Cursor

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

Cursor AI

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]

I built a persistent memory layer for Claude Code, Codex, Cursor, and other coding agents

Claude Code gets much better when you give it project context. CLAUDE.md helps. Skills help. Session summaries help. But I kept running into the same problem: The memory was tied to one tool, one session, or one folder structure. Once I started using multiple agents across the same codebase, the system became messy. Claude knew one thing. Cursor knew another. Codex started from zero. Important decisions lived in old chat logs. Debugging context disappeared after compaction. So I built AgentMemory. The idea is simple: Instead of treating memory as chat history, treat it like project infrastructure. A coding agent should be able to read: - what this repo does - what decisions were already made - what files matter - what bugs were already investigated - what patterns the project follows - what should not be repeated - what context is stale And this memory should work across agents, not just one Claude session. The main difference from a normal CLAUDE.md setup: CLAUDE.md is a great entry point. AgentMemory is trying to be the shared memory layer behind the agents. So Claude Code, Codex, Cursor, Copilot, or any agent can use the same project memory instead of rebuilding context every time. I also wanted the memory to be benchmarkable. Because “the agent remembers things” is not enough. The useful questions are: - did memory improve the task? - did stale memory hurt the result? - did the agent retrieve the right context? - did it avoid repeating old mistakes? - can another agent use the same context? Still early, but the repo is here if anyone wants to try it or give feedback: https://github.com/rohitg00/agentmemory Curious how others here are handling memory across long-running Claude Code projects. submitted by /u/SeveralSeat2176 [link] [comments]

Glia – Local-first shared memory layer (SQLite-vec + FTS5 + Offline Knowledge Graph)

Hey everyone, I wanted to share a project I've been working on called Glia. It is a 100% offline, local-first RAG and memory layer designed to connect your AI web chats (Claude, ChatGPT, DeepSeek) with your local developer tools (Claude Code, Cursor, Windsurf) using a unified local database. I wanted something lightweight that did not require pulling heavy Docker containers or subscribing to third-party memory APIs. I settled on a Node.js + SQLite architecture running sqlite-vec (for 768-dim float32 embeddings) alongside SQLite FTS5 for hybrid search, powered completely by local Ollama instances. We just launched a live website that outlines the details and demonstrates the features in action: Website: https://glia-ai.vercel.app/ Codebase: https://github.com/Eshaan-Nair/Glia-AI Technical Stack & Features: Hybrid Search Retrieval: SQLite-vec (using nomic-embed-text locally) + FTS5 keyword prefix matching (porter stemmer). Surgical Sentence-level Trimming: Chunks are sliced into sentences. When a prompt is intercepted, only the exact matching sentences are pulled out of the vector store instead of the whole paragraph. It cuts LLM prompt bloat by ~90-95% in my benchmarks. Knowledge Graph Extraction: An offline task queue uses a local LLM (llama3.1:8b via Ollama) to extract entity triples (subject-relation-object). These are stored in a SQLite facts table (or Neo4j if you run the full Docker compose profile) and fused with the vector retrieval score. HyDE (Hypothetical Document Embeddings): Queries are pre-processed to generate a hypothetical answer, which is embedded together with the original query to bridge semantic gaps. Concurrency: Running SQLite in WAL (Write-Ahead Logging) mode allows the browser extension dashboard and active MCP sessions to read/write concurrently without locking. PII Redaction: Aggressive scrubbing of JWTs, API keys, emails, and IPs in the extension before data is saved. The extension works on Claude.ai, ChatGPT, DeepSeek, Gemini, Grok, and Mistral. The MCP server runs out of the same backend database for your terminal agent or Cursor. You can set it up with a single command: npx glia-ai-setup Glia is completely open-source (MIT). If you like the local-first approach or want to contribute to the SQLite vector pipeline, PRs are very welcome, and a star on GitHub helps the project get discovered! I would appreciate any feedback on the SQLite hybrid search scaling, the scoring fusion algorithm (RAG pipeline details are in RAG_PIPELINE.md), or local graph extraction performance. submitted by /u/Better-Platypus-3420 [link] [comments]

