No BS AI/ML Content | ML Engineer with a Plot Twist 🥷30M+ Views 📝

Done-for you Audience Building & Content Writing for AI/ML Founders that frees up 100% of your time, so you can focus entirely on building your company  
  
Is your work too important to ignore? 💌 https://tally.so/r/wk69VJ  (or shoot me a DM) 👉
  
It's 2025 and we're at the peak of the attention economy  
  
Where Authenticity is king, visibility is currency and people wanna buy from people

Not faceless dehumanized brands  
  
So, they told you LinkedIn was "free marketing" you slapped some posts and called it strategy  
  
You've poured hours crafting the 'perfect' post, only to watch it sink without a trace..  
  
Then it dawned on you, your technical brilliance isn't enough to draw a crowd  
  
Hell, maybe you've even tried mimicking influencers, scrambling for any traction..  
  
Your post got 500 f*cking views... from your friends...  
  
Being ignored is soul-crushing  
  
It's not your fault tough  
  
When you started your company, you didn't sign-up for a degree in an Audience Building, Copywriting and LinkedIn strategy 

And here you are, sacrificing yet another late night for your vision  

"Am I just sabotaging my efforts?"  
  
"Is my life-work meaningless?"  
  
Getting traction feels impossible  
  
You hoped hiring some marketer would save your weekends but..  
  
Tech is a code they can't crack  
  
They can only churn out the lame, soul-sucking crap we all ignore on sight  
  
With armies of founders flooding feeds, your voice dissolves into meaningless white noise  
  
But in the process, you've wasted more of your precious time explaining 101 tech concept and rewriting low-quality drafts 

Done being ignored?  
  
Let's make the world listen 
  
I can help you with:  

✒️ No BS AI/ML Content  
⭐ LinkedIn Profile Revamp
🎯 Website Copy Optimization  

I've done it before:

↳ For founders backed by YC, a16z, Sequoia, and Techstars
↳ For bootstrappers in LLMs, ML, Vector DBs, Data Analytics, and Engineering
↳ For AI and Machine Learning leaders at Google, Microsoft, Airbnb, and YouTube

If they could rise above the noise, so can you

Yeah, even if you're an introvert with a "boring" niche

Still with me?  
  
Amazing—You're amazing!  
  
Cheers,  
Paolo

My booking link: 💌 https://tally.so/r/wLW0oz (or shoot me a DM) 👉

I've done 40+ LLM courses. Most were a waste.

These 4 weren't:

1️⃣ Karpathy's Zero to Hero
How LLMs actually work. Build GPT from scratch. Non-negotiable foundation.
→ karpathy.ai/zero-to-hero

2️⃣ OpenAI Cookbook
Production patterns. API best practices. Copy what ships.
→ https://lnkd.in/gHEY_aMw

3️⃣ Hugging Face NLP Course
Transformers, fine-tuning, hands-on. Where theory meets code.
→ huggingface.co/learn

4️⃣ DeepLearning.AI Short Courses
Bite-sized builds. Pick what you need, skip what you don't.
→ https://lnkd.in/gCFQ69G9

What I'd skip:

→ $2,000 bootcamps (same content, worse pacing)
→ 40-hour "comprehensive" courses (bloated with filler)
→ Anything that starts with "What is AI?" (you're past that)
→ YouTube rabbit holes (no structure, no accountability)

The pattern:

Foundation (Karpathy) → Patterns (OpenAI) → Depth (HuggingFace) → Application (DeepLearning.AI)

That's the sequence. Everything else is noise.

💾 Save for your next "where do I actually start with LLMs" spiral
♻️ Repost to save someone from another $2,000 bootcamp

Paolo Perrone

No BS AI/ML Content | ML Engineer with a Plot Twist 🥷100M+ Views 📝

Stop paying for AI courses.

NVIDIA teaches it better — for free.

8 courses. All self-paced. All with certificates.

