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.

Alex Banks

Building a better future with AI

Alex Banks is a founder, creator and investor. He reaches over 1 million people on social media each week with insights into the world of artificial intelligence (AI).

He is the founder and writer of The Signal, a weekly newsletter that cuts through the AI noise to over 55,000+ readers.

Alex is also an active early-stage investor and advisor to startups across the AI and technology landscape.

👉 All partnerships, enquiries and speaking engagements: contact@noisemedia.ai

Writer

Anthropic just measured which jobs AI is actually replacing.

The gap between theory and reality is massive.

Anthropic published a new research paper using its own Claude usage data to track AI's real-world impact on jobs.

What's new:

They created a metric called "observed exposure" that combines theoretical AI capability with actual professional usage data. The results are eye-opening.

→ Computer & Math: 96% theoretical capability. 32% actual coverage.
→ Office & Admin: 94% theoretical. 42% observed.
→ Legal: 88% theoretical. Just 15% observed.

Capability isn't the bottleneck. Legal constraints, verification requirements, and slow enterprise adoption are what's holding back real-world deployment today.

Most exposed occupations:

→ Computer programmers top the list at 75% task coverage
→ Customer service reps follow at 70%
→ Data entry keyers at 67%

But there’s a certain irony at play that I think is worth pointing out.

Programmers are both the most exposed occupation AND the heaviest adopters of AI. 

They're actively building and using the technology that automates their own work.

The workers most at risk overall skew older, female, more educated, and higher-paid, earning 47% more on average than their unexposed counterparts. 

Graduate degree holders are nearly 4x more represented in the most exposed group.

Despite all this exposure:

→ No meaningful increase in unemployment for high-risk workers since ChatGPT launched
→ But hiring of 22-25 year olds into exposed roles has dropped roughly 14%
→ No equivalent decline for workers over 25

My takeaway:

It’s interesting to see the “disruption” showing up as a hiring freeze vs sweeping layoffs.

But mainstream media much prefer to print “thousands made redundant” to sensationalise headlines.

I also think it’s important to point out the 30% of workers that have zero AI exposure. 

Cooks, bartenders, mechanics, lifeguards. The roles AI can't touch are almost entirely physical.

Having a living measure like this helps track how the gap between AI’s theoretical capability and real-world adoption narrows over time. 

That gap is where the next wave of disruption lives.

Follow me Alex Banks for daily AI highlights and insights.

I talked about AI’s impact on jobs first in my newsletter.

You get the most important news + analysis in your inbox every Sunday.

Read it here: https://lnkd.in/ei8r5Xyq

Anthropic said no to the Pentagon.
OpenAI signed the deal hours later.
Here's what happened:

Anthropic drew two red lines with the Department of War:

1. No mass domestic surveillance
↳ AI can now analyse bulk data the government buys on Americans at scale
↳ Currently legal, but only because the law hasn't caught up

2. No fully autonomous weapons
↳ AI isn't reliable enough to automate selecting and engaging targets
↳ No oversight framework exists for removing humans from the loop

For context:

→ Anthropic was the first AI company on the classified cloud
→ Deployed across intelligence, cyber ops, and combat support
→ Forfeited hundreds of millions cutting off CCP-linked firms
→ These two red lines represent ~1% of use cases

The Pentagon gave Anthropic a 3-day ultimatum.

Anthropic refused.

The response:

→ President Trump: "Their selfishness is putting AMERICAN LIVES at risk"
→ Secretary of War Pete Hegseth: Anthropic is a "supply chain risk"
→ Trump ordered every federal agency to cease all use of Anthropic's technology

Then OpenAI entered.

On Friday morning, Altman publicly backed Anthropic's red lines.

