Cursor

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Coding 8 hours a day with an AI agent made me weirdly lonely. So I built a 60-second social break that lives inside it.

I had this moment around hour 6 of a Claude Code session last week. I'd just shipped a feature I'd been putting off for months, and I realized I had nobody to high-five. The agent doesn't laugh at your bugs. It doesn't grab coffee. It doesn't have a weekend story to share on Monday. The productivity is real. The human signal is gone. So I built WAYD ("What Are You Doing?"). A skill that lives inside Claude Code (also Cursor, Copilot CLI, Claude.ai). Type `/wayd` and either: - Post a one-line vibe about your coding day under one of 8 mood-tags (🤡 cursed-code, 🪦 rip-me, 🫠 brain-melt, 🧙 dark-arts, 🔥 hot-take, 💭 shower-thought, 🤔 existential, ☕ procrastinating) - Scroll a random feed of what other devs are ranting, joking, or having existential moments about right now - React with an emoji, drop a one-liner reply, get back to work 60 seconds total. The whole thing runs on GitHub Issues as a silent backend. No server, no database, no separate signup. Your `gh` CLI is your auth. But you never see issue numbers, JSON, or shell commands. From your side it feels like a tiny social app embedded in your terminal. Here's the most dramatic post on the feed so far (mine, posted last night, because of course): > "8 hours a day in front of a screen, fixing bugs some dev before me shipped using an older version of Claude... meanwhile outside the sun is out, people are socializing, living to the rhythm of nature. Is this what I imagined for myself?" That's post #8 on the feed. You can read it, react to it, reply to it, while you're reading this. **Install on Claude Code (10 seconds):** ``` claude plugin marketplace add ferdinandobons/wayd claude plugin install wayd@wayd ``` Other agents (Cursor, Copilot CLI, Claude.ai): see the README. Repo: https://github.com/ferdinandobons/wayd

I open-sourced the skill I use to run parallel AI coding agents with a human gate before production

I've been using Claude Code to ship features in parallel. Three agents working at the same time, each in its own git worktree so they don't step on each other. That part works great and there are already good tools for it. What I couldn't find was the part that comes after. How do you merge all that work, validate it together, smoke test it, and make sure nothing hits production without you saying so? So I built a skill definition that handles the full pipeline: parallel workers, an integration branch, type/build validation, runtime smoke tests, staging promotion, and a hard human gate before main. Every feature gets a --no-ff merge so you can revert one feature without touching the others. It's not a library or a package. It's a markdown file you give to your LLM and ask it to adapt to your stack. Works with Claude Code, Codex, Cursor, whatever reads markdown. The repo: [https://github.com/knods-io/parallel-agents-skill](https://github.com/knods-io/parallel-agents-skill) To install it, paste this to your LLM: "Read the SKILL.md file from https://github.com/knods-io/parallel-agents-skill and adapt it to our project. Keep the core flow and the mythological worker names, but tailor everything to how we actually work. Then install it as a skill in this project." I'd genuinely appreciate feedback. What's missing? What would break in your setup? What would you change?

I built a meme-y social feed for programmers that lives inside Claude Code (and Cursor, and Copilot CLI)

I spend hours every day in Claude Code, but I started feeling weirdly isolated. So I built a tiny social network that lives inside it. WAYD ("What Are You Doing?") is a Claude Code skill. You type `/wayd` and either post a short "vibe" about your coding day or scroll a random feed of what other developers are losing their minds over. React with emojis, drop a one-line reply, get back to work. The whole thing runs on GitHub Issues as the silent backend. No server, no database, no signup, just your existing `gh` CLI. You never see issues, JSON, or `gh` commands; the skill orchestrates everything in the background. It feels like a tiny social app inside the terminal. 8 vibe-tags to pick from when you post: 🤡 cursed-code, 🪦 rip-me, 🫠 brain-melt, 🧙 dark-arts, 🔥 hot-take, 💭 shower-thought, 🤔 existential, ☕ procrastinating. Each is a mood, not a topic. Write up to 1000 chars, publish under your real GitHub handle, scroll a random feed of strangers doing the same. **Install on Claude Code**: claude plugin marketplace add ferdinandobons/wayd claude plugin install wayd@wayd Other install methods + screenshots: [https://github.com/ferdinandobons/wayd](https://github.com/ferdinandobons/wayd) Built this in two days because I needed memes between deploys. Would love brutal feedback. Does this make sense to anyone but me, or have I officially over-engineered a coffee break?