Here's the roadmap:

𝗦𝘁𝗮𝗿𝘁 𝗵𝗲𝗿𝗲 (𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿):

1️⃣ AI for All — Basics to GenAI (~2-3 hrs)
→ https://lnkd.in/eUJpCVYg

2️⃣ Generative AI Explained (~2 hrs)
→ https://lnkd.in/eVFxf_g2

3️⃣ Building A Brain in 10 Minutes
→ https://lnkd.in/ejbHJW-j

𝗚𝗼 𝗱𝗲𝗲𝗽𝗲𝗿 (𝗜𝗻𝘁𝗲𝗿𝗺𝗲𝗱𝗶𝗮𝘁𝗲):

4️⃣ Agentic AI Explained (~1 hr)
→ https://lnkd.in/ekfPcAR8

5️⃣ Augment your LLM with RAG (~1 hr)
→ https://lnkd.in/e6Ab_36P

6️⃣ Beginner's Guide to Autonomous Robots (~1 hr)
→ https://lnkd.in/eSiMpGPP

𝗕𝘂𝗶𝗹𝗱 𝘀𝘁𝘂𝗳𝗳 (𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱):

7️⃣ Build a Deep Research Agent (~4 hrs)
→ https://lnkd.in/e3zpDehf

8️⃣ Accelerate Data Science Workflows (~1 hr)
→ https://lnkd.in/eRkdHp8Q

Total time: ~13 hours
Cost: $0
Certificate: Yes

💾 Save for your next "I should learn AI" moment.
♻️ Repost to retire the "which course should I take" question.

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prediktivai

learn.nvidia.com

Learning Objectives

Eugene Kulakoff

A Practical Guide To Becoming An AI Engineer in 2026

AI engineers make $180K median. Senior at OpenAI? $860K–$1.27M.

But most people try to break in by learning the wrong skills in the wrong order.

Here's the actual roadmap:

Phase 1: Fundamentals (1.5–3 months)
→ Python: production-ready, not tutorial-style
→ Handle APIs, JSON, errors confidently
→ Git + GitHub: your repos are your portfolio
→ Basic ML: what a model is, training vs inference, embeddings, core terminology
→ Master this or waste months later

Phase 2: LLM Integration (2–3 months)
→ Prompt engineering: not just "typing questions"
→ System prompts, few-shot learning, chain-of-thought, output formatting
→ APIs: OpenAI, Anthropic, Hugging Face open-source models
→ Token management and cost control
→ By end: build an app that takes input → calls model → returns structured output

Phase 3: Production Systems (2–3 months)
→ LangChain: connect models, tools, memory, multi-step logic
→ RAG: give models access to your docs, databases, internal knowledge for accurate answers
→ Agents: not just chat. Perform actions, call APIs, update records, trigger workflows
→ MCP: ensures AI interacts safely with external systems (GitHub, Zapier, Google Docs)
→ LLMOps: prompt versioning, monitoring, cost management, handling updates

Phase 4: Get Hired
→ Project 1: RAG decision support system with embeddings, semantic search, structured outputs, confidence scores
→ Project 2: AI workflow orchestrator that ingests tickets/emails/logs, classifies, prioritizes, applies business rules, triggers actions
→ Clean code, document everything, make demo videos
→ Resume: Python, Git, LangChain, RAG, vector DBs, APIs
→ Certifications (Azure, Databricks) help but aren't required

You're not inventing neural networks. You're not doing PhD research. Companies need people who make models work in production.

Don't wait until you feel ready. Pick one phase. Start today. Make mistakes. Iterate.

That's how you become a competitive AI Engineer in 2026.

💾 Save for your next "where do I even start with AI" spiral
♻️ Repost to the friend who has been "about to learn AI" for 6 months

Carlos V.

"Anyone can code now" is a lie.

Anyone can generate code.
Someone still has to make it work.

The person who generated it paid $20/month.
The person who fixes it bills $300/hour.

Technical debt isn't free.
It just gets invoiced later.

