Cursor

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Open-source Website to Mobile coding-agent plugin/skills

I’ve been working on a plugin/skill set for Claude Code, Cursor, and Codex called WebToMobile. The idea is simple: if you have a website or web app and want to turn it into a mobile app, the agent should not just start generating random React Native screens. Instead, it follows a migration workflow: Audits your website, GitHub repo, or local project Maps web routes/pages to mobile screens Separates reusable code from rewrite-required code Flags mobile-native gaps like auth, storage, cookies, OAuth redirects, uploads, push, etc. Creates a Markdown migration plan/checklist Waits for your approval Builds in Expo React Native Runs QA/review checks before claiming anything is done Important distinction: - If you give it only a live URL, it can help with UI/UX and visual structure. - If you give it the repo/local code, it can do a much deeper migration plan and implementation. It includes commands like: /web-to-mobile /mobile-resume /mobile-scan /mobile-review /mobile-audit /mobile-qa I built it because “make this website into an app” is usually too vague for AI agents. They need a defined path, not just a better prompt. Repo: https://github.com/suntay44/web-to-mobile-magic-plugin Would love feedback from people building with Expo, React Native, Claude Code, Cursor, or Codex. submitted by /u/suntay44 [link] [comments]

Claude Code efficiency

Hey, everybody! I’m currently using Claude code to build my own app, I tell Claude AI what I want to do/implement into my app and he writes me a prompt which then I feed into Claude code. I’ve been doing this and have been writing my app in React Native, so far so good, I’ve implemented an API and use Supabase as a back-end. My current stack is Claude Code for, well, Coding and fixes within the code, Claude Ai to write and create the idea of the implementation, supabase for the back end and Cursor to locally host my app to see the version before deploying into my domain. What I want to ask is, am I using Claude code to its potential? I feel like I use him quite efficiently and savvy, but I still feel like I’m not using him to its proper potential or not getting a 100% out of all the uses it has. Does anyone have any tips, skills, agents or any advice along those lines that would help me improve my app building or general usage within Claude? submitted by /u/NickToons203 [link] [comments]

[Open Source] I built a full Git MCP server in Go that doesn't just wrap bash. It uses tree-sitter, handles real plumbing (write-tree), and runs 100% locally.

I was tired of watching LLM agents fail at basic Git operations. Standard integrations pass raw text, hang on pagers, or scream because they can't parse unstructured ⁠git diff⁠ outputs. git-courer is a full Model Context Protocol (MCP) server written in Go that treats Git properly. No bash spawning, no unstructured text to parse. Everything communicates via structured JSON. Here is an actual commit message it generated completely locally: fix: fix mcp server connection handling WHY The previous implementation lacked proper error handling for connection failures in the MCP server, leading to unhandled panics or silent failures when the local LLM backend was unreachable. WHAT * Added connection timeout logic to the local client calls. * Implemented retry mechanisms with exponential backoff for transient backend errors. The Architecture & Tool Pack Read Tools (status, diff, history, blame): Completely structured JSON and fully paginated. A single ⁠status⁠ call replaces over 5 standard Git commands for the agent. Write Tools (commit, merge, rebase, branch, stash, stage, sync...): Every single mutation auto-creates a backup before executing. If the LLM messes up, a ⁠RESTORE⁠ command brings you back exactly where you were. Safety Model: Destructive operations (hard resets, force pushes, branch deletions) require an explicit ⁠confirmed=true⁠ gate. The agent is forced to ask you first. ⁠dry_run=true⁠ is also available for peace of mind. The Semantic Annotator (Why it's different) Instead of just feeding raw code to the LLM, git-courer uses ⁠go-enry⁠ + ⁠go-tree-sitter⁠ to parse the AST and tag every hunk semantically before the LLM even sees it. It detects tags like ⁠NEW_FUNC⁠, ⁠MOD_SIG⁠, ⁠MOD_BODY⁠, ⁠DELETED⁠, and ⁠BREAKING_CHANGE⁠. The commit type (⁠feat⁠, ⁠fix⁠, ⁠refactor⁠) is determined deterministically from these AST tags rather than guessed by the model. The Commit Pipeline Atomic Commits: One staged area = one commit. It actively prevents the agent from creating giant, messy multi-feature commits. In-Memory Previews: The ⁠PREVIEW⁠ tool uses ⁠write-tree⁠ to snapshot the staging area into a ⁠job_id⁠. The working tree is never touched during the preview stage. ⁠APPLY⁠ then uses ⁠commit-tree⁠ + ⁠update-ref⁠ to seal the deal cleanly. Client & Backend Support 13 Clients Configured Automatically: Runs out of the box with ⁠git-courer mcp setup⁠ for Claude Code, Cursor, Windsurf, OpenCode, Cline, Roo Code, VS Code, Zed, Claude Desktop, Continue, and more. 100% Local-First: Works with any backend exposing an OpenAI-compatible ⁠/v1⁠ API (Ollama, LM Studio, llama.cpp). The project is fully open source. I’d love to hear your thoughts on the architecture, the plumbing pipeline, or any features you'd like to see added! Repo: github.com/Alejandro-M-P/git-courer submitted by /u/blakok14 [link] [comments]