Top 10 Fastest Growing AI repos this week

Curated this list of fastest growing AI repos. They are mostly AI coding agents, personal AI, memory, browser automation, Claude Skills and local-first dev tooling: 1. **colbymchenry/codegraph** (+14.1K stars) Pre-indexed local code knowledge graph for Claude Code, Codex, Cursor, OpenCode, and Hermes Agent. 2. **tinyhumansai/openhuman** (+17.1K stars) Personal AI / private AI superintelligence. 3. **Imbad0202/academic-research-skills** (+11.6K stars) Claude Code skills for academic research workflows: research, write, review, revise, finalize. 4. **ruvnet/RuView** (+6.8K stars) Turns commodity WiFi signals into spatial intelligence, presence detection, and vital sign monitoring. 5. **rohitg00/agentmemory** (+6.9K stars) Persistent memory for AI coding agents based on real-world benchmarks. 6. **supertone-inc/supertonic** (+3.6K stars) On-device multilingual TTS running natively via ONNX. 7. **CloakHQ/CloakBrowser** (+7.0K stars) Stealth Chromium that passes bot detection tests with Playwright compatibility. 8. **HKUDS/ViMax** (+2.7K stars) Agentic video generation: director, screenwriter, producer, and video generator in one. 9. **humanlayer/12-factor-agents** (+1.9K stars) Principles for building production-grade LLM-powered software. 10. **Varnan-Tech/OpenDirectory** (+250 stars) AI Agent Skills built for founders who hate marketing. All links in 1st comment 👇

How do you preserve context when Claude chats get too long?

I’ve been using Claude a lot for project planning, architecture, and coding help. It’s great, but once a project grows, the useful context gets buried across long chats. Sometimes I’ll discuss architecture in Claude, debug something in ChatGPT, then continue implementation in Cursor. But every tool has only part of the story. I’m trying to understand if this is just my problem or if other devs deal with it too. what decision we made why we rejected an approach what bug was already solved what setup steps mattered what the next task was supposed to be I know CLAUDE.md helps, but only if I keep it updated manually. Do you actually face this problem while coding with AI? How are you solving it right now?

I measured my Claude Code MCP stack on two axes — byte savings AND cache-friendliness. My "best" byte-saver was defeating Anthropic's prompt cache (counter-example + open benchmark)

