Cursor

TL;DR On June 29, 2026, Cursor released a native iOS app in public beta, available on all paid plans, for iPhone and iPad You can launch cloud agents from your phone - pick a repo, describe the task by voice or text, use slash commands, choose a frontier model, and let an agent run in an isolated VM Remote Control lets you take an agent already running on your desktop and keep steering it from your phone, with an option to keep the machine awake while you’re away Live Activities put agent status on your lock screen; you get push notifications, can review demos, screenshots and logs, inspect diffs, and merge pull requests without opening a laptop A launch promo gives 75% off Composer 2.5 runs in the mobile app through July 5, 2026 This lands months after SpaceX’s move on Cursor - and reframes the editor as an orchestration surface rather than a place you type code I’ve written about Cursor enough times on this blog that a phone app could have been a footnote. It isn’t. Not because the app itself is revolutionary - it’s a well-made mobile client - but because of what it quietly admits about how the work has changed. For most of software history, the editor was where you sat and typed. Cursor’s iOS app is built on the assumption that you mostly aren’t typing anymore. You’re directing. ...

TL;DR Start with What Actually Belongs in My AI Dev Stack in 2026 - the canonical stack essay Then An AI Tooling Learning Path - phased skill-building order Deep dives below cover comparisons and spec-driven workflows; single-tool posts are briefs, not entry points Canonical essays What Actually Belongs in My AI Dev Stack in 2026 An AI Tooling Learning Path: Logical Phases for 2026 Context Engineering - the production skill behind reliable coding agents Spec-Driven Development - when the brief becomes the product Deep dives Claude Code vs Cursor: A 6-Month Comparison GitHub Spec Kit and Spec-Driven Development GitHub Spec Kit in 2026: SDD Goes Mainstream My AI-Augmented Design Workflow When to Fine-Tune vs When to RAG Briefs (moment-in-time) These are useful snapshots, not the starting point: ...

TL;DR Composer 2.5 is Cursor’s most capable in-house coding model yet, built on Moonshot’s open-source Kimi K2.5 checkpoint with about 85% of total training compute spent on Cursor’s own continued pretraining and RL The model is purpose-built for the agent loop inside Cursor - long-horizon tasks, hundreds of tool calls, multi-step instructions - rather than as a general-purpose chat model Cursor claims parity with Claude Opus 4.7 and GPT-5.5 on its own CursorBench v3.1 (63.2%) and a strong 79.8% on SWE-Bench Multilingual Pricing is dramatically lower: $0.50 / $2.50 per million input/output tokens on the default variant, with included usage doubled for the first week Together with SpaceXAI, Cursor is now training a much larger successor model from scratch on Colossus 2 with around 10x the compute - so 2.5 is a waypoint, not the endgame For a while, Cursor was an IDE wrapped around someone else’s models - Claude, GPT, Gemini. That story has shifted. With Composer 2.5, released this week, Cursor has shipped its most capable first-party coding model yet, and it is a serious enough piece of work that it deserves real consideration as a daily driver rather than a budget fallback. ...

TL;DR A combination of Cursor in the IDE, Claude Code and Codex in the terminal, and GitHub Spec Kit as the living contract has collapsed the discuss-design-document loop from days to under ten minutes Every meeting is transcribed and checked into GitHub alongside the design corpus, giving AI agents access to the full historical record - not just curated decisions but the debates that shaped them Model selection matters: cheaper, faster models for throwaway sketches and small refactors; expensive models (Opus) for large cross-repo work where the cost of a wrong answer is high The real transformation is cognitive flow - removing friction between thinking and recording means decisions get made and captured while the problem is still fresh, with almost no context switching AI is now suggesting improvements faster than the author can implement them; the next bottleneck is compaction, not generation - asking the model to reduce documents to their load-bearing claims rather than produce more content Since making a combination of Cursor in the IDE and Claude Code and Codex in the terminal the centre of my working day - with ChatGPT for general questions and GitHub Spec Kit holding the design contract - the way I move from a question on Slack to a documented design decision has changed beyond recognition. ...

TL;DR The order you learn AI tools matters as much as which tools you learn - most people start with terminal agents or editors before they understand how models actually fail The seven-phase path runs: fundamentals, chat interfaces, AI-native editors, terminal agents, local models, orchestration, and review and evaluation Terminal agents (Claude Code, Cline, Aider) represent the biggest mindset shift - you move from driving with suggestions to specifying and letting the model execute Local models via Ollama belong in phase five, once you have felt the pain of API costs and know which tasks actually need frontier capability Review, evaluation, and capture (phase seven) is the phase most developers skip - and the one that separates AI-curious from AI-competent The hardest part of learning AI tooling in 2026 is not any single tool. It is the order you meet them in. ...