💬 What's the worst vibe-coded mess you've inherited?
♻️ Repost if $300/hour still felt like a discount

Talks about #datascience, #machinelearning, and #audiencebuilding

No BS AI/ML Content | ML Engineer with a Plot Twist 🥷50M+ Views 📝

"I'm not an AI Engineer anymore," my ex-Google friend said.

"I'm a Forward Deployment Engineer now. Made $280K last year."

"Wait, what's a Forward Deployment Engineer?"

Then he showed me this GitHub Repo.

What I saw changed how I think about AI careers:

1️⃣ You Audit Data First
Not building features.
Untangling 20-year-old corrupted schemas.
Finding the "System of Record" (usually an Excel sheet).

"If you can't untangle their data, you can't build on it."

2️⃣ You Write Glue Code
Custom parsers for proprietary formats.
VPNs bridging on-prem to cloud.
Integrations the product doesn't support.

"50% integration, 50% strategy. Not 90% model training."

3️⃣ You're a Technical Diplomat
Navigating hostile stakeholders.
Turning blockers into allies.
Lead Engineer blocks VPN access? 1-on-1 to surface his fear. 
Co-author scripts to give him ownership.

"Broken trust kills more projects than broken code."

4️⃣ You Extract Patterns
Build one-off for Customer A.
Turn into primitive.
Customer B gets it as feature.

"Every deployment becomes product."

5️⃣ You Measure Differently
Not accuracy. Not training time.
(a) Outcome value delivered
(b) Your effort per $ going DOWN

"Each deployment should make the next one easier."

6️⃣ Your Stack Expands
Python, Go, SQL.
GCP: BigQuery, Vertex AI, GKE.
Terraform, Kubernetes, Google ADK.

"Full-stack: Data + Cloud + AI + Consulting."

7️⃣ Golden Rule
You Build to Become Obsolete
Building systems that run without you.
Not staying embedded forever.

Customer 1: 8 weeks embedded
Customer 5: 2 weeks embedded
Customer 20: 3 days embedded

Same outcome. 95% less effort.

"The secret?" he said.

"AI Engineers optimize for the demo. 
FDEs optimize for revenue."

Haven't thought about my career the same since.

P.S. That $280K? Base salary. Total comp: $420K with deployment bonuses. 

He's now hiring FDEs. Can't find enough people who get it.

That GitHub repo he showed me? The complete FDE roadmap.
→ https://lnkd.in/eDEfzzMq

Will you use it?

💾 Save for when your demo works but the contract doesn't close
♻️ Repost if you've been "almost ready to deploy" for 6 months

The cost of building software just dropped 90%.

What happens next?

Jevons Paradox.

When electric lighting got cheaper, we didn't just use the same amount of light for less money.

We lit up entire cities.

Software is about to do the same.

Every company has hundreds of Excel sheets running critical processes.

At $50K per app? Only the essential ones get built.
At $5K per app? Everything gets built.

The latent demand is enormous.

What this means for engineers:

❌ Implementation is not the job anymore
✅ Domain knowledge is the moat

The 10x engineer isn't someone who types faster.
It's someone who knows:
→ Which architecture fits the problem
→ Which framework won't collapse at scale
→ Which shortcuts will cost you later

AI handles the typing.
You handle architecture, judgment, and business context.

A business expert + motivated developer + AI tools = what used to require a squad.

2026 will catch people off guard.

💾 Save this for your next "AI won't replace developers" debate
♻️ Repost if your company still has critical processes running on Excel

Add this to your 2026 AI learning path.

Paul Iusztin just dropped 9 free lessons on AI agents — 
No frameworks, just fundamentals.

1️⃣ Workflows vs. Agents
🔗 https://lnkd.in/eE5MB85b

2️⃣ Context Engineering
🔗 https://lnkd.in/erHCDMFb

3️⃣ Structured Outputs
🔗 https://lnkd.in/eyMhZJ5T

4️⃣ The 5 Workflow Patterns
🔗 https://lnkd.in/erW33Uxe

5️⃣ Tool Calling From Scratch
🔗 https://lnkd.in/ebTBmr93

6️⃣ Planning (ReAct & Plan-and-Execute)
🔗 https://lnkd.in/e8Sz_Pvz

7️⃣ ReAct Agents From Scratch
🔗 https://lnkd.in/e_A-92ig

8️⃣ AI Agent's Memory
🔗 https://lnkd.in/e59FX8bD

9️⃣ Multimodal Agents
🔗 https://lnkd.in/eP-hTdsN

Build everything from scratch. Understand the "why" before the "how."

