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Welcome to the “how do I actually learn how LLMs work” guide. If you’ve got a CS background and you’re tired of the endless machine learning prerequisites, this is for you. I built this with past me in mind, I wish I had it all drawn out like this. This roadmap should have you comfortable with building, training, and exploring and researching.

The links at the end let you go as deep as you want. If you’re stuck, rewatch or reread. If you already know something, skip ahead. The phases are your guardrails, not handcuffs. By the end, you’ll have actually built the skills. Every resource, every project, every link is there for a reason. Use it, adapt it, and make it your own. I hope you don’t just use this as a collection of bookmarks.

Remember, you can always use DeepResearch when you’re stuck, need something broken down to first principles, want material tailored to your level, need to identify gaps, or just want to explore deeper.

This is blogpost #4 in my 101 Days of Blogging . If it sparks anything; ideas, questions, or critique, my DMs are open. Hope it gives you something useful to walk away with.

The short version:

You will:

The approach here is simple.

Learn by Layering: Build Intuition ➡️ Strengthen Theory ➡️ More Hands-on ➡️ Paper Deep Dives ➡️ Build Something Real.

You’re going to use four kinds of resources:

Real technical ability is fading. Worried about AI replacing you? Build real technical depth. LLMs are leverage, a force multiplier, but only if you know what you’re doing. You’re not losing to AI. You’re losing to people who use AI better than you because they actually understand the tech. Get sharper.

This goes way beyond privacy or ideology. As optimization and model alignment get more personal (and more opaque), your only actual safety net is full local control. If you’re building a business, a workflow, or even a habit that depends on a remote black box, you’re not the customer; you’re the product. Full-stack ownership isn’t just to show off. It’s pure risk management.

The future belongs to those who can build, debug, and document, not just rent someone else’s toolchain. Bootcamps don’t cut it anymore.

“Every day these systems run is a miracle. Most engineers wouldn’t last five minutes outside their cloud sandbox.”

Our industry is obsessed with AI hype, most devs have never seen the bare metal, never written a real doc, and never owned their own stack. Meanwhile, the only thing standing between us and our systems’ total collapse is duct tape, a few command-line obsessives, and the shrinking number of people who still know how to fix things when the it all stops working. We’re staring down an industry where the median troubleshooting skill is somewhere between “reboot and pray” and “copy-paste from Stack Overflow”.

So please, stop the doomscroll and quit worrying about being replaced. LLMs amplify you; they don’t substitute for you. The edge is in the hard parts: critical thinking, debugging, taste for clean architecture, putting it all together. That’s not going anywhere. The job is shifting not getting eliminated: more architecture, more security, more maintenance, more troubleshooting. Still deeply human, and still non-trivial to automate.

This is blogpost #3 in my 101 Days of Blogging . If it sparks anything; ideas, questions, or critique, my DMs are open. Hope it gives you something useful to walk away with.

Yesterday morning I hosted an X/Twitter Audio Space on how LLMs, open-source, and the gravitational pull of platform centralization are forcing us all to rethink what it actually means to be a developer. The cloud got us coddled… The cloud was a mistake , and I believe that next decade’s winners won’t be the ones who just ship the most code (LLMs are really good at that BTW), but the ones who get obsessed with understanding, documenting, and actually owning their tools, top to bottom.

Let’s set the scene. Google Cloud outage just crashed the internet. X/Twitter is in full panic mode , Cursor/Claude Code/Windsurf/etc aren’t working anymore. LLMs have become the default code generator, human programming skills are fading. For me, I didn’t even notice the outage until I got online. My local agents, running on my hardware from my basement , kept running.

TL;DR: I feed the template below into Gemini 2.5 Pro to build the DeepResearch prompt. Then I use the output to run DeepResearch with. You’ll find more context further down, but the main idea is simple: Just drop your core ideas between TOPIC BEGINS HERE and TOPIC ENDS HERE. The rest builds itself.

Google just doesn’t cut it anymore: I’m the guy who wired a mini–data center into his basement. When you’ve got almost 3-dozen GPUs humming at 3 a.m. and a brain that treats half-baked ideas like Pokémon’s you gotta catch ’em all, shallow Googling just doesn’t cut it. I needed a research system that could keep up with the chaos in my head, force clarity, and let me ship faster than my cats can yank the UPS cable while livestreaming (true story ).

DeepResearch and this framework turn my chaotic untangled thoughts into informative, in-depth, comprehensive reports. I also use it to learn anything and I have it wired into the loop of how I code with agents. Today, I’m sharing this workflow with you.

This is blogpost #2 in my 101 Days of Blogging . If it sparks anything; ideas, questions, or critique, my DMs are open. Hope it gives you something useful to walk away with.