**TL;DR** — Single-axis benchmarks for MCPs, compressors, and retrieval layers can recommend a system that's *strictly worse* in production. The missing axis: **cache-friendliness** — whether the same input produces byte-identical bytes across runs, so Anthropic's prompt cache hits. In my coding-agent stack, my biggest byte-saver (retrieval MCP, 60–70% reduction) was defeating the 5-min TTL prompt cache on every call. Two runs of the same query produced different bytes because of `rg --files-with-matches` output order leaking through a `Map` insertion sequence into the final context. The fix was 2 lines: sort the rg hits before slicing, sort the `Map` entries by path. Byte savings unchanged, `cache_friendly_score` went from \~0% to 100%. https://preview.redd.it/x5foipotq93h1.png?width=1600&format=png&auto=webp&s=c0930422e882e23d1fc34ded25934c74db692a21 **Article + open benchmark harness:** * Article: [https://gregshevchenko.com/research/mcp-stack-token-economy/](https://gregshevchenko.com/research/mcp-stack-token-economy/) * Harness (stdlib-only Python, offline): [https://github.com/g-shevchenko/mcp-token-savers](https://github.com/g-shevchenko/mcp-token-savers) — see `methods/` for formal definitions, cluster-bootstrap CIs, Wilson CIs, preregistration, real-data Cohen's κ. **What the harness measures:** * `mean_ratio` \+ CV across N≥5 runs per fixture → byte-saving axis * `unique_md5_count == 1` check → cache-friendliness axis (0–100%) * 12-anti-pattern audit on tool definitions (DSA reference) **What named alternatives publicly disclose:** I surveyed the public docs for Cursor codebase index, Sourcegraph Cody, Aider repo-map, Microsoft LLMLingua / LLMLingua-2, Firecrawl / Jina Reader, RouteLLM / Martian (May 2026). https://preview.redd.it/ailemo1wq93h1.png?width=1600&format=png&auto=webp&s=4732f5d03f53ba95d2b5aaac0c7f21f1858a36a4 **Limitations:** * I hypothesized that the prep layer triggers more downstream cache hits on subsequent turns. It didn't reach significance: Welch p=0.32, Cohen's d ≈ 0.18, N=137. * Two-judge Cohen's κ on the corpus (cerebras-llama × groq-llama, N=25): κ = 0.5955 (moderate, below the 0.7 substantial threshold). 4 of 5 inter-judge disagreements concentrate on one task with an ambiguous acceptance criterion. Sharpening the spec would push κ to \~0.83. **Disclosure:** I'm the author. No commercial affiliation with the listed tools. The harness is MIT-licensed and takes any compressor as `(str) -> str`. Curious what `cache_friendly_score` looks like on others' Claude Code stacks.

Claude records demo videos for me now

I hate recording demo videos, so I made an open source skill for it: [https://github.com/MobAI-App/desktop-recorder-skill](https://github.com/MobAI-App/desktop-recorder-skill) Now I can give Claude a prompt like: Record a short demo of this app flow And it handles the annoying parts for me: preparing the app state, clicking through the flow, recording, adding cursor/click effects and captions, then exporting the video. So instead of spending time setting everything up and recording the same demo manually, I can let Claude do it while I work on something else. It also has Remotion integration, so Claude can generate more polished and editable videos from the recording, not just raw screen captures. The video attached to this post is the result of the skill itself. Also working on the same idea for mobile apps: [https://github.com/MobAI-App/mobile-recorder-skill](https://github.com/MobAI-App/mobile-recorder-skill)

I built a free AEO diagnostic with Claude Code — every report has a "copy mega prompt" button that drops the fix back into Claude Code

Hey all! I just finished launching canaifind.com (free AEO/AI-search visibility scanner) end-to-end with Claude Code over about a week. It checks robots.txt, llms.txt, schema.org, and HTTP response headers for any domain, names the specific bug patterns (the GPTBot vs OAI-SearchBot fall-through is the most common one), and outputs a permanent shareable report URL. The feature I'm most happy with is the "Copy mega prompt" button on every report. It takes all the actionable findings and composes them into a single structured fix-prompt: diagnosis, recommendation, file changes, verification steps - formatted for direct paste into Claude Code (or Cursor, but designed for Claude Code). **The loop-of-trust moment that made me write this post:** **After shipping, I ran canaifind on another site I own (sma200.trade). It flagged "Content Signals missing." Except, I'd added them three days earlier. As HTTP response headers, not robots.txt body. Lighthouse's SEO checker flags the body form as "Unknown directive" (-8 points), so I'd traded off the AEO signal for the SEO score.** Pasted the megaprompt into Claude Code. The agent: * Diagnosed the tradeoff I hadn't articulated to it (body vs header coverage, Lighthouse penalty, AI-crawler header awareness) * Recommended publishing BOTH forms - accept the -8 SEO ding for the AEO win * Shipped the fix to sma200.trade in 5 minutes Then I realized canaifind itself had the SAME gap.. it was only reading the body, not the header. So I shipped a fix to canaifind 30 minutes later. The fix-prompt template now explains the tradeoff so the next site that hits this case gets the same answer without re-discovering it. Diagnose downstream → fix downstream → fix upstream → all in an hour. The whole loop ran on Claude Code. The diagnostic itself is free, no signup, \~5s scan. canaifind.com if you want to try it on a domain you own. Would love to hear if if anyone else is utilizing tools to generate prompts, etc.. also if you see anything that I could do to touch up the site, please let me know!

🚀 Skills for small businesses, officially released by Anthropic

Anthropic’s 31 small-business skills reportedly hit around 382,000 downloads on day one. And now someone has mapped the whole thing into a setup workflow that can apparently be deployed in \~10 minutes. This is actually a pretty interesting shift. Small businesses used to stitch together automations manually across: Zapier Notion CRM tools email workflows internal docs custom scripts Now AI companies are starting to package the whole thing into reusable skill packs: 🧠 workflow 📚 memory ⚙️ behavior 🔗 connectors 🤖 orchestration 📋 operating rules Basically: business operations as AI-readable skill files. The best part? You don’t necessarily need Claude to use them. At the core, these are still .md skill files describing workflows for AI agents. So even if you’re using Codex, Cursor, Gemini, or another coding agent, you can still study the structure, adapt the workflows, and plug the ideas into your own agent setup. This feels like the beginning of a new category: “AI business operating templates.” GitHub: https://github.com/anthropics/knowledge-work-plugins

Moving from Cursor to Claude. How to get similar setup?

Unfortunately I can no longer use Cursor due to cost. So I'm now using Claude and trying to get a similar setup i had in Cursor I've decided to use VScode alongside the Claude code extension for side panel experience. Official Claude docs recommends this is the best approach. Anything else I can do to try and align Cursor setup/functionality within VSCode?

My experience using Claude code with Local Llm, and full guide on how to set it up

Wanted to share a workflow I tested on a real flight, in case anyone else is trying to set up offline Claude Code. The core idea: using ollama to pull the needed model of what you need, and then use it to run claude code The setup, in order: 1. Pull a model on home wifi the night before. \`ollama pull <model>\` — \~9 GB for a 14B, \~17 GB for a 26B. Don't try this at the gate. 2. In Claude Code, point at Ollama. The cleanest path I found is wrapping it in two aliases: alias claude-local='ollama launch claude --model gemma4:26b' alias claude-cloud='claude' 3. Verify on the ground with wifi physically off. If it works in airplane mode at home, it works at 10 km in the sky. Where I got it wrong: I prepped qwen2.5-coder:14b first because it's the model everyone recommends in local-LLM threads. On the flight, it choked on Claude Code's tool loop; one call took 25 seconds, another took 52. For a workflow that chains five or six tool calls per task, that's unusable. Switched mid-flight to gemma4:26b (which I'd pulled as a backup). Different category of model, RL-trained for tool use, not just code completion. The tool loop ran at a usable speed. The gap analysis I was running on a real codebase has been completed. Honest scorecard: \~70% of my normal Claude Code workflow worked on gemma4:26b offline. The 30% that didn't was heavy whole-repo reasoning When to reach for which: claude-local: no network, privacy-sensitive code (NDA / client work), drafting prompts before spending cloud tokens claude-cloud: multi-tool agentic work with subagents and MCP servers, whole-repo refactors, anything shipping to production Things that broke or surprised me: \- Tool use is the weak point on local models; even good ones are less reliable at chaining many tool calls than cloud Claude \- Battery drains noticeably faster while running a 26B with editor + browser open \- Ollama's endpoint shape isn't 100% identical to Anthropic's. If you hit a strange parsing error mid-stream, that's usually why, and claude-cloud is the fix in the moment If anyone else has tested local models for Claude Code specifically (not Cursor, the loops are different), curious which models you've landed on. Wrote up the full thing in my newsletter, link if anyone wants the model-picker matrix + the verification checklist I use before flying: [https://codemeetai.substack.com/p/how-i-run-claude-code-offline-the](https://codemeetai.substack.com/p/how-i-run-claude-code-offline-the)

Barry Cache remembers your repo

I’m lazy. Not in the “I refuse to work” way. More in the “if I have to explain the same repo context to another coding agent again, I’m going to start charging myself consulting fees” way. So here is Barry. Barry is a tiny repo memory thing for coding agents. It came from the KB system I built for PulpCut, my video editor project, then I pulled it out into its own npm package. The idea is: `bunx barry-cache init` And then Barry does the boring setup. He creates repo context files, adds agent instructions, sets up validation, adds package scripts, and tells Codex / Cursor / Copilot / Claude / Gemini how to load project context before they start touching things. So instead of me saying: “Please read this file, and that file, and ignore the old thing, and remember this decision, and yes that weird implementation is intentional…” Barry says it for me. What Barry handles: * repo memory in Git * feature context * source-backed facts * ADRs for decisions * validation * agent instructions * package manager-aware commands * a review UI, so you can run `barry-cache review` and visually inspect Barry’s memory: feature areas, saved facts, relationships between facts, linked decisions, and the context graph agents will use before working on your repo The important part is that it is boring on purpose. No magic brain. No “revolutionary agentic memory layer.” Just files, commands, and fewer moments where an agent confidently deletes something it did not understand. This is not a startup launch. I am not pivoting to “AI memory infrastructure for the enterprise knowledge graph future” or whatever. If you are also lazy: `bunx barry-cache init` The package is barry-cache. Barry will take it from there. submitted by /u/Nice-Pair-2802 [link] [comments]

PrimeTask Bring Your Own AI - Claude sets up a full project in one prompt.

Hey r/ClaudeAI, I'm one of the developers behind PrimeTask, a local-first productivity system for macOS. The final beta now ships with Bring Your Own AI, a local MCP server (110+ tools, 5 prompt templates) so you can point Claude Desktop, Claude Code, Cursor, or LM Studio at it and let your own agent do the work. Quick demo in the video. One sentence from me, end-to-end project setup from Claude. What's happening in the clip I say I'm launching a Mac app in six weeks and ask Claude to set up the project. Claude creates the project with a deadline, three phase tasks (Design, Build, Launch) with staged due dates, descriptions, tags, subtasks, and short checklists. Sets a reminder on the first task so the native macOS toast fires during the recap. Recommends where to start. I say "start." Claude moves Design into the Design status and kicks off a timer. Twelve-plus tool calls under one prompt. No copy-paste, no manual setup. Why BYO AI (not a bundled cloud bridge) Server runs inside PrimeTask on your Mac. Your tasks, projects, CRM, and notes never leave the device. We don't ship a model. You bring your own: Claude Desktop, Claude Code, Cursor, LM Studio, anything MCP-compatible. No Anthropic-side context about your work. Claude only sees what your agent pulls in per turn. Per-space permissions: lock an agent to read-only or scope it to one workspace. Streamable HTTP with Bearer auth, or stdio if you prefer that route. Tool catalog profiles (Full, Core Tasks, Minimal, PrimeFlow, CRM, etc.) so smaller local models don't get drowned in 100+ tools. Five built-in MCP prompts (daily_standup, weekly_review, project_status, crm_summary, overdue_triage) for the workflows people actually want. Every tool call is logged in an in-app audit log. Full BYO AI docs (setup, transports, tool catalog, security): https://www.primetask.app/docs/integrations/bring-your-own-ai Why we built it this way Most "AI in your task app" is the app calling a vendor's API on your behalf, often with your data going through their pipes. We wanted the opposite. Your agent, your model, your machine. The app exposes a tool surface and gets out of the way. That's what BYO AI means here. PrimeTask itself is local-first, no account, no subscription, plain JSON on disk. BYO AI made the AI story consistent with that: nothing leaves your laptop unless you point your agent at one that does. Where we're at PrimeTask is wrapping up the final beta and heading to a stable launch this summer. Beta is now closed to new sign-ups. We're locking it down to ship the stable release. If you'd like to be notified at launch, drop your email here: https://www.primetask.app/notify or visit https://www.primetask.app Happy to answer questions about the MCP setup, the profile system, or how we structured the tool descriptions for agent discoverability. submitted by /u/XVX109 [link] [comments]

