Implement a 24/7 security scanning system where a local AI model continuously scans your codebase for issues, and a powerful cloud model periodically reviews and validates the findings for a cost-effective solution.
On a machine with high unified memory like a Mac Studio, load a powerful but potentially slow local model (e.g., GLM 5.2). Configure this agent to perform a security scan on your codebase at a regular interval, such as every 30 minutes.
Program the local model to output all of its findings into a structured text file, such as a Markdown report. This report will serve as the input for the next step.
In a powerful cloud-based AI environment like Claude Code, set up a recurring loop to run once a day. The blog post mentions using a command like /loop 24 hours.
The daily task for your cloud model is to read the Markdown report generated by the local model. It will use its superior reasoning abilities to analyze the list of potential issues.
The cloud model acts as the expert 'closer,' reviewing the code snippets associated with the findings, determining which issues are legitimate security risks, and suggesting how to fix them.
Build your own local AI hardware setup by choosing the right machines (Mac, DGX, custom PC) and connecting them with Tailscale and an AI agent like OpenClaw for automated model deployment across your network.
Create a fully autonomous software development pipeline using AI agents. This workflow takes you from collaborative task generation, through automated code building and reviewing, to one-click deployment via a Slack emoji.
Use an AI tool with browser control capabilities to automate repetitive tasks like managing your LinkedIn inbox. Set clear criteria to have the AI read and reply to high-value messages on your behalf.
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