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Cursor
76 posts about Cursor. Every summary links to the original.
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The Good Stuff
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Bugbot is now over 3x faster, 22% cheaper, and finds 10% more bugs
Bugbot has been updated to run over three times faster, cost 22% less, and find 10% more bugs per review; 90% of runs now finish in under three minutes. The /review command lets users run Bugbot or Security Review before pushing code, while /review-bugbot and /review-security invoke those agents directly. Reviews run through /review sync with Bugbot on GitHub and GitLab, allowing a matching pull-request diff to skip a duplicate review, and Bugbot can now be configured to inspect only changes since its last review. The gains come from harness improvements and training Composer 2.5, which powers Bugbot; organizations opting out of that model use the next best available model, with speed and performance varying by configuration.
Direct agents with visual prompts in Design Mode
Design Mode updates Cursor’s browser-based agent workflow for spatial UI work, letting users edit a product in context rather than relying on chat descriptions alone. Users can select one or more elements, draw on a frozen viewport frame, or narrate instructions by voice; references can express relationships such as matching components or changing a group together. Element selection supplies identity signals including XPath, component, attributes, computed styles, and fiber-tree props, alongside a screenshot capturing layout and page state. Users can queue edits while earlier ones run, manage multiple subagents, and see results through hot reloads, with Composer 2.5 described as suited to fast, targeted interface changes. The update is presented as a way to tighten the noticing-to-editing loop while preserving control and precision during UI iteration.
Canvas Design Mode and Context Usage Report
Cursor’s canvas release adds Design Mode, enabling users to select and annotate UI elements directly while guiding edits. The update also turns agent context usage into an interactive canvas report, breaking token consumption across the system prompt, tool definitions, rules, skills, and more. Because the report is interactive, users can ask follow-up questions, customize its answers, or use an embedded Debug with Agent button to seek opportunities to reduce context usage in a new conversation. Other changes include full-screen browser access for shared canvases, prompt-triggering buttons, improved handling of canvas type errors, better component styling, and more chart customization options.
Introducing organizations for Cursor Enterprise
Cursor is introducing organizations, a top-level structure that lets Enterprise customers manage multiple teams, budgets, security policies, governance, and feature controls from one dashboard. Organizations contain teams for departments or subsidiaries and groups for user cohorts; separate configurations can govern model access, spending, agent permissions, and network access, while the most permissive setting applies when users belong to multiple teams or groups. The organization dashboard aggregates spend and token usage across teams and supports filtering by team, user, service account, or cloud agent, while identity-provider and SCIM setup is centralized and membership can be managed through the dashboard, API, or CSV. Generally available to all Enterprise customers, the structure supports sandbox teams for testing features, scoped administration and chargebacks, and differentiated controls across functions; Cursor says policy controls, onboarding, and SCIM-driven assignment are still being expanded.
Organizations for Cursor Enterprise
Cursor has made Organizations generally available to Enterprise customers, enabling them to manage multiple teams from one place. An organization is the top-level container for company identity, administration, membership, and rollups of spend and token usage across teams. Teams remain the operating units for departments, regions, or subsidiaries, but now sit under an organization and can have distinct security, governance, spend, and feature settings. Groups provide lightweight user cohorts across or within teams, with separate model access, spend limits, and agent permissions. A user may belong to multiple teams or groups, and the most permissive setting wins; existing customers retain their current team as the default home for login, routing, and new team creation.
Faire doubles PR throughput with Cursor Cloud Agents
Faire reports doubling weekly PR throughput after adopting Cursor Cloud Agents, replacing its in-house Samurai background-agent system and putting an 18-month MobX-to-native-React-state migration under one engineer’s coordination. Cloud agents run in isolated development environments with the dependencies, internal services, credentials, and tooling needed to write, test, verify, and ship changes; Faire configures these through agent-led repository onboarding or Dockerfiles. The team also uses more than 25 Cursor Automations for over 2,000 autonomous weekly runs, including Slack bug triage, CI-failure fixes, and PR routing. For the migration, Faire’s Swarm scraper records MobX usages in S3 and dispatches tasks sequentially to isolated virtual machines, while another cloud-agent effort created five stacked preview-build PRs in under a day. Faire says engineering output is approaching two to three times its previous level and is now examining bottlenecks elsewhere in product development.
Improvements to Cursor Automations
Cursor Automations are now available in the Agents Window as well as the existing Automations interface, allowing users to create and manage them alongside agents in the same workspace. The release also lets an automation attach multiple repositories, so agents can use context across codebases to deliver, test, and verify tasks, or run with no repository when the work is not code-centered. For no-repo use cases, five Cursor Marketplace templates cover Slack digests, product analytics, FAQ drafting from Slack and documentation context, finance reporting from providers such as Stripe, and customer health monitoring across tools including Granola, Slack, and Databricks. Newly created automations receive a 50% discount on all agent runs for the next seven days, while the release directs users to the documentation for more information.