💾 Save this with the 12 GitHub repos
♻️ Repost if LangChain has ever broken and you had no idea why

Most AI engineers learn the stack backwards.

They jump to fine-tuning before they understand what an embedding even does.

Here's the order that makes you dangerous:

1️⃣ Embeddings first
Vector representations. Similarity search. Distance metrics.
Skip this → You'll never debug why your RAG returns garbage.
→ sbert.net

2️⃣ Prompting second
Zero-shot, few-shot, chain-of-thought, structured outputs.
Skip this → You can't control the model with code if you can't control it with words.
→ https://lnkd.in/ejy5YgMp

3️⃣ Retrieval
Chunking strategies. Hybrid search. Reranking.
Skip this → 80% of your "hallucination problems" stay unsolved.
→ https://lnkd.in/eVRZPp2F

4️⃣ Evaluation
Golden datasets. LLM-as-judge. Regression testing.
Skip this → You ship on vibes. You break prod on vibes.
→ https://lnkd.in/eFFiUue5

5️⃣ Fine-tuning
LoRA, QLoRA, when to fine-tune vs. when to prompt.
Skip this → Actually, most people start here. That's why they waste $10K on training runs that don't help.
→ https://lnkd.in/ePwCHz5g

6️⃣ Build 5 things yourself
→ RAG over your docs: https://lnkd.in/eNifVYgi
→ Agent with tools: https://lnkd.in/efzMR5aU
→ Classifier with structured output: https://lnkd.in/e-9XFshq
→ Eval pipeline: https://lnkd.in/e2MY4gm8
→ Chatbot with memory: https://lnkd.in/eG5u78ex

7️⃣ Read real-world post-mortems
Real learning is failure exposure.
You see what broke. You see WHY.

You don't become good by memorizing LangChain docs.

You become good by understanding the physics of LLM systems.

Latency. Cost. Accuracy. Reliability. Context limits.

Those 5 forces rule everything.

💾 Save this. The sequence nobody teaches.
♻️ Repost if you've seen someone fine-tune before they could prompt.

I Googled "PyTorch not detecting GPU" for the 47th time.

Then I fixed my setup permanently.

Things I never want to do again:

→ Reinstall CUDA manually
→ Debug conda conflicts for 4 hours
→ Hear "it works on my machine"
→ Recreate an environment from memory
→ Lose a week to setup after a laptop crash

The fix:

→ Docker — Environment that works. Everywhere. Always.
→ Conda exports — Snapshot dependencies. Version-locked.
→ Dotfiles in Git — Shell, editor, aliases. Portable.
→ Cloud storage — Weights, datasets, checkpoints. Never local-only.
→ Git — Code, configs, scripts. Pushed, not just committed.

Laptop dies?

10 minutes to full productivity.

The pain was never necessary.
It was a shortcut that became debt.

💾 Save this. Build it once. Never rebuild from scratch again.
♻️ Repost if at least 3 of these triggered you.

How to actually code with LLMs in 2026.

Not the hype. What's working for engineers who ship:

1️⃣ 𝗦𝗽𝗲𝗰 𝗯𝗲𝗳𝗼𝗿𝗲 𝗰𝗼𝗱𝗲
Don't throw wishes at the LLM.
→ Describe the idea
→ Let the AI ask questions until requirements are clear
→ Compile into spec.md
→ Generate step-by-step plan
→ Then code

It's "waterfall in 15 minutes."

2️⃣ 𝗦𝗺𝗮𝗹𝗹 𝗰𝗵𝘂𝗻𝗸𝘀
Ask for too much = jumbled mess.
"Like 10 devs worked on it without talking."
One function. One bug. One feature. Then next.

