Whenever I start teaching, something amazing happens: past troops ping to join. I’ve loved watching this for years — an honor I don’t take lightly. I post slides and curriculum in our announcements so they can follow; I do it to become the best trainer.

3 myths keeping veterans out of AI engineering: 1. "You need a math background." I have a police science background. 2. "It takes years." Seniors can transition within a year. 3. "The market is saturated." It's starving for people who show they can ship.

I have this theory about timing in AI engineering: The window where "I just pivoted into this" is still an acceptable answer in interviews is closing. Not closed. Closing. In 2024 it was wide open. In 2026 it's narrowing. In 2028 it will be mostly shut. Every month you wait, the bar moves up.

The entry-level tech market is brutal right now. Companies want juniors who think and ship like seniors. That's exactly why @VetsWhoCode.io doesn't build students. It builds engineers — with production commits, real evals, and a portfolio that can hold its own in any interview.

Quick ask, then back to your feed: @VetsWhoCode is 100% donor funded. Every veteran we train is free to them and not free to us. If you've gotten value from anything I've posted this month — a framework, a hot take, a reminder to ship — consider sending us $10. Thanks you.

Hiring manager: "Tell me about a time you used an LLM in production." Wrong answer: "I built a chatbot demo." Right answer: "I built X. Users complained about Y. I added evals to catch it. Bug rate dropped from Z% to W%. Here's the repo." Specifics. Failures. Fixes. Numbers. That's what wins offers.

Rule we live by inside @VetsWhoCode: Ship something every Friday. Anything. A script. A blog post. A pull request. A demo video. A failed experiment with notes. 52 things per year. 156 in three years. By then you don't need to apply for jobs. They find you.

If you're reading this in 2026: The window where "I just learned this last year" is an acceptable answer in an AI engineering interview is closing. Not closed. Closing. Every month you wait, the bar moves up. Every month you ship, your odds compound. The math is brutal and it's also fair. Get to it

If a veteran asked me where to get serious about AI engineering in 2026, my answer wouldn't be a book or a Coursera certificate. It would be @VetsWhoCode. Free. Mentored by working engineers from Google, Microsoft, and GitHub. A decade of placements behind it. vetswhocode.io

The smallest useful AI project you can ship this weekend: A script that takes a folder of your own PDFs, lets you ask questions about them in the terminal, and answers with citations. ~150 lines of Python. Touches embeddings, vector search, retrieval, and prompting. That's a real RAG system.

"Why learn to code if AI will do it?" Because the people directing the AI need to understand what it's producing. Someone has to architect the system, review the diff, catch the subtle bug, and own the outcome. AI raised the floor on what one engineer can build. It also raised the ceiling.

Underrated career move: comment thoughtfully on 3 AI engineers' posts every day for 30 days. Real comments. Real questions. Not "great post." By day 30, you'll have warm relationships with 90 people in the industry. That's how jobs actually get filled.

What does an AI engineer actually do all day in 2026? – 30% writing code (Python, mostly glue between APIs and data) – 20% writing and running evals – 20% in meetings about prompts, costs, and edge cases – 15% reading logs from yesterday's failures – 10% talking to users – 5% reading papers

Veterans, listen: The single biggest unlock for transitioning into AI engineering is building in public. One project. One repo. One thread of progress posts. Recruiters don't read resumes anymore. They read your GitHub and your timeline. Make sure both tell a story.

"How do I stop the LLM from hallucinating?" You don't. You contain it. – Ground answers in retrieved documents (RAG). – Cite sources in the output. – Add a verification step that checks claims against the source. – Refuse to answer when confidence is low. – Log everything so you can audit later.

Every AI engineer I know — including the ones at top labs — has felt like a fraud at some point this year. The field moves too fast for anyone to feel "caught up." The cure isn't catching up. It's shipping. Build one thing this week. You'll learn more than reading 100 papers.

Three projects every aspiring AI engineer should ship before applying to jobs: A RAG app over a real dataset you care about. (Not the docs of a tool. A real one.) An eval harness for that RAG app, with at least 50 test cases. A simple agent that can take an action in the real world.

Real engineering judgment: If a regex solves it, use a regex. If a SQL query solves it, use a SQL query. If a deterministic function solves it, write the function. Reaching for an LLM when you don't need one is how you get slow, expensive, unreliable software. Use the right tool.

The mistake when learning AI at scale: You build a slick demo on GPT-4-class models without ever looking at the bill. Then it ships. Then accounting sees the invoice. Token economics is part of the job. Know your input/output costs per call. Cache aggressively. Use the smallest model.

When do you use what? Prompt engineering: the answer lives in the model already. You just need to ask the right way. RAG: the answer lives in YOUR documents. The model has to read them first. Fine-tuning: you need the model to consistently follow a specific style, format, or domain language.

Here's something I tell every veteran walking into @VetsWhoCode: The military trained you to test things until they break. Pre-flight checks. Pre-mission rehearsals. After-action reviews. That mindset is exactly what AI engineering is starving for right now.

Nobody talks about evals enough. If you can't measure whether your LLM app is getting better or worse, you're not engineering. You're vibing. First eval doesn't have to be fancy. 20 example inputs, expected outputs, a script that scores them. Done. Now you have a feedback loop.