Introducing Machinaos[Fully Opensource]: OS That converts LLM Tokens to Work.

claude On May 13 Anthropic Culled the Usage of "Claude -p" Command which instantly killed the heavily 25x subsidization usage of Claude . People were using Openclaw , Hermes Agent and others things through claude cli using the "-P" command , but now the usage will be charged as Claude SDK API credits from their Pro[100$] or MAX[200$] Budgets. Using claude through their SDK is ~25x more expensive and burns credits super Fast. Once i Tried to Generate a Simple PDF report from my emails and it burned ~10$ in the Calude SDK Credits. Also Claude Code usage is very generous and barely hits the Weekly Quotas. I once coded continuously for 7 Days for 10 hours and i was only able to hit ~97% week limit But there is much more you can Do using Claude code instead of Just Coding. You can Add Tools and Sub Agents, etc and Convert it to Cowork and Design too. BTW Claude Cowork and Claude Design are Supper Token Hoggers and Hits Quotas Fast. Once I was using Calude Design and told it generate around 10 Design Themes and it burned through weekly quota with a Hour usage. Meanwhile I was Already Building Machinaos: OS That Converts LLM Tokens to Work for Me. I connect my socials , emails , web tools, browser, etc and use it to generate websites, read emails and generate PDF Reports and mails them to others emails or to someone on my Socials like WA. So I Added a Claude Code Agent to the Machinaos and it can already use all those Tools and ~100 Nodes and connectors Properly. https://reddit.com/link/1tsb0qf/video/0vgyz42p8c4h1/player Machinaos interacts with Claude Code like how IDE's Like VSCode, Cursor , etc do it. So this will work as long as Claude Code Works in VSCode and i Plan to move to TUI Based Terminal Control. Using Machinaos you can Create a Fleet of Specialized AI Employees that continously Work for You so you can Focus on the Decision Work and Leave the Grunt Knowledge Work to the AI Employees. https://reddit.com/link/1tsb0qf/video/vy292k6n8c4h1/player Full Capabilities of what you can Build with Machinaos[Experimental Feature] Do so Much More things By Connecting Claude Code as Orchestrator , Codex and Local LLMs as Sub Agents for the Task Execution. Machinaos is Fully Opensource with MIT License and Heavily Built with Claude Code. Github: https://github.com/zeenie-ai/MachinaOS Discord: https://discord.gg/c9pCJ7d8Ce Do Star on Github , it Matters a Lot. submitted by /u/Dry-Foundation9720 [link] [comments]

Sonnet 4.7?