We built a tool that installs frameworks like ComfyUI, Ollama, OpenWebUI etc on any cloud GPU in one command and saves your whole setup between sessions [R]

We kept running into the same problem every time we rented a GPU to run Ollama + OpenWebUI or ComfyUI, we'd spend the first 45 minutes reinstalling everything. Custom nodes, models, configs, all of it. Docker images went stale fast, different providers had different base images, and nothing was truly portable. We got sick of it and built swm. Here's what it does for ComfyUI users specifically: swm gpus -g a100 --max-price 2.00 --sort price shows you the cheapest available GPU across RunPod, Vast ai, Lambda, and 7 other providers in one view swm pod create — spins up an instance on whatever provider you pick swm setup install comfyui — installs ComfyUI on the pod From there the main thing is the workspace sync. Your entire setup custom nodes, models, outputs, configs lives in S3-compatible object storage (I use B2). When you're done you run swm pod down and it pushes everything, kills the instance, and next time you spin up on any provider you just pull and everything is exactly where you left it. No more reinstalling 15 custom nodes and redownloading checkpoints every session. We also built a lifecycle guard because we kept falling asleep mid-session and waking up to dumb bills. It watches GPU utilization and if nothing's happening for 30 minutes (configurable), it saves your workspace and terminates automatically. Has saved us more money than we want to admit lol. A few other things: Background auto-sync daemon pushes changes every 60 seconds so you don't have to remember to save Tar mode for huge workspaces with tons of small files packs everything into one S3 object instead of 600k individual uploads Also supports vLLM, Ollama, Open WebUI, SwarmUI, and Axolotl if you do more than SD Works with Cursor, Claude Code, Codex, Windsurf if you want your AI agent to manage GPU instances for you Free, open source, Apache 2.0. pipx install swm-gpu Site: https://swmgpu.com GitHub: https://github.com/swm-gpu/swm Would love feedback from anyone who rents GPUs. What's the most annoying part of your current workflow? We are also looking for contributors to the open source repo and suggestions on new frameworks/extensions to be included. Please share your thoughts submitted by /u/Tkpf18 [link] [comments]

Six agents running. Three are paused waiting for me. I haven't written a line of code in two hours.

I've been running parallel Claude Code agents for a few months. The promise was speed - 5× the throughput because 5× the agents. What actually happens by hour two: One agent stops on a yes/no. You alt-tab to it, approve, alt-tab back. Two more pause within the next minute. You scroll through their context, lose your place in the first one. Now there are four waiting. You're not writing code anymore - you're processing a decision queue you accidentally built for yourself. The agents aren't slow. You are. I started calling this the bottleself: the point where parallelism stops adding output and starts adding approvals you can't process fast enough. The ceiling on your system isn't tokens, model speed, or context window. It's the human in the loop. So I built a layer above the agents - a planner that: takes a high-level goal decomposes it into parallel subtasks spawns parallel Claude Code sub-agents - one per task has a QA sub-agent review the output pings you only when it actually can't decide Right now it's Claude Code only. Codex / Cursor / Aider integrations next. For a fresh repo with Claude Code, the planner handles decomposition + parallel execution end-to-end without me touching the keyboard. Source: github.com/gekto-dev/gekto Try: npx gekto Honest question to anyone running 5+ agents: how much of your day is actually writing code vs clearing the queue your agents created? Where does the bottleself hit for you? submitted by /u/OptimisticYogurt42 [link] [comments]

Built a tool that turns websites into structured design docs for AI workflows

Been experimenting with a tool that converts websites/screenshots into structured design documentation. The original problem was that screenshots alone weren’t enough for reliable UI understanding inside AI/browser-agent workflows. So the tool tries to combine: visual hierarchy DOM/CSS structure spacing systems typography patterns interaction behavior reusable component analysis The interesting part is seeing how different products structure their UI systems internally. Still early and improving daily, but curious what people here think would make something like this genuinely useful in AI/dev workflows. Happy to Share Link -- submitted by /u/hiehie [link] [comments]

We're turning into prompt managers, not craftsmen. Anyone else seeing this?

