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

Read more code.

And fewer articles, tutorials, or surface-level books.

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 code - 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 the rapid pace at which AI is going.
But this is a massive learning opportunity if you know where to look.

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

It happens when you:
- Read real production code
- Study PRs or commit history
- See how teams structure repos, 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 (subscribe if interested) https://buff.ly/2rwqlNL

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

Ashish Patel 🇮🇳

Sr.Data Scientist & AI Consultant

🔥 6x Linkedln Top Voice | AI Research Scientist & Chief Data Scientist at IBM | Generative AI Expert | Author - Hands-on Time Series Analytics with Python | IBM Quantum ML Certified | 11+ Years in AI | MLOps | IIMA |

I have over 12+ years, Author, Data Scientist and Researcher with 8+ Years of Experience of Data Science technology and Research experience in wide functions including predictive modelling, data preprocessing, feature engineering, machine learning and deep learning. Currently, I work as Sr.Aws AI ML Solution Architect(Chief Data Scientist) at IBM India Pvt Ltd. Helps to Collaborate with IBM and AWS specialists to design and build complex enterprise solutions with the foundation of Red Hat Open Shift, AWS Infrastructure, and IBM Software technology. Standardize solutions in accordance with the AWS Well-Architected Framework across all pillars, including operational excellence, security, performance efficiency, cost optimisation, and reliability.

If you catch me outside of work, I'm an adventurous traveller and avid cricket lover!

𝗖𝗼𝗺𝗽𝗲𝘁𝗲𝗻𝗰𝗶𝗲𝘀: Data science, Machine learning, Deep learning, Audio Processing, Python, Computer Vision, SQL, Keras, TensorFlow, Librosa, Pyaudio, Mlflow, tube flow, mlops, DVC, MLOps on AWS, Kubeflow on AWS, 

Enterprise LLMs Services: Amazon Bedrock, Amazon Titan, IBM WatsonX.ai, Azure OpenAI, NvidiaNemo, Google PaLM

LLMs Knowledge: GPT-4, Claude, ChatGPT, OpenChat, UltraLM, OpenCoderPlus, WizardLM, Nous Hermes, Vicuna, Baize-v2, Guanaco, Minotaur, Davinci003, Falcon, Alpaca Farm PPO Sim (GPT-4), Pythia, Cohere, Alpaca Farm PPO Human, Cohere Chat, Alpaca, Falcon 7B Instruct, Davinci001.

MLOps on AWS Services: Amazon SageMaker, AWS CodePipeline, AWS CodeBuild, AWS CodeCommit, AWS CodeDeploy, AWS Batch, AWS Glue, AWS Data Pipeline, Amazon Elastic Container Service, AWS Farget, AWS Lambda, AWS EKS(Elastic Kubernetes Service), AWS ECS(Elastic Container Service), AWS APP Runner, AWS Cloud Formation, AWS ECR(Elastic Container Registry), AWS CloudWatch, AWS Auto Scaling, AWS CloudTrail, AWS EC2, AWS EBS(Elastic Block Store), AWS S3, AWS Route53

I also like meeting new people over a cup of coffee. Feel free to reach out at ashishpatel.ce.2011@gmail.com 

I am a passionate blogger, Researcher like to write about Machine learning and deep learning related Articles. you can visit my blog 

𝗟𝗶𝗻𝗸: https://medium.com/ml-research-lab.
book: https://www.amazon.in/B-V-Vishwas-ebook/dp/B08GLG46PQ/
Orcid Profile: https://orcid.org/0000-0002-5385-3897
Google Scholar : https://scholar.google.com/citations?hl=en&authuser=1&user=MfHiCbkAAAAJ

P.S. If you're passionate about creating tech-focused products to help people reach their potential, I am happy to connect at https://www.linkedin.com/in/ashishpatel2604

Sr. AWS AI/ML Solution Architect(Chief Data Scientist)

▪️Design and/or delivery of solutions, assets, and content at sustainable AI
▪️Apply methodology and governance around project development.
▪️Document and articulate clear understanding of customer/partner success criteria.
▪️Apply critical thinking, technical knowledge, customer/partner insights to solve problems with industry methodologies, and frameworks.
▪️Collaborate with IBM and AWS specialists to design and build complex enterprise solutions with foundation of Red Hat Open Shift, AWS Infrastructure, and IBM Software technology.
▪️Standardize solutions in accordance with the AWS Well-Architected Framework across all pillars, including operational excellence, security, performance efficiency, cost optimization, and reliability.
▪️Define, lead, and execute architecture design workshops and white-boarding sessions to build implementation plans.
▪️Develop deep relationships with key customers through solution integration to drive long-term IBM and AWS adoption.
▪️Team well with our key IBM & Red Hat business units, and strategic partners to support our partnership.
▪️Consult as a subject matter expert on our IBM/AWS technology and services portfolio.
▪️Author use cases, reference architectures, blog posts, and other field and external content.
▪️Work in sandbox environments to demonstrate IBM Hybrid Cloud Capabilities with AWS.

400+ ready-to-use templates for Claude Code.

Prebuilt AI agents.
Custom slash commands.
MCP integrations.
Zero setup from scratch.

Benefit:
You stop wasting time rewriting the same Claude instructions for every project.

Instead of:
• re-prompting agents
• reconfiguring commands
• rebuilding integrations

You install and move.

What this actually gives developers:
• domain-specific agents (security auditor, code reviewer, API designer)
• ready commands for testing, deployment, workflows
• MCP integrations for GitHub, PostgreSQL, Stripe, AWS
• optimized defaults that don’t need babysitting

This turns Claude Code from a tool into a system.

Proof:
The library aggregates:
• official Anthropic skills
• community-built agents
• real scientific & production workflows

Plus developer tooling most people don’t even think about:
• session analytics
• real-time conversation monitoring

And yes — it’s 100% open source.

Bottom line:
Most teams don’t fail at AI dev because models are weak.
They fail because every project starts from zero.

This removes that tax.

Link to the GitHub repo is in the comments.

♻️ Share if this saves someone from re-inventing their Claude setup

80+ ML best-practice recipes to take scikit-learn from notebook to production

Most scikit-learn examples stop at “the model runs.”
Real systems need more than that like lot of real world practises that big companies data scientist following including Microsoft, Google and OpenAI as well.

We need:

• leakage-safe preprocessing
• reproducible pipelines
• correct cross-validation
• models that survive deployment, not just demos

This is exactly what the scikit-learn Cookbook – Third Edition delivers:
80+ production-tested ML best practices and recipes, built on scikit-learn 1.5, covering the full path from experimentation to deployment.

What you’ll actually build (with code):

Chapter 1 – Core API & Pipelines
Estimators, transformers, pipelines
Code: https://lnkd.in/1gwq1sre

Chapter 2 – Preprocessing & Feature Engineering
Leakage-safe data workflows
Code: https://lnkd.in/5hjd9su4

Chapter 3 – Dimensionality Reduction
PCA and modern reduction techniques
Code: https://lnkd.in/jufvkc1x

Chapter 4 – Distance-Based Models
kNN, distance metrics
Code: https://lnkd.in/1lt2exu7

Chapter 5 – Linear Models & Regularization
Production-grade linear baselines
Code: https://lnkd.in/11b2gzwp

Chapter 6 – Advanced Logistic Regression
Extensions and advanced usage
Code: https://lnkd.in/1g9wfgiv

Chapter 7 – SVMs & Kernels
Optimized kernel methods
Code: https://lnkd.in/26zhj0gf

Chapter 8 – Trees & Ensembles
Random forests, boosting
Code: https://lnkd.in/83g1vzfd

Chapter 9 – Text & Multiclass Classification
Vectorization and multiclass pipelines
Code: https://lnkd.in/4awunn23

Chