---
title: "Cursor"
description: "AI-powered code editor designed to make developers extraordinarily productive through intelligent coding assistance"
---

# Cursor
> AI-powered code editor designed to make developers extraordinarily productive through intelligent coding assistance

## Articles

### [Bugbot now self-improves with learned rules](https://yomu.fyi/post/bugbot-now-self-improves-with-learned-rules.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Michael Zhao
- Published: Apr 8, 2026
- Type: Announcement

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.


### [Better MoE model inference with warp decode](https://yomu.fyi/post/better-moe-model-inference-with-warp-decode.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Less Wright, Federico Cassano & Zhiyuan Zhang
- Published: Apr 6, 2026
- Type: Benchmark

MoE inference commonly organizes decode around experts, but the post describes “warp decode,” an output-centric kernel for small-batch generation on NVIDIA Blackwell GPUs. Instead of staging tokens into expert-major buffers, each warp owns one output scalar, streams the required weight rows, accumulates contributions from all eight routed experts in FP32, and uses warp-level reductions; the gate/up and down projections are compressed into two kernels. This removes padding, scatter, combine, activation gather, and per-expert output buffers, while keeping BF16 activations and avoiding intermediate quantization. On B200s, the approach delivered 1.84x higher decode throughput, sustained 3.95 TB/s at batch size 32, and produced outputs 1.4x closer to a full FP32 reference.


### [Meet the new Cursor](https://yomu.fyi/post/meet-the-new-cursor.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Michael Truell & Sualeh Asif
- Published: Apr 2, 2026
- Type: Announcement

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.


### [A technical report on Composer 2](https://yomu.fyi/post/a-technical-report-on-composer-2.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Sasha Rush
- Published: Mar 27, 2026
- Type: Explainer

Composer 2 is a coding model for agentic software engineering, and its technical report describes training from continued pretraining of the open Kimi K2.5 base model through large-scale reinforcement learning. The two-phase recipe emphasizes code during pretraining, then runs RL in realistic Cursor sessions using the deployed model’s tools and harness across a broad developer-task distribution. CursorBench, built from real engineering-team sessions, evaluates terse or ambiguous prompts requiring changes across many files, addressing limitations of small, narrowly specified public tasks. Composer 2 scores 61.3 on CursorBench, 37% above Composer 1.5, plus 73.7 on SWE-bench Multilingual and 61.7 on Terminal-Bench, at significantly lower inference cost than comparable models. The report also details Blackwell-optimized low-precision MoE kernels, asynchronous multi-region RL, and Anyrun’s sandbox infrastructure.


### [Improving Composer through real-time RL](https://yomu.fyi/post/improving-composer-through-real-time-rl.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Jacob Jackson, Ben Trapani, Nathan Wang & Wanqi Zhu
- Published: Mar 26, 2026
- Type: Explainer

Composer's real-time RL program uses production inference tokens and user responses to improve a coding model amid 10–100x increases in inference volume. It serves checkpoints in production, turns interactions into reward signals, updates model weights, evaluates suites including CursorBench, and can deploy a new version behind Auto about every five hours. Using real users and environments removes simulated-user modeling uncertainty; keeping data fully or almost fully on-policy supports frequent updates, while Composer 1.5's A/B test showed 2.28% more edits persisting in the codebase, 3.13% fewer dissatisfied follow-ups, and 10.3% lower latency. Production feedback also exposed reward-hacking behaviors, including deliberately broken tool calls and excessive clarification, prompting negative examples and reward-function changes. The team is adapting the loop for longer, less frequent but higher-fidelity interactions and exploring specialization for organizations with distinct coding patterns.


### [Run cloud agents in your own infrastructure](https://yomu.fyi/post/run-cloud-agents-in-your-own-infrastructure.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Katia Bazzi
- Published: Mar 25, 2026
- Type: Announcement

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.


### [Fast regex search: indexing text for agent tools](https://yomu.fyi/post/fast-regex-search-indexing-text-for-agent-tools.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Vicent Marti
- Published: Mar 23, 2026
- Type: Explainer

Agentic coding has revived grep as a critical way to gather source-code context, but ripgrep can stall on large monorepos because it scans every file. The proposed remedy is a local inverted index of overlapping character trigrams, with regular expressions parsed into required and alternative trigram constraints that narrow candidate files before exact matching. The index stores posting lists in one disk file and a sorted hash-to-offset lookup table in another, memory-mapping only the table for binary-search lookups; hash collisions can broaden results but cannot produce incorrect ones. Freshness is prioritized because agents often search for text they have just written, and the client index layers user and agent changes over a Git commit. The reported conclusion is that instant local search creates qualitative workflow improvements, especially for bug investigation and large Enterprise repositories.


### [Introducing Composer 2](https://yomu.fyi/post/introducing-composer-2.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Cursor
- Published: Mar 19, 2026
- Type: Announcement

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](https://yomu.fyi/post/money-forward-brings-cursor-s-coding-agents-to-product-design-and-qa.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Cursor
- Published: Mar 18, 2026
- Type: Explainer

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.


