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This Claude Code project setup guide literally makes Claude Code 10x better🤯

SOMEONE OPEN SOURCED A SMALL MODEL TRAINED SPECIFICALLY AS A PERSONAL AGENT ROUTER DECIDES WHAT RUNS LOCAL VS CLOUD AUTOMATICALLY ROUTES TASKS TO THE RIGHT MODEL BASED ON COMPLEXITY SMARTER AGENT ORCHESTRATION WITHOUT THE OVERHEAD

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- Codex = coding + reasoning. ($20/mo) - Convex = backend. (Free) - Netlify = deploying. (Free) - Porkbun = domain. ($8/yr) - Lemon Squeezy = payments. (~5%) - GitHub = version control. (Free) - Mailgun = emails. (Free tier) - WorkOS = auth. (Free) - Bunnydotnet = CDN. (Cheap) - Plausible = analytics. (Free/self-host) - Logtail = logs. (Free tier) - Valkey = Redis alt. (Free) - Weaviate = vector DB. (Free) Total monthly cost to run a startup: ~$20–30 There has never been a cheaper time to build.

- Codex = coding + reasoning. ($20/mo) - Convex = backend. (Free) - Netlify = deploying. (Free) - Porkbun = domain. ($8/yr) - Lemon Squeezy = payments. (~5%) - GitHub = version control. (Free) - Mailgun = emails. (Free tier) - WorkOS = auth. (Free) - Bunnydotnet = CDN. (Cheap) - Plausible = analytics. (Free/self-host) - Logtail = logs. (Free tier) - Valkey = Redis alt. (Free) - Weaviate = vector DB. (Free) Total monthly cost to run a startup: ~$20–30 There has never been a cheaper time to build.

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🚨 If you’re using Claude without this, you’re wasting money. This repo adds: • token optimization • memory persistence • learning loops • verification systems • agent orchestration Upgrade your workflow. Link👇

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🚨 BREAKING: CHINA just released a Python framework for building AI agents. 100% OPEN SOURCE. It has visual agent design, MCP tools, memory, RAG, and reasoning. All built in. All working together. It's called AgentScope. You describe your agent system. It builds the architecture, wires the tools, and runs the whole thing. You come back and there's a working multi-agent pipeline. Not a prototype. Not a demo. The actual system. Not a wrapper. Not a chatbot builder. A full Agent-Oriented Programming framework that thinks in agents from the ground up. Here's what it does out of the box: → Visual agent builder so you design your entire system before writing a single line of code → Native MCP tool support, plug any external tool directly into any agent in your pipeline → Built-in memory so every agent remembers context, decisions, and history across sessions → RAG pipeline ready to connect your own documents, databases, and knowledge bases → Reasoning modules that let agents plan, reflect, and self-correct without human input → Multi-agent coordination so your agents collaborate as a system, not a pile of isolated API calls Here's how it thinks: You define your goal. AgentScope maps the agent roles. Each agent gets its tools, its memory, its reasoning layer. They coordinate. Results flow back up. You get a finished output. A single complex task might route through a planner agent, a researcher agent, a coder agent, and a critic agent, each doing its job, then converge into one clean deliverable. Here's the wildest part: AgentScope is built by Alibaba DAMO Academy. The same lab behind Qwen. They didn't assemble this from existing pieces. They designed the entire framework from first principles around how agents actually need to think, remember, and work together. Most frameworks give you building blocks. AgentScope gives you an architecture. The community has already started plugging it into data pipelines, research workflows, and full automation systems the team never planned for. 100% Open Source. Apache 2.0 License.