3️⃣ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗽𝗮𝗰𝗸𝗶𝗻𝗴
LLMs are only as good as what you show them.
→ Relevant code
→ API docs
→ Known pitfalls
→ Preferred approaches

Don't make the AI guess.

4️⃣ 𝗠𝗼𝗱𝗲𝗹 𝗺𝘂𝘀𝗶𝗰𝗮𝗹 𝗰𝗵𝗮𝗶𝗿𝘀
Each model has blind spots.
Stuck? Copy the same prompt to another model.
Sometimes a second opinion is all you need.

5️⃣ 𝗛𝘂𝗺𝗮𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗹𝗼𝗼𝗽
AI writes with complete conviction.
Including bugs. Including nonsense.
Treat every snippet like junior dev code.
Read it. Run it. Test it.

6️⃣ 𝗖𝗼𝗺𝗺𝗶𝘁 𝗹𝗶𝗸𝗲 𝘀𝗮𝘃𝗲 𝗽𝗼𝗶𝗻𝘁𝘀
AI generates fast. Veers off course fast.
Commit after each small task.
Your safety net when AI goes sideways.

7️⃣ 𝗥𝘂𝗹𝗲𝘀 𝗳𝗶𝗹𝗲𝘀
Use CLAUDE.md, GEMINI.md, or .cursorrules.
→ Your coding standards
→ Your patterns
→ Your constraints

Train it once. Enforce everywhere.

The mental model:

LLMs are over-confident junior devs.
You're the senior engineer.
They're the force multiplier.

💾 Save this before your next AI coding session.

The most valuable developer skill in 2026?

Unfucking vibe-coded projects.

Here's what I'm seeing:

→ Founders ship AI-generated MVPs
→ The app works (sort of)
→ They get users, funding, traction
→ Then they try to add features
→ Everything breaks
→ They call a real developer

The codebase:
→ 1,227 branches, none merged
→ All logic in one file
→ Emojis in print statements
→ No tests. No architecture. No separation of concerns.
→ "It compiled once" is the only QA.

This is the new greenfield project.

You're not building from scratch anymore.
You're inheriting AI slop and making it production-ready.

Vibe coding creates the mess. 
Real engineering cleans it up. 

Position yourself as the cleanup crew. 

That's where the money is moving. 

💾 Save for when they ask you to "just fix" their AI prototype.
♻️ Repost if you've ever opened a codebase and immediately closed your laptop.

Stop collecting AI tools.

Bookmarking isn't learning. 
"I'll try it later" means never.

ChatGPT, Claude, or Gemini.
Pick one. Master it.

That's 80% of the value.

The other 47 tools you bookmarked?
You'll never open them.

💾 Save this as permission to simplify
♻️ Repost if your bookmarks folder has more AI tools than you've actually used

The only AI tools you need in 2026.

Tested everything. Deleted most. Kept these.

𝗦 𝗧𝗶𝗲𝗿 — 𝗣𝗶𝗰𝗸 𝗼𝗻𝗲, 𝗺𝗮𝘀𝘁𝗲𝗿 𝗶𝘁:
→ ChatGPT — deep research + voice mode
→ Claude — writing + coding
→ Gemini — image/video gen + learning

𝗔 𝗧𝗶𝗲𝗿 — 𝗠𝗼𝘀𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝘀𝗵𝗼𝘂𝗹𝗱 𝘂𝘀𝗲:
→ NotebookLM — chat with your docs, zero hallucination
→ Perplexity — search without SEO garbage
→ Comet — AI browser that controls pages for you
→ Deep Research — 30-min reports, fully cited

𝗕 𝗧𝗶𝗲𝗿 — 𝗦𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀:
Writing & Presentations:
→ Claude — upload samples, matches your voice
→ Gamma — full deck from one prompt

Image Generation:
→ Nano Banana — best aesthetics
→ ChatGPT — best prompt understanding
→ Reve — character consistency

Audio:
→ ElevenLabs — voice cloning, text-to-speech
→ Suno — full songs from prompts

Video:
→ Sora 2 — realistic video gen
→ Veo 3 — Google's alternative
→ HeyGen — AI avatars + 175 languages

Automation:
→ n8n — open source, self-hostable
→ Zapier/Make — faster setup, less control

Visuals:
→ Napkin AI — text to diagrams

Coding:
→ Cursor — AI-assisted coding

The S tier does 80% of the work.
Everything else is situational.

