The Best LOCAL Agentic Coding Workflow (Complete Guide)
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Top Comments (10)
One thing this workflow surfaces that's easy to overlook: model latency under agentic load is fundamentally different from interactive chat. When an agent loops — tool call → model response → next call — even a 1.5s/token slow-down compounds badly across a 10-step chain. So the hardware running your local model matters more for agents than for one-shot chat. Two profiles worth distinguishing: Workstation GPU (what this video shows): best for heavy models (70B+), batch jobs, fast single-response latency on large contexts. Power draw is high, so you probably close it when you're not actively coding. Always-on edge box (Jetson-class, low-power mini PCs): 10–15W, runs 24/7, handles persistent background agents, memory consolidation, watch tasks. You sacrifice model size but gain availability — the agent is there when you need it, not only when you decide to spin it up. Neither is strictly better; the use case drives the choice. A dedicated low-power box for always-on background tasks alongside a bigger GPU box for heavy generation is a real setup worth considering if you're serious about local agentic workflows. (Disclosure: we build ClawBox, a Jetson Orin Nano pre-configured for always-on OpenClaw agents — so I have a stake in the always-on angle. Tim's guide covers the GPU workstation side well; I'm just flagging the complementary hardware picture.)
This is probably the most practically useful video for those who want to get their hands dirty.
This is what I’ve been waiting for… thanks
Good to see completely local model finally getting some attention. I've been working on a custom agentic system for the past year. Entirely custom harness, no Ollama, no LMStudio. I need it to be custom because it's the basis of an academic work.
Deploy your site using here.now completely for free, copy the prompt for your agent here! https://here.now/r/twt
Nice of you to upload this right when I was considering running my own LLMs! Definitely gonna be making use of this
Fantastic tutorial, hands on, focused, and right to the point. Thank you.
This is a great workup. I am going through this right now trying to size my dev/stage/prod environments so perfect timing.
Great video, Tim!
Great tutorial , thanks for sharing
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Top Comments (10)
One thing this workflow surfaces that's easy to overlook: model latency under agentic load is fundamentally different from interactive chat. When an agent loops — tool call → model response → next call — even a 1.5s/token slow-down compounds badly across a 10-step chain. So the hardware running your local model matters more for agents than for one-shot chat. Two profiles worth distinguishing: Workstation GPU (what this video shows): best for heavy models (70B+), batch jobs, fast single-response latency on large contexts. Power draw is high, so you probably close it when you're not actively coding. Always-on edge box (Jetson-class, low-power mini PCs): 10–15W, runs 24/7, handles persistent background agents, memory consolidation, watch tasks. You sacrifice model size but gain availability — the agent is there when you need it, not only when you decide to spin it up. Neither is strictly better; the use case drives the choice. A dedicated low-power box for always-on background tasks alongside a bigger GPU box for heavy generation is a real setup worth considering if you're serious about local agentic workflows. (Disclosure: we build ClawBox, a Jetson Orin Nano pre-configured for always-on OpenClaw agents — so I have a stake in the always-on angle. Tim's guide covers the GPU workstation side well; I'm just flagging the complementary hardware picture.)
This is probably the most practically useful video for those who want to get their hands dirty.
This is what I’ve been waiting for… thanks
Good to see completely local model finally getting some attention. I've been working on a custom agentic system for the past year. Entirely custom harness, no Ollama, no LMStudio. I need it to be custom because it's the basis of an academic work.
Deploy your site using here.now completely for free, copy the prompt for your agent here! https://here.now/r/twt
Nice of you to upload this right when I was considering running my own LLMs! Definitely gonna be making use of this
Fantastic tutorial, hands on, focused, and right to the point. Thank you.
This is a great workup. I am going through this right now trying to size my dev/stage/prod environments so perfect timing.
Great video, Tim!
Great tutorial , thanks for sharing