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Developer Experience
69 posts about Developer Experience. Every summary links to the original.
Compose Multiplatform 1.12.0 Released
JetBrains has announced the release of Compose Multiplatform 1.12.0 with targeted capabilities for artificial intelligence integrations, web rendering, and desktop application layouts. The update incorporates an experimental Model Context Protocol server inside Compose Hot Reload, allowing AI agents to trigger reloads, capture screenshots, inspect the semantic tree, and simulate user interactions directly. For web deployments, the framework now provides automatic font fallback by downloading necessary Noto font subsets on demand when unresolved characters occur during rendering. Desktop developers receive an experimental v2 window and dialog application programming interface in the androidx.compose.ui.window.v2 package, which distinguishes requested states from actual window states and permits precise control over positioning, sizing constraints, and multi-screen placement. These combined enhancements improve developer workflows and runtime flexibility across multiple target platforms.
Elvira MustafinaOpenTelemetry Comes to IntelliJ IDEA, GoLand, PyCharm, and WebStorm
With the 2026.2 release, JetBrains expanded its OpenTelemetry plugin from Rider to IntelliJ IDEA, GoLand, PyCharm, and WebStorm. The tool captures logs, metrics, traces, and service maps locally without requiring an external observability backend. Developers can search structured log records, plot metric values over time, inspect distributed spans, and verify communication paths across databases and message queues during local runs. To ingest telemetry, the plugin automatically configures OpenTelemetry Protocol environment variables for supported run configurations and terminal sessions, or accepts forwarded data from existing collectors. Additionally, experimental Model Context Protocol support allows AI coding agents to query gathered logs, spans, and service topology.
Egor KlimovIdeas Worth a Longer Conversation: The JetBrains Research Podcast
The JetBrains Research Podcast investigates foundational software development and computer science questions through discussions with industry researchers. In team psychology, Cat Hicks demonstrates that collaborative culture and organizational belonging reduce developer anxiety around automated tooling while curbing the risks of overproduction pressure. Historical analysis by Tomáš Petříček shows that programming spans five competing cultures rather than pure specification implementation, explaining why automated code generation historically encounters limits. Regarding artificial intelligence evaluation, Ibragim Badertdinov details how SWE-rebench isolates model capability through dataset decontamination, leveraging the strict verifiability of software test execution for training feedback. Additionally, conversations with Alexander Kulikov and Anna Kogan examine how foundational mathematical understanding aids AI oversight and address adoption hurdles for open-source computer vision infrastructure.
Katie FraserGithub ·
Your alt text passes automated checks. That doesn’t mean it’s any good.
Automated accessibility checkers reliably flag missing alt text attributes but frequently miss unhelpful descriptions like raw filenames or repetitive labels. To evaluate image description quality without generating excessive false positives, GitHub built an alt text plugin for the GitHub Accessibility Scanner. The tool combines five deterministic, zero-credential rules that evaluate strings and visual layout spacing with an opt-in vision model check for subjective context. Page context including headings and surrounding prose is extracted alongside images to guide the model using structured outputs and explicit anti-nitpick instructions. While deterministic checks catch unwritten text, the model-driven rule serves as an opt-in prompt for human review rather than an absolute verdict.
Taarik AshenafiHelp AI Coding Agents Write Up-To-Date Code With Modern Golang Skills
AI coding agents often generate outdated Go patterns because older syntax dominates training data and new features fall past model cutoffs. To address this gap, the GoLand team released Modern Go Guidelines, an open-source set of skills and CLI tools covering Go 1.0 through Go 1.27. The tool inspects a project's go.mod file or a specific version flag to supply only the language features and standard library additions compatible with that environment. Using a progressive disclosure approach, the CLI provides concise rule identifiers via a list command and expands into before-and-after examples through an explain command. This mechanism minimizes token consumption while helping agents produce up-to-date, compilable code without modifying project files.
Artem PronichevHow We Optimized the Qwen 3.6 Model for Our Junie Agent
Deploying the Junie coding agent locally on Apple MacBook M5 hardware requires overcoming steep performance bottlenecks during model execution. Engineers optimized the entire stack using Qwen3.6-27B rather than Qwen3.8-27B, which severely degrades without token-heavy reasoning enabled. At the agent layer, Junie appends requests directly into a rolling context and caches initial prompt prefixes to maximize KV-cache reuse across tasks. For model inference, the team disabled reasoning, selected 4-bit quantization, and patched MLX-VLM to execute self-attention prefill matrix operations using faster 8-bit instructions on the M5 processor. Combining these prefill improvements with simultaneous multi-token prediction and n-gram speculative decoding delivered up to a 2x generation speedup.