Claude leaking a new Sonnet model? submitted by /u/YardStunning2324 [link] [comments]

Puppetmaster dramatically decreases token costs + increases context

Puppetmaster is an orchestrator + router that sits on top of the agent CLIs you already pay for (Cursor, Claude Code, Codex, OpenAI) or a plain shell when there's no harness at all. You hand it work, and it routes each task to the cheapest model that can actually do it, runs the workers as independent processes, and stores everything as durable typed state instead of one giant transcript. This is the "context-hack" Puppetmaster graphs your directories and prevents context stretching between agents. https://github.com/professorpalmer/Puppetmaster submitted by /u/ProfessorPalmer [link] [comments]

This system cuts wasted tokens by making Claude map your code before writing anything

What it does: Stops the fluff — Forces Claude to skip polite prose and filler. Every response is pure logic and code, nothing else. Maps before it touches — Claude has to understand your entire codebase structure before writing a single line. No more broken APIs. Pushes back on you — If your prompt is vague or your logic is weak, Claude argues back. It won't just blindly write bad code. Catches bugs before they exist — Proactively flags race conditions, security issues, and duplicate logic during the mapping phase itself. Kills the sycophancy — No more "great idea!" responses as context grows. It stays sharp and critical till the end of the conversation. How to use it: Go to the gist and copy the CLAUDE.md file In Claude Code or Cursor, add it as your project-level system prompt or drop it in your root as CLAUDE.md Copy SHARED-GROUNDING.md too is a short rule that makes Claude explain what it's doing before every tool call. Start a new conversation. Don't continue an existing long chat the whole point is a clean context Give it a complex task and watch it ask clarifying questions and map dependencies before touching anything One catch: Don't use this for small scripts or quick fixes. The upfront token cost isn't worth it unless you're building something complex. The Files You'll Feed submitted by /u/Choice-Highlight-369 [link] [comments]

I built a local context compiler for coding agents — real benchmark on a NestJS repo, including where it backfires

Disclosure up front: this is my own open-source project (@lubab/madar, MIT). Not selling anything, but it's mine, so weigh the numbers accordingly. When you ask a coding agent (Claude Code, Cursor, etc.) "how does X work" in a big repo, it usually opens a pile of files to figure out how everything connects before it can answer. That discovery is most of the token cost — and it repeats every session. Madar maps your repo once, locally, and hands the agent a small "context pack" over MCP: the files and call paths that actually matter for your question. The bet is that the agent starts from that instead of rediscovering the codebase each time. I finally ran a clean before/after. Same question ("how is the idea report generated"), same real backend (NestJS + BullMQ, ~800 files), Claude Code doing the work. Baseline = no Madar. Numbers are Anthropic-reported, not my estimates: Plain agent With Madar Input tokens 1,000,776 223,539 Cost $1.84 $0.69 Turns 16 5 Tool calls 15 4 So roughly 78% fewer input tokens and 63% cheaper to reach the same answer on that run. Where it backfires (the part I actually care about): It's ONE question, ONE repo, ONE agent. Not a general claim. Two things carried the result: the graph was scoped to the backend service, and built with --spi. Point it at a whole monorepo graph and the pack gets big enough that it can cost more tokens than it saves. Scoping isn't optional. "How does X work" (explain) is the case I've tested. Edit/review tasks are much less proven. It's also deterministic — no embeddings, no ML deps, no calling out to a model to build the graph. Just static analysis of your TS/Node code, locally. If you want to try it and tell me where it regresses, that's genuinely the feedback I need: npm i -g @lubab/madar madar generate . --spi madar claude install # or cursor / copilot / codex / gemini Repo: github.com/mohanagy/madar Honest question for the sub: for those of you running Claude Code / Cursor on big repos — is the "rediscover the codebase every session" token cost actually your bottleneck, or is it something else? Trying to figure out if this is even the right problem to attack. submitted by /u/CaptainProud4703 [link] [comments]

Here's 100+ evals on Opus 4.8

We aggregated 100+ evals on Opus 4.8 to see what changed. The big gains vs 4.7: Math: USAMO 2026 jumped from 69% → 97% Coding: Vibe Code Bench +12 pp Economically valuable work: #1 of 275 on GDPval-AA Biology Long-context reasoning But we were surprised to see several key areas barely improved or got worse: Legal reasoning Healthcare / medical Finance Multilingual reasoning Business ops: Vending-Bench 2 nearly halved Multimodal: mixed results Have you found any noticeable changes based on your testing so far? submitted by /u/davidthesong [link] [comments]

Grateful to be accepted into Claude for Open Source Program

Just got the email from Anthropic. Claude Max 20x free for 6 months for open source maintainers. Really thankful for this. I have been building CodeBurn, a CLI that shows where your AI coding tokens go. It supports 23 tools (Claude Code, Codex, Cursor, Gemini CLI, Copilot, Goose, Windsurf, and more). Reads session data from disk. No API keys, no wrappers, nothing leaves your machine. It breaks down cost by model, project, and task type. Has a waste detector with copy-paste fixes and a head-to-head model comparison using your own data. With this support there is a lot more coming for the open source community. If you use AI coding tools, check it out: npx codeburn@latest GitHub: https://github.com/getagentseal/codeburn submitted by /u/MurkyFlan567 [link] [comments]

pg-mnemosyne-mcp – Give your Cursor & Claude Code assistants persistent PostgreSQL memory and task tracking

Hi everyone! I was tired of my AI coding agents losing context across different chats or stepping on each other's toes when running multi-agent sessions. So I built **pg-mnemosyne-mcp**, a high-performance Model Context Protocol (MCP) server for PostgreSQL. It does three things really well: **Persistent Super Memory**: Let's your AI store key-value memories with tags directly in a local or cloud Postgres DB. **Dynamic Task checklists**: Prompts a specialized task board for AI tracking. **Agent Coordination Hub**: If you run multiple agents (e.g. Claude Desktop and Cursor), they register their active files and tasks in a shared database to prevent merge conflicts. Setup is a single command: `pg-mnemosyne init --dsn "..."` (it auto-configures Claude Desktop, Cursor, Roo Code, Windsurf, Claude Code, and more). It's fully open-source! If this sounds useful to your workflow, I'd love for you to try it out or drop a ⭐ to support the project! 👉 **GitHub**: https://github.com/Janadasroor/pg-mnemosyne-mcp 👉 **PyPI**: https://pypi.org/project/pg-mnemosyne-mcp/ submitted by /u/janadasroor [link] [comments]

Loom for Claude

Yo! Solo founder, built this to help myself while working on my main startup. Turned out to be pretty useful so I thought I'd wrap it up for others to use. The problem: I use Cursor and Claude Code daily. The slow part isn't typing prompts anymore (Wispr Flow + voice mode already solved that) — it's explaining which screenshot goes with which sentence. "The button on the right of the second screenshot, the orange one, no, that one..." Dis Dat: press ⌃⌥⌘Space, talk while pointing your cursor at things, press again. A link lands on your clipboard. Paste it into Cursor, Claude Code, Codex, Lovable, v0... The agent goes and fetches your feedback — what you were saying, where you pointed — and ships the changes. Free to try, $19/mo for unlimited. Works with any AI vibe coding soon. Mac only for now (Apple Silicon + Intel). Also building a mobile version. open any page on your phone, talk as you scroll, and the link lands on your Mac ready to paste. So you can react out loud to your own product without sitting at your desk. Coming soon; happy to share more if anyone's curious. Things I'd genuinely value feedback on: What's the workflow you'd want this to slot into that I'm missing? What other agents would you want this to work with first? Anyone tried something similar and bounced off it... what killed it? I'll be here all day. Roast away. submitted by /u/Emergency_Bar_428 [link] [comments]

1 in 4 agent skills had vulnerabilities. This is the local check I wish I had before installing random AI tooling

