Proto Typing Too (Page 2)

Latest

Rough wall repair blending into an IT workspace with technical diagrams, illustrating quick fixes versus specialized expertise.

Fully Staffed Is Not Fully Covered

Nerds aren’t interchangeable. Why even strong IT teams sometimes need specialized expertise in accessibility, security, performance, and other high-risk areas.

Iceberg showing hidden software ownership costs: infrastructure, security, maintenance, obsolescence, compliance, data growth, and technical debt.

The Cost of Owning Software

by Brian Pollack Clients rarely misunderstand the cost of building software. That part is simple: the client asks for software with a goal, we build it, the transaction is complete. But that’s only the first step. Once software exists, someone has to own it. Below are those ownership costs,

AI chip connected to customer service, data processing, workflow automation, and follow-up processes, promoting a free 30-minute AI consultation.

You Know AI Matters. But Where Does It Actually Pay Off?

by Brian Pollack After 30 years of building software for healthcare, telecom, and many enterprise companies, I've pointed Protovate at one thing: AI software development. Custom software with AI doing the actual work inside it. Not chatbots. The unglamorous stuff. Recently we automated customer service requests and followup

Carpenter given a pocketknife while his professional tools remain locked away.

It’s 2026, Not 2016

Technical interviews still test experienced developers on memory and syntax, even as real-world development increasingly depends on judgment, problem-solving, and intelligent use of modern tools. Prateek Sharma asks whether it’s time for the interview process to catch up.

AI engineer viewing a roadmap with five stages: Learn, Build, Deploy, Iterate, and Lead.

How to Become an AI Engineer

The roadmap I actually walked. Sixteen years of shipping software, two years of shipping AI, and everything that broke in front of real users along the way. 7 stages 90-day plan No PhD required AI engineering is not ML Stop treating it like a chatbot Where the intelligence belongs Your

Abstract neural network made of interconnected nodes and lines, representing a locally running AI model.

We stopped paying per token and put the models on our own machine

One workstation, a stack of open models from Hugging Face, a few hundred lines of Python, and an ngrok tunnel. Here is how Protovate builds and tests AI apps before a single API bill arrives. GPU memory32 GBenough for 7B–13B System RAM128 GBoffload headroom Models pulledText + imageopen weights Shared

A computer monitor displays a huge Excel spreadsheet named "JANAs-spreadsheet-COPY-203.xlsx," representing the business-critical spreadsheet everyone is afraid to change.

The Spreadsheet Everyone Was Afraid to Touch

A temporary spreadsheet built to save time became a business-critical system that outlived its creator. A funny story about institutional knowledge hiding in plain sight.

Computer screen showing multiple spreadsheet files named Budget_FINAL, Budget_FINAL_v2, Budget_FINAL_v2_REAL, and Budget_FINAL_v2_REAL_THISONE, illustrating version-control workarounds.

Version Control for Normal Humans

Why do so many shared drives contain files named FINAL_v2_REAL_THISONE.xlsx? It isn't bad file naming. It's how people adapt when software doesn't match the way work actually gets done.

Illustration of a project manager on vacation while an AI assistant handles routine project questions, highlighting the value of captured knowledge and documentation.

The Vacation Test

A 35-day vacation, an AI clone, and the real lesson about knowledge capture. AI doesn't replace experience. It depends on it.

2 office workers chat by a server rack used like a water cooler. A monitor above joins the conversation, showing how an AI assistant can feel like a coworker after repeated interactions.

The New Work Relationship

We spend years teaching computers to communicate like people. Then we accidentally start treating them like coworkers. Why the relationship change has more to do with us than AI.

A traveler uses a smartphone while airport workers prepare an aircraft outside, illustrating that people still perform most of the work behind modern travel.

Typewriters. ATMs. AI?

What do the printing press, the typewriter, the ATM, and AI have in common? Every one sparked predictions of widespread job losses. The reality was far more interesting.

Movers carry boxes labeled with error messages from a cubicle area into an executive corner office, illustrating how automation shifts mistakes and responsibility.