Stanislav ErokhinJunie Can Now Run Entirely on Your Mac – No Credits, No Cloud
JetBrains has introduced Junie Local, a feature that runs the Junie coding agent entirely on local hardware without cloud dependencies, subscriptions, or credit meters. Users execute a single command inside Junie to download a 4-bit quantized Qwen3.6-27B model, requiring an Apple M5 Mac with 64 GB of RAM. The team prioritized prefill throughput over pure generation speed by utilizing 8-bit arithmetic instructions on the M5 Neural Accelerator, which increased prefill throughput by approximately 40%. Performance optimizations also include KV-cache reuse, speculative decoding to double generation speed, and disabling reasoning to preserve performance. In JetBrains' evaluations, the local configuration performed comparably to cloud-based Sonnet 4.5 on everyday tasks while ensuring source code, diffs, and prompts never leave the local environment.
Dmitry SavelevWhy AI-Generated Code Is Easy but Engineering Trust Is Hard
At Salesforce, engineering teams discovered that AI coding agents could produce internally consistent code and passing test suites that still failed to meet actual requirements. To ensure correctness before writing code, the team developed an agentic Spec-Driven Development workflow centered on explicit specifications and gated verification. The system separates evidence-based repository lookups handled by agents from subjective judgment calls escalated to human engineers. Implementation plans must cite repository evidence reviewed by a Skeptic Agent, while a Compliance Matrix traces each success criterion directly to executable test proof. Finally, a multi-agent review system evaluates the implementation under an asymmetric rule allowing an independent judge to downgrade passing checks but never override failed gates.
Scott NybergFrom all-or-nothing to task-based OAuth consent
Cloudflare introduced OAuth scope customization to replace all-or-nothing consent flows for third-party applications. Previously, users had to approve every requested scope or deny access completely, compelling developers of tools like MCP servers to construct custom pre-consent selection interfaces. With the new feature, developers can designate specific permissions as optional alongside required scopes when configuring an OAuth client. During authorization, users can deselect these optional scopes, while unrequested client scopes remain hidden to maintain task-specific consent screens. Because resulting access tokens contain only the granted subset of permissions, client applications must inspect returned scopes after code exchange rather than assuming full authorization.
Miller VargasPyCharm for AI-assisted Django Workflows
According to the 2026 Django Developers Survey, ninety percent of respondents incorporate artificial intelligence into their weekly or daily workflows. While agents generate code rapidly, developers remain accountable for understanding, evaluating, and shipping changes within their applications. PyCharm addresses these demands by integrating support for external agents, local models via Ollama and LM Studio, and customizable agent skills across codebases. The IDE maintains version-specific framework assistance, including auto-completion for Django 6.0 template partials, alongside architectural inspection tools and visual diffs. Furthermore, developers can trace application components, inspect API endpoints, execute HTTP requests, and query database migrations directly through built-in tooling.
Will VincentReady for Go 1.27 on Day One
GoLand 2026.2 introduces day-one support for Go 1.27 language features, tooling improvements, and profiling capabilities. The Go 1.27 release adds generic methods, promoted field names in struct composite literals, improved function type inference, and a dedicated profile for detecting goroutine leaks. To assist with code maintenance, GoLand integrates official go fix modernizers directly into editor inspections, the Problems tool window, and optional pre-commit checks. Developers can capture, visualize, and analyze goroutine leak profiles alongside existing CPU, memory, and mutex profiles within the IDE. Additionally, updated Modern Go Code Guidelines provide AI coding agents with Go 1.27 context and API changes aligned with the version specified in go.mod.
Artem PronichevSecure all your internal vibe-coded applications — in one click
AI-assisted development enables employees to build applications rapidly, but unmanaged deployments can accidentally expose internal company data to the public Internet. Cloudflare introduced direct Cloudflare Access integration for Cloudflare Workers, allowing organizations to enforce authentication policies directly at the account or individual Worker level rather than per hostname. When enabled, incoming requests are authenticated before reaching application code across custom domains, routes, workers.dev subdomains, and preview environments. Developers can access authenticated identity data such as emails and groups directly via the Worker context object without manually validating JSON Web Tokens. This capability was implemented on Cloudflare's Rust-based FL2 proxy, which separated Worker routing from Worker execution so routing runs safely prior to Access policy enforcement.
Chythra MalapatiAirbnb ·
Flexible Authentication: Reimagining authentication for millions of users at Airbnb
Airbnb overhauled its login and registration architecture by transitioning to a server-driven framework called Flexible Authentication. The new system separates user identification from verification, using a backend policy engine to dynamically serve the optimal auth challenge and fallback options based on regional and user context. This shift eliminated dead ends, reduced client bundle sizes, and dramatically sped up experimentation velocity across Web, iOS, and Android.
Jose SantosHow Standardizing Product Telemetry Reduced Time to Insight by 97%
Salesforce faced scaling bottlenecks when individual engineering teams maintained fragmented, custom telemetry pipelines requiring manual data transformations and dashboard creation every release. To resolve these silos, the engineering organization established the Product Data Platform (PDP) using a standardized custom schema built atop Monitoring Cloud infrastructure. This framework mandates core telemetry fields for consistent metric generation while offering governed optional attributes for product-specific flexibility. An AI-driven Model Context Protocol tool assists developers by recommending compliant instrumentation patterns directly within their workflows. Today, the unified platform processes 45 billion rows of data daily across 19,000 distinct events and 2,000 product features, reducing dashboard refresh latency by 97 percent from one month to daily updates.