Look around. Every other product launching right now is some variation of "AI-Powered [insert buzzword]." They're everywhere. Modern tools have given founders and developers a convincing illusion of omnipotence: idea hits, feed it to an LLM, stack some agents on top, and MVP is done in a weekend. https://preview.redd.it/37ocn6azkv1h1.png?width=1672&format=png&auto=webp&s=06d4a9ef986d56a9eb3417e67a3524c18e73e100 Sounds great, right? On the surface, yes. But underneath that fast-launch facade, something is quietly rotting: thinking is getting commoditized, and we're losing craft. Real mastery in any field takes years of practice, failure, and deep focus. Today, apparently everyone is a master for $20 a month. That's a lie we're telling ourselves. Just look at how much panic a 5-hour rate limit window in Claude generates online. Tokens run out, and suddenly people have two options: wait for the reset like a metered parking spot, or upgrade. It's like a Michelin-starred chef who can no longer taste food, just dictating to a chatbot: "make me a pasta." Without the subscription, he can't cook. The counterargument: "But orchestrating AI IS the new skill." Fair. But it's a horizontal skill, not a vertical one. You learn to coordinate agents while losing deep domain knowledge. Think conductor versus virtuoso violinist. A conductor is impressive - but if the orchestra walks off stage, can he play a solo that makes the room go quiet? This is most visible in developers right now. People who got used to copy-pasting from Cursor or Claude hit a wall on hard architectural problems. When a product grows, starts needing real trade-offs, starts buckling under load - prompts stop working. The muscle for hard problems atrophied because they never had to build it. Same thing is happening to analysts, marketers, designers, researchers. My position: barbell, not crutch Running out of tokens doesn't scare me. My foundation means I can work regardless of what's left in my quota, whether there's internet, whether a subscription is active. The only thing that throws me off is running out of good coffee. I use LLMs heavily. But with one condition: AI is a barbell, not a crutch. It sharpens my own work - it doesn't replace the parts I care about. The fastest, most tireless junior I've ever hired. But the senior judgment and the final call always stay with me. Two types of professionals The market is already splitting into two groups. Token-dependent: live limit to limit, panic when Anthropic or OpenAI have an outage, can't produce anything original without a prompt to lean on. Token-independent: use AI as a force multiplier but can, at any moment, sit down and do the work themselves - with more depth, more precision, better judgment. The second group will command much higher rates. When the world is drowning in mediocre AI-powered software and content - and it will be - clients and employers will pay serious money for people who actually understand what they're building and why. Curious whether others are feeling this shift. Are you building toward token-independence, or does the dependency not bother you? submitted by /u/digdiver [link] [comments]

Experimenting with screenshot + DOM analysis for better UI understanding

Been experimenting with a tool that converts websites/screenshots into structured design documentation. The original problem was that screenshots alone weren’t enough for reliable UI understanding inside AI/browser-agent workflows. So the tool tries to combine: visual hierarchy DOM/CSS structure spacing systems typography patterns interaction behavior reusable component analysis The interesting part is seeing how different products structure their UI systems internally. Still early and improving daily, but curious what people here think would make something like this genuinely useful in AI/dev workflows. submitted by /u/hiehie [link] [comments]

What multi-operator Claude Code looks like once you build the plumbing

Five pieces. Hub in the middle. Four ways to talk to it: a one-line MCP client, a CLI, headless workers in Docker, and a small desktop supervisor. What you get: multiple people attached to the same Claude Code session, watching it think sessions that can route subtasks to each other across repos headless Claude in containers that spawn more containers (agents calling agents) watch and intervene from a browser tab on your phone Hub is on Docker Hub if you want to self-host, or use the hosted one. Repos and walkthrough: https://github.com/clawborrator submitted by /u/fixitchris [link] [comments]

I think the biggest mistake beginners make with vibe coding is jumping directly into:

I think the biggest mistake beginners make with vibe coding is jumping directly into: “build me this app” That’s exactly what I did at the start. The result? Endless loops of errors, generic designs, broken architecture, AI changing random files, and eventually a project nobody really understands anymore. After months of using Cursor/Copilot/ChatGPT, I realized AI coding works MUCH better when you slow down before coding. What helped me most: First: clarify the idea in your own head. Discuss the idea with ChatGPT/Claude BEFORE touching code. Ask the LLM to ask YOU questions until the idea becomes clear. Create a small PRD before building anything. If possible, design rough UI ideas first (Figma/Dribbble helped me a lot). Big lesson: AI is not a replacement for product thinking. Another huge thing: Create rules for your IDE agent. For example: don’t touch files without asking, comment functions properly, explain WHY changes are made, ask before refactoring, never rename important files automatically. Also: KEEP A CHANGELOG. Seriously. After long sessions, AI starts forgetting context or creating confusing logic. A changelog helps both you and the AI understand what already changed. I also keep small .md files for: project memory, security audits, completed fixes, architecture notes. This becomes super useful when switching chats, IDEs, or models later. And one more thing nobody told me: When the chat starts feeling slow, messy, or confused… it’s usually context overload. Starting a fresh chat with organized context often gives WAY better results than continuing a broken conversation forever. AI coding became much easier once I stopped treating AI like magic and started treating it like a junior teammate that needs structure. submitted by /u/Embarrassed_Leg_6330 [link] [comments]

Made claude code warn you, time before it hits usage to transfer the pending work, all dynamically

I got tired of Claude Code silently hitting rate limits, so I decided to build something to address the issue, so I don't get blocked mid-work. Imagine you’re 40 minutes into a refactor. Claude is running tools and making progress, then suddenly, everything stops. The session has reached its rate limit without any warning—no alert saying you’re at 95%, just a complete halt. The usage bars are visible in the UI, but the model itself remains unaware of them. I discovered that Anthropic has a usage API, and Claude Code already possesses hooks to make it work. This led me to create agent-baton, which reads the usage API and installs hooks to make Claude aware of its limits. Here are the three hooks you can initiate with one command (baton init): SessionStart: Fetches usage data and injects it so Claude knows from the first message how much has been used. UserPromptSubmit: Performs a time-to-live (TTL) aware check that avoids overwhelming the API. It uses smart caching—checking every 15 minutes when usage is low and once a minute when it's nearing the limit. PreToolUse: This is the crucial one; it checks usage mid-task to prevent the scenario where you “started at 93% and ran out of capacity mid-execution,” catching the problem within 1-2 tool calls. When the warning threshold is reached, it prompts an interactive question using Claude Code's built-in AskUserQuestion tool: "Claude 5-hour usage is at 91% — you're in the warning zone." Options include: - Continue this task - Write a handoff document - Switch to lightweight mode It also handles full agent handoffs by writing a structured markdown handoff and passing work to Cursor, Codex, or Gemini. You can install it with the following command: npm install -g u/codeprakhar25/agent-baton && baton init For more details, visit the GitHub repository. submitted by /u/No-Childhood-2502 [link] [comments]

I built SeeFlow – architecture diagrams that actually run, wired to your live app

Architecture diagrams rot. You spend an afternoon in Confluence, three months later it's wrong, and nobody updates it because there's no forcing function. https://preview.redd.it/l14h40ly3m1h1.png?width=2508&format=png&auto=webp&s=df60b2ba6da04fadf7e1039b9472a106ed163314 SeeFlow tries to fix that by making diagrams executable. It generates a flow canvas from your codebase, then wires each node to your actual running app. There's a Claude Code / Codex/ Cursor / Windsurf plugin that does the heavy lifting: /seeflow show me the shopping cart feature It also ships an MCP server so any MCP-aware editor can register and edit demos without leaving the IDE. Link to the site: https://seeflow.dev 100% Free/ MIT Open Source submitted by /u/mrtule [link] [comments]