TL;DR After six months of daily use, neither Cursor nor Claude Code wins outright - they represent two distinct philosophies that complement each other in a hybrid workflow Cursor’s strength is deep IDE integration: seamless codebase indexing, best-in-class multi-file Composer Mode, and zero context switching for feature development and UI work Claude Code’s strength is agentic execution: it runs tests, reads output, fixes code, and loops until passing - ideal for debugging, test-driven fixes, and housekeeping tasks The real winner underlying both tools is the Claude 4 family (Sonnet 4.6 for most work, Opus 4.7 for the harder agentic loops); the choice of tool determines how you interact with that intelligence, not which intelligence you get The practical split: use Cursor as your primary environment for feature work, use Claude Code when you need something to just run and fix itself It’s been six months since the landscape of AI coding tools shifted from “helpful autocomplete” to “autonomous agents.” During this time, I’ve used both Cursor and Claude Code (Anthropic’s CLI tool) for every major project. ...

TL;DR A single AI tool cannot handle everything - a proper AI dev stack in 2026 needs distinct layers for spec writing, fast editing, heavy agentic work, cheap model tasks, review, research, and capture Spec-driven development is the most underused part: writing requirements and acceptance criteria before generation dramatically improves AI output and reduces wasted iterations Tools like Cursor AI handle fast, in-flow editing while Claude Code or Cline are better suited to multi-file refactors and autonomous implementation from specs Letting the same model that generated code also review it is a weak loop - a separate review pass with a different model or explicitly critical prompt is essential The real shift is treating AI not as a bolt-on assistant but as part of the workflow architecture itself, with each tool assigned a clear, specific responsibility There is a big difference between using AI for development and having an actual AI development stack. ...

TL;DR GitHub Spec Kit reached v0.5.0 in 2026, evolving from a documentation toolkit into a full extensibility platform for AI-assisted development Claude Code CLI is now a native skill within Spec Kit, making spec-to-code pipelines seamless and built-in The ecosystem has exploded with dedicated tools like AWS Kiro and Tessl, while multi-agent support covers Copilot, Cursor, Gemini CLI, and more Spec-Driven Development prevents architectural drift by making the spec the single source of truth - versioned, reviewable, and respected by AI agents Getting started is now low-effort: write a spec.md, pick any AI tool, and let the spec drive implementation Six months ago, we explored how GitHub Spec Kit was beginning to reshape software development. In early 2026, that promise isn’t just materializing - it’s accelerating. The project has hit version 0.5.0, the ecosystem has exploded, and Spec-Driven Development has transitioned from “interesting idea” to actual industry standard. ...

TL;DR SpaceX has signed an option to acquire Cursor (made by Anysphere) for $60 billion, or pay $10 billion for the joint work if it walks away Cursor’s valuation has risen 24x in fifteen months - from $2.5 billion in January 2025 to a $60 billion option price in April 2026 The deal sits under SpaceX rather than xAI directly, because SpaceX holds the balance sheet after the SpaceX - xAI merger valued at $1.25 trillion For xAI, buying Cursor is a faster route to developer relevance than out-marketing OpenAI’s Codex or Anthropic’s Claude Code If the acquisition closes, three of the main AI coding interfaces will sit inside three frontier labs - raising questions about model neutrality and pricing pressure on independent tools It’s rare to see an option contract make the front page, but that is what landed on 21 April 2026. SpaceX disclosed that it has signed a deal with Cursor - the AI coding tool made by Anysphere - giving it the right to buy the startup outright for $60 billion later this year, or to walk away with a $10 billion payment for the joint work the two teams are doing in the meantime. ...

TL;DR GitHub Spec Kit is a structured framework of version-controlled markdown files (spec.md, constitution.md, boundaries.md, etc.) that serve as the single source of truth for a software project Spec-Driven Development (SDD) means writing the specification first, then generating and refactoring code in alignment with it - preventing architectural drift over time Integrating Spec Kit with Cursor AI turns the spec from a static document into an active constraint the AI understands and respects The spec-first loop (define, implement, refine, repeat) creates development that is clearer, faster, and easier to maintain than ad-hoc planning SDD is especially powerful for long-term projects and large teams where shared mental models and consistent architecture matter most Spec-Driven Development is starting to reshape how modern software is planned, built, and maintained. Among the tools pushing this shift forward, GitHub Spec Kit stands out as one of the clearest, cleanest ways to bring structure and intention into your workflow. It turns the usual chaos of planning into something organised, navigable, and repeatable - and when combined with AI-powered editors like Cursor, it becomes even more powerful. ...