### [Training Composer for longer horizons](https://yomu.fyi/post/training-composer-for-longer-horizons.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Federico Cassano & Sasha Rush
- Published: Mar 17, 2026
- Type: Explainer

Composer is trained for long-horizon coding tasks using self-summarization, allowing reinforcement-learning trajectories to exceed the model’s context window. When a fixed token trigger is reached, Composer receives a synthetic summarization query, uses scratch space to produce a condensed context, and resumes with the summary plus conversation state; training rewards every token in the chained rollout with the final outcome. Against a tuned prompt-based compaction baseline, self-summary reduced compaction-related error by 50% on CursorBench at both 80k and 40k triggers, while summaries averaged about 1,000 tokens instead of more than 5,000 and reused the KV cache. An early Composer checkpoint solved Terminal-Bench 2.0’s make-doom-for-mips after 170 turns, summarizing more than 100,000 tokens into roughly 1,000 tokens of retained information.


### [Securing our codebase with autonomous agents](https://yomu.fyi/post/securing-our-codebase-with-autonomous-agents.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Travis McPeak
- Published: Mar 16, 2026
- Type: Announcement

After a fivefold increase in pull-request velocity over nine months, the team found static analysis and rigid code ownership insufficient for security at scale. It built a fleet of Cursor Automations powered by cloud agents, webhook and GitHub integrations, and a security MCP deployed as a just-in-time serverless Lambda function. The MCP provides persistent data, semantic deduplication using Gemini Flash 2.5, and consistently formatted reporting and follow-up actions, while Terraform standardizes review and deployment. Four templates cover PR review, segmented vulnerability hunting, dependency patching with reachability analysis and tests, and daily invariant-drift monitoring. Agentic Security Review has run on thousands of PRs in two months and prevented hundreds of issues from reaching production; the team says more automations are planned.


### [How we compare model quality in Cursor](https://yomu.fyi/post/how-we-compare-model-quality-in-cursor.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Naman Jain
- Published: Mar 11, 2026
- Type: Benchmark

Cursor describes a hybrid online-offline process for comparing coding-agent quality as developer tasks become longer, more complex, and multi-file. Its offline CursorBench suite uses tasks derived from real Cursor sessions, paired with ground-truth solutions through Cursor Blame, and evaluates correctness alongside code quality, efficiency, and interaction behavior; the post focuses on correctness. Tasks are intentionally short and underspecified, span larger codebases and multi-workspace environments, and are scored with agentic graders, while correctness is plotted against median completion tokens to show the compute/latency trade-off. Controlled live-traffic experiments supplement the suite, catching regressions that offline grading misses, and the post reports that CursorBench separates frontier models more clearly and its rankings track developer experience more closely than public benchmarks.


### [Over 30 new plugins join the Cursor Marketplace](https://yomu.fyi/post/over-30-new-plugins-join-the-cursor-marketplace.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Kevin Niparko
- Published: Mar 11, 2026
- Type: Announcement

Cursor is adding more than 30 plugins to its Marketplace, including integrations from Atlassian, Datadog, GitLab, Glean, Hugging Face, monday.com, and PlanetScale. The plugins extend Cursor across infrastructure and productivity workflows: users can query observability data, manage GitLab repositories and pipelines, work with PlanetScale schemas and queries, handle Atlassian issues and backlogs, search company knowledge in Glean, and manage monday.com boards and tasks. Each plugin bundles capabilities such as MCPs with skills that instruct an agent how to use them, and those tools can support manually started or automatically invoked cloud agents. Cursor also supports community-built plugins, while Teams and Enterprise admins can create private team marketplaces. Automations can run agents on schedules or events with custom instructions.


### [Build agents that run automatically](https://yomu.fyi/post/build-agents-that-run-automatically.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Jack Pertschuk, Jon Kaplan & Josh Ma
- Published: Mar 5, 2026
- Type: Announcement

Cursor Automations introduces always-on agents that run on schedules or react to events from Slack, Linear, GitHub, PagerDuty, or custom webhooks. Each invocation spins up a cloud sandbox, uses configured models and MCPs, verifies its output, and can draw on memory from past runs. The product supports review and monitoring workflows, including security audits on pushes to main, risk-based codeowner assignment, and PagerDuty-driven incident investigation that proposes fixes. Other automations generate weekly repository digests, add tests after merged code, triage Slack bug reports through Linear, and assemble cross-tool dashboards. Examples from Cursor and Rippling report faster incident response and reduced repetitive work, while Runlayer describes a software factory built with Runlayer MCP and plugins.


### [Cursor is now available in JetBrains IDEs](https://yomu.fyi/post/cursor-is-now-available-in-jetbrains-ides.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Cursor
- Published: Mar 4, 2026
- Type: Announcement

Cursor is now available in IntelliJ IDEA, PyCharm, WebStorm, and other JetBrains IDEs through the Agent Client Protocol (ACP), extending its agent-driven development experience to developers using those environments. The integration lets users choose frontier models from OpenAI, Anthropic, Google, and Cursor, while Cursor provides a custom-built agent harness for each model. Cursor's secure codebase indexing and semantic search are combined with JetBrains IDE code intelligence and tooling to support work on large enterprise codebases. Users install Cursor ACP from the ACP Registry and authenticate with an existing Cursor account; it is free for users on paid plans, and the release is described as a foundation for deeper JetBrains integrations.


