Alternatives
Products that do what Nuvoa | Parallel Coding Made Easy does
Log into multiple Openai, Anthropic & Antigravity Accounts
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Multi Agent Continuous Context Harness - MACCHA solves the problem that every AI coding session starts from zero. It combines a file-based 7-tier context architecture with a working memory engine (Memanto) that features vector embeddings, confidence decay, and semantic conflict detection — so Antigravity, OpenCode, and Claude Code all share the same persistent, self-improving brain. No 24/7 daemon needed.
Jun 2026 · github.com
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Hello all, I'm a software developer. Over the last few months more and more of my work has turned into using coding agents instead of typing the whole code myself. Usually a few claude sessions at once, sometimes codex, one per feature or per revealed bug. I ran them in a split terminal for a few weeks, and quickly spotted two main problems. The first is that I couldn't easily tell which agent was stuck waiting on me and which was still working, so I'd cycle through sessions and checking on them. The second one: agents sharing a single branch step on each other. Two of them could be editing…
Jul 2026 · shikigami.dev
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I've been running Claude Code and Codex together every day. At some point I figured out you can use tmux to let them talk to each other, so I started doing that. Once they could coordinate, I kept adding more agents. Before long I had a whole team working together. But any time I rebooted my machine, the whole thing was gone. Not just the tabs. The way they were wired up, what each one was doing, all of it. Nothing I'd found treats your agent setup as a topology, as something with a shape you can save and bring back. So I built OpenRig, a multi-agent harness. A harness wraps a model. A "rig"…
Apr 2026 · github.com
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Hey HN, Over the past 10 months I've been using Claude Code heavily, and one limitation kept coming up: you can really only run one coding agent at a time. While one agent is refactoring something, the rest of the repo is basically blocked unless you start manually juggling branches and working directories. The core issue is that AI coding agents operate directly in your filesystem. If two agents run in the same working directory they quickly start stepping on each other’s changes. Git worktrees turned out to be a surprisingly good primitive for solving this. So I built ChatML, a Desktop app…
Mar 2026 · github.com
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Hi everyone, I've been working on a CLI tool that can help to easily run any model in claude, Codex, Gemini, Pi, and OpenCode. It's also an API keys manager, supports multiple providers or OpenAI/Claude/Gemini accounts. You can add openrouter, poe, Vercel AI gateways etc. It has a built-in provider that is free to all, which is using Deepseek-V4, no login or API key required, add your own when you're ready. After installation you can try claude instantly (No config, no login): aivo claude Hope it's useful to someone.
Apr 2026 · getaivo.dev
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I built Hydra because I kept losing my flow when Claude Code hit usage limits mid-task. I would copy context, open another tool, and then re-explain everything. This would be super annoying for me. Hydra wraps your AI coding CLIs (Claude Code, Codex, OpenCode, Pi, or any terminal-based tool) in a single command. It monitors terminal output for rate limit patterns, and when one provider runs out, you switch to another with one keypress. Your conversation history, git diff, and recent commits are automatically copied to your clipboard so you can paste and keep going. The fallback chain is…
Apr 2026 · github.com
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The magic in AI coding assistants isn't the code -- it's the prompts. I studied the externally observable behavior of Claude Code and recreated it from scratch in Python with the exact same behaviors. It works with any model -- OpenAI, Gemini, Claude. What's surprising: 1. You can keep the core agent really simple, just 280 lines of Python. As long as it supports hooks, custom sub-agents and Model Context Protocol (MCP), then all the rest of the coding-assistant-specific behavior and tools can be factored out into a separate MCP server. 2. The magic is in the prompts (1200 lines of…
2025 · github.com
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We built an open source layer to orchestrate multiple Codex agents in parallel. Found myself and some friends running Codex agents across multiple terminals. Thats why me and a friend built emdash. Each agent gets its own isolated workspace, making it easy to see who’s working, who’s stuck, and what’s changed.
Sep 2025 · github.com
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