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Industrial Real Estate Investor and Business Operator

Barrett O'Neill

Follow me to learn how you can leverage AI to boost your productivity and accelerate your career. Scaled products to 8 Million+ users and built the world's biggest AI newsletter with 1 Million+ readers.

Zain Kahn

Before you spend $2,000 on an AI course, read this.

A lot of people are buying expensive AI courses right now.
But honestly, most of them don’t need to.

Some of the best AI learning resources in the world are already free.
And they’re not built by self-proclaimed AI experts online.They’re created by the companies actually building the models, chips, tools, and infrastructure behind this entire AI wave.

If you want to learn AI properly, it makes sense to learn directly from the source.

But before that, Check out 100+ such resources shared in this community of 200K+ AI/ML Engineers: https://lnkd.in/e64Jvdrt

Here's a list of 10 free AI learning platforms from industry leaders:
𝟭 - 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰:
https://lnkd.in/e5fK7QUA

𝟮 - 𝗚𝗼𝗼𝗴𝗹𝗲:
http://grow.google/ai

𝟯 - 𝗠𝗲𝘁𝗮:
https://lnkd.in/et6wz-ta

𝟰 - 𝗡𝗩𝗜𝗗𝗜𝗔:
https://lnkd.in/e8aHmFxc (GOATed)

𝟱 - 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁:
https://lnkd.in/ej85NeZc

𝟲 - 𝗢𝗽𝗲𝗻𝗔𝗜:
http://academy.openai.com

𝟳 - 𝗜𝗕𝗠:
http://skillsbuild.org

𝟴 - 𝗔𝗪𝗦:
http://skillbuilder.aws

𝟵 - 𝗗𝗲𝗲𝗽𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝗔𝗜:
http://deeplearning.ai

𝟭𝟬 - 𝗛𝘂𝗴𝗴𝗶𝗻𝗴 𝗙𝗮𝗰𝗲:
https://lnkd.in/eajVCrvv

If you’re serious about learning AI, the biggest difference usually comes from consistency, not another course.

A simple system works surprisingly well:
• Pick one learning path
• Spend 30–45 minutes a day on it
• Build small projects as you go
• Share what you learn publicly
• Repeat for 90 days

That alone puts you ahead of most people who only consume content.
Paid courses aren’t always bad.
But too often people end up buying motivation instead of building real skills.

Follow me to learn how you can leverage AI to boost your productivity and accelerate your career. Scaled products to 10 Million+ users.

This guy from Cambridge plays with Claude Code like he owns it.

He talks about his workflow in 2026 and here's what's new: 

1. Video-Based Specification (Spec Phase)

> Screen Recording: 

Instead of writing a spec from scratch, find an existing product that is similar to your idea. Record your screen while using it and talking through your specific feature ideas and changes.

> Generate PRD: 

Upload this video to Gemini 1.5 Pro (referred to as Gemini 3 Pro/Free Pro in the video context) and ask it to generate a Product Requirement Document (PRD).

> Refine Spec: 

Use the "Ask User Question" tool in Claude Code. Prompt it to interview you about the generated spec to fill in missing details (e.g., "How should the emoji picker be positioned?").

> Package Discovery: 

Feed the refined spec into ChatGPT with "Heavy Thinking" (likely OpenAI's o1/o3 models) to search for and recommend specific, well-maintained GitHub packages (e.g., for a WYSIWYG editor) to avoid building complex components from scratch.

2. The Orchestrator Role

> Design Feedback Loops: 

Your primary job is not to write code but to design loops where the agent can build, fail, and learn.

> Monitor & Update: 

Watch the agent’s reasoning. If it makes a mistake, don't just fix the code—update the claude. md (project instructions file) to prevent that specific mistake from happening again.

> High-Level Decisions: 

Make the architectural decisions the agent can't, such as choosing the database or specific tools.

3. Model Choice & Tools

> Model Selection: 

Use Opus 4.5 for building large-scale features and GPT-5.2 for architecture and debugging (specific future models mentioned in his 2026 context).

> Voice Dictation: 

Use HyperWhisper to dictate prompts significantly faster than typing.

4. Execution & "Parallel Vibe Coding"

> Parallel Agents: 

For small, well-defined tasks (like extracting hard-coded strings for translations), spin up multiple sub-agents to work on different parts of the project simultaneously.

> Avoid Conflicts: 

Do not use parallel agents for large features within the same project to avoid complex merge conflicts ("meshing issues").

> Sub-agents for Multi-Project Fixes: 

If a bug exists in a template used by multiple projects, spin up one sub-agent per project to fix them all in parallel.

5. Review & Maintenance

> Planning Mode: 

Use Claude Code’s "Planning Mode" to prevent Architectural Drift, ensuring the agent sticks to the original design vision over time.

