Practical AI workflows for senior network, security, and infrastructure engineers who are tired of doing everything the hard way. Audio from the G Talks Tech YouTube channel plus audio-first episodes.
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This episode is the audio from a YouTube video. Some segments reference on-screen demos. Watch the full video at https://youtu.be/dDE8gajTKkQ Local AI for network engineers: your configs never leave your desk. This episode sets up Ollama properly on a MacBook, benchmarks 6 local models against real Cisco configs, generates handoff documentation from a full 14,000-token show run all capture, and finishes with a 9B model driving real network tools through LM Studio’s permission gate. Free, private, and measured on camera, including the parts where local AI needs a fix. What you’ll learn How to i
This episode is the audio from a YouTube video. Some segments reference on-screen demos. Watch the full video at https://youtu.be/dbkwtuXuPPQ If you’re the senior engineer on a small team, you’re probably being told to put AI agents on your network while being the one who answers for the outage if it goes wrong. In this episode I build a bounded AI agent with real network tools (FastMCP, Netmiko, Claude Code as the harness, NetBox as the source of truth) and turn it loose on three Cisco IOS XE devices in my CML home lab. It finds a real CVE by checking my running configs against Cisco’s live P
This episode is the audio from a YouTube video. Some segments reference on-screen demos. Watch the full video at https://youtu.be/LZrmRdSMiJ0 Every network engineer has asked an AI chatbot for help, then had to manually copy and paste show command output back and forth the whole time. This episode walks through building a custom MCP server with FastMCP and Netmiko that gives Claude Desktop live, read-only access to real Cisco IOS lab devices, so you can ask questions in plain English and get answers pulled straight from your running network. What you’ll learn How to build an MCP server from sc
This episode is the audio from a YouTube video. Some segments reference on-screen demos. Watch the full video at https://youtu.be/OEfeS8pyn_s It’s 11 PM, your maintenance window is going sideways, and ChatGPT just spit out 10 config changes. Two of them will break your network, three are noise, and you don’t have time to figure out which is which. This episode walks through the four-part prompt structure that fixes that, runs four prompts live against a real Cisco lab in Claude, and shows where AI still gets things wrong so you know when to override it. What you’ll learn How to structure a net
This episode is the audio from a YouTube video. Some segments reference on-screen demos. Watch the full video at https://youtu.be/LA3_eIaBM1E Manually copy-pasting show command output into a browser chat works fine for a lab, but it falls apart the moment you need to run it against a whole site, on a schedule, or hand it off to a teammate. This episode walks through a fully automated network documentation pipeline built on Python, Netmiko, and the Claude API. One YAML inventory, one command, and you get back a finished runbook plus an optional draw.io diagram in minutes. What you’ll learn How
This episode is the audio from a YouTube video. Some segments reference on-screen demos. Watch the full video at https://youtu.be/z89tfs7HV0I Network documentation is always outdated. Configs change constantly, but the docs never keep up. This episode walks through a complete AI-powered workflow that takes raw show command output from live Cisco devices and turns it into a finished operational runbook and network diagram in about 10 minutes. What you’ll learn How to collect show commands from network devices using Python and Netmiko How to redact sensitive data (IPs, hostnames, credentials, se
This episode is the audio from a YouTube video. Some segments reference on-screen demos. Watch the full video at https://youtu.be/kdyXSark_ck Every network engineer knows the vague ticket: the app is slow, so it must be the network. This episode walks through a practical framework to rule the network in or out fast, using evidence instead of guesswork, and finishes with a PowerShell script that runs the checks and produces paste-ready proof for your ticket response. What you’ll learn Why the network is everyone’s default suspect and what that costs you A rule-in vs rule-out framework for fast