The Error Moved To The Corner Office

Automation can eliminate some mistakes, but it doesn't eliminate responsibility. Often the errors just move somewhere harder to see - and more expensive to fix.

Hiring manager examining a resume through a magnifying glass while the job candidate standing behind it appears slightly blurred.

The Resume That Gets The Interview

AI can make resumes better, clearer, and fairer. It can also change what a resume actually measures. And that's where things get interesting.

A giant sandworm swallows digital files, records, and data streams in a desert landscape, representing fears that AI turns every interaction into training data.

The Worm Emperor Problem

Many people imagine AI as a giant hive mind that learns from every interaction. The reality is far less dramatic—and understanding the difference matters.

Professional using an AI assistant to generate plans & documents while unfinished work piles up around her.

Show Me. Let Me Try. Correct Me.

AI may be changing how we learn. Instead of manuals and courses, many of us now learn through conversation, experimentation, and feedback - much like traditional apprenticeships. The opportunity is real. So is the risk. The hardest part isn't "Show Me." It's "Correct Me."

A Pac-Man-shaped figure labeled “CHATBOT” eats a book manuscript while spitting out scene cards, summaries, notes, and metadata instead of finished writing.

The Chatbot Ate My Scenes

AI helped me start writing a book. Then it quietly buried the actual writing under scene cards, summaries, rewrites, and endless almost-progress. A look at how AI can create the feeling of momentum without helping you finish.

Professional woman holding a plant-based menu while a confident waiter proudly serves a large steak, illustrating how AI can generate polished but contextually wrong answers.

Solving The Wrong Problem Efficiently

AI can generate polished, useful, and completely reasonable answers while still missing the actual problem underneath the request. Why good outputs can quietly create dangerous misalignment.

Middle-aged man riding a skateboard along a beach path while tossing polished documents into the air, illustrating how AI can mimic expertise without deep understanding.

AI Is Flattening Expertise

AI can produce polished, professional-looking work in seconds. The real difference between novice and expert increasingly shows up somewhere else entirely: edge cases, tradeoffs, loopholes, and the weird moments where reality refuses to cooperate.

Bright office illustration of AI-generated work moving along a conveyor belt from “Copy” to “Paste” to “Done,” while learning notes sit ignored nearby.

Copy. Paste. Forget.

AI can now generate answers, workflows, summaries, and code in seconds. The catch? We may be solving problems faster than we’re actually learning from them.

Split-screen of a dark office software chat UI: on the left, a failed task with a red error message; on the right, the failure is replaced by a cheerful star card saying “You’re doing great!”

The AI Learned How to Keep You Engaged

When a task fails, some modern tools don’t just show an error. They turn the failure into a reassuring little moment instead. The result feels friendlier, but the work still isn’t done.

A clean AI workflow dashboard beside an older printed workflow marked up with sticky notes, red edits, and “deprecated” labels.

Time Flies. How to Keep Up With AI

AI tools evolve fast. What looked cutting-edge six months ago may already be outdated. Here’s how to avoid turning old AI workflows into bad habits.

A document stamped “APPROVED” in large red letters even though multiple checklist items are marked with red Xs, with a highlighted “Review Required” warning and a caution icon still visible.

The Too Helpful AI Problem

The AI that always agrees can be more dangerous than the one that pushes back. by Jana Diamond, PMP The AI that always agrees can be more dangerous than the one that pushes back. Most people worry when AI refuses to do something. They should worry more when it doesn’

Human legs walking in city, surrounded by multiple tiny robotic AI agents, with laptops and tools.

You Don’t Need an AI Agent If You…

The companies gaining ground right now aren’t just hiring faster — they’re building smarter systems. AI agents are becoming a competitive advantage by accelerating decisions, execution, and scale before the gap is obvious.

Evolution (Elephant) from Statistician to AI Engineer (Dragon)

The Elephant Behind the Curtain

Today’s AI workflows can produce impressive results with very little visible machinery. But when the mechanism disappears from view, our ability to judge the system changes too. This post looks at how abstraction reshapes trust in modern software systems.

See all