [article](), [awareness](), [code-completion](), [code-security](), [developer](), [developer-productivity](), [large-language-models-llms](), [megawatt](), [pmm](), [security](), [snyk-code](), [vulnerabilities]() ### AI overview An overview of AI developer tools for coding, planning, and security. It describes coding assistants, chatbots and LLMs, and security companions, emphasizing that code-generation tools should be paired with security tooling to help avoid vulnerabilities. ### Source excerpt Explore the top AI coding and security assistants like GitHub Copilot, Snyk Code, and more that are revolutionizing development - code faster, more efficiently, and securely. ## Kotlin 1.9.20: Streamlining Source Sets in Multiplatform Project - Jigar Brahmbhatt DevFeed: [Kotlin 1.9.20: Streamlining Source Sets in Multiplatform Project - Jigar Brahmbhatt]() Original publisher: [Read original article]() Published: 2024-02-01T00:00:00Z Content type: article Language: en Sources: [Touchlab | Enterprise Mobile Innovation & Development]() Topics: [Kotlin](), [Kotlin Multiplatform](), [multiplatform](), [gradle-plugin](), [code-completion](), [Code](), [Development]() Tags: [boilerplate](), [code-completion](), [development](), [gradle](), [gradle-plugin](), [kotlin](), [kotlin-multiplatform](), [multiplatform](), [new-features]() ### AI overview This article explains how Kotlin 1.9.20 improves Kotlin Multiplatform source set configuration. It covers the default source set hierarchy template, simplified iOS target setup, custom source sets, manual template application, and enhanced IDE code completion. ### Source excerpt Explore how Kotlin 1.9.20's new features significantly enhance the experience of setting up target source sets ## Why you should consider migrating your Gradle scripts to Kotlin DSL DevFeed: [Why you should consider migrating your Gradle scripts to Kotlin DSL]() Original publisher: [Read original article]() Published: 2023-05-10T00:00:00Z Content type: tutorial Language: en Sources: [Kt. Academy]() Topics: [Gradle](), [Kotlin](), [domain-specific language](), [Development]() Tags: [build](), [code-analysis](), [code-completion](), [dependencies](), [development-process](), [dsl](), [gradle](), [interoperability](), [kotlin](), [migrate](), [workshop-learning-programming]() ### AI overview This article explains Gradle Kotlin DSL, describing its benefits over Groovy-based Gradle scripts, including static typing, code completion, type-safe APIs, concise syntax, and improved maintainability. It also outlines steps for migrating build scripts to Kotlin DSL. ### Source excerpt Discover the benefits of migrating your Gradle scripts to Kotlin DSL and streamline your development process. ## Autocomplete for Behaviors in PhpStorm with Mixins & Veneers DevFeed: [Autocomplete for Behaviors in PhpStorm with Mixins & Veneers]() Original publisher: [Read original article]() Author: andrew@nystudio107.com (Andrew Welch) Published: 2022-12-20T15:42:00Z Content type: tutorial Language: en Sources: [nystudio107 | Articles on modern web development.]() Topics: [phpstorm](), [code-completion](), [Content Management System](), [Development]() Tags: [added](), [autocomplete](), [behaviors](), [cms](), [code](), [code-analysis](), [code-completion](), [completion](), [craft](), [insights](), [intellisense](), [learn](), [mixins](), [phpstorm](), [project](), [using](), [veneers]() ### AI overview A tutorial on restoring PhpStorm IntelliSense code completion for behaviors added to Craft CMS projects. It explains how to use mixins and veneer classes so PhpStorm can recognize dynamically added properties and methods through static code analysis. ### Source excerpt Learn how to get PhpStorm IntelliSense code completion for Behaviors added to your Craft CMS project using Mixins & Veneers ## Maestro: The Orchestration Language Powering Shopify Flow DevFeed: [Maestro: The Orchestration Language Powering Shopify Flow]() Original publisher: [Read original article]() Author: Thiago Tonelli Published: 2022-05-13T18:45:00Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Orchestration](), [Shopify](), [Automation](), [Data structures](), [Usability](), [code-completion]() Tags: [automation](), [code-completion](), [data](), [orchestration](), [scale](), [shopify](), [usability](), [use-cases]() ### AI overview Shopify Engineering describes Maestro, a domain-specific orchestration language created to support the Flow engine's scalability, fault tolerance, and execution requirements. The article explains how Shopify decoupled the visual Flow editor from its execution engine, introduced a horizontally scalable Maestro engine, and migrated existing workflows through a translation layer. ### Source excerpt An overview of Shopify Flow's new orchestration language Maestro, highlighting its design and implementation as well as how neatly it integrates with and addresses the requirements. ## Code Completion. Часть 4: обучение модели DevFeed: [Code Completion. Часть 4: обучение модели]() Original publisher: [Read original article]() Author: p0b0rchy (JetBrains) Published: 2021-10-11T09:02:36Z Content type: article Language: ru Sources: [JetBrains RU]() Topics: [code-completion](), [машинное обучение](), [ide]() Tags: [catboost](), [code-completion](), [ide](), [metric-learning](), [ml](), [tag-055aee430837](), [tag-baed6d151ab4]() ### AI overview This fourth article in a code-completion series describes problems encountered while training a machine-learning model. It explains anonymizing collected IDE data, investigating anomalous autocomplete usage through logs, and excluding users with alphabetical suggestion sorting from the training dataset and experiments. ### Source excerpt Хотим поделиться специфическими проблемами, возникшими у нас при обучении модели, и решениями, которые мы для этих проблем придумали. В прошлый раз мы остановились на том, что из-за необходимости защищать код пользователей даже от себя самих нам необходимо анонимизировать все собранные данные. Вызову окна автодополнения сопоставляется набор параметров, каждый из которых -- либо число, либо перечислимый тип. А вот сам код, на котором пользователь вызвал окно, мы не сохраняем. Если при сборе данных мы допустим ошибку, нельзя будет просто заглянуть в код и все понять. Нам придется устраивать целое расследование без всякой гарантии успеха. Например ## Code Completion. Часть 3: где взять данные для обучения? DevFeed: [Code Completion. Часть 3: где взять данные для обучения?]() Original publisher: [Read original article]() Author: p0b0rchy (JetBrains) Published: 2021-09-20T10:27:02Z Content type: article Language: ru Sources: [JetBrains RU]() Topics: [Code](), [code-completion](), [машинное обучение](), [jetbrains]() Tags: [code](), [code-completion](), [data-protection](), [dataset](), [ide](), [jetbrains](), [tag-055aee430837](), [tag-9bf5e01ce62e](), [tag-baed6d151ab4]() ### AI overview This article explains how JetBrains approaches building training data for machine-learning-based code completion. It describes why user code cannot be collected and why datasets derived only from completed open-source repositories do not adequately represent real development scenarios. ### Source excerpt В предыдущей статье цикла мы установили, что современной системе автодополнения нужно машинное обучение -- чтобы ранжировать варианты подсказок. Машине, как и человеку, для обучения нужны данные. Мы используем подход "обучение с учителем". Он предполагает, что мы показываем алгоритму примеры задач, для которых нам заранее известно правильное решение. Алгоритм извлекает из этих примеров закономерности и учится принимать правильные решения даже в тех ситуациях, которых он раньше не видел. Результат обучения критическим образом зависит от качества и объема этих примеров. И именно здесь у нас возникают трудности. С одной стороны, собирать базу на основе кода наших пользователей нельзя: результат вашего труда остается только у вас, на серверы JetBrains он не попадает. С другой стороны, из доступных репозиториев с открытым кодом полноценный датасет собрать не получается. Делая так, мы обучали бы алгоритм работать только с готовым (рабочим) кодом. Сегодня обсудим, как мы выкручиваемся из этой ситуации. Для начала давайте более подробно рассмотрим, почему не получается работать с готовыми репозиториями. А потому что они "готовые" [Next page]()