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Why I’m moving my AI dev workflow to Local LLMs (and why clients should care) 🤖💻
The "AI coding" hype is everywhere, but as a Senior Developer, I’ve found that the real magic happens when you take the LLM off the cloud and run it on your own hardware.
I’ve been deep-diving into Local LLMs and Agentic coding workflows lately, and it’s completely changed my architectural process. Here’s why this is a game-changer for high-stakes development:
1️⃣ Privacy & Security: For my clients, data is everything. Running models locally (using Ollama/LM Studio) means proprietary logic never leaves the machine. No leaks, no training on sensitive IP.
2️⃣ Zero Latency, Pure Focus: Agentic workflows—where AI agents handle the boilerplate, unit tests, and documentation—allow me to focus 100% on system design and complex logic.
3️⃣ Custom Context: By giving local agents deep context of a specific codebase, they become "Junior Partners" who actually understand the architecture, rather than just guessing.
AI isn't here to replace the developer; it’s here to automate the "noise." A Senior Architect plus a fleet of local agents is the most efficient way to build scalable software in 2026.
Are you still using cloud-only AI, or have you made the switch to a local-first workflow? What’s the biggest "agentic" win you’ve had this week? 👇
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Where should an agent’s permission check live?
I built DeadBolt at NextEleven to put that check at the point where a tool action actually runs, outside the model’s instructions. Stable 2.0.0 is now available, free and open source under Apache-2.0.
For example: an agent requests a file write. Your executor asks DeadBolt first. A denial leaves the protected function uncalled. You can set tool policies, require one-use approvals, pause a run, or kill its future admissions. Store or transport failure denies protected dispatch.
You can embed it in Rust, use a local sidecar from Python or another language, or wrap a trusted stdio MCP server.
The boundary matters: DeadBolt protects routes you connect. It is not a sandbox for arbitrary code, and kill does not automatically cancel work already running.
Install, examples and captioned walkthroughs:
https://nexteleven.dev/deadbolt
If you’re building agents, what would make a permission gate easier to integrate into your stack? I’m happy to answer setup questions and consider useful feedback.
nexteleven.dev
DeadBolt — Give agents tools. Keep control.
A local execution gate for software agents, built by NextEleven.
For me, a small MCP example would help: approve a file write, then show what happens if the agent changes the path or content before execution. Is the one-use approval tied to those exact arguments?