Reading up on Cursor
100 deep · digging since nov 19, 25
- GitHub - tobi/walgit
walgit is a git server implemented as a single binary that runs in front of an object store (S3/GCS), requiring no local state beyond a cache.
- unslop — cursor/plugins
The article presents a step‑by‑step method called 'Unslop' for detecting and removing AI‑generated writing patterns while injecting human voice effectively.
- The Evolution of the Agent Harness - by Dan McAteer
The article argues that as AI models internalize agent harness capabilities, the remaining harness evolves into an interface for managing scarce human attention rather than directing model behavior.
- The asteroid currently hitting frontend web development
Frontend educators are stepping back as AI agents lower the risk of frontend code, shifting focus from developer experience to agent-friendly tools and performance.
- One Week of Building and Reviewing Code With LLM Agents
Logging a week of agent‑driven backend work, the engineer shows LLMs generated most code, isolated agents found real bugs, while multi‑tool agreement often missed defects.
- The Making of Cursor's Icons
The article details how Cursor's design team created its distinctive icon set, outlining their iterative process, inspiration sources, and the tools used to achieve a cohesive visual language.
- Cursor launches Origin, GitHub alternative
Cursor unveils Origin, a self‑hosted GitHub‑like platform that integrates AI‑powered code assistance to offer developers an alternative to traditional source‑hosting services.
- The next GitHub is not worth winning — David Poblador i Garcia
The author argues that most development work is ephemeral and should stay local, proposing a staging area where experiments remain on‑device until they merit pushing to GitHub.
- The cost of caring about software - Alex Rios Substack
AI reduces code-writing effort but shifts verification cost onto reviewers, creating an externality where comprehension debt rises despite faster generation.
- GitHub Outages Show the Limits of Reactive Scaling — Rahmi Pruitt
GitHub’s eight‑hour outage shows that reactive scaling fails when hidden concurrency limits are hit and client retries amplify demand, proving that agents need proactive traffic pacing.
- Git at any scale
Cursor argues Git’s packfile‑based design hinders scalable hosting and presents Continuity, a S3‑backed write‑ahead log system that enables linear, consistent replication.
- Git at any scale
Cursor explains why Git's packfile design hinders scalable hosting, reviews past solutions like Spokes, and introduces Continuity, a S3‑based write‑ahead log system that provides linearizable, elastically scalable Git storage.
- Cursor launches Origin code hosting platform as GitHub outage exposes opening in AI coding race
Cursor launched Origin, an AI‑native code‑hosting platform that mirrors GitHub, aiming to capture developer attention as GitHub’s reliability falters amid rising AI‑agent usage.
- The Shapes of Agent Memory – Files, Stores, and Experience
Structured stores beat file memory on accuracy and token cost; files win only for small memories or 'I don’t know' answers, and trained experience helps weak models only.
- ReplyHey: find your customers on Reddit
ReplyHey scans Reddit, X, and LinkedIn daily for problem statements matching your product, scores them, drafts personalized replies, and provides a rule‑respecting posting plan.
- Solo — AI marketing in chat or your own agent
Cogny Solo provides a free AI marketing chat with preloaded skills and a $9/mo unmetered MCP option to connect agents like Claude Code or Cursor for SEO and ads.
- Write for people
The author laments that auto-generated technical explanations are becoming overly verbose and unintelligible, arguing that simplicity and human effort in writing are essential for clarity.
- Introducing Grok 4.6
Grok 4.6 advances long-running agent capabilities and visual/interactive work, matching GPT-5.6 Sol on the AA Intelligence Index and launching in Cursor and Grok Build with 2x usage for the first week.
- AI is removing the middle class of software engineering
AI enables rapid code generation, allowing engineers with weak judgment to create unmaintainable systems faster, exposing and amplifying poor engineering practices until projects collapse under their own complexity.
- Import from another agent
ChatGPT and Codex now support importing setup and recent work from Claude Code, Claude Cowork, or Cursor via a guided flow that preserves existing configurations and enables sync.
- The model picker is a dead end
Lovable's model independence means deeply customizing instructions, tools, and context for each model rather than treating them as interchangeable, using a control plane to dynamically assign tasks based on real-time build progress and optimizing for working applications, not benchmark scores.