Scott NybergGithub ·
Using the GitHub Copilot SDK for Java
The GitHub Copilot SDK for Java offers a framework-agnostic client library to orchestrate AI agent sessions and tool execution directly from server-side Java code. Unlike framework-dependent alternatives, the SDK supports direct model providers such as OpenAI, Azure, and Anthropic through custom endpoint configurations without requiring a Copilot subscription. Developers can register tools declaratively using the experimental @CopilotTool annotation processor or dynamically through inline lambda definitions with ToolDefinition.from. When integrated into a Jakarta EE 11 application on Open Liberty, agent workflows run on container-managed virtual threads that propagate CDI and transaction contexts during blocking calls like sendAndWait. Real-time event subscriptions capture model execution steps and tool invocations to stream updates over WebSockets without exhausting platform threads.
Edward BurnsEverything we launched during Agents Week
Agents Week introduced a series of products and primitives across Cloudflare to support an Agentic Internet where autonomous software and humans interact. Developing capable agents requires addressing technical challenges spanning identity, communication, orchestration, memory, observability, and security. Cloudflare introduced the Agent Development Lifecycle alongside tools like Cloudflare Agents for live tracing, replay, and human-in-the-loop approvals. The releases also included programmable CI/CD pipelines capable of repairing failures, Cloudflare Wallets for agent transactions, and Kitesurf, an agent-first browser running in V8 isolates. New protocols and tools like WebMCP, MCPv2, and Cloudflare AI Search aim to make web data and applications readable, discoverable, and searchable by autonomous software.
Shelley JonesAnnouncing Cloudflare Ambassadors, Community Engineers, and another $1M in open-source funding
Cloudflare introduced an updated community initiative featuring two tracks: Cloudflare Ambassadors and Cloudflare Community Engineers. The Ambassador program selects community leaders for terms lasting up to two years to organize events, lead student groups, and create learning spaces with company-provided credits, resources, and marketing assets. The Community Engineer track offers grants from a new one million dollar open-source fund spanning the next two years to support maintainers of projects such as Astro, Agents SDK, EmDash, Hono, and Vinext. To maintain its Discord server of nearly 100,000 members, a new Discord committee composed of staff and Ambassadors will guide community discussions while automated spam protections handle routine moderation.
Kristian FreemanCloudflare AI Search: give your agents a search engine for your data
Cloudflare introduced developer experience improvements and a preview pricing model for Cloudflare AI Search. Previously, users had to manually connect separate Cloudflare primitives like Workers AI, AI Gateway, Vectorize, R2, and Browser Run to build a search solution. The platform now automates crawling, ingestion, embedding, and retrieval across structured and unstructured data sources. Developers can index sites without sitemaps using link discovery, expose public /search and /mcp endpoints across namespaces, bind search instances directly to Workers, or secure endpoints with Cloudflare Access. Cloudflare uses this system for developer documentation, its corporate blog, and the Cloudflare Dev Stack MCP server to supply coding agents with cited documentation.
Nelson DuarteGithub ·
Turn one giant AI-generated pull request to a reviewable stack
Coding agents often consolidate full-stack features into monolithic pull requests spanning thousands of lines, resulting in delayed reviews and lower feedback quality. To resolve this bottleneck, engineers can decompose comprehensive features into ordered, single-concern layers using GitHub stacked pull requests and the gh-stack CLI extension. Distinct specialized agents handle individual branches—ranging from foundational data access modules up to client interfaces—while CI checks continuously evaluate each layer against the stack base. When reviewers request upstream modifications, running gh stack sync executes a cascading rebase across all dependent branches, preserving commit signatures and keeping the entire pull request chain mergeable.
Julia MuiruriHow Salesforce Built an Agentic Engineering Enablement Strategy for Thousands of Software Engineers
Salesforce addressed the challenge of scaling agentic engineering across thousands of software developers by focusing on organizational learning rather than tooling. While individual engineers initially developed disparate, uncoordinated workflows, the Technology, People, Innovation, and Learning (TPIL) team unified these approaches into the Proficiency Level (PL) Framework. The framework maps developer growth across four mindset-driven stages: AI-Assisted, AI-Validating, AI-Orchestrating, and AI-Native. To facilitate progress across these stages, TPIL introduced global AI camps, weekly working sessions, and manager coaching guides. Rather than tracking course completions or telemetry scores, Salesforce measured transformation through behavioral indicators, such as shifts toward validating agent output, designing multi-agent workflows, and asking higher-level questions regarding human judgment.
Scott Nyberg