Lab Dot AI

Coding is no longer the bottleneck in AI
Deciding what to build now matters more

These are distilled notes I took
from a Stanford CS230 lecture with Andrew Ng and Laurence Moroney.

AI didn't slow down.
Our thinking did.

Code is cheap.
Judgment isn't.

AI sped up software creation so much that the constraint moved:
from building things
to choosing the right thing to build

That's why roles are collapsing.

The most valuable engineers now:
talk to users
define the problem
write the spec
then write the code

Not faster typists.
Better decision-makers.

The job market didn't crash.
It corrected.

Hype hiring faded.
Delivery expectations rose.

What wins in 2025+:
deep understanding
business impact
shipping real work

Ideas are everywhere.
Finished systems are rare.

Vibe coding isn't free.
Fast code can still be bad code.
And bad code compounds debt.

Responsible AI isn't optional anymore.
It's a product and brand risk.

Reliability now comes from process:
clear intent
planned tool use
checking results

Not clever prompts.

The industry is splitting:
big models for scale
small models for control

Prompting plateaus.
Fine-tuning compounds.

Most AI projects fail.
Not because of models.
Because of bad decisions upstream.

AI removed the coding bottleneck.
It exposed the thinking one.

If you're upskilling:
learn to define problems
own outcomes end to end
optimize for judgment

Tools change fast.
Good decisions last.

I'm documenting what I'm learning as the AI landscape shifts.
Follow if you want more of this.

P.S. Link to the Stanford lecture in the comments.

Most tech careers will stall without these skills
The 10 skills I teach for AI mastery in 2026

If you work in tech, tools alone will not keep you relevant.

These are the skills that actually compound.

1. LLM foundations
How models work, are trained, their limits, and how to control hallucinations and context

2. Prompt engineering
Design prompts that reliably shape reasoning, outputs, and workflows

3. AI-powered research and fast learning
Use AI to synthesize hundreds of sources and compress learning cycles

4. Private AI systems
Run secure, self-hosted AI with open models and full control over data

5. RAG foundations
Build systems that answer from your data, not generic public training data

6. Agent-driven automation
Use coding agents to automate analysis, documentation, and workflow generation

7. Model Context Protocol (MCP)
Skip the hype and apply MCP to connect tools, data, and actions into AI systems

8. Agentic AI foundations
Design and build AI agents in Python from first principles

9. AI-augmented workflows
Automate work and life processes using no-code and low-code tools

10. What's next
Track trends, avoid dead ends, and plan continuous AI upskilling

I teach these skills in Practical AI for Systems Engineers.
If you want depth, not hype, visit: https://labdotai.com/practical-ai-for-se

I turned 15+ years in Systems Engineering into one course.
Built for engineers who want to go from zero to AI-ready fast.

I'm packaging my experience - from 15+ years across Systems Engineering roles to leading a global team that won Dell's internal AI hackathon at their RTP campus (USA), into a structured "zero to hero" course built to make AI practical in real engineering work.

No sales talk, no fluff, no hype.

Just foundations, best tools, and approaches that work for me.

We start from fundamentals and move fast. You'll see how AI fits engineering reality.

2023: I only chatted with AI
2025: I led 4 peers to an AI hackathon win in the U.S.

Was it easy?

No, I had plenty of self-doubt.
"Why are you doing this? You can't compete with developers using AI".

But I kept learning.
I kept building.
I kept pushing past comfort zones.

Here's what I discovered:
- AI removes tedious tasks so you can focus on what energizes you.
- Agentic dev + no-code tools let anyone build useful automations.
- Launching your own MVP is possible without strong dev skills.

Today I don't fear losing jobs - I create them.
And I focus my best energy where it matters most.
I skip non-productive meetings and get real work done.

If something sounds too intimidating, my first thought is:
"Should I delegate it, or use AI to help me?"

If you feel late to AI, you're not.
I'll show you how to catch up fast.

30+ AI tools tested in my IT workflow.

These 12 actually save me hours weekly.

- Perplexity → research in minutes

- NotebookLM → quick learning

- ChatGPT, Claude, Google AI Studio → brainstorm + polish content

- Claude Code, Cursor → accelerate dev and automations

- n8n → automate workflows

- Ollama → run LLMs locally

More in the carousel.

Tested and working well for me - these are the tools I keep.

What tool would you add?

We won an AI hackathon with a 5 minute demo
No complex stack. Just ruthless focus on value.

A few months ago, I led an international team to win Dell's internal AI hackathon in the AI-FOR / Modern Dev category in RTP, USA.

We qualified for the global finals by solving a real business problem with a working prototype, not a slide deck.

What actually made the difference:

- Good preparation beats raw brilliance. We narrowed the idea until it could be explained and demoed in five minutes.

- You do not need to be a developer to build useful AI prototypes anymore. Today's tools remove that barrier.

- Solve problems directly connected to your day job. Relevance creates instant clarity and buy-in.

- Leverage your team's full range of expertise. Technical depth plus domain context wins.

- Skip default AI frameworks when they limit differentiation. Building from fundamentals uncovers features others miss.

- Step outside your comfort zone. Hackathons reward people who show up and commit.

If you have never joined a hackathon, what real problem from your work would you solve with AI?

The most unexpected moments often spark the biggest breakthroughs.

At the end of July, my garden leave and my time at Dell Technologies officially came to a close. With the formalities done, I started my next chapter: founding Lab Dot AI.

Why now?

Winning Dell's internal AI Hackathon at the RTP campus revealed something I couldn't ignore. There's a growing gap between AI awareness and the practical skills needed to apply it in real systems.

The mission

After more than 20 years in the Systems Engineering community, my focus is simple:

- Share lessons learned, including mistakes, so others can avoid them

- Shorten the AI learning curve for Systems Engineers

- Provide honest, vendor-neutral insight grounded in real-world work


The opportunity

I believe Agentic AI is the next major wave of automation, more impactful than anything we've seen before. Every meaningful breakthrough in my career came from eliminating repetitive work. AI now makes that faster, more powerful, and accessible to far more people.

If you're a Systems Engineer or Presales who wants practical AI skills, not slide decks, follow along. This is exactly what I'm building.