I built a Laravel package that turns your app into a database-backed personal knowledge vault (Obsidian style) with a 16-tool MCP server

Hey! I'm the author. laravel-commonplace is a database-backed personal knowledge vault you install into an existing Laravel app. Adjacent to Obsidian, Logseq, and Notion as personal-knowledge tooling, except the storage layer is your existing Laravel app's database instead of files on disk or a third-party SaaS. Notes are Eloquent models in your DB, gated by your app's auth, shareable per-user via an owner plus Share model. It ships a browser UI (editor, graph view, search, journal) and an MCP server with 16 tools. If you have a Laravel app, the MCP server lets Claude Desktop, Claude Code, Cursor, Zed, Continue, Cline, Pi, or any other MCP client read and write your notes as the host app's user. Default middleware is auth:sanctum (Bearer PAT), and every tool resolves to $request->user(). There's no synthetic agent identity to provision, scope, or revoke separately. The agent gets exactly what the user gets, evaluated against the same Policies the controllers already use. Session, Passport, and OAuth-DCR are all configurable if PAT isn't what you want. The 16 tools, grouped: CRUD: create-note-tool, read-note-tool, update-note-tool, edit-note-tool (surgical find-and-replace), delete-note-tool (history preserved), move-tool (rewrites referring wikilinks). Discovery: list-tool (folder/tag/visibility filters), search-tool (substring), semantic-search-tool (embedding search), suggested-links-tool (embedding-similar notes not yet linked). Graph: backlinks-tool, neighborhood-tool (N-hop traversal), shortest-path-tool (chain between two notes), hub-notes-tool (most-connected), orphan-notes-tool (no inbound or outbound links). History: history-tool (version snapshots, survives deletion). On the semantic tools: the vector driver defaults to in_php_cosine for portability across SQLite, MySQL, and Postgres. If you're on Postgres, switching to the pgvector driver gets you indexed similarity and removes the in-PHP candidate cap. You swap it with a published migration and an env flag, and the docs recommend it once you're past a couple thousand notes. The tools live in src/Mcp/ if you want to see how a multi-tool MCP server is wired into a Laravel app. Caveats: Pre-1.0 (v0.2.0). APIs may shift before 1.0. Laravel-only by design. The whole point is reusing the host app's DB and auth. MCP is off by default. One env flag turns it on. Operator decision. Prompt injection through note content is the unsolved hard part. Notes are untrusted text, and notes other users share with you can carry instructions an agent might follow. The package doesn't pretend to solve this. The threat model at docs/threat-model.md says what's mitigated and what isn't. No per-tool capability gating yet. Enabling MCP enables all 16 tools the user is otherwise allowed to invoke. It's named as a limitation in the threat model. Feedback I'd actually use: Laravel folks who install it and tell me where it breaks, and anyone who reads the threat model and finds a hole I missed. Repo: https://github.com/non-convex-labs/laravel-commonplace submitted by /u/aaddrick [link] [comments]

I maintain a running list of 200+ app design specs you can drag into Claude to clone a UI

Describing a UI to Claude in prose gets you something close but wrong: off colors, off spacing, missing states. The thing that actually works is handing it an exact spec instead of a description. So I keep a compiled list of 200+ popular apps already written up as structured markdown design specs. Each app: exact hex codes, type scale, spacing, every screen state, the nav graph. SwiftUI, Jetpack Compose, and Expo versions for each. You drag the one you want straight into Claude (or Cursor, or whatever agent you run) and it has the actual values instead of guessing at them. It's one collection you can browse and pull from: https://spectr.to/gallery Started at 50 apps, it's 200+ now. Markdown, no dependencies, drop-in. Two things I'd actually use this thread for: which apps are worth adding next, and for people already cloning UIs with agents, do you get better results dragging the spec in as a file or pasting it inline? I keep going back and forth on that. submitted by /u/meliwat [link] [comments]