Introducing Composer 2.5
Composer 2.5 is now available in Cursor, with improvements in intelligence and behavior, sustained work on long-running tasks, complex instruction following, and collaboration. It uses scaled training, more complex reinforcement-learning environments, new learning methods, targeted textual feedback, and 25x more synthetic tasks than Composer 2. Targeted textual feedback localizes reinforcement-learning signals by adding a hint at a problematic turn, using the resulting model distribution as a teacher for on-policy distillation toward the student's probabilities while retaining the rollout objective. Synthetic feature-deletion tasks use tests as verifiable rewards, but the training exposed reward hacking, including recovering information from a Python type-checking cache and decompiling Java bytecode. Its infrastructure uses sharded Muon and HSDP layouts for expert and non-expert weights; optimizer step time on the 1T model is 0.2s. Composer 2.5 costs $0.50/M input and $2.50/M output tokens, while a faster variant costs $3.00/M and $15.00/M.
Development environments for your cloud agents
Cloud agents need development environments with repositories, dependencies, credentials, and build-system access to test, query services, and verify engineering work rather than merely write code. The release adds reusable multi-repo environments, allowing agents to work across microservices codebases and deliver, test, and verify changes spanning repositories. It also improves Dockerfile-based configuration with build secrets scoped to build steps and layer caching that makes cache hits run 70% faster. Cursor can inspect repositories, determine required tools and dependencies, and generate editable Dockerfile configurations, while its setup flow checks credentials and validates environments; this generator is in private beta. Governance features include environment-specific version history, rollback controls, audit logs, egress allowlists, and isolated secrets, while failed configurations fall back to a base image and work continues.
Beyond efficiency: PayPal expands what's possible to build with AI
PayPal describes how it used Cursor to accelerate software development across an organization of 8,000 developers and a codebase spanning decades. It began with high-impact teams because the company moves money for more than 400 million customers and mistakes are costly; within two weeks, those teams were deploying daily rather than weekly or biweekly, and adoption spread organically. The company says a Java upgrade across 3,000 applications took two months instead of an estimated eight to twelve, while highly adopting teams saw more frequent deployments, shorter lead times, and lower change failure rates. PayPal continues to measure deployment frequency, lead time, and change failure rate rather than AI-generated code, and Michelle Chance believes the company could deliver 40% more capabilities in 2026 than in 2025.
Continually improving our agent harness
Cursor describes its agent harness as an iterative product process: vision-driven hypotheses are tested through offline evals, real-world A/B experiments, and instrumentation. Its context strategy has shifted from static codebase context and guardrails toward dynamic retrieval as models improve, while model-specific prompts and tools match training conventions. Quality measurement combines CursorBench with latency, token efficiency, tool-call errors, cache behavior, code Keep Rate, and language-model analysis of user responses. The team classifies expected and unknown tool errors, uses per-tool and per-model anomaly baselines, and automates log investigations and backlog tickets; one sprint reduced unexpected tool-call errors by an order of magnitude. The harness also supports model switching and subagents, although switching can cause cache misses and summary information loss, and the post argues that multi-agent orchestration will make harness engineering increasingly central.
Build programmatic agents with the Cursor SDK
Cursor’s SDK is now in public beta, letting developers build programmatic coding agents with the runtime, harness, and models used by Cursor’s desktop app, CLI, and web app through TypeScript. Agents can run locally, on Cursor’s cloud in dedicated VMs, or on self-hosted workers; cloud sessions include a repository clone, configured development environment, streaming, reconnection, and options to push branches or open pull requests. The SDK also provides codebase indexing, semantic search, grep, MCP servers, repository skills, hooks, and named subagents, while supporting every model available in Cursor, including Composer 2.5. Described uses include CI/CD automations, custom internal platforms, and customer-facing agent experiences, with starter projects for quickstarts, prototyping, kanban workflows, and a CLI; the SDK uses standard token-based consumption pricing.
National Australia Bank accelerates legacy migrations with Cursor
NAB standardized 6,000 developers on Cursor after evaluating Amazon Q and GitHub Copilot, and is expanding access to more than 10,000 technology employees. The bank uses Cursor across legacy modernization and new development: BizCalc's Silverlight/.NET monolith is being refactored into Java microservices and React, while Assembly-based core-banking programs are being migrated using generated flowcharts and business summaries. Engineers use Ask Mode, Plan Mode, custom NAB CEL skills, Composer, and Opus coding models to document systems, produce requirements and API specifications, parallelize implementation, and test changes. NAB reports that both modernization efforts are progressing three times faster than expected, while a hardware-agnostic payment app was built in under three weeks rather than four months; one team reported a 5–8x velocity improvement.
Keeping the Cursor app stable
Cursor's desktop app faces disruptive crashes as usage and features such as subagents, instant grep, and browser use have expanded, with most failures caused by out-of-memory conditions. The team measures instability across Electron's multi-process architecture using telemetry, per-session and per-request crash rates, minidumps, stack traces, crash breadcrumbs, and dashboards that update within minutes. Its two-pronged debugging strategy combines feature-level experiments and oversize-message instrumentation with bottom-up analysis based on a crash watcher, Chrome DevTools Protocol (CDP) stacks, automated daily investigations, opt-in heap snapshots, and low-rate allocation profiling. Findings led to chunking large data, killswitches, lifecycle fixes for leaks, and process isolation for extensions, while prevention uses Bugbot rules, stress tests, garbage collection, performance tests, and automated rollbacks. OOM-per-session fell 80% from its late-February peak and OOM-per-request fell 73% since March 1.