RAG in plain English: Step 1: take your documents. Step 2: turn them into searchable chunks. Step 3: when a user asks a question, find the relevant chunks first. Step 4: hand those chunks + the question to the LLM. That's it. That's the trick. Now go build one.

The bar for "AI engineer" in 2026 is higher than "senior backend dev" was in 2021. That's not gatekeeping. That's reality. The good news: the ramp is also shorter than it's ever been. The Hashflag Stack is 17 weeks — and it's built for veterans with real lives. We've watched it work.

What's the one thing you wish someone told you before you started learning AI engineering? I'll start: "You don't need a PhD. You need a deployed app, a GitHub repo, and a story about why you built it." Veterans — drop yours. We're building the VWC curriculum out loud.

The 17-week ramp we use at @VetsWhoCode — The Hashflag Stack: Phase 1: Foundations. Terminal, Git, JS, Python — by hand, no AI. Phase 2: Software Engineering. Next.js, FastAPI, real apps. Phase 3: AI Engineering. LangChain, RAG, evals. Phase 4: Production Mastery. Ship, monitor, scale.

The fastest way to actually understand what's happening inside the LLMs you're building on? Build with them every day for 17 weeks under mentors who have real world experience. That's the @VetsWhoCode AI Engineering track. No theory-only work. You build, you break, you ship. vetswhocode.io

Veterans have an unfair advantage in AI engineering and nobody talks about it: – You're trained to operate under ambiguity – You think in systems and SOPs – You ship under pressure – You don't quit when something breaks Those are the exact traits that separate AI engineers from tinkerers.

The biggest mistake aspiring AI engineers make: They learn 12 frameworks before they ship 1 thing. Pick a problem you actually have. Solve it with an LLM. Deploy it. Break it. Fix it. That single loop teaches more than a year of tutorials.

Hot take: if your entire AI skill set is "I'm good at prompting," you're not an AI engineer. You're a power user. AI engineers ship systems. That means RAG pipelines, evals, guardrails, retries, observability. At @VetsWhoCode, that's where we start training. Not at the prompt box.

I run @VetsWhoCode. I've watched hundreds of veterans transition into tech. Clean code still matters. It's the floor. The ceiling in 2026 is the engineer who writes clean code AND can take a messy business problem, hand part of it to an LLM, and ship a working answer by Friday. Both. Always both.

What it actually takes to be an AI Engineer in 2026: Strong Python fundamentals Comfort with APIs and HTTP Tokens, embeddings, context windows RAG and vector search Evals — knowing when your model is wrong Notice "prompt engineering" isn't on the list. It's table stakes.

Did you know @VetsWhoCode teaches AI Governance?

NotebookLM is a great tool to build learning resources like this Fine-Tuning Playbook, where we walk through the process of customizing foundational models for specific tasks.

I recently put together a video about how the shifts in software engineering during the AI age closely resemble another craft I follow closely: martial arts and combat sports. I'm noticing many of the same patterns I grew up watching now unfolding in tech: www.youtube.com/watch?v=Rsgt...

I’ve started seeing AI as infrastructure. We built an audio pipeline so every post can be listened to. AI translates, infrastructure delivers, humans keep ownership. From the user side: just press play. #AI #accessibility vetswhocode.io/blogs/ai-as-...

In 2010 I sat in Barnes & Noble with a coding book and a chip on my shoulder. A decade later we’ve trained 300+ vets, driven $20M+ in earnings, and placed people at Microsoft, AWS, Google. The gap wasn’t talent—it was translation. So we built J0dI3, an intelligence layer.

We've had our hands full with things at VWC—watch this video for the latest updates: youtu.be/tUg5IjxMB9o?...

If you're concentrating on upskilling within the industry, the best app to use is NotebookLM. I buy tech books on Humble Bundle and create plenty of resources that help me learn wherever I am.

Don't th ank me, you did the work.

Threads is fun but I am able to be more technical over here.

I had a blast on my latest talk. Being able to speak about GitHub Copilot at Microsoft. Last speaking engagement of the year and for a minute, cause my new role is even more demanding.

Facts. Most all small nonprofits are small cause they are actually spending the money on providing services, not on fundraisers and galas and shit.

hey i heard you were talking about me but haven’t bought a ticket yet… so like what are you waiting for?! 🎃 www.tixtree.com/e/magnolia-c...

It’s one of the reasons why AI safety has become a passion of mine. I’ve been in the meetings where white dudes are dismissive and say, “we will have legal handle it.”

I say this from grand scale experience: content moderation is one of the toughest jobs out there.

This feels like a prayer almost. I love it.

I will not grieve people whose only connection to my life is through the wrongs they have done. I owe them nothing and I reserve the right to celebrate how their death makes my life an inherently better place. That doesn't make me their killer, that simply makes me someone they cannot wrong again.

It was my job to study and analyze Charlie Kirk. I took every piece of content he made and created a data pipeline to leverage it as a knowledge base with AI. If you have anyone in your immediate circle saying anything beyond the fact that he was a horrible right-wing propagandist, run.

The project he had the alarmed me the most that frankly no one was speaking about was the 5C charter school program, a Christian charter school program that focused on indoctrinating K-12 with his ideology.