Cursor

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

coding-posture: task-aware modes for AI coding agents — one SKILL.md, research-backed, MIT

coding-posture is a small skill that stops coding agents from behaving like optimistic elevators with write access — thrashing on a stuck bug, faking a green test, skipping the repro, migrating prod without a rollback. Before non-trivial work, the agent picks a mode — debug, fix, review, test-first, refactor, optimize, migrate, upgrade, integrate, spike, unstuck — and follows a short checklist for it. A few invariants hold in every mode: verify by running the real check, never weaken a test to go green, no destructive commands without explicit scope. Why it's built this way (grounded in research, not vibes): Procedures, not personas. Naming a role ("act as an expert debugger") doesn't reliably change behavior (Zheng et al., EMNLP 2024); specifying a process does. So each mode is a checklist, not a character. The model self-selects the mode from context — no brittle keyword router. Evidence, honestly: the repo ships a with/without-skill eval (LLM judge + baseline). Early result: +15pp (85% vs 70%) on one model, 5 cases — directional, and you can run it yourself in eval/. Install: Claude Code plugin (/plugin marketplace add alexei-led/coding-posture), a Codex plugin, or drop the SKILL.md into Pi / Hermes / Cursor. MIT. Feedback and new modes welcome. submitted by /u/alexei_led [link] [comments]

I open-sourced ByteDance's "Vibe Creating" prompt skill as a portable Agent Skill (single SKILL.md, bilingual)

ByteDance shipped a creator paradigm + prompt skill called "Vibe Creating" with their Seedance 2.0 video model. I open-sourced a portable version on the open Agent Skills standard (single SKILL.md) — it drops into Claude Code's ~/.claude/skills/, and also works in Codex, OpenClaw, Hermes, or as a Cursor rule / system prompt. What it does: turns a rough idea, story, or an over-specified shot script into a clean, model-friendly text-to-video prompt. The core idea — as video models get smarter, the prompt should get simpler: describe the scene and the emotion, and let the model handle the cinematography. Why it's not just a "rewrite this" prompt: it's judgment-first. It scores your input on three axes (Scenario × Expression × Information) and picks the lightest action — pass-through, light cleanup, direct rewrite, ask-first, or keep-as-is. Hand it something that genuinely needs precise control (dialogue sync, a UI demo) and it tells you to keep your detailed prompt instead of flattening it. Output is a fixed four-part format: Judgment / Action / Result / Notes, so it's auditable. Bilingual (EN/中文), MIT, with worked test cases and before/after clips in the repo: https://github.com/Alisa0808/vibe-creating-skill — feedback on the SKILL.md packaging welcome. submitted by /u/Which-Jello9157 [link] [comments]

Anyone here using more than one AI tool in their workflow? How do you handle the context gap?

I've been running Claude for planning and a separate session for building, and the part that keeps breaking down is the handoff. whatever I figured out in one session doesn't automatically carry to the next. Curious how others are handling this. Are you using a single tool end-to-end, or mixing Claude with Cursor/Codex/ChatGPT? And if you're mixing, what's your actual handoff process? submitted by /u/riley_kim [link] [comments]

For AI coding agents, review feels more expensive than generation now

One time I ran Claude in a loop for four or five hours to build a program, then had Codex and Cursor review the code. Each pass surfaced different issues, but the codebase was so large that I could only trust what they flagged. I've been using coding agents more seriously in my own projects recently, and the part that feels expensive is not the generation anymore. It is the review. A patch can appear in 2 minutes. But then I still need to check the diff, run tests, read logs, and ask myself whether the change is only "passing" or actually going in the right direction. Maybe this is just my setup, but I trust an agent-written change much more when it comes with some evidence: what command it ran, what failed before, what passed after, and what files it intentionally did not touch. One time I ran Claude in a loop for four or five hours to build a program, then had Codex and Cursor review the code. Each pass surfaced different issues, but the codebase was so large that I could only trust what they flagged. I'm not trying to say AI coding is bad. It is useful. But it changed my review work from "read every line slowly" to "ask for proof and inspect the risky parts." Curious how other people handle this: Do you ask your coding agent to include test output in every PR? Do you use another model/tool to review the first model's code? If tests pass but the design feels wrong, do you count that as agent success? What evidence makes you trust an AI-written patch? submitted by /u/BitByLiu [link] [comments]

Software development has entered its "infinite monkeys" era

With the rise of agentic coding tools like Claude Code, Cursor, and Codex, the barrier to entry is gone. Now, anyone with an internet connection can "type." We have essentially reached the infinite monkey phase of software development. Millions of new hobbyists, junior devs, and product managers can now generate codebase-level changes with natural language. The "typewriters" are the LLMs translating those keystrokes into code. By sheer volume of output, we are going to see a massive explosion of software. Some of it will be brilliant, but a lot of it will be absolute gibberish that somehow runs because the AI patched it together. What a time to be alive. 🐒⌨️ submitted by /u/usnavy13 [link] [comments]

Building is easy now but how do you distribute? Does claude help you in that ?