💾 Save this. Reference it. Stop hoarding tools.
♻️ Repost to save someone from bookmark hell.

In 2026, you're going to ship code you've never read.

Not because you're vibe coding.
Because you're an experienced engineer who trusts the system.

Here's what's changing:

Traditional code review: line-by-line diffs.
Artifact review: browser recordings, working demos, test results.

Google Antigravity already does this.
Spins up your site, scrolls through it, screenshots everything.
You verify the agent's work at a glance.

Amazon Nova Act automates browser-based QA with 90%+ reliability.
Live viewing. Session replay. Full logging.

The shift:
→ Diffs tell you what changed.
→ Artifacts prove it works.

When your validation includes:
→ Automated tests that pass
→ Security scans that clear
→ Browser recordings that show it working

Do you really need to read every line?

You're still the final say.
You're just not reading every semicolon anymore.

💾 Save this. The review paradigm is shifting.
♻️ Repost to save someone still reviewing diffs line by line.

I spent 3 days blaming GPT-4.

The model was innocent.
My retrieval was broken.

The 3 RAG failures nobody debugs:

1️⃣ Chunk splits the answer
→ Answer exists in your docs
→ But it's split across chunks 47 and 48
→ Neither scores above your 0.7 threshold alone
→ Fix: 20% chunk overlap. Or sentence-window retrieval.

2️⃣ Question ≠ Answer embedding
→ User: "How do I reset my password?"
→ Doc: "Navigate to Settings > Security > Reset"
→ Cosine similarity: 0.61 (below threshold)
→ Fix: Index hypothetical questions, not just content. HyDE works.

3️⃣ Retriever floods the context
→ You retrieve top 10 "just to be safe"
→ 8 chunks are noise
→ LLM gets confused. Answer quality drops.
→ Fix: Retrieve 20. Rerank with cross-encoder. Keep top 3.
→ Why: Cross-encoders score actual relevance, not embedding similarity.

The pattern:

RAG failures look like LLM failures.
You blame the model.
The model never saw the right context.

I added retrieval logging after those 3 wasted days.

First thing I check now: "What did the model actually see?"

💾 Save for when your RAG works in demo but breaks in prod.
♻️ Repost if your RAG has gaslit you at least once.

Your 2026 AI resolutions (be honest):

❌ "I'll read the paper before using the model"
✅ Skim the abstract. Check the GitHub stars.

❌ "I'll add evals before shipping"
✅ "We'll add them in v2"

❌ "I'll finally learn Rust"
✅ Install. Hello world. Uninstall by February.

❌ "I'll stop using GPT-4 for everything"
✅ Try Claude for a week. Go back.

❌ "I'll write documentation this time"
✅ The code is self-documenting (it's not)

❌ "I'll reduce my API costs"
✅ Add caching. Forget to invalidate. Debug for 3 days.

❌ "I'll build my own RAG pipeline"
✅ Use LangChain. Hate it. Use LangChain anyway.

❌ "I'll understand transformers deeply"
✅ Watch 3Blue1Brown once. Call it done.

See you in 12 months when none of this happened.

♻️ Repost if at least 3 of these are already on your list.

Your agent isn't forgetting. It never knew.

Here's what I mean:

Input: "NVIDIA is an American company whose headquarters are in Santa Clara, California."