- Ryan Greenblatt – What happens once AI can automate AI research?
The discussion explores whether automating AI R&D could trigger recursive self-improvement, potentially yielding years of progress in months, with debate on verifiability, data bottlenecks, and alignment risks.
- BCMS - Headless CMS for developers and their clients
BCMS is a headless CMS enabling AI agents, clients, and developers to manage content via UI, API, or agents, with TypeScript generation, widget support, and integrations for Next.js, Nuxt, and more.
- GitHub - micro/mu at console.dev
Mu is an MCP server and web app that gives agents access to real-world tools like news, mail, and weather through a unified interface, with self-hosting and CLI support.
- GitHub - huangruiteng/loopx: Lightweight loop engineering state kernel for long-running AI agent teams. Agent-loop agnostic across Codex, Claude Code, and other coding agents, with durable goals, quota-aware auto-wake, executable todos, evidence logs, and verifiable handoffs.
LoopX is a lightweight, agent-agnostic state kernel that manages long-running AI agent work by preserving objectives, gates, todos, evidence, quota, and handoffs across bounded turns.
- MCP Server | AppSignal
AppSignal's MCP server integrates with AI coding tools to deliver real-time performance insights, error tracking, and debugging support directly within developers' editors.
- IntelliJ IDEA Goes LSP: Java and Kotlin Intelligence Comes to VS Code, Cursor, and Agentic Flows - The JetBrains Blog
JetBrains released a preview extension bringing IntelliJ IDEA's Java and Kotlin intelligence to VS Code and Cursor via LSP, with free 30-day trials.
- Cursor: AI coding agent
Cursor is an AI coding agent that autonomously builds, tests, and reviews code across terminals, Slack, and GitHub while integrating top models.
- How we set up our cloud agent environment
Cursor rebuilt its monorepo to support cloud agents by matching local dev, simplifying the interface, and adding a self‑healing environment, boosting agent‑authored PRs from 10% to over half.
- The AI Aesthetic - Jim Nielsen’s Blog
The article explores emerging AI-driven design motifs—sparkles, streaming and shimmering text, tiny icons, beige/orange palettes—and questions which will become lasting UI conventions.
- Copper | shadcn
Copper is a macOS app that merges a to‑do list, clipboard, and scratchpad for AI‑assisted workflow, storing notes locally and costing $39 one‑time.
- How much can you delegate to agents? - by Jina Yoon
The article presents a practical framework for deciding how much autonomy to grant AI agents based on task checkability and undo cost, outlining four levels from assistant to self‑driving mode.
- DeepSQL - 24/7 DBA: Cut Database Costs, Optimize Queries, MCP + Slack
DeepSQL offers a self‑hosted AI agent that rewrites slow queries, recommends indexes, and delivers context‑aware SQL via MCP, CLI, and Slack to cut database costs.
- hilos · team chat where people and AI agents share the room
Hilos introduces a team chat workspace where AI coding agents act as first‑class members, automatically creating branches, previews, and pull requests for human review.
- Agent swarms and the new model economics
Cursor’s new agent swarm design builds SQLite in Rust with far higher success and far lower code size and cost than its earlier swarm.
- 5 Trends That Defined AI Engineering at World’s Fair 2026
AI engineering shifted from building with agents to building reliable systems around them, focusing on loop engineering, skills, and forward deployed engineers.
- Social Media Scraping APIs for YouTube, TikTok, Instagram, Facebook, X & LinkedIn
SocialKit provides a unified API that extracts transcripts, summaries, comments, and stats from YouTube, TikTok, Instagram, Facebook, X, and LinkedIn as clean JSON.
- The Making of Claude Code \ Anthropic
Anthropic shares the inside story of Claude Code's development from an internal CLI tool to a widely used coding agent, highlighting design decisions and team insights.
- Show HN: Smart model routing directly in Claude, Codex and Cursor
A smart model routing tool for Claude, Codex, and Cursor claims to reduce costs and improve speed by dynamically selecting the best model for each request.
- Skill engineering and the case against one-shot AI design
Paul Bakaus argues AI agents need “skill engineering” to give designers precise control, rejecting full automation in favor of human judgment steering the final 20% of creative work.