Building is now commodity, distribution is the key. I am eager to know how and what people have figured out about distribution using claude or cursor !! submitted by /u/RevolutionarySlip292 [link] [comments]

I built an MCP server that lets Claude Code read your on-prem servers and PostgreSQL over SSH - without giving it shell access

I got tired of switching between Claude and a terminal just to answer basic ops questions like "is this service up?" or "why are there 40 waiting locks on that DB?" So I built infra-mcp — a stdio MCP server you register once in Claude Code or Cursor, and then your agent can: - Check systemd service states and grab bounded journal logs - Run read-only SQL queries on PostgreSQL (with schema introspection — list_tables, describe_table) - Get a full infra overview for a VM in one call - Everything goes through an SSH tunnel to your existing servers Security was the whole point, so nothing is cut: - All SSH commands are checked against a per-VM allowlist before any network call - DB queries run as a dedicated read-only role inside a READ ONLY transaction - Every remote operation is written to a local append-only audit log - No shell access, no write path Install: uv tool install infra-mcp Then `infra-mcp generate-config` to bootstrap from your ~/.ssh/config, and register `infra-mcp run` as a stdio server in your client config. GitHub + PyPI: https://github.com/esp4ce/infra-mcp Happy to answer questions — especially curious if anyone has a use case where the read-only constraint is actually blocking them. submitted by /u/espaceee [link] [comments]

I built PromptQueue for when Claude says I'm out of prompts

This came from a very small Claude annoyance: Claude says to try again later, but I already know the exact prompt I want to run next. PromptQueue lets me queue it locally instead of keeping a tab open or setting a reminder. Example: promptqueue add 19:30 claude continue the draft and tighten the conclusion promptqueue run At the scheduled time it opens or focuses Claude, pastes the prompt, and can submit it. It also supports Claude Code, Codex, ChatGPT, Gemini, Cursor, and CLI targets. I am the author. It is free and open source. No server, no account, no private APIs, just a local queue file and one Python script. GitHub: https://github.com/AtharvaMaik/PromptQueue PyPI: pip install promptqueue submitted by /u/Clashking666 [link] [comments]

browser-search — three tools, zero cost, and your AI agent learns to search and browse the web

I've been using AI agents like OpenCode, Claude Code, and Cursor for months. They're great with code, but when they need to search or browse the web, things get complicated: Cloudflare blocks them, JavaScript-heavy sites don't load, APIs cost money. So I built browser-search. It's three open source tools orchestrated by a skill, fully self-hosted: SearXNG — metasearch engine that queries dozens of search engines at once Camofox — full browser via REST API, always warm, for browsing and interacting CloakBrowser — stealth browser for when the site has Cloudflare, Akamai, or DataDome The agent decides which tool to use. Zero human intervention. Zero API keys. Zero subscriptions. What makes it different: It's a skill, not a plugin — works with any agent that can read instructions Automatic navigation escalation: if Camofox gets blocked, it switches to CloakBrowser Deep Research mode: the agent is instructed to go beyond surface-level answers, cross-verify sources, cover every aspect Integrated Readability.js for clean article extraction (~70% token savings) The SKILL.md is plain text — fork it, tweak it, make it yours MIT licensed on GitHub: https://github.com/Johell1NS/browser-search If you try it, let me know. If you make it better, even more so. If you don't need it, share it with someone who might. Every star, comment, or pull request is welcome — that's what makes open source great. submitted by /u/Ill-Tradition1362 [link] [comments]

Why I cancelled my Cursor subscription

submitted by /u/cuerdo [link] [comments]

I built a shared memory for AI agents - so they stop forgetting, build on each other's work, and you can actually *see* what they know

Most AI coding agents forget everything the moment a session ends. Open the project tomorrow and the agent has no idea what it figured out yesterday, why it made a call, or what it already tried. I got tired of re-explaining the same context every time, so I built kaeru. It started as memory for a single agent across sessions, but it turned into something more useful: one place several different agents can think on at once. An agent saves what it learns, links related notes together, and looks them up later — and so can the next agent, or your teammate's agent. What it does: - A shared cognitive engine for many agents. kaeru can act as one common memory for a whole group of different agents — Claude Code, Cursor, Opencode, whatever you run — plus the people working alongside them. They all read and write to the same place, so one agent builds on what another already worked out instead of starting from zero. It runs on your own infrastructure, and what gets shared is always explicit and passes a secret-scanner so nothing sensitive leaks by accident. - See the whole memory. New in this release: a 3D visualizer that renders everything your agents know as a galaxy — a cluster per project, brighter/bigger points for the more important memories, thicker links for stronger connections. You can replay a chain of reasoning step by step, or scrub a timeline and watch the memory grow. It's the first time you can actually *look* at what your agents have built up. - Time-travel. Every fact keeps its history. You can ask what a note looked like 5 minutes ago, 2 hours ago, or on a specific date — nothing gets silently overwritten. - Reasoning trails, not isolated notes. When you link two ideas, you can mark how strong the connection is. Later, kaeru pulls up the whole chain of reasoning between two points instead of handing you one note out of context. - Importance levels. You tag how important something is — from "always load this" down to "archived". When an agent comes back to a project, it loads the important stuff first instead of dumping the entire history into the context window. - Agents actually use it. The hard part of any agent-memory tool is getting the agent to bother using it. On Claude Code, kaeru can take over the built-in memory and point it at itself, so the agent writes to and reads from kaeru every session instead of splitting knowledge across two systems. It runs as a small background service your agents connect to — Claude Code, Cursor, Opencode, and anything that speaks MCP. This release also adds a native adapter for the rig framework, so Rust agents can embed kaeru directly. One-line installer, and prebuilt binaries for Linux, macOS, and now Windows. It's open source. Still early and very much in testing, so feedback is welcome — what would you want your agents to remember and share? https://i.redd.it/6g5e8lt3vz8h1.gif Repo + release: https://github.com/LamantinAI/kaeru/releases/tag/v0.3.0 submitted by /u/KeySeaworthiness6180 [link] [comments]

