Alternatives
Products that do what CMEM Cloud does
Cross-device cross-agent context synced in real-time
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Feb 2026 · marketplace.visualstudio.com
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Hey HN! I'm José, and I built Recall to solve a problem that was driving me crazy. The Problem: I use Claude for coding daily, but every conversation starts from scratch. I'd explain my architecture, coding standards, past decisions... then hit the context limit and lose everything. Next session? Start over. The Solution: Recall is an MCP (Model Context Protocol) server that gives Claude persistent memory using Redis + semantic search. Think of it as long-term memory that survives context limits and session restarts. How it works: - Claude stores important context as "memories" during…
Oct 2025 · npmjs.com
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I've been working on CloudRouter, a skill + CLI that gives coding agents like Claude Code and Codex the ability to start cloud VMs and GPUs. When an agent writes code, it usually needs to start a dev server, run tests, open a browser to verify its work. Today that all happens on your local machine. This works fine for a single task, but the agent is sharing your computer: your ports, RAM, screen. If you run multiple agents in parallel, it gets a bit chaotic. Docker helps with isolation, but it still uses your machine's resources, and doesn't give the agent a browser, a desktop, or a GPU to…
Feb 2026 · cloudrouter.dev
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Hey HN! We're Deshraj and Taranjeet. We've been building working on a startup called Mem0, building an open-source memory layer for AI apps and agents (https://news.ycombinator.com/item?id=41447317). We also kept running into our own daily frustrations with AI assistants forgetting everything between conversations. Over a weekend, we decided to hack together a Chrome extension to solve this for ourselves. The problem was simple: we were constantly re-explaining our context across platforms when switching between ChatGPT, Claude, and Perplexity. Start a coding discussion in…
2024 · github.com
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Single-agent LLMs suck at long-running complex tasks. We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress. How it works: 1. Orchestrator agent that manages task decomposition 2. Sub-agents for parallel work 3. Subscriptions to task state and progress 4. Real-time sharing of intermediate discoveries between agents We tested this on a Putnam-level math problem, but the…
Feb 2026 · github.com
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1 Orchestrator. 18 AI Agents. 6 PM Workflows. In Claude Code
Mar 2026
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