Writings, Regrets, and Re-skillings during the AI Revolution

Author: Professor Lamb

  • Network Concepts, No Textbook 8: Same Street? The Subnet Mask

    Before every packet leaves, your computer asks one question: is this destination on my street? Yes means deliver it directly through the switch. No means hand it to the default gateway, the router’s address on your street (a door you cannot reach is not a door).

    The subnet mask is how the computer answers. Where the mask says 255, that part of the address is street. Where it says 0, that part is house. So 255.255.255.0 under 192.168.10.5 means the street is 192.168.10 and the house is 5. Change the mask and you move the end of the street. With 255.255.0.0, the street becomes 192.168, and 192.168.20.6 suddenly counts as same street. Same two addresses, different mask, different answer.

    Basically, the mask decides where the street ends. One street is one network and one broadcast domain. Every machine on that street has to stop and read every broadcast. Too many machines on one street, and they spend the day listening to shouting. So we cut big streets into smaller ones. Week five only draws the line between street and house. Later weeks take the line apart bit by bit. The skill that matters first is hearing the question: same street?

  • Network Concepts, No Textbook 7: What an IP Address Is

    A MAC address names the network card. It is burned in at the factory. It says which card, and nothing about where the card is right now. Nobody delivers mail to a serial number. So every device gets a second address that says which network it sits on. That is the IPv4 address.

    An IPv4 address is a 32-bit number. Humans write it as four numbers from 0 to 255 with dots between them (dotted decimal), like 192.168.10.2. The machine never sees the dots. It sees thirty-two ones and zeros. Each of those four numbers is an octet: eight bits, which is why no octet ever goes past 255. The front of the address is the street (which network). The back is the house number (which device on that street). Routers care about the street.

    Basically, MAC is identity of the hardware; IP is location on the map. Floor two never leaves the building. Floor three is the address that does. Everything that sends or receives a packet needs one: laptops, printers, phones, cameras, and managed switches (so you can talk to the switch, not so it can switch). A Mac address names the card. An IP address names where it is.

  • Network Concepts, No Textbook 6: Twisted Pair and Category Ratings

    Most of the copper in a building is twisted pair: eight wires in four pairs, each pair twisted around itself. The twist is not decoration. It is the entire engineering. Two wires twisted together cancel each other’s interference. Alexander Graham Bell patented that idea in 1881 to keep streetlight noise out of telephone lines. We still use it.

    Crosstalk is the enemy, signal leaking from one wire into the next. Tighter twists mean less crosstalk and a faster cable. Category ratings are the grade of that fight. Three numbers are worth saying out loud. Cat 5e does one gigabit to 100 meters. Cat 6A does ten gigabit to 100 meters. Cat 8 does forty gigabit, but only about 30 meters, which is why it is a data center answer and not a building answer. Cat 5e is the floor. Nothing below it belongs in a network today.

    U T P (unshielded) is almost every cable in the walls. S T P (shielded) wraps the pairs in foil or braid for noisy places: factory floors, elevator shafts, runs beside power. Do not walk the whole Category table from one to eight. Six of those rows are obsolete telephone grades. Students need three facts, not eight rows. The twist is why the cable works. The Category number says how far and how fast before crosstalk ruins it.

  • Network Concepts, No Textbook 5: Copper, Fiber, or Radio

    Every link in every network on earth is copper, fiber, or radio. There is no fourth thing. That sentence is the organizing idea for a whole week of ITN 101, and it survives long after Category numbers fade.

    Copper carries electricity. It is cheap, easy to terminate, and it picks up noise. Fiber carries light down a glass or plastic core. Because there is no electricity in the cable, motors, elevators, and power lines have nothing to grab; the trade is cost, kink sensitivity, and harder troubleshooting. Radio carries nothing at all. It just goes. Convenient, shared, and unpredictable (which is exactly why the wired links win in a classroom with historically bad Wi-Fi).

    Transmission media is the physical stuff a signal travels through. Last week’s word was link, the connection between two nodes. This week we open the link and look inside it. The plural is media; the singular is medium. Hold those three families on the board and everything else hangs off them: twisted pair and coax under copper, single-mode and multimode under fiber, Wi-Fi and cellular under radio. If students leave with only one idea from the media day, make it this one. Copper, fiber, or radio. Pick the medium that matches the job.

  • Network Concepts, No Textbook 4: Cable Management Is Documentation You Can Touch

    Nobody frames a photo of a patch panel, but maybe they should. Cable management — the velcro, the horizontal managers, the service loops — is documentation you can touch. A rack where every cable runs in a channel and drops vertically to its port tells you the truth about itself. A rat’s nest tells you nothing, and it lies about everything.

    The argument I make in ITN 101 is not about beauty. It is about the 2 a.m. version of you. That person is tired, the building is dark, and something is down. In a dressed rack, the tired person can trace a cable with a finger and be sure. In a tangled rack, the tired person starts unplugging things to see what happens, which is how a small outage becomes a big one.

    Two rules do most of the work. First, velcro, never zip ties — because you will need to move that cable someday, and a zip tie turns a five-minute change into surgery. Second, leave a service loop: a little slack, neatly coiled, so the cable can reach one rack-unit up or down without being re-terminated. Slack is forgiveness, built in ahead of time.

    Students hear this as housekeeping. It is closer to honesty. A network is a physical thing before it is a logical one, and the physical part keeps score.

  • Network Concepts, No Textbook 3: Label Both Ends

    Textbooks treat documentation as a chapter near the back. In ITN 101 we treat it as a habit that starts with a four-cent label.

    The rule is simple: label both ends of every cable. Not one end — both. Because the end you can see is never the end you need. You are always standing at the wrong end, in a dim closet, holding a flashlight in your teeth, trying to decide whether the blue one goes to the printer or the camera. A label on the far end does not help you. A label in your hand answers the question.

    Students groan at this. It feels like busywork, the networking equivalent of being told to clean your room. Then lab week three arrives, somebody’s link will not come up, and the team with labeled cables finds the fault in ninety seconds while everyone else plays cable roulette.

    A label is documentation at the exact point of need, readable without logging into anything. Start there. The diagrams can come later, and they will be accurate, because the labels forced them to be.

  • Network Concepts, No Textbook 2: Network Scopes

    Once you know a network is nodes connected by links, the next plain question is scale. How big is this thing? Network people answer with scope words: LAN, WAN, and a few cousins. The words are not fashion labels. They tell you roughly how far the links reach and who owns the space between them.

    A LAN (local area network) is the network in one place you control: a classroom, a small office, one floor of a building. Ethernet and Wi-Fi live here. A WAN (wide area network) stretches across cities or continents. You usually rent the long haul from a carrier; the internet is the WAN most people mean. Between those poles sit useful middle sizes. A MAN covers a city or campus. A PAN is personal, phone to earbuds, a few meters of radio. A CAN is a controller network on a factory floor or in a vehicle.

    Basically, start with distance and ownership, not the acronym. Is everyone under one roof with cables you can pull? That is LAN thinking. Are you crossing public infrastructure you do not own? That is WAN thinking. Day one of class deliberately refuses to chase LAN versus WAN before nodes and links are solid. The mesh lab builds the schema with hands. The labels come after the picture.

  • Network Concepts, No Textbook 1: Why We Skip OSI Early

    Textbooks love to open networking with the OSI model. Seven layers, two mnemonics, a stack of vocabulary before anyone has touched a cable. That is a clean map if you already know the building. It is a bad first week if you do not.

    In ITN 101 we skip the layers early on purpose. Week one is nodes, links, and protocols, said in ordinary English. Weeks two and three are the stuff you can trip over: copper, fiber, radio, connectors, and the boxes in the rack. Only after students have held a patch cable and watched a switch learn a MAC address do we put floor numbers on those boxes. A hub is floor one. A switch is floor two. A router is floor three. Suddenly the map has somewhere to hang.

    Basically, a model is useful when it names things you have already met. Before that, it is seven labels floating in air. The OSI model is still coming (Network+ tests it, and the industry speaks it). We just refuse to start there. Troubleshooting is the real payoff of layers: you check one floor at a time instead of guessing in a building with no numbers. That skill lands better once the cable, the frame, and the address are already in the room.

  • AI Power Tips 5: Iterate, Disclose, and Explain It Back

    The last habit has three parts. Together, they separate a power user from someone who got lucky.

    Iterate. The first response is a draft. (I made the longer case for that in ChatGPT at Work 6.) For students, the key is to iterate against the assignment, not against a vague feeling. Paste in the requirements. Ask what the draft is missing. Tell the AI exactly what to keep and what to fix, one or two things at a time. Then jot down what changed and why. That note is useful to you later, and it shows your own thinking if anyone asks.

    Disclose. Say how you used AI, in plain language, whenever an assignment allows it. “I used ChatGPT to generate practice questions and to check my citations. The analysis and writing are mine.” Disclosure is not a confession. It is the same professional habit as citing a source or crediting a teammate. Students who build it now are protected later, in courses with different rules and in jobs where undisclosed AI use can end a career.

    Explain it back. This is the test I care about most. Could you explain every part of what you submitted, out loud, without the AI in front of you? Why this approach, what this line of code does, where that number came from? If not, the work is not yours yet, no matter who typed it. Before submitting, close the chat and explain your work to a friend, a pet, or an empty room. The parts you stumble on are the parts to go back and learn.

    Put together, the habits from this series read like one sentence: choose the model, delegate only what is permitted, check the output with four questions, iterate, disclose, and be able to explain the work.

    Done consistently, they also produce something useful for a job search: a documented example of real work you did with AI, including what you delegated, how you checked it, and what you changed. That makes a better interview answer than “I use ChatGPT sometimes.”

    The tools will keep changing. Next year’s models will be faster and more capable, and some of today’s paid features will be free. A student with these habits will use each new tool well on day one. That is the point of teaching the habits instead of the product.

  • AI Power Tips 4: Four Questions Before You Trust It

    AI output arrives in the same confident tone whether it is right or wrong. That is the trap. A polished paragraph with a fabricated citation looks exactly like a polished paragraph with a real one.

    Many students respond with vague suspicion (“AI makes stuff up”) and then use the output anyway, because checking everything feels endless. What helps is a short routine. Before you trust anything an AI produced, ask four questions.

    1. What is it assuming? Every answer rests on assumptions about your situation. The AI may assume a different version of the software, a different state’s rules, a different course level, or a goal you never stated. Name the assumptions, then check whether they match your task.

    2. Does the logic hold? Follow the steps. Does each one actually lead to the next, or does the answer leap from a premise to a conclusion that only sounds connected? In code, trace what each line does. In an essay, look for a “therefore” doing work it has not earned.

    3. What is the evidence? Which claims can you check against an independent source, like the textbook, the official documentation, the original article, or the data itself? Another chatbot does not count. Open every citation. Recompute the numbers. Run the code.

    4. What happens if it is wrong? This question sets how hard you check. A clumsy word in a practice email costs nothing. A wrong subnet mask breaks the lab network. A wrong figure in a graded report costs points and credibility. Spend your checking time where the consequences are.

    That last question keeps the routine sane. You do not have to audit everything to the same depth, but you do have to know what is riding on each answer.

    A good way to practice is with planted errors. Take an AI output with a few problems hidden in it, such as a fake source, a wrong number, or an outdated fact, and hunt them down. After a few rounds, students start to notice the texture of a confident mistake: the smooth sentence that says nothing you can check.

    Employers are not paying for someone who can get AI to produce text. Anyone can do that. They are paying for someone who can tell when the text is wrong.