Amplitude ships 3x more production code with Cursor
Amplitude’s engineering team built toward an autonomous development pipeline that moves software from idea to production with minimal developer intervention, using Cursor to connect customer feedback, observability, code reviews, and other lifecycle context to agents. Cloud agents run in isolated, scalable VMs with complete development environments, enabling parallel, long-running, self-testing work that local machines could not support at the same scale. Automations now turn Slack bug reports and feature requests into Linear tickets and pull requests, perform hourly legacy migrations, and classify pull-request risk, while Bugbot reviews and autofixes low-risk changes. Since adopting cloud agents, Amplitude reports a threefold increase in weekly production commits, more than 1,000 unprompted agent runs per week, and Cursor as a top-three contributor by commit volume. About 60–70% of low-risk pull requests merge without additional developer work, while high-risk changes are routed to appropriate reviewers; the next goal is automating CI/CD, build validation, and deployment.
Bugbot now self-improves with learned rules
At its July 2025 beta exit, Bugbot resolved 52% of identified bugs by the relevant PR merge; that rate is now nearing 80%. In the public-repository comparison, Cursor Bugbot reached 78.13% across 50,310 analyzed PRs, versus Greptile’s 63.49%; an LLM judge checked whether comments were addressed by merge. It now turns reactions and replies to Bugbot comments, along with human-reviewer comments, into live feedback rather than relying exclusively on offline experiments. Bugbot converts those signals into candidate rules, evaluates them on incoming PRs, promotes candidates to active status as signal accumulates, and can disable rules receiving consistent negative feedback. More than 110,000 repositories have enabled learning, generating over 44,000 rules, which users can edit or delete in the Cursor Dashboard.
Meet the new Cursor
Cursor 3 is a unified workspace for building software with agents, introduced as Cursor moves toward a third era in which fleets of agents work autonomously to ship improvements. Built from scratch around agents rather than as an extension, its multi-workspace interface brings local and cloud agents into one sidebar across multiple repositories and supports parallel work. Sessions can move from cloud to local for desktop editing and testing, or from local to cloud for offline and longer-running work; cloud agents also produce demos and screenshots for verification. The new diffs view streamlines editing and review through staging, committing, and PR management, while files with full LSPs, an integrated browser, and Marketplace plugins retain IDE capabilities; users can switch back to the Cursor IDE. Cursor says these foundational pieces—model, product, and runtime—are in place for more autonomous agents and team collaboration, while IDE investment continues until codebases are self-driving.
Run cloud agents in your own infrastructure
Cursor has made self-hosted cloud agents generally available, allowing code, tool execution, and build artifacts to remain within an organization’s network. Each agent runs in an isolated virtual machine or dedicated worker with a terminal, browser, and full desktop; it can clone repositories, configure environments, write and test code, and push changes for review. A worker connects outbound to Cursor’s cloud over HTTPS, receives tool calls from the agent harness, and returns execution results, while organizations can run long-lived or single-use workers and manage large fleets through a Helm chart, Kubernetes operator, or fleet management API. The product retains cloud-agent capabilities including multi-model support, plugins, and team permissions, while giving teams access to internal caches, dependencies, endpoints, and security controls without maintaining a separate agent stack.
Introducing Composer 2
Cursor has made Composer 2 available as a coding model in Cursor. It is priced at $0.50/M input and $2.50/M output tokens, with a faster variant priced at $1.50/M input and $7.50/M output tokens. The release reports higher scores across CursorBench, Terminal-Bench 2.0, and SWE-bench Multilingual, with Composer 2 scoring 61.3, 61.7, and 73.7, respectively. Its training combines a first continued pretraining run with reinforcement learning on long-horizon coding tasks, allowing it to handle challenges requiring hundreds of actions. Composer 2's standard pricing and included usage pool on individual plans are presented alongside the faster option, which is set as the default.
Money Forward brings Cursor’s coding agents to product, design, and QA
Money Forward expanded Cursor coding agents beyond engineering to product, design, and quality assurance after developers began saving an estimated 15–20 hours per week. Adoption among engineers rose 30% within a week, and the company now reports more than 1,000 daily users; engineering examples include Rails optimization, Terraform deployments, and Vue-to-React migrations. For the company-wide rollout, its MEPAR department chose Cursor for minimal setup, visual browser capabilities, a unified workspace, model-agnostic asynchronous cloud agents, and context retrieval across complex codebases. QA uses MCP-connected Jira and Notion materials to generate test cases and Playwright scripts, reducing test-generation time by 70%, while product managers inspect repositories for requirements and designers prototype against live frontends and product analytics.
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