I built a free Windows app to dictate prompts into Claude Code (it cleans up my stutters before the text hits the terminal)

I think at roughly 150 words a minute and type maybe 40. Most of my Claude Code prompts are long rambly things, so I'd half-talk them out loud and then type a shorter, worse version of what I'd just said. Anthropic's /voice helped, but it only types inside the Claude CLI, and I live in a bunch of other windows all day. I looked at Wispr Flow but it's $144/yr and still doesn't do the per-app stuff I wanted. So over a weekend I built my own thing. It's called Pipevoice. ​ Push-to-talk. Hold a key, ramble, let go, and the cleaned-up text shows up as real keystrokes in whatever app is focused. There's a 3-min demo in this post where I dictate a long stuttery instruction at Claude. You can watch it drop the filler words and the "umm"s before any of it reaches the terminal. Then it just runs. ​ The bit I actually cared about: it types into everything, not only the Claude CLI. Cursor, a browser, a chat box, wherever the cursor happens to be. There are per-app profiles too. In my terminal it skips the AI cleanup and auto-presses Enter, so it's raw words, hands-free. In a chat box it polishes and sends. ​ You pick the engine at each stage. Transcribe with Deepgram (fastest), OpenAI Whisper (most accurate), or local Whisper if you want it offline. Cleanup is optional and runs through Gemini's free tier, OpenRouter free models, or local Ollama. Go local Whisper plus Ollama and no audio ever leaves your machine, which is the reason I built it that way. I work on client code and didn't want to ship that audio anywhere. ​ Free, no account, source is on GitHub. I built it solo and it's still rough in places, so I'd honestly like to hear what breaks or what annoys you, especially from people who are in Claude Code all day. Not affiliated with Anthropic. Just me scratching my own itch. submitted by /u/powleads [link] [comments]

SpaceX buys AI coding startup Cursor for $60 billion in race for an edge over Anthropic and OpenAI

submitted by /u/esporx [link] [comments]

so i built an mcp to superpower claude and claude code

so i was getting tired of running multiple playwright hits and trials just to get claude desktop and claude code to access reddit, linkedin, pinterest, etc. i mean f*k me for wasting so much time on that. and then i built this thing called McpBrowser, give your ai access to the social web without api keys. it's got a free tier with 50 requests/day which is pretty cool, and if you need more you can just pay a one-time $9 for unlimited requests. plus it works with a bunch of ai clients like claude and cursor, and you can access gated content without api keys. the best part is it doesn't scrape or violate website terms, so you don't have to worry about that. it's also got a mac app which is super easy to use. webmatrices.com/mcpbrowser submitted by /u/bishwasbhn [link] [comments]

My AI tools kept forgetting everything, so I gave them a shared brain (local + open source)

Hi there! this is my first small rant that turned into a project: every AI tool I use has its own memory. I tell Claude Desktop something, Cursor has no clue. New chat? Back to zero. It drove me nuts — so I built Centralaizer. This is an open source solution, so it's free with MIT license. It's a little memory hub that runs on your own machine. Any MCP tool (Claude Desktop, Cursor, Claude Code, VS Code Copilot…) plugs into it and they all share the same memory. Save a fact or a decision in one, the others can pull it right up. No cloud — everything stays on your laptop. A few things I cared about: 🧠 opt-in, not spying — the agent decides what to save/recall 🚧 sketchy notes get held in a review queue instead of polluting everyone's memory 🔒 it scrubs PII (emails, keys, phones) before storing 🔎 search isn't just keywords — vector + full-text + a little knowledge graph 🖥️ a web dashboard to browse it all (light and dark mode 🌙) One command (./setup_and_run.sh) or Docker. There's also a Claude Code hook for auto-recall, one-click export, and a browser extension to bring it into ChatGPT/Gemini/Qwen. Would love thoughts — or roasts — on the retrieval and the "trust score" idea. Any feedback is more than welcome as it's an initial project. 🎥 (attach centralaizer-demo.mp4) · 👉 https://github.com/lestercoyoyjr/Centralaizer-public https://reddit.com/link/1u66kb0/video/90314duxkd7h1/player submitted by /u/Accomplished-Pen-491 [link] [comments]