I’ve been working in AI for close to a decade, starting well before today’s LLM-driven wave.

My work is grounded in understanding AI across layers — from hardware, AI models, and data to system design, reliability, and scale. I approach AI as an end-to-end engineering problem: not just modeling, but how models interact with data, infrastructure, and real-world constraints.

Alongside this, I write The AI Merge, a technical newsletter on AI foundations and systems engineering, focused on clear thinking and practical system design rather than hype.

I’m currently building an AI Systems course, centered on end-to-end system design, for engineers who want a deeper, systems-first understanding of modern AI.

🗞️ Read The AI Merge: https://read.theaimerge.com

Alex Razvant

A lot of courses out there teach AI & LLMs, but very few touch on Inference

Sure, it's easy to grasp the fundamentals of what LLMs are and how to build systems around them, but scaling out an LLM out of a lab of 1-2 H100s running it and a single concurrent user session is a big challenge.

Efficient inference is one of the largest barriers to LLMs in production.

Batching, managing memory, concurrent user sessions, GPU saturation, and sharding with/or MIG (Multi-Instance GPUs), parallelisation are key concepts for engineers to know, understand, and work with or around them.

I've had this list of resources saved for a while, which fills some of those gaps.

1. Good Courses / Videos
- CS336 Stanford (Lecture 5-11): https://buff.ly/9o4jRxl
- GPU MODE: https://buff.ly/bPEHYL0 
- LLM Inference Patterns: https://buff.ly/4ASDS5c
- LLM Inference (NVIDIA): https://buff.ly/muDMcor 

2. Repositories / Blogs
- GPU Performance Engineering (E.Andere): https://buff.ly/sfGJNMK 
- AI Performance Engineering (C.Fregly) https://buff.ly/8ey3GPa 
- Inside NVIDIA GPUs (A.Gordic): https://buff.ly/wmgNwMg 
- Understanding LLM Inference (my article): https://buff.ly/NrNU3Qz 

I don't think there's a clear, step-by-step guide on how to master AI Perf Engineering.
The best bet is to study the core pieces of the models and the hardware they run on.

Then, learn the rest on demand.

undefined undefined

Neural Bits CTA

Join 4100+ engineers and get a weekly newsletter on advanced AI/ML. 👉 https://neuralbits.substack.com/

Kubrick

Ready to start building?

--- Resources ---
📌 Kubrick Code Repository
https://github.com/multi-modal-ai/multimodal-agents-course

📌 Neural Bits Newsletter (mine)
https://multimodalai.substack.com/

📌 The Neural Maze Newsletter (Miguel's)
https://theneuralmaze.substack.com/

Senior AI/ML Engineer | Author of NeuralBits Newsletter – helping you build efficient AI Systems

Hard work always pays off, be consistent!

I'm a Senior Machine Learning Engineer with a keen interest in developing AI/ML solutions for real world problems and helping others get started on their Machine Learning Journey.

Let's connect @:
📘 https://medium.com/@alexandrurazvant
✉️ alexandrurazvant@gmail.com
🌐 https://www.neuraleaps.com

Upskill your AI Engineering!

AI Engineering Hub

Senior AI Engineer | Writing The AI Merge Newsletter | Helping Engineers Build AI Beyond Demos

The 5 structure-layers of any LLM Model.
(Understand these, and you'll understand every OSS model on HF)

With each open-source LLM released, you'll find the model weights on Hugging Face.

When you go to the Files section, you won't find a `model.xyz` file, but multiple files, usually 10-15 that compose the entire LLM.

Here's the blueprint to understand them:

1️⃣ 𝘼𝙧𝙘𝙝𝙞𝙩𝙚𝙘𝙩𝙪𝙧𝙚 𝘾𝙤𝙣𝙛𝙞𝙜𝙪𝙧𝙖𝙩𝙞𝙤𝙣 (config.json)
This one contains metadata on model architecture, layer activations, sizes, vocabulary size, number of attention heads, model precision, and more. 

The transformers library knows how to parse this config and build the model architecture. 

2️⃣ 𝙈𝙤𝙙𝙚𝙡 𝙒𝙚𝙞𝙜𝙝𝙩𝙨 (the .safetensors splits)
LLMs have B (billions) of parameters.
For safer download, model weights are split into chunks/shards.
The formats used for weights are either Binary or Safetensors.

Safetensors format is an alternative to the default Pickle serializer that PyTorch (pt) uses, as it’s vulnerable to code injection.

3️⃣ 𝙇𝙖𝙮𝙚𝙧 𝙈𝙖𝙥𝙥𝙞𝙣𝙜 (the *.index.json)
Since the models are large and weights come as part files (e.g., 0001-of-0006, 0002-of-0006, etc.), this file stores a sequential map of the model architecture.

This specifies what layers are part of what model chunk.

4️⃣ 𝙏𝙤𝙠𝙚𝙣𝙞𝙯𝙚𝙧 𝘾𝙤𝙣𝙛𝙞𝙜 (tokenizer.json)
The tokenizer config file contains metadata about the tokenizer used to train this model. 

It contains the class name used to instantiate the tokenizer, the layer names, the structure of how the inputs are processed before passing through the model, and special tokens.

The LLM generally uses 𝘴𝘱𝘦𝘤𝘪𝘢𝘭_𝘵𝘰𝘬𝘦𝘯𝘴 as markers to stop generation.

5️⃣ 𝙂𝙚𝙣𝙚𝙧𝙖𝙩𝙞𝙤𝙣 𝘾𝙤𝙣𝙛𝙞𝙜 (generation_config.json)
These configuration files contain metadata for Inference, such as Temperature and TopP/TopK thresholds or context window size, with which the model was trained. 

When you load your model using Transformers.from_pretrained(), all these files are processed to set up everything you need to actually use that model.

---
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The 4 patterns of LLM Inference.

Deploying LLMs is not the same as deploying traditional ML models. The differences show up most clearly at inference time. 

Some principles such as optimized models, infrastructure, GPU saturation and usage or distributed systems concepts still apply.

But LLM inference adds a new layer of complexity, as Transformer models inference is way more nuanced.

With LLMs, the output is generative and not a fixed size having batch-size input = batch-size output that more traditional ML or Deep Learning models had.

LLM inference splits into two stages, Prefill and Decode, this creates 4 distinct patterns an AI Engineer/Dev should know about before optimizing or deploying.

⇢ In the prefill phase, the model processes the entire input prompt and computes the key-value (KV) cache. In benchmarks, we'll often see this metric as `pp` or `prompt processing` speed, which is fast.

⇢  In the decode phase, it autoregressively generates tokens one at a time, updating the KV cache at each step. In benchmarks, this is refered to as `tg` or text-generation speeds, and this process is memory-bound as tokens have to travel within the GPU's memory during KV cache updates.

Understanding this gives you these four distinct LLM inference patterns:

SISO -- Short Input, Short Output
→ Low latency with fast generation. If using this pattern, aim to optimize for high concurrency.

LILO --  Long Input, Long Output
→ The most compute-intensive requires distributed inference, ideally with shared KV cache across nodes or tensor parallelism.

LISO -- Long Input, Short Output
→ This one has heavy prefill and quick decode. GPUs with enough VRAM would parallelize the prefill and generate fast.

SILO -- Short Input, Long Output
→ Light prefill, which runs fast but is heavier on decode. Decode latency dominates here.

---

𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: The longer the output, the more time is spent in the decode loop.
Before fine-tuning your own LLM and deploying it, also consider the inference pattern your system will target.

Summary:
- In LLM inference, there are 4 patterns.
- For LISO, the generation and TFTT are fast.
- On SISO, everything is fast.
- For LILO,  a lot of computing.
- For SILO, prefill is fast, generation is slow.

---

Learn AI through Fundamentals, Engineering and Systems.
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If you'll ever try to run LLMs locally - you'll bump into llama.cpp

Most guides tell you what to use for Edge/CPU/low-end GPU inference - but not what’s actually happening under the hood. 

No context on:
- How is the model checkpoint parsed?
- How are tensors unpacked and mapped?
- How do GGML, GGUF, and llama.cpp fit together?

✅ So I went through the codebase myself. 

I spent a few hours digging through llama.cpp, PRs, and community threads, then turned everything into a single sequence diagram that shows the flow end-to-end. 

The key pieces of the stack:
> GGML - the ML Tensor Library and execution primitives (ops, mappings, kv-cache)
> GGUF - the format for storing quantized LLM models and Quantization types.
> llama.cpp - is the C++ inference engine, building on top of GGML & GGUF, wiring everything together.

If you’re running local LLMs, this is the stack you’re relying on.

If you want to understand why things break, how quantized models are actually loaded, this breakdown will help. 

---
I share more practical advice on AI Fundamentals, Engineering and Systems.
Join here (free): https://buff.ly/iqFpm9x

You should read more code.

If you’re already a Software Engineer or AI / ML Engineer, one of the fastest ways to level up right now is to study how big AI teams write and ship - in public. 

Many top companies and major contributors to the AI space have been open-sourcing their internal frameworks, kernels, or infrastructure libraries. 

You might feel overwhelmed by how fast AI is going. But this is a great learning opportunity if you know where to look.

Learning doesn’t just happen from papers and tutorials, especially if you already build software.

You should:
- Read real production code
- Study PRs or commit history on major/popular repos.
- See how teams structure codebases, design APIs, configs, tests

Check what gets reviewed, what gets rejected, and why.
This kind of learning will upgrade your engineering intuition. 

Here are some excellent codebases to learn from.

✅ From DeepSeek:
1. FlashMLA  - A library of high-performance attention kernels.
Link: https://buff.ly/XbFnPA7 

2. DeepEP - A Communication Library for high-throughput MoE workloads.
Link: https://buff.ly/3qxMc3S

3. 3FS - A Distributed Filesystem for AI Workloads, for AI training and inference.
Link: https://buff.ly/ovfW47A

✅ From NVIDIA:
1. Dynamo - A datacenter-scale Inference Serving Framework for GenAI workloads.
Link: https://buff.ly/WyAMzBz 

2. NeMo  -  A Scalable Generative AI Framework, for training and deploying LLMs and VLMs.
Link: https://buff.ly/L9KTuJp 

✅ From vLLM:
1. The vLLM Library  - One of the most popular LLM inference engines, designed for scale.
Link: https://buff.ly/GLCfoIM

✅ From SGLang:
1. The SGLang Engine - A high-performance serving engine for LLMs & multimodal models.
Link: https://buff.ly/m4HhsWQ 

✅ From llama.cpp
1. The llama.cpp Engine - A minimal C/C++ inference engine for LLMs with broad hardware support and quantization options.
Link: https://buff.ly/u9yaXwp

📌 Don't study everything, you won't be able to.

Instead, pay attention to:
⇾ Code structure & abstractions
How are core functionalities separated (core logic vs hardware-specific optimizations)?

⇾  PRs & commit history
What context do authors provide? How do reviewers push back?

⇾  Interfaces & public APIs
How are public APIs designed?

⇾  Tests & benchmarks
How do the test suites look? What CI pipelines do they have, profiling? 

Don’t just use these libraries. 
Read them.

That will build your intuition and help you get better at your craft, either as a curious learner or an engineer as part of a larger team at your job.

---
I distill my learnings with my audience on The AI Merge.
Check it here (8700+ engineers already do that) https://buff.ly/2rwqlNL

I have a surprise for my European audience, for NVIDIA GTC 2026 (March 16–19)

🎁 I’m giving away 1 x NVIDIA DGX Spark to someone who joins me for Virtual NVIDIA GTC 2026. 

I’ve spent the last 6 months building on NVIDIA DGX Spark, and when building AI Systems locally, the Spark is an incredible machine to learn, prototype, fine-tune, build and iterate fast.

Especially if you want more control over your entire stack.

NVIDIA GTC is the premier global AI conference, where developers, and researchers come together to explore the next wave of AI across every industry and application.

✅️ Virtual GTC is free. 

𝗛𝗼𝘄 𝘁𝗼 𝗲𝗻𝘁𝗲𝗿 𝘁𝗵𝗲 𝗗𝗚𝗫 𝗦𝗽𝗮𝗿𝗸 𝗴𝗶𝘃𝗲𝗮𝘄𝗮𝘆:
𝟭. 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 for GTC 2026 using my link: https://buff.ly/aKgT9BA
𝟮. 𝗔𝘁𝘁𝗲𝗻𝗱 at least 1 virtual session (keynote does not count) - virtual sessions are free
𝟯. 𝗙𝗶𝗹𝗹 𝗼𝘂𝘁 𝗺𝘆 𝗴𝗶𝘃𝗲𝗮𝘄𝗮𝘆 𝗳𝗼𝗿𝗺 𝗮𝗳𝘁𝗲𝗿 𝗮𝘁𝘁𝗲𝗻𝗱𝗶𝗻𝗴

I’ll share the giveaway form link as we get closer to GTC 2026. 𝗦𝘁𝗮𝘆 𝘁𝘂𝗻𝗲𝗱.

Here are a few sessions that caught my eye (links below):
1. AI Factories in Europe: Building the Foundations for Scalable Intelligence [S81899] 
2. Accelerate AI Through Open-Source Inference [S81902] 
3. Teach AI to Code in Every Language With NVIDIA NeMo [S82306] 
4. From Data to Meaning: Vision-Language Models Shaping the Cities of Tomorrow [S81867] 
5. Your Learning Pathway: Get Certified for Career Success [C81544] 

If you’re planning to attend, remember to register here: https://buff.ly/aKgT9BA 

Follow me here and stay tuned for the giveaway form.

Underrated advice to grow as an Engineer

1. Go Depth first (2–3 years)
Pick one stack (language + framework + system) and get good at it.

Breadth gets easy after.
I know it's tempting to know a bit out of everything, but you'll spread yourself too thin, too soon.

2. If you're a junior/mid - talk to senior/staff engineers (smartly)

Don't ask “What should I do?” 
Instead, ask: “Here’s what I tried, here’s what happened, what would you check next?”

They've seen enough systems and code to spot patterns early on.

2. Get good at Code Reviews
Express your points, think past the small code component - oversee the entire system.
Don't nitpick for code changes unless necessary. 

3. No Vibe-coding
With each feature/component you build, spend time gathering context, asking for options, plan, review, verify and write the hard paths yourself.

One practice that works for me is, after drafting a plan for my coding Agent (in Plan Mode), I ask if it's got any questions for me, before starting to work.

It usually comes up with great points.

3. Study outside work
That's an easy switch.
You do it already by consuming content, scrolling through posts and watching videos.
But make sure you focus on what actually helps you, not all the news/hype out there.

Work getting better, use AI as leverage.

A better way to manage complex configurations in your AI/ML projects.

AI/ML systems usually have more moving parts and more configuration surfaces (data, training, evaluation, inference, orchestration). Keeping track of which configuration subset was used can get difficult fast. 

Sure, as per Factor III (Config) in 12-factor apps, environment-specific config can be stored as env variables.

However, env vars alone are usually not enough. You might need hierarchical configs, runtime composition, overrides etc.

For instance, on a current project I'm working on, I have:
- 6 MLOps pipelines that build the raw_data > deployed model lineage.
- 2 edge services that ingest video, route frames to Inference Servers
- 3 MCP servers, each with their own connection details, tools and resources
- 2 Agents, wired through A2A - each with their own subset of instructions and MCP

In this setup, during testing, services often require runtime overrides (MLOps pipelines), composition or swap (training/eval), and sometimes they share the same configuration domain. 

When configuration gets more complex, I found Hydra + OmegaConf to be the way to go.

Here's what you need to know:
1. 𝗢𝗺𝗲𝗴𝗮𝗖𝗼𝗻𝗳 is a hierarchical configuration system explicitly designed for complex applications like ML pipelines. 

You can:
↳ Merge multiple configuration files directly through runtime composition.
↳ Access fields via attribute notation "config.model.optimizer" or dict notation config["model"]["optimizer"]
↳ Add Type safety against schemas such as dataclasses or Pydantic base models.

2. 𝗛𝘆𝗱𝗿𝗮 builds on top of OmegaConf, creating a full-featured framework.

You can:
↳ Group multiple YAMLs into a single configuration file and reference them directly by filename.
↳ Run a sweep, same application - multiple configurations in parallel.

Concrete examples:
1. Your hydra config could look like this (config.yaml)
``` config.yaml
defaults:
  - data: mydata
  - training: trainAB
  - inference: inferAB
  - _self_ 
```

When loading the `config.yaml` file using Hydra, we get a nested configuration object, from which we can access any field 
(cfg . inference . batch_size / cfg . training . batch_size)

2. Override any config value at runtime. 
For example, if in the config.yaml, if you have:
```
model:
 optimizer: SGD
```

At runtime, you can override the value with:

```
python train model.optimizer=Adam
```

𝗧𝗵𝗶𝘀 𝗮𝗹𝘀𝗼 𝘄𝗼𝗿𝗸𝘀 𝗳𝗼𝗿 𝗻𝗲𝘀𝘁𝗲𝗱 𝗰𝗼𝗻𝗳𝗶𝗴𝘀.
↳ Run multiple configurations simultaneously using Hydra multirun.

```
python train -m data.dataset=A1, B12, C55
```

This will launch multiple runs (one per dataset value) and create separate output directories for each run. 

---

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Qwen3.5 4B and 9B around the corner? 👀

Code is cheap nowadays. Design and planning is not. 

Some, keep measuring output by volume. 

I've been doing AI Assisted Programming for the past 2.5 years, on real codebases.

Online, I keep seeing many claim to push 10k Lines of Code a day to production and not reviewing anything.

I think that’s mostly marketing.
The more code added to a codebase, the larger the surface area.

Software isn't written once. It's designed to evolve.
What works now, should be easily changed or improved in the future.

The question you should ask: 
“Can we ship changes that are consistent and safe to release?”

A few safeguards that actually worked for me:
1. Treat AI code as proposal, not truth. Don’t accept diffs you can’t explain.
2. Put a harness on the agent. (Cursor Rules / AgentsMD / ClaudeMD / Skills / Plugins)
3. Keep Agent Rules, Skills and Plugins small, scoped and enforceable.
4. Add guardrails only after noticing Coding Agent failures.
5. Keep PRs reviewable. Minimal context + a 1-pager attached on the PR for big moves.
6. Reviews should't focus on preference. Skip nitpicks. Focus on the entire workflow.
7. Pre-commit + CI parity. Catch drift before you waste a CI cycle.
8. Write a handoff note to your tomorrow-self so you catch-up faster on the next day.

For an Engineer, AI helps, but only when you stay in control of constraints.

---
I explain complex topics in AI, AI Engineering and building AI Systems.

For free.

Join here: https://buff.ly/DnpHEXD

Six Tips on AI Assisted Programming

CarbabaAI, a UAE-based B2B SaaS company, pioneers document intelligence using AI-driven compliance systems and QA validators to optimize enterprise workflows. As Founder, contributions include innovative solutions that reduced document processing time by 40%, showcasing a commitment to operational efficiency and transformative technologies.  

With a biomedical degree major with a data science minor from Swinburne University of Technology, expertise spans machine learning, AI systems, and data analytics. Previous roles, such as Software Developer at CliniNote, focused on real-world data solutions for healthcare. Additionally, mentoring at Monash University bridged academic concepts with practical applications, reflecting a dedication to responsible AI-driven industry innovation.

Ghadeer A.

The agents that actually work aren’t using LangChain. They’re doing this 👇 

Most builders hide behind tools.

Frameworks feel powerful.

But they hide real mechanics.

You ship features.
You miss understanding.

Build the core yourself.

This free, 0$ open-source repo
teaches AI agents from scratch.
Using Python and local LLMs.

No frameworks.
No abstractions.
Just first principles.

	1.	Basic LLM Chat
Send prompts to a local model.
See raw input and output.

	2.	System Prompts
Define behavior with constraints.
Control the agent precisely.

	3.	Structured Output (JSON)
Force reliable, parsable data.
Turn text into code-ready output.

	4.	Routing Logic
Classify user intent cleanly.
Choose specific execution paths.

	5.	Tools & Capabilities
Call external Python functions.
Give the agent real power.

	6.	The Agent Loop
Implement Observe-Decide-Act.
Autonomy through tight loops.

	7.	Memory Management
Persist context across sessions.
Maintain coherent conversations.

	8.	Planning
Break goals into clear steps.
Execute with structured intent.

	9.	Atomic Actions
Isolate tool execution safely.
Prevent crashes from single failures.

	10.	Atom of Thought
Use dependency graphs for reasoning.
Handle complex multi-step tasks.

Real agency is not magic.
It is loops and state.
It is structured decisions.

Stop renting intelligence.
Start engineering it.
Master the fundamentals.

Link to repo (in the comments) 👇

➕ Follow Ghadeer for more insights
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Ghadeer  Al Ruwaishedi

Document-to-Decision Architect | Building Enterprise AI Systems that Turn Documents into Trusted Decisions

🚨 BREAKING: Someone just open-sourced the Claude Code playbook engineers were gatekeeping.

If you’re building AI agents, stop what you’re doing.

This repo is insane.

I just stumbled on what might be the most valuable Claude Code resource on GitHub right now.

Not tutorials.
Not theory.

Actual production workflows.

Inside you’ll find:

→ Clean sub-agent architectures for real AI teams
→ The only MCP servers you actually need (DeepWiki, Context7, Playwright)
→ Battle-tested workflows from AI legends like Andrej Karpathy and Boris Cherny
→ Hidden slash commands that aren’t even in the docs

This isn’t just a repo.

It’s basically a blueprint for building your own AI workforce.

Instead of prompting one model…

You orchestrate specialized agents that collaborate on tasks.

Research agent.
Coding agent.
Testing agent.
Documentation agent.

All working together.

The crazy part?

You could spin up a fully customized AI team tonight.

No paid course.
No gatekeeping.

Just pure open-source gold.

I dropped the repo link in the comments 👇

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🚨BREAKING: MIT just killed the AI course industry. (Bookmark this 👇)

Most AI courses are overpriced

People feel behind on AI.
They buy expensive courses.
They still don’t know where to start.

MIT removed the confusion.

Here’s what they released for free.

1️⃣ AI 101
Learn AI from absolute zero.
Perfect for true beginners.
https://lnkd.in/dtJ47_Nk

2️⃣ Introduction to Deep Learning
Neural networks explained clearly.
Strong technical foundation.
https://lnkd.in/dV652vnD

3️⃣ Artificial Intelligence
How machines reason and learn.
Classic AI problem-solving.
https://lnkd.in/d8KJgztk

4️⃣ Introduction to Machine Learning
Core ML algorithms simplified.
Beginner-friendly approach.
https://lnkd.in/dCYxsqui

5️⃣ How to AI (Almost) Anything
Apply AI to art and music.
Hands-on creative projects.
https://lnkd.in/dFwdMnvD

6️⃣ Understanding the World Through Data
Decode reality with data.
ML meets real-world problems.
https://lnkd.in/d7V8anyu

7️⃣ Artificial Intelligence in K-12 Education
Foundations of AI explained.
Where education is heading.
https://lnkd.in/drhYa8Nj

8️⃣ Introduction to Algorithms
Learn to think efficiently.
Critical for serious AI work.
https://lnkd.in/dYGsgRPt

9️⃣ Foundation Models and Generative AI
Modern AI systems explained.
Yes, the tech behind ChatGPT.
https://lnkd.in/dFMym7EW

The best AI education is free.
The best source is MIT.
The only cost is effort.

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Ghadeer  A.

No alternative text description for this image

NVIDIA quietly dropped the best free AI curriculum available. I mapped the smartest path through it 👇

Most will still waste it.

Everyone is buying AI courses.
Stacking certificates.
Missing execution.

They learn tools.
They skip architecture.
Meetings expose the gap.

That’s not a skills problem.
It’s a direction problem.

Start with FOUNDATIONS.

Generative AI Explained
https://lnkd.in/gGzipKBq
Build real mental models first.

Building A Brain in 10 Minutes
https://lnkd.in/giHj9tCM
Understand how models are wired.

AI for All: From Basics to GenAI
https://lnkd.in/gGzipKBq
Connect basics to GenAI reality.

Then get TECHNICAL.

Getting Started with AI on Jetson Nano
https://lnkd.in/gbP9QUQZ
See how AI runs on the edge.

Introduction to AI in the Data Center
https://lnkd.in/gq4GZ5SP
Understand scale and infrastructure.

An Even Easier Introduction to CUDA
https://lnkd.in/gq4GZ5SP
Learn how GPUs do the work.

Accelerated Computing with CUDA C/C++
https://lnkd.in/g9F5aKzS
Performance changes outcomes.

Then focus on DEPLOYMENT.

NVIDIA NIM Microservices
https://lnkd.in/gJSZW6Dc
From model to production systems.

Generative AI with NeMo
https://lnkd.in/gFaDpab8
Enterprise-grade customization.

Finally, see REAL APPLICATION.

Generative AI in Digital Health
https://lnkd.in/gApPyj5W
Healthcare use cases in action.

AI Weather Models with NVIDIA Earth-2
https://lnkd.in/gCCtcUNt
AI at planetary scale.

The people getting promoted
aren’t the most certified.

They connect skills to systems.
They fix stalled pilots.
They explain execution clearly.

Pick one course. Finish it.
Tie it to business impact.

Ignorance is now optional.

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🚨 BREAKING: You can run Claude Code completely free now.

No API costs. No rate limits. 100% local on your machine.

Just Claude Code running locally fast, private, and 100% yours.

Here’s how to set up Claude Code on your own machine (free + fully private): 👇

STEP 1: Install Your Local AI Engine

Ollama runs AI models locally on Mac and Windows.

STEP 2: Download a Coding Model

Pick based on your hardware:

Powerful machine → qwen2.5-coder:32b
Regular laptop → qwen2.5-coder:7b
Lightweight → gemma:2b

STEP 3: Point Claude Code to Your Machine

This is the key step.

Set your base URL to local:

export ANTHROPIC_BASE_URL = http ://localhost:11434/v1

STEP 4: Start Coding Locally

Navigate to any project folder and run:

claude-code --model qwen2.5-coder:7b

Why this matters:

- No $20/month API costs
- No rate limits (run 24/7 if you want)
- Complete privacy (code never leaves your machine)
- Works offline after model download
- Customize with any Ollama-compatible model

This is how coding agents should work.

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🚨Most developers use Anthropic’s Claude. Almost none use it like this. 

This 𝗖𝗟𝗔𝗨𝗗𝗘.𝗺𝗱 file will make you 10x engineer 👇

It combines all the best practices shared by Claude Code creator:

Boris Cherny (creator of Claude Code at Anthropic) shared on X internal best practices and workflows he and his team actually use with Claude Code daily. 

Someone turned those threads into a structured 𝗖𝗟𝗔𝗨𝗗𝗘.𝗺𝗱 you can drop into any project.

It includes:
• Workflow orchestration
• Subagent strategy
• Self-improvement loop
• Verification before done
• Autonomous bug fixing
• Core principles

This is a compounding system. 

Every correction you make gets captured as a rule. 

Over time, Claude's mistake rate drops because it learns from your feedback.

If you build with AI daily, this will save you a lot of time.

Comment “BUILD”

And I’ll send you:

- The full CLAUDE.md file.
- A step-by-step install guide.
- How to wire it into any repo.
- How to use subagents properly.

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🚨BREAKING: Anthropic Drops AI Dev Gold For $0.00. Most devs will ignore this 👇

AI feels overwhelming.
Too many tools. No path.
Anthropic built one.

Claude Code mastery. 
Ship production-ready AI.
Build with real workflows.

API building simplified.
Auth, requests, scaling.
From prompt to product.

Python MCP deep dive.
Control context like a pro.
Smarter outputs. Less chaos.

Free courses.
Free certificates.
Massive career leverage.

Claude Code + APIs + MCP.
Anthropic just raised the bar.

Link to courses: https://lnkd.in/d6uDYTpX

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graphical user interface, text, application, chat or text message

🚨 BREAKING: An Anthropic hackathon winner just gave away his entire Claude Code system for $0.00.

He beat 100+ builders at the Anthropic × Forum Ventures hackathon.

Built zenith. chat in 8 hours using Claude Code.

Walked away with $15K in API credits.

Then did something even crazier.

He open-sourced the entire system he used.

Inside the repo:

→ 14+ agents
→ 56+ skills
→ 33+ commands
→ MCP configs, hooks, and rules
→ AgentShield security scanner
→ Continuous learning system
→ Full cross-platform support

50,000+ stars. MIT licensed.

One install command.

This is basically a plug-and-play AI agent infrastructure.

If you’re building with Claude Code or AI agents…

this is a goldmine.

I’ll send the repo + setup guide.

Comment “ZENITH” and follow me.

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Anthropic

🚨10 GitHub repos with 600k+ combined stars. Most AI builders haven’t touched one

You save posts.
You bookmark threads.
You never build.

That is the problem.
Noise feels like progress.
But it builds nothing.

So I filtered the hype.
Only practical notebook-first repos.
Only structured learning paths.

Here are 10 that matter 👇:

	1.	microsoft/generative-ai-for-beginners
~105k stars. Structured GenAI course.
https://lnkd.in/daVemJpw

Full Microsoft curriculum in notebooks.
Build real GenAI apps step by step.

	2.	rasbt/LLMs-from-scratch
~83k stars. GPT built line by line.
https://lnkd.in/d8-e9pPs

Educational LLM implementation.
You understand transformers by coding.

	3.	microsoft/ai-agents-for-beginners
~49k stars. Agent systems explained.
https://lnkd.in/dyWxyqpR

Tools, memory, planning, workflows.
From theory to working agents.

	4.	microsoft/ML-For-Beginners
~83k stars. 26 ML lessons.
https://lnkd.in/dc-aSusG

Classic machine learning foundations.
Clear notebooks. Real exercises.

	5.	openai/openai-cookbook
~71k stars. Production patterns.
https://lnkd.in/dsCeebWa

Official API examples in notebooks.
Recipes you can ship to users.

	6.	jackfrued/Python-100-Days
~177k stars. Discipline over hype.
https://lnkd.in/dzJErdpW

100 days of Python exercises.
Brutal consistency builds fluency.

	7.	pathwaycom/llm-app
~54k stars. Real RAG systems.
https://lnkd.in/dpi3JaUc

Deployable LLM app templates.
Production-ready data pipelines.

	8.	jakevdp/PythonDataScienceHandbook
~46k stars. Data science core.
https://lnkd.in/dcKM-tQH

NumPy, Pandas, Matplotlib, SKLearn.
Foundations that never expire.

	9.	CompVis/stable-diffusion
~72k stars. Model-level insight.
https://lnkd.in/dkXWksnT

Original text-to-image codebase.
Learn diffusion by reading source.

	10.	facebookresearch/segment-anything
~53k stars. Vision breakthrough.
https://lnkd.in/d9uKEiku

Interactive image segmentation model.
Understand modern CV systems.

Most people consume AI content.
Few build with structured repos.
The difference compounds daily.

Serious builders study code.
Then they ship relentlessly.

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Stanford University just put the AI playbook online. Most people will never open it.

Everyone talks about AI.
Few study it seriously.
Even fewer build it.

The gap is widening fast.
Access is no longer the excuse.

Here is the 7-course roadmap 👇
From zero to AI engineer.
In the right order.

	1.	Artificial Intelligence (CS221)
Search, logic, planning, decisions.
The foundations of intelligent systems.
https://lnkd.in/gM892TxC

You learn how machines reason.
Not prompts. Principles.
	2.	Machine Learning (CS229)
Supervised vs unsupervised learning.
Core algorithms and intuition.
https://lnkd.in/gWgtbwZc

You stop copying tutorials.
You understand why models work.

	3.	Deep Learning (CS230)
Neural networks, CNNs, RNNs.
Practical training best practices.
https://lnkd.in/gYyD2Fwa

This is modern AI infrastructure.
Vision, speech, generation.

	4.	Machine Learning Theory (CS229M)
The math behind generalization.
Why models succeed or fail.
https://lnkd.in/gN4v2u9w

No more black boxes.
You see the mechanics.

	5.	Reinforcement Learning (CS234)
MDPs and Q-learning.
Systems that learn from actions.
https://lnkd.in/gKhtZYsV

Agents improve through feedback.
Like humans. At scale.

	6.	Natural Language Understanding (CS224U)
Meaning, intent, dialogue systems.
How machines interpret language.
https://lnkd.in/gg2BAW9k

Beyond autocomplete.
Into structured understanding.

	7.	NLP with Deep Learning (CS224N)
Translation, Q&A, summarization.
Sentiment and large language models.
https://lnkd.in/gWvnNXB2

This is how ChatGPT-class systems run.
Under the hood.

These are Stanford courses.
Now free. Online. Public.

The barrier is not money.
It is commitment.

Consumers scroll.
Engineers build.

Decide who you become in 2026.

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🚨 I regret paying for deep learning courses after finding this.

I’ve spent money on “premium” AI courses.
Some were good.
Most were… slides + buzzwords.

Then I found this full Deep Learning course by Professor Bryce.

And it exposed a hard truth:

Most people don’t struggle with deep learning 
They struggle with how it’s taught.

This course does what almost none do:

• Starts from first principles
• Explains the math visually (without ego)
• Connects why things work, not just how
• Covers the full stack: NN → CNN → RNN → LSTM → Attention → Transformers → GANs → AlphaGo
• Zero hype. Zero shortcuts. Pure understanding.

You don’t just learn models.
You learn how deep learning thinks.

If deep learning ever felt like:
“Magic”
“Black boxes”
“Copy-paste notebooks”

You skipped this layer.

And no amount of new tools will fix that.

This is the kind of foundation that:
• makes papers readable
• makes architectures intuitive
• makes you dangerous (in a good way)

It’s free.
It’s timeless.
And it’s better than most paid programs.

Link to playlist: https://lnkd.in/dajKU8Hk

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🔥Claude Code processed 10,000 files in minutes. I used to do this manually for weeks

Speed just replaced effort. 👇

Manual workflows do not scale.
Humans become the bottleneck.

Weeks lost to repetitive tasks.
Energy drained on low-leverage work.
Momentum destroyed by busywork.

Let agents handle the execution.
You focus on direction.

1️⃣ Total system authority

Claude Code works inside your machine.
Not trapped in a chat window.

It runs shell commands natively.
It manages repos and dependencies.
It reads and rewrites entire directories.

2️⃣ Your stack. One interface.

No more jumping between tools.
Everything connects at the terminal.

Update repos and pull requests.
Sync tickets and notify teams.
Move from docs to deployment instantly.

3️⃣ Speed compounds daily

Top builders remove friction.
They engineer momentum.

Switch models based on workload.
Compress context when needed.
Stop processes without losing state.

4️⃣ Memory that persists

Context no longer resets.
It evolves with your project.

Define build and test standards.
Lock in your code patterns.
Document structure once, reuse forever.

5️⃣ Prompt with precision

Clear constraints drive clean output.
Vague input creates chaos.

Break work into stages.
Approve plans before execution.
Chain analysis into action.

6️⃣ The new reality

Engineering is orchestration now.
Syntax is automated.

Stop being the typist.
Start being the architect.

Leverage beats effort.
Direction beats repetition.
Speed beats grind.

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You’re NOT behind on AI. You just haven’t seen this list yet. (Bookmark this 👇)

Harvard. Google. Anthropic. MIT.

All free.
All in one place.
All starting today.

Most people scroll past gold like this.

The ones who save it?
They’re the ones getting promoted in 12 months.

Here’s the full roadmap. Beginner to expert. $0.

🟢 BEGINNER — Start here. No excuses.

→ Anthropic Academy: Claude 101
https://lnkd.in/d6uDYTpX

→ Google AI Essentials
https://grow.google/ai

→ AWS Generative AI Essentials
https://lnkd.in/djzk-_jY

→ Elements of AI (University of Helsinki)
https://lnkd.in/dzgMRKCU

🟡 INTERMEDIATE — Build real skills.

→ DeepLearning.AI Short Courses
https://lnkd.in/dXjnT-tZ

→ Harvard CS50: AI with Python
https://lnkd.in/dncUUZss

→ Hugging Face AI Agents Course
https://lnkd.in/dPSAKrz7

🔴 EXPERT — Separate yourself.

→ Anthropic: Claude Code in Action
https://lnkd.in/d6uDYTpX

→ Practical Deep Learning for Coders (Fast.ai)
https://course.fast.ai

→ Google Generative AI Learning Path
https://lnkd.in/d5i2A-Q3

AI will not replace you.

But the person who spent their Sunday on this list?

They will.
Save this post.
Tag someone who keeps saying “I’ll start learning AI soon.”

Soon is today.

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🚨BREAKING: GitHub is the new university.
Most people are still paying for the old one

Degrees won’t teach modern AI.

AI moves faster than classrooms.
Courses are outdated on arrival.

Theory without building fails.
Real skill comes from shipping.

These repos teach the real game👇:

1.	OpenAI Cookbook
https://lnkd.in/dsCeebWa
Real examples using modern AI APIs.
Production patterns you can copy.

2.	OpenAI Agents SDK
https://lnkd.in/daunkrqS
Learn to build real AI agents.
Tools, orchestration, workflows.

3.	OpenAI Evals
github.com/openai/evals
Measure model performance properly.
Serious AI builders test everything.

4.	PydanticAI
https://lnkd.in/dThGCfFu
Structured outputs for LLM apps.
Cleaner, safer AI pipelines.

5.	Hugging Face Agents Course
https://lnkd.in/d3KW4c5K
Step-by-step agent building course.
Learn by shipping real projects.

6.	AI for Beginners
https://lnkd.in/e3nY2tbb
Microsoft’s full AI learning path.
Concepts explained with projects.

7.	Hugging Face 101 Course
https://lnkd.in/dcB786Mc
The fastest intro to the HF stack.
Models, datasets, and tooling.

8.	Hugging Face Smol Course
https://lnkd.in/dqpXjDJd
Learn small model development.
Perfect for efficient AI systems.

9.	AI Engineer Handbook
https://lnkd.in/dgqyfiR8
Covers the modern AI stack.
RAG, agents, evaluation, deployment.

10.	AI Engineering Field Guide
https://lnkd.in/dbUPTMv3
Practical patterns from real teams.
What AI engineers actually ship.

The best AI education is public.
You just have to build.

Bookmark this list.
Start shipping real projects.

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DeepSeek AI didn’t build a better AI. They stole one. Anthropic just released the receipts 👇

Silicon Valley applauded.

We worship speed.
We ignore sourcing.

24,000 fake accounts at scale.
That is not growth.

16 million prompts harvested.
That is not efficiency.

Claude’s best capabilities targeted.
Call it what it is.

The timeline cheered.
Investors celebrated.
No one asked questions.

Burn the illusion.

A dev responded differently.
They released 155k of their own
Claude Code conversations.

Generated with Opus 4.5.
On purpose. In public.

Then open sourced the tooling.
Export your own data.
Redact sensitive details.

Make it searchable.
Upload it to Hugging Face.
Audit what trains the models.

If AI feeds on us,
we audit the feast.

This is not about drama.
It is about POWERRR.

Data is leverage.
Leverage decides winners.

Comment “LIBERAL”
and I’ll send the steps
to liberate your own data.

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> Claude is FREE
> ChatGPT is FREE
> Gemini is FREE
> Grok is FREE
> Perplexity is FREE
> DeepSeek is FREE
> n8n is FREE (self-hosted)
> LangChain is FREE
> LangGraph is FREE
> CrewAI is FREE
> Hugging Face is FREE
> LlamaIndex is FREE
> Ollama is FREE
> Cursor is FREE (starter)
> VS Code is FREE
> GitHub is FREE
> Replit is FREE
> Supabase is FREE
> Vercel is FREE
> Cloudflare Workers is FREE
> Zapier is FREE (starter)
> Make. com is FREE (starter)
> Notion is FREE
> Google Colab is FREE
> Kaggle Notebooks is FREE
> Youtube is FREE

You have access to the exact same tools used by the engineers building frontier AI.

The gap between you and them isn’t money.

It’s 90 days of focused execution.

And yes…

your first weeks will probably look like this 👇

Everyone goes through this phase.

The difference between observers and builders is simple:

They kept going.

Start today.

Meme credit Eduardo Ordax 

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95% of “AI agents” aren’t agents. 
They’re prompt wrappers with dreams.

Real autonomy isn’t magic.
It’s layers. And almost everyone is missing three of them.

Here’s the stack the top 3% of builders actually use: 👇

🔹Layer 1: LLMs — The Raw Intelligence

Great at language. Useless at autonomy.
	•	Tokenization + inference
	•	Prompt shaping
	•	Direct API access

This is where most teams stop — then wonder why nothing is reliable.

🔹Layer 2: AI Agents — Intelligence That Acts

A brain finally gets hands.
	•	Tools + function calling
	•	CoT / ReAct reasoning
	•	Task decomposition
	•	Long-term memory

One agent is smart. But one agent alone can’t run a real workflow.


🔹Layer 3: Agentic Systems — Intelligence That Cooperates

This is where autonomy becomes repeatable.
	•	Multi-agent communication
	•	Roles + specialization
	•	Routing + scheduling
	•	Multi-agent RAG for shared context

This is the leap from “bot” → “operation.”

🔹Layer 4: Agentic Infrastructure — Intelligence That Survives Reality

The layer everyone forgets… until their system fails.
	•	Observability + traceability
	•	Error handling + retries
	•	Security + compliance
	•	Human-in-the-loop control
	•	Workflow automation

Without this layer?
Your agents are demos.
With it?
Your agents become deployable systems.

Most teams think they’re building agents.
They’re not.
They’re stacking prompts and praying.

Real autonomy lives at Layer 4.
Real reliability comes from architecture, not clever prompting.

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🚨BREAKING: Qwen3.5 Small just made your GPU bill a product decision, not a technical one 

Big models are officially on notice.

Compute costs are exploding.
Edge AI felt impossible.
Small models meant compromise.

Not anymore.

Meet Qwen 3.5 Small.
0.8B. 2B. 4B. 9B.
Same Qwen3.5 foundation.

Native multimodal built in.
Improved architecture.
Scaled RL for real reasoning.

0.8B / 2B → tiny and fast.
Made for edge devices.
Low latency. Low burn.

4B → lean agent base.
Multimodal and production-ready.
Way stronger than it looks.

9B → compact powerhouse.
81.7 on GPQA.
87.7 on OmniDocBench.

84.5 on Video-MME.
Closing the gap with giants.
Without giant infrastructure.

This is the shift.
Efficiency is strategy.
Performance per dollar wins.

Small is no longer cute.
Small is competitive.

Check it out links in the comments 👇

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🚨BREAKING: A $380B company just proved your marketing team is too big 

One person ran growth.

Most companies build bigger teams.
More managers.
More dashboards.

But execution keeps slowing.

Anthropic tried something different.

For 10 months

Their entire growth marketing team = 1 person.

Paid search.
Paid social.
SEO.
Email.
App stores.

Here’s the exact system he used 👇

Step 1 — Export your ads

Download every ad as a CSV.

Include:

CTR
Conversions
Spend

Upload it to Claude.

Claude instantly finds weak ads
and generates better variations.

Step 2 — Split AI into specialists

Agent #1 → Headlines only
(max 30 characters)

Agent #2 → Descriptions only
(max 90 characters)

Now you have hundreds of ideas.

Step 3 — Automate the creatives

He built a Figma plugin.

It pulls the copy.
Finds templates.
Generates full ads automatically.

100 ads in 0.5 seconds.

Step 4 — Connect Claude to Meta

He built an MCP server.

Claude reads the Meta Ads API.

Ask:

“Which ads converted best?”
“Where am I wasting spend?”

Claude answers instantly.

Step 5 — Add memory

Every test gets logged.

Next ads automatically use
what worked before.

The system improves every cycle.

The results

Ad creation:
2 hours → 15 minutes.

10x more experiments.

One marketer.
One AI system.
A $380B company.

This is AI-native marketing

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Claude_code_guide

🚨عــاجــل Qwen3.5 Small خلّى فاتورة الGPU
قرار منتج مو صداع تقني.

النماذج الضخمة؟
صار عليها ضغط رسميًا.

تكاليف الحوسبة قاعدة تولّع.
Edge AI كان شكله مستحيل
والنماذج الصغيرة كانت دايم تعني “أداء أقل”.

مو بعد اليوم.

تعرّفوا على Qwen 3.5 Small:
0.8B · 2B · 4B · 9B
بنفس أساس Qwen3.5 القوي.

متعدد الوسائط من الأساس.
معمارية أقوى.
وتعزيز بالتعلم المعزز عشان استدلال فعلي مش مجرد كلام.

0.8B / 2B → صغير وسريع جدًا.
مثالي للأجهزة الطرفية.
Latency قليل. تكلفة تشغيل قليلة.

4B → قاعدة خفيفة لبناء Agents بجد.
Multimodal وجاهز للإنتاج.
أقوى بكثير مما تتوقع.

9B → قوة مدمجة.
81.7 على GPQA
87.7 على OmniDocBench
84.5 على Video-MME
يقرب من الكبار
بس بدون بنية تحتية تكسّر ميزانيتك.

هنا التحول الحقيقي:
الكفاءة صارت استراتيجية.
الأداء مقابل كل دولار هو اللعبة.

الصغير مو “كيوت” بعد.
الصغير صار منافس

الرابط في التعليقات 👇

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