I believe that if you genuinely want to move the needle, whether you’re an indie builder, a founder hunting for market clarity, or just someone tired of getting subpar answers, you need a proper system. Something structured that transforms vague curiosities into pinpoint insights, ruthlessly forces clarity, prevents endless rabbit holes, and delivers actual value (think high signal, zero noise). To me, that’s DeepResearch.

Before DeepResearch, I’d “just check one thing” and suddenly I’m 138 tabs deep with outdated blogposts and conflicting info. DeepResearch fixed that, but only because I learned how to use it. There’s a method to the clarity.

DeepResearch, among many things, could be:

Traditional research often ends up vague, ambiguous, and misses key insights entirely. So I built-and obsessively iterated-a prompt framework designed explicitly to fix these problems. This approach guides both me and the AI to:

This became the Ultimate DeepResearch Prompt Builder Template, the backbone of every serious AI-driven research I execute.

Writing is how we come to understand ourselves, a gift to our future selves, a record of what once mattered. It grounds our thoughts and gives them shape.

This one is for me. I hope you enjoy it too.

The past few months have given me a lot to think about. Life can happen to you out of nowhere, faster than a finger snap, and you’ve only got yourself-mostly-to keep it together.

In life, you’re either getting smarter or dumber. Stronger or weaker. More efficient or completely helpless. Subject to dependence or reliance. The latter is becoming exponentially easier, and the trend will only accelerate in the years ahead.

“I want to live happily in a world I don’t understand.” ― Nassim Nicholas Taleb, Antifragile: Things That Gain From Disorder

Don’t be that guy.

Being prepared is fundamental to your survival, but not only that… Being prepared is our only duty in life: to ourselves, to our loved ones, and to everything we care about. So, I am no longer taking time for granted, and I will always be prepared.

Actions-per-minute matter. A lot. We’re entering an era where productivity multipliers, across the board, are approaching infinity. That has to be harnessed, deliberately and fast. Or else…

So, I’ve made a decision: I’m going to stress-test myself—across the board, for an extended amount of time. No more skipped workouts. No more pushed plans. No more dragging out already-soft deadlines. I have to show up. Fully. For all of it.

On Thursday, May 1, 2025, I hosted a space titled “Titles, Power, and Playing the Game” with my friends Skyler Payne and Giyu . Skylar is ex-Google, ex-LinkedIn, so he’s got those big corps under the belt; he even once politicked so hard, someone wrote a blind post about him. Giyu, meanwhile, just landed his first full-time job and is already climbing the ladder fast. But like anyone new to the corporate world, he’s been running into a few dilemmas along the way. That’s what sparked this audio space and the article you’re reading now.

Earlier in my career, whenever I heard “corporate politics,” I usually thought of greasy handshakes and backroom deals. Stuff I’d typically tune out because, frankly, I’d rather be building and learning. Very engineer-minded. I eventually realized that “corporate politics” wasn’t about manipulation, it was about learning to navigate the human network to actually get things done.

Giyu, who was hired with no previous full-time experiences but had a couple of years of consulting under his belt, was struggling with what job title to take. He got into an org that got him promoted within the first 3 days of his onboarding. He works directly with the CTO, and does a lot of heavy lifting: architecting, leading data, managing stakeholders across teams; but all at a newer company. He was hesitant to grab a big title like “Head of” or “Director” too early, worried it might mess with his path later. Title inflation is real, but also titles mean different things everywhere.

Skylar warned against jumping at a VP title too soon, especially in smaller shops where it might not have the real weight: “Titles signal your scope,” he said. Taking a VP title when you’re basically a super-strong tech lead can misrepresent things and cause headaches down the line when you talk to bigger companies. It can look like ego or make it hard to accept a title that fits the scope elsewhere.

But beyond the labels, Skyler pushed a deeper question: What kind of work do you actually want to be doing? Fire-fighting daily? Building medium-term? Long-term strategy? Hands-on coding vs. guiding others? Giyu felt strongest about medium-term building, maybe a 30/70 split coding vs. guiding.

Skyler broke down the usual paths:

Based on that, Skyler suggested “Staff+” made sense. It reflects the impact, partners with the CTO, but keeps future doors open. The key takeaway is to let the work you want to do drive the title, not the other way around. Focus on the what, the title follows the substance.

Skyler asked, “do you wanna do big things?” If so, you gotta get people on board and be seen. That alignment is politics. It’s about relationships, sure, but also visibility. You can’t just build cool stuff in the dark and expect things to happen.

He said it’s essentially about influence: selling your vision. Both he and Giyu stressed it’s not about being fake, but genuinely connecting and figuring out what makes people tick. Giyu admitted it felt weird at first, but seeing its value changed how he operated.