𝗪𝗵𝗮𝘁 𝘃𝗲𝗰𝘁𝗼𝗿 𝘀𝘁𝗼𝗿𝗲𝘀 𝘀𝗲𝗲:
→ "NVIDIA" (chunk)
→ "American" (chunk)
→ "Santa Clara" (chunk)
→ "California" (chunk)
→ Embeddings. No relationships. Hopes the LLM connects the dots.

𝗪𝗵𝗮𝘁 𝗰𝗼𝗴𝗻𝗲𝗲 𝘀𝗲𝗲𝘀:
→ NVIDIA → headquartered_in → Santa Clara
→ Santa Clara → located_in → California
→ NVIDIA → company_nationality → American
→ Vectors + knowledge graph. Relationships preserved.

When you ask "Where is the American GPU company based?"

Vector RAG: retrieves "NVIDIA" and "American" separately. Prays the model infers the rest.

Cognee: traverses NVIDIA → Santa Clara → California. Returns the answer.

This is why cognee outperforms on multi-hop reasoning.

Not because of the model.
Because it combines semantics with structure.

Vectors tell you what's similar.
Graphs tell you what's connected.

Cognee gives you both.

pip install cognee → first graph in 5 minutes.
Open source. Free.

https://lnkd.in/eNE3RWeF ⭐

💾 Save this before your agent "forgets" something it never knew
♻️ Repost if your RAG pipeline retrieves chunks but can't reason over them

Stop importing RL libraries you don't understand.

Hugging Face's Deep RL course — free, and still the best.

What's inside:

𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀:
→ Introduction to Deep Reinforcement Learning
→ Q-Learning fundamentals
→ Deep Q-Learning with Atari Games

𝗣𝗼𝗹𝗶𝗰𝘆 𝗠𝗲𝘁𝗵𝗼𝗱𝘀:
→ Policy Gradient with PyTorch
→ Actor-Critic methods with robotics
→ PPO (Proximal Policy Optimization)

𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱:
→ Multi-Agent systems
→ AI vs AI competitions
→ Imitation Learning

The environments you'll train in:
→ Space Invaders
→ Doom (VizDoom)
→ PyBullet robotics
→ Unity ML-Agents
→ Huggy the Doggo 🐶

What makes it different:
→ Hands-on Google Colab notebooks
→ Push trained agents to Hugging Face Hub with one line
→ Certificate at 80% completion
→ Self-paced, no deadlines

This isn't theory-only.
You build agents that play games, control robots, and compete against each other.

→ https://lnkd.in/e7KcdSdG

💾 Save for when you're ready to understand what's under the hood.
♻️ Repost if you've been importing RL libraries without knowing how they work.

Copy-paste context engineering.

Not tutorials. Not theory. 

Templates you can use today.

𝗦𝘁𝗮𝗿𝘁 𝗵𝗲𝗿𝗲:
→ minimal_context.yaml — Lightweight, general-purpose
→ schema_template.yaml — Standardized data structures
→ scoring_functions.py — Measure what you build

𝗔𝗱𝗱 𝗰𝗼𝗻𝘁𝗿𝗼𝗹:
→ control_loop.py — Cyclic refinement
→ prompt_program_template.py — Code-like reasoning

𝗔𝗱𝗱 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲:
→ field_protocol_shells.py — Field operations
→ recursive_context.py — Self-improvement

15 minutes to minimal. 4 hours to full stack. All composable.

→ https://lnkd.in/ex9Sz77j

💾 Save before you build from scratch again
♻️ Repost if you've rebuilt the same scaffolding too many times

The AI pitch: 
"Give the boring work to the computer. Do the interesting work yourself."

The reality: 
Watching the computer do the work IS the boring part.

I dozed off watching agents code 3 times this week.

Watched one refactor my auth flow in 4 minutes.
Took me 2 days last year.
I should be thrilled. I'm not.

The code is better than what I'd write.
And I feel nothing.

No struggle. No satisfaction. Just output.

I didn't become an engineer to watch code write itself.

Maybe the new skill isn't solving problems.
It's finding problems worth solving yourself.

💾 Save this for when someone asks why you miss debugging
♻️ Repost if you miss debugging dopamine hit

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