A recent paper analyzed 31,132 agent skills in the wild and found that 26.1% had at least one vulnerability: prompt injection, data exfiltration, privilege escalation, or supply-chain risk. That number changed one habit for me: before I run a repo with agent configs, I scan the files the agent will obey. Because the scary files usually do not look scary. AGENTS.md, MCP configs, Cursor rules, hooks, plugin manifests, skills - these are not just docs. They decide what your agent can run, inherit, fetch, and trust. The local check I use now is lintai: https://github.com/777genius/lintai Install / run: npx lintai-cli scan . For CI: npx lintai-cli scan . --format sarif > lintai.sarif For a deeper review: npx lintai-cli scan . --preset preview No SaaS. No telemetry. Local, fast and deterministic. If you do not use npm: curl -fsSL https://github.com/777genius/lintai/releases/latest/download/lintai-installer.sh | sh "$HOME/.local/bin/lintai" scan . Not a sandbox. Not a silver bullet. Just a fast preflight before giving an AI-agent repo trust. Github: https://github.com/777genius/lintai Site: https://777genius.github.io/lintai/ Curious what other people are using to review agent trust files before running them. submitted by /u/IlyaZelen [link] [comments]

thing i wish i'd known about ai tools when i started using them seriously a year ago

the biggest unlock wasn't the model getting better. it was me getting better at knowing when to use which tool. year-ago me: opened chatgpt for everything because it was the first tab. asked it questions, got mediocre answers, accepted them, moved on. now me: actually thinks about which tool fits the task. claude for writing and reasoning. perplexity (used to, less now) or kagi for "find me a source." cursor for code. notebooklm for synthesizing across many documents. chatgpt voice for thinking-out-loud. granola for meeting notes. each one has a specific role. this sounds obvious typed out. it wasn't obvious when i was just starting. i thought i was supposed to find The One Tool and master it. turns out the skill is matching tool to task. the tools are mostly fine. the user choosing the wrong tool is most of why outputs are bad. second thing: don't trust any tool that doesn't show its work. perplexity citations matter. claude saying "i'm not certain about this" matters. tools that just confidently produce output with no provenance are dangerous if you're going to act on the output. early on i trusted everything equally. now i grade tools by how clearly they show me what they don't know. third thing: the cheap subscriptions add up faster than you think. i ran the math at one point — what i spent in my first year of "trying ai tools" was more than what i'd have paid a human freelancer to do the things i was trying to automate. would have been faster, too. AI tools have a real cost-benefit math and it's not always in your favor, especially early when you're still figuring out what works. if i'd known those three things a year ago, i'd have wasted less money and gotten better outputs sooner. posting in case it helps anyone earlier in the curve. submitted by /u/Honest-Purchase-9113 [link] [comments]

I spent $340 on AI subscriptions last month. Wrote down what I actually used each one for. It was depressing.

Going through the credit card statement, here's what I had active: Claude Pro (40), ChatGPT Plus (20), Cursor (20), Perplexity Pro (20), Notion AI (10), Granola (20), ElevenLabs Starter (5), Midjourney Basic (10), Gamma Pro (10), Beautiful.ai (12), Otter Pro (17), Loom Business (15), Zapier Pro (30), Make Core (10), Tactiq Pro (8), Descript Creator (15), Reclaim.ai Pro (8), Motion (19), Superhuman (30), one i can't remember the name of (10), some ai-something for instagram captions (11) Then I sat down and wrote next to each one the last time I'd actually used it. Not opened it, used it for a real piece of work. Claude (yesterday), ChatGPT (yesterday, voice mode in car), Cursor (yesterday), Perplexity (3 days), Granola (every meeting), Gamma (2 weeks), Zapier (a month, but the automations are still running), ElevenLabs (3 months ago), Midjourney (couldn't remember), Beautiful.ai (couldn't remember), Otter (replaced by Granola, just forgot to cancel), Loom (4 months), Tactiq (replaced by Granola, also forgot), Descript (used twice in 6 months), Reclaim/Motion (both, can't tell them apart, forget which one schedules my meetings), Superhuman (used the AI features twice), the instagram one (literally cannot remember signing up) Cancelled 11 things this morning. Saving $145/month. Nothing in my workflow actually changed. The pattern isn't that AI tools are bad. It's that I treat subscribing like trying. Every "I want to try this" became a recurring charge I forgot about. submitted by /u/OneSeaworthiness2676 [link] [comments]