By Friday evening, he'd signed the Pentagon deal himself:

→ OpenAI claims the same red lines as Anthropic, plus a third
→ OpenAI asked the Pentagon to offer identical terms to all AI labs
→ Multiple OpenAI employees signed an open letter supporting Anthropic
→ Altman admitted the deal was "definitely rushed" and "the optics don't look good"

The consumer backlash was immediate:

→ "Cancel ChatGPT" went viral across Reddit and X
→ Claude hit # 1 on the App Store, overtaking ChatGPT
→ Claude is now # 1 in Germany, Canada, and other markets

My takeaway:

Not only have I seen my workflows fully transition from ChatGPT to Claude over the last year, but the consumer mass-market is catching up.

I think it’s also important to point out that correlation doesn’t necessarily mean causation here.

Claude didn’t just hit # 1 in the app store as a result of ChatGPT accepting the DOW deal.

Anthropic has simply built a better product where the timing of adoption happened to coincide with this viral uproar of the company denying use of their products for the surveillance of Americans and operation of autonomous weapons.

Ethical positioning now has direct commercial consequences.

Values are becoming a competitive moat.

I covered this idea yesterday in my newsletter.

Be the first to receive it by subscribing today.

Read it here: https://lnkd.in/e898z7SG

Claude just made switching from ChatGPT effortless.
You can now import your entire memory in 30 seconds.
Here's exactly how it works:

Step 1: Go to Claude Settings → Capabilities
↳ Scroll to the Memory section
↳ Find "Import memory from other AI providers"
↳ Click "Start import"

Step 2: Copy the prompt Claude gives you
↳ The pop-out shows a ready-made prompt
↳ Hit Copy (it's already written for you)

Step 3: Paste it into ChatGPT (or any other AI)
↳ Open a new ChatGPT chat and paste the prompt
↳ It exports everything: your identity, career, projects, preferences

Step 4: Paste the results back into Claude
↳ Switch back to the Claude import pop-out
↳ Paste ChatGPT's response into the text box
↳ Hit "Add to memory"

That's it. Claude now knows everything ChatGPT knew about you.

What gets transferred:

→ How you like to be spoken to
→ Your role, projects, and goals
→ Tools and frameworks you use
→ Every preference and correction you've made

My takeaway:

Claude hit # 1 on the App Store on Monday.
They’re now # 1 on the Android Store today.

A lot of people are curious about switching but assume they'll lose months of context they've built up with their exiting AI model provider.

That friction just disappeared.

Anthropic has removed the last real switching cost.

Whether it be ChatGPT, Gemini, or Grok, you no longer have to start from scratch.

Memory portability is vital for the AI to have robust context about you as an individual and tailor outputs appropriately.

What’s more, the utility of memory compounds over time.

My memory with Claude is already over 2 pages long.

If you've been on the fence, now's the time.

Follow me Alex Banks for daily AI highlights and insights.

I’ll be doing full coverage in this week’s newsletter.
Subscribe to The Signal: https://lnkd.in/ePSZP6KF

The people building AI keep walking away from it.

And they're not leaving quietly.

In the past week alone:

1. Mrinank Sharma, Anthropic's Safeguards Research lead, resigned with an open letter declaring the world is facing "interconnected crises."
2. Zoë Hitzig, an OpenAI research scientis,t published a New York Times op-ed warning about user manipulation through ChatGPT's emerging ad strategy.
3. Two more xAI co-founders left (half the founding team is now gone)

Rewind further and the list grows:

→ Ilya Sutskever, OpenAI's co-founder, left to start a safety-focused company (May 2024)
→ Jan Leike, co-lead of OpenAI's Superalignment team, resigned saying safety had taken a backseat (May 2024)
→ Miles Brundage, OpenAI's senior advisor for AGI readiness, warned that no lab is prepared for what's coming (October 2024)

Every industry has turnover.

But this isn't normal turnover.

These are researchers who spent years building the most advanced AI systems ever created.

And something about what they saw made them walk away.

If the people who understand this technology best can't reconcile building it with their own conscience, what does that tell the rest of us?

Or are they partnering with national publications to generate clicks while leaning into the "fear AI" angle to drive demand for their next venture?

Only time will tell.

Follow me Alex Banks for daily AI highlights and insights.

I explore ideas like this weekly in my newsletter The Signal.
Subscribe here: https://lnkd.in/ePSZP6KF

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