- The Pulse: a new trend, smart model routing - The Pragmatic Engineer
Several vendors now offer intelligent model routers that automatically select the cheapest sufficient LLM per task, promising 20-30% cost savings, a trend expected to become table stakes.
- Understanding is the new bottleneck
As AI agents write code faster than humans can review, understanding remains critical for creative participation, not just verification.
- Documentation · oak
Oak is a version-control and storage layer built for AI coding agents, offering branch-per-session workflows, lazy mounts for instant monorepo access, and full git export for data portability.
- What Active Rubyists Are Using in 2026: A Maintainer's Read of the RubyKaigi Survey - DEV Community
A maintainer's analysis of RubyKaigi 2026 survey data reveals that Ruby 4.0 adoption matches 3.4 within six months, Claude Code dominates at 80% usage, and VS Code/Cursor lead editors while mise/asdf challenge rbenv.
- Autoresearch: The feedback loop behind self-improving agents
Autoresearch uses outer loops with feedback signals and human input to let agents improve and maintain systems, reducing bottlenecks while keeping humans central.
- “It’s Hard to Eval” Is a Product Smell – Hamel's Blog
Products that are hard to evaluate programmatically are likely hard for users to verify, so design for verification first.
- Build from anywhere with Cursor for iOS
Cursor released a native iOS app in public beta that lets developers launch and control AI coding agents from their phone, with cloud agent support and remote control.
- Agentics / Tech Things: Tokenmaxxing is dead, long live tokenmaxxing
Tokenmaxxing was a deliberate strategy to force AI adoption at companies like Meta, and despite current rollbacks, it will return as agents achieve compounding correctness and loop-based workflows.
- Claude Code turned every engineer into three. Now companies need more product thinkers
AI coding tools like Claude Code have tripled engineering output, shifting the bottleneck from coding to product decisions, requiring engineers to focus on fundamentals and product thinking.
- hasp · model 01
Hasp is a local secret broker that injects credentials into coding agent processes without the agent ever reading the plaintext value.
- SpaceX to buy Cursor for $60B
Commenters debate Cursor's value at a $60B acquisition price, praising its model-agnostic agentic workflow and enterprise traction while others dismiss it as a commodity in a saturated market.
- API for Cursor - Cursor Composer in Any Coding Agent
API for Cursor is an open-source macOS app that exposes Cursor's Composer as a local OpenAI-compatible API for use in any coding agent.
- GitHub - millionco/react-doctor: Your agent writes bad React. This catches it
React Doctor is a deterministic scanner that audits React codebases for issues across state, effects, performance, architecture, security, and accessibility, and integrates with coding agents and CI pipelines.
- A Guide to AI Inference Engineering - ByteByteGo Newsletter
LLM inference splits into compute-bound prefill and memory-bound decode, driving optimization techniques like batching, quantization, speculative decoding, and disaggregation.
- The Quiet, Galactic Ambitions of Cursor CEO Michael Truell - Business Insider
Cursor CEO Michael Truell navigated a fraught reliance on Anthropic, built in-house AI models, and struck a deal for a potential $60B acquisition by SpaceX to secure computing power.
- We Should Take Text Optimization More Seriously
Text optimization—modifying prompts, context, memory, and harnesses—is a legitimate, sample-efficient learning mechanism that deserves the same rigorous study as weight optimization.
- Claude Fable 5 and Claude Mythos 5 \ Anthropic
Anthropic launches Claude Fable 5, a safety-nerfed Mythos-class model for general use, and Mythos 5 for vetted cyber defenders, both at half the price of Mythos Preview.
- Direct agents with visual prompts in Design Mode
Cursor's Design Mode lets users point, draw, or narrate UI changes in the browser, with agents editing the underlying code in real time.
- Give your agent its own computer
LangSmith Sandboxes give each AI agent its own hardware-isolated microVM with filesystem, shell, and package manager, enabling secure code execution without risking host infrastructure.
- Musings on Markets: Revisiting the SpaceX Valuation: A Post-Prospectus Update!
A finance professor updates his SpaceX valuation to $1.3 trillion after the IPO prospectus reveals heavy AI investment, dual-class control, and risky governance.
- I think Anthropic and OpenAI have found product-market fit
Anthropic and OpenAI may have reached product-market fit based on enterprise willingness to pay $200/month for tokens, though valuation and cost sustainability remain contested.