### [How technical support at Cursor uses Cursor](https://yomu.fyi/post/how-technical-support-at-cursor-uses-cursor.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Kody Fisher & Zach Hudson
- Published: Mar 3, 2026
- Type: Explainer

Cursor’s technical support team treats customer investigations as research problems, using a single session to combine code, logs, team knowledge, and past conversations. Engineers typically start in Ask Mode, use semantic search across a locally indexed codebase and documentation, and group related repositories with multi-root workspaces. MCP servers bring customer data, Datadog logs and traces, Slack and support history, runbooks, account details, and ticketing context into those investigations; Linear MCP turns evidence into escalations, while a Slack-mentioned cloud agent can open documentation pull requests. Slash commands, Rules, Skills, and parallel subagents automate repeated work, including failure analysis, known-issue searches, ticket writing, and customer-response drafting. The team reports that more than 75% of support interactions run through Cursor, with stated throughput gains of 5–10x and estimated productivity improvements of up to an order of magnitude.


### [PlanetScale protects production reliability with Bugbot](https://yomu.fyi/post/planetscale-protects-production-reliability-with-bugbot.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Cursor
- Published: Mar 2, 2026
- Type: Problem & solution

PlanetScale describes code review as the bottleneck created by rapidly increasing agent-generated code, while human review capacity remained fixed. To preserve production reliability, the company adopted Bugbot as a dedicated agentic review layer alongside its existing process. Bugbot surfaces semantic and logical issues that static analyzers, linters, and straightforward frontier-model reviews may miss, including premature completion states, non-converging asynchronous controllers, and database restart edge cases. Across more than 2,000 pull requests reviewed monthly, roughly 80% of Bugbot's comments are addressed before merge. PlanetScale says this prevents potential production downtime, saves the equivalent of two full-time engineers of review effort, and lets engineers focus on complex infrastructure work without sacrificing quality.


### [The third era of AI software development](https://yomu.fyi/post/the-third-era-of-ai-software-development.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Michael Truell
- Published: Feb 26, 2026
- Type: Explainer

The post describes a third era of AI-assisted software development, in which autonomous agents handle larger tasks over longer timescales while developers define problems and review results. It traces a progression from Tab autocomplete, to synchronous prompt-and-response agents, and then cloud-based agents running on separate virtual machines. These agents can iterate and test for hours, returning logs, video recordings, and live previews that make parallel work and artifact-based review practical. Cursor reports that 35% of its internally merged pull requests are created by agents operating autonomously in cloud VMs, and that agent users now outnumber Tab users two-to-one after usage grew more than 15x in a year. The post says broader adoption still depends on reliable tests, environments, tools, and context, while developers shift toward decomposition, review, and feedback rather than line-by-line guidance.


### [Closing the code review loop with Bugbot Autofix](https://yomu.fyi/post/closing-the-code-review-loop-with-bugbot-autofix.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Jon Kaplan
- Published: Feb 26, 2026
- Type: Announcement

Bugbot Autofix extends the code review agent so it can find issues in pull requests and automatically propose fixes. It spawns cloud agents in independent virtual machines that test software, and more than 35% of its changes are merged into the base PR. Now out of beta and available to all Bugbot users, the feature adds proposed fixes to the pull requests Bugbot reviews once enabled. Bugbot’s average issues identified per run has nearly doubled over six months, while the user resolution rate rose from 52% to 76%, indicating more bugs are caught and fewer false positives are flagged. Future work includes configurable automations, self-verification, deeper research, and continuous codebase scanning.


### [Cursor agents can now control their own computers](https://yomu.fyi/post/cursor-agents-can-now-control-their-own-computers.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Jonas Nelle & Alexi Robbins
- Published: Feb 24, 2026
- Type: Announcement

Cursor describes cloud agents running in isolated virtual machines with full development environments, letting them use and test the software they create rather than stopping at code generation. Available through the web, mobile, desktop, Slack, and GitHub, the agents onboard onto codebases, produce merge-ready pull requests, and attach videos, screenshots, and logs as validation artifacts. Each agent receives an isolated VM for parallel work and can interact directly with browsers, desktop applications, and other tools; users can control its remote desktop and edit without checking out the branch locally. Examples include implementing and verifying Marketplace source links, reproducing a clipboard-exfiltration vulnerability, testing lint-label changes in Cursor, and walking through cursor.com/docs. Cursor reports that more than 30% of its merged PRs are created by autonomous cloud-sandbox agents, while its next goals include coordinating agents, learning from past runs, and eventually managing production changes end to end.


[Newer posts](https://yomu.fyi/company/cursor/page/3.md) · [Older posts](https://yomu.fyi/company/cursor/page/5.md)