> Shape of Diffs: 

Inspect the "shape" of the code changes (diffs) to ensure they are manageable and align with expectations before accepting them.

> Forking for Learning: 

If the agent does something surprising or complex, fork the session. In the forked session, ask "Why did you do that?" or request diagrams to understand the code without polluting the context of the main working session.

Get more tutorials here: https://lnkd.in/e64Jvdrt

Traditional computer science degrees can't keep up with this pace.

Here are the 15 engineering blogs that actually teach real-world scale:

1 → THE CODE
Scale at global level.
Teaches you how to code with AI and AI Agents
https://lnkd.in/e64Jvdrt

2 → Airbnb Tech Blog
Product engineering done right.
User focused systems.
https://airbnb.tech/blog/

3 → Meta Engineering
Billions of users infrastructure.
Performance at extreme scale.
https://engineering.fb.com

4 → AWS Architecture
Cloud design patterns revealed.
Production ready blueprints.
https://lnkd.in/gqjh88PS

5 → Netflix TechBlog
Streaming at massive scale.
Chaos engineering explained.
https://lnkd.in/gNE8Cs6Z

6 → Discord Engineering
Real time systems mastery.
Handling millions of connections.
https://lnkd.in/gea6kSwt

7 → Google Research
Cutting edge tech breakthroughs.
Tomorrow's solutions today.
https://lnkd.in/gjzDu8wp

8 → NVIDIA Developer
GPU powered computing insights.
AI infrastructure secrets.
https://lnkd.in/dt_BW7qF

9 → Slack Engineering
Collaboration platform internals.
Distributed systems at scale.
https://slack.engineering

10 → Cloudflare Blog
Internet infrastructure deep dives.
Security and performance wins.
https://lnkd.in/gqa-HE6h

11 → Figma Tech Blog
Collaborative design systems.
Browser performance magic.
https://lnkd.in/g2KF_eX9

12 → Shopify Engineering
E-commerce at scale.
Black Friday traffic handled.
https://lnkd.in/g7DVzRAc

13 → Stripe Engineering
Payment systems that work.
Financial infrastructure explained.
https://lnkd.in/gfMr-jEr

14 → Microsoft Engineering
Enterprise scale solutions.
Developer tools insights.
https://lnkd.in/g3Mi8XES

15 → GitHub Engineering
Code collaboration at scale.
Developer platform secrets.
https://lnkd.in/gPHaD6AC

16 → Bonus
System design deep dives.
https://lnkd.in/g-tP7eEc

Save this for later.
Repost ♻️ for aspiring engineers.

Follow me for more AI playbooks and engineering resources.

Claude Code is only as good as your orchestration workflow.

I spent 6 months testing it so you don't have to. 

[ P.S. You can get my Ultimate Claude Code guide for engineers here at no cost: https://lnkd.in/e64Jvdrt ]

So Boris Cherny (the creator of Claude Code at Anthropic) recently shared the internal best practices his team actually uses daily. 

Someone brilliantly distilled those threads into a structured CLAUDE .md file you can drop straight into any project root.

It acts as a system prompt. Turns Claude into a far more autonomous, rigorous engineering partner.

Here's what it does:

→ 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻: Mandates "Plan Node Default" for any task over 3 steps. Uses subagents liberally to keep the main context window clean.

→ 𝗦𝗲𝗹𝗳-𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁 𝗟𝗼𝗼𝗽: This is the real magic. After ANY correction, it updates a tasks/lessons .md file. You're building a compounding system where the mistake rate drops over time because it actively learns from your feedback.

→ 𝗩𝗲𝗿𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗕𝗲𝗳𝗼𝗿𝗲 𝗗𝗼𝗻𝗲: Can't mark a task complete without proving it works. Diffs behavior. Runs tests. Checks logs. The bar? "Would a staff engineer approve this?"

→ 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗕𝘂𝗴 𝗙𝗶𝘅𝗶𝗻𝗴: Zero hand-holding. Point it at failing CI tests or error logs... it just goes to work. No constant context switching from you.

→ 𝗦𝘁𝗿𝗶𝗰𝘁 𝗧𝗮𝘀𝗸 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Forces a "Plan First" approach written to a todo .md with checkable items before any implementation starts.

And perhaps the most important part?

It forces the AI to prioritize simplicity, find root causes instead of temporary fixes, and minimize the blast radius of every change. Senior developer standards. Not shortcuts.

If you're spending hours a day in the terminal with AI, setting up a strong .md instruction file like this isn't optional anymore. It's the difference between AI that drifts and AI that compounds.

It takes time to set up. But if you do, you're ahead of almost everyone else.

Lemon is the most powerful tool I've used since ChatGPT.

You literally say things with your voice and it does it for you instantly.

Pure magic.

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