- Cursor · The Cursor Developer Habits Report
Coder productivity has doubled year-over-year, with AI-generated code surviving review at higher rates, while top 1% of users far outpace median developers.
- agent-skills/skills/autoreview/SKILL.md at main · openclaw/agent-skills
The piece defines a structured pre-commit code review skill for AI agents, specifying contracts, scope governance, and engine isolation across multiple review engines including Codex, Claude, and others.
- Robinhood Lets Customers Use AI to Trade Stocks, Make Credit-Card Purchases - WSJ
Robinhood is launching a feature allowing customers to delegate trading and credit-card spending decisions to AI agents like Claude and Cursor via dedicated accounts.
- Using AI to write better code more slowly
Using multiple LLMs for rigorous code review and bug finding can produce higher-quality code than the common fast-output approach, though it slows development velocity.
- {{IW4QaZoc2}}
Perplexity open-sources Bumblebee, a read-only scanner that checks developer machines for risky packages, extensions, and AI tool configs during supply-chain incidents.
- What we’ve learned building cloud agents
Cursor's cloud agents perform best when given full development environments, durable execution via Temporal, and a harness that shifts control to the agent.
- Taste MCP · Use your design taste in any AI tool
Taste captures screenshots of UI you love, extracts design tokens with AI, and builds a taste profile that AI coding assistants use via MCP.
- Introducing Composer 2.5
Composer 2.5, a major update to Cursor's AI coding assistant, improves long-horizon task performance via targeted RL feedback and synthetic data, built on Kimi K2.5.
- Agent Hooks: Deterministic Control for Agent Workflows
Hooks enforce deterministic controls in agent workflows by attaching handlers to lifecycle points, moving repeatable rules out of model memory into explicit code.
- Development environments for your cloud agents
Cursor introduces new tools for configuring cloud agent development environments, including multi-repo support, Dockerfile-based configuration, and enhanced security controls.
- Development environments for your agents
Cursor launches cloud agent development environments with multi-repo support, Dockerfile-based config as code, and per-environment governance controls.
Takes
Cursor Designer, Ryo Lu: "Right now I'm running 10-20 GrokBot agents that automate 90% of my routine I have a Chief of staff agent. He knows about all my other bots and manages everything" In a 20-minute podcast, a Cursor Designer explained how to build a team of GrokBot agents that will work for you 24/7 Worth more than a $500 course on agentic engineering Watch today, then read how to build a Grok agents team from scratch in article below
@maestrooth
Git at Scale (by cursor) has been one of the most interesting blog posts i've read in a while. It came right when I was frustrated with Shopify's internal git system. As an exercise, I've implemented it over the weekend as open source. It's a single rust binary that you can point at any S3 type object store. It uses WAL and CAS primitives and requires no other data store. It also implements bundle-uri so large git repos (like our mono) are very fast to download as a chain of static bundles. Also comes with basic familiar UX.
@tobi
the models have no moat (OpenAI, Anthropic, XAI) the IDEs have no moat (Cursor, Windsurf) the harnesses have no moat (Cognition, Factory, LangChain) the app builders have no moat (Replit, Lovable, Bolt) the wrappers have no moat (Harvey, Abridge, OpenEvidence) the inference providers have no moat (Together, Fireworks, Groq) the voice layer has no moat (Sierra, Decagon, ElevenLabs) the data labeling companies have no moat (Scale, Surge, Mercor) the AI infrastructure has no moat (Baseten, Modal, Railway) the neoclouds have no moat (CoreWeave, Lambda, Crusoe) the generative media companies have no moat (Runway, Higgsfield, Suno) apparently nobody in AI has a moat except the venture firm ☠️
@nikunj
Cursor has officially joined SpaceX. We’re grateful to become part of such a special company, and it has been a privilege working with the SpaceXAI team. Lots ahead.
@mntruell
I’ve been using it for ~2 hours and I’m absolutely blown away at how I could automate a bunch of my workflows. Cursor team built a way for “normies” to automate stuff. Feels like an OpenClaw 2.0 moment. (Zero affiliation, I upgraded to Ultra to use it and absolutely worth it.)
@GergelyOrosz
I got early access to Grok Bot and I'm hooked. I haven't been this excited about a new AI product in a while. It's like OpenClaw, but super easy, reliable, and less scary to use. I think this will be a huge new product line for Cursor/Grok/SpaceX. I've already found so many ways to use it that have meaningfully made my life better: 1. Matchmaking people looking for jobs with companies who are hiring (see below) 2. Auto-replying to support emails (saves me hours!) 3. Scanning my credit card statements and finding recurring subscriptions to cancel 4. Sending me (really good!) briefs for upcoming podcast guests See below for my actual set of agents that I've been using and chat with daily. Great work on this team Grok Bot. (I'm not an investor in this, nor do I have any ties to this product/company. I'm just a fan!)
@lennysan
Launching Copper, a Mac app for capturing things you want to keep and prompts you want to try next while working with AI. The more I use AI, the more I find myself collecting little things I don't want to lose. You're in ChatGPT and think, "I'll need this later," but you don't want to stop what you're doing. Then you're in Claude. Cursor. Chrome. Back to ChatGPT. Before long, you've got little things scattered everywhere. An answer you want to keep. A link. An idea. Or three follow-up prompts before the current one has even finished generating. I had this problem, so I built Copper. Copper combines the useful parts of a to-do list, a clipboard, and a scratchpad. It's built specifically for AI-assisted work. It sits next to where you work and is always one shortcut away. It works with all your AI apps, terminals and browsers. Whenever I find something worth keeping, I capture it. If I think of a few prompts while the AI is still responding, I type them into Copper. Then I send them back into ChatGPT, Claude, or Cursor and check them off as I go. Copper is local and private. It doesn't sync anything, doesn't collect anything, and doesn't need an account. I've used Copper every day for the past few months. It has completely changed how I work. If this feels like something you'd use, it's $39. One-time purchase.
@shadcn
Built this using Higgsfield + Cursor Composer 2.5 + my custom landing-page-design-skill.md file. It reminds me that models don't matter. Skills do.
@elayadesigns
Introducing Cursor Router, our intelligent model router that selects the right model for the task at hand. Router delivers frontier-quality results at 60% lower cost.
@cursor_ai
I made a prompting utility app. Like a prompting assistant. It works with all your existing apps, chatgpt, claude, cursor, web. It's been incredibly useful. I'm thinking about distribution (I think AI has made distributing the source much more interesting). If you buy it, would you rather receive A or B (poll in thread)?
@shadcn
SCOOP: Perplexity is quietly building an AI coding tool that takes on Claude Code and Cursor. It's meant to build software end-to-end. The tool is being used internally (for now) under the codename 'Teammate.'
@CharlesRollet1
This could well end quicker than most people assume, because coding agents in the cloud are coming, and they are coming fast, especially inside places like Cursor (I was in their offices yesterday, and local agents will prob go away soon as I read the room)
@GergelyOrosz
Introducing Cursor for iOS. Build from anywhere by launching always-on cloud agents. Or remotely control agents running on your computer from the app. Composer 2.5 is 75% off in the app now through July 5.
@cursor_ai
Last week, I spoke at @cursor_ai compile on the new PM, and what it means when anyone can build anything
@clairevo
Introducing /automate, a skill for agents to set up automations for you. Describe your task in plain language. Cursor configures the triggers, instructions, and tools.
@cursor_ai
.@mntruell launched Cursor 8 times, and nobody cared. Don't give up.
@marclou
anyone built a reliable system/solution for indexing and searching threads across claude code, codex, cursor..etc? All the built-in ones are not that great.
@shadcn
We're launching code storage and git hosting. Origin gives teams and agents a place to host, review, and collaborate on code. Available this fall. Join the waitlist. https://cursor.com/origin-waitlist
@cursor_ai
Building recursive agent systems
@leerob
Michael Truell (@mntruell) fell in love with coding at 12. The company he co-founded, @cursor_ai, went from 15 people to 700 in two years. Today, over 60% of the Fortune 500 build with its AI coding platform.
@claudeai
People still using Cursor/Codex/VSCode to code?! I don't get it... I know we used to call it "vibe coding" Today, I call it professional product engineering. Done staring at code in an editor, we're way pass that. This is how you should do it in 2026 👇
@SimonHoiberg
Suddenly it hit me. What happened to DeepSeek? Sora? GitHub Copilot? Llama? Cursor? Perplexity? What happened?
@shub0414
Today we're launching Paxel: a free tool that analyzes your Claude, Codex, and Cursor coding sessions and gives you a profile of how you build with AI. It runs locally inside Docker, and your code never leaves your machine. Try it at http://paxel.ycombinator.com
@ycombinator
State of Memory in Agent Harness
@mem0ai
Introducing Impeccable 3.5, the best way to design in production: iterate on real UI with your AI agent, in the codebase you actually ship. Turns out many popular design skills, including Impeccable and Anthropic's frontend-design, weren't actually very good at...design (the workflow was valuable, but the output didn't magically make LLMs like GPT great designers). We measured it across thousands of generations: 74% of pages used the cream AI-default background, 76% reached for extreme letter-spacing, 90%+ failed the contrast floor. So we started fixing slop systematically, specific to each model. The skill now compiles rules for the exact defects each model makes, instead of shipping one generic file to everyone. The biggest jump is in GPT-5.5 and Codex. Also new: ◆ It now knows the difference between a new project and an existing one. Existing codebase, it reads your design system and preserves your identity. Greenfield, it seeds a fresh palette from 129 hand-curated anchors so every cold start doesn't drift to the same safe colors. ◆ Live Mode is now in beta, and works at two scales. Type a direction into the new Steer bar, or speak it, and the agent reads the whole page and edits it in place. Or pick a single element, steer it with a sub-command, live-edit any copy, and accept the variant straight back to source. Insert mode scaffolds brand-new elements between the ones already there. Recovery survives HMR, hidden heroes, and dev-tool overlays. ◆ A rebuilt anti-pattern detector. Torn off jsdom and onto a real CSS cascade resolver: roughly 20x faster, dependency-free, and now small enough to run inline inside the skill, not just the CLI and extension. 14 new rules, 41 total. ◆ The skill keeps itself current, checking once a day and offering to update. Plus /impeccable init and a bare /impeccable that reads your repo and tells you the next move. Free, open source. Claude Code, Codex, Cursor, and more.
@pbakaus
First, open the Cursor browser into a new project. Then, navigate to http://magicpath.ai and log in.
@skirano
Today's Training Data episode takes us BTS on the infrastructure challenges required to do large RL runs at scale, featuring @ellev3n11 (Composer Lead at @cursor_ai) and @dzhulgakov (Co-Founder at @FireworksAI_HQ). The Cursor team trained Composer 2 on Fireworks by starting with a strong base model (Kimi 2.5) and performing large-scale mid-training on code tokens and web data to learn common patterns and libraries, followed by a large-scale Reinforcement Learning run to learn how to navigate the Cursor harness, call tools, and write correct code. Today's episode dives into the systems and infrastructure challenges of making that large RL run happening, and there were many (!!), from numerical mismatch to global distribution to synchronizing rollouts across asynchronous pipelines to keeping track of expert activation across runs and more. Extremely nerdy in-the-weeds challenges that Federico and Dima were delighted to nerd out on together :) Beyond RL infra, we also discussed Online vs Simulated rollouts, self-summarization for long-horizon agents, environment design ("the most powerful RL environment is the product itself"), and other technical nuggets. PS: We filmed this episode before the SpaceX news, while the Cursor team was still compute-constrained. While Cursor now has *all* the flops, the takeaways and hurdles crossed ring true for any serious application-level company that is racing to post-train their own models. I believe that more serious application companies will go the way of Cursor and post-train their own models. 00:00 Introduction 00:53 Why Cursor Trained Composer 2 04:55 Specialization vs Bitter Lesson 06:16 Composer 2 Training Recipe 16:32 Scaling RL Infrastructure Globally 23:32 Floating Point Drift 25:11 MoE Sensitivity Explained 26:25 Router Replay Fix 27:19 Real Time RL Loop 31:49 Long Horizon Agents 34:29 Why RL Everywhere 37:34 LLM as Judge Rewards 39:14 RL in Hard Domains 40:13 Build Your Own Environments 44:34 Closing Thoughts
@sonyatweetybird
the most used skill internally at cursor right now /thermo-nuclear-code-quality-review - deletes complexity instead of moving it - blocks files over 1k lines - flags thin wrappers and leaked logic - rejects PRs that work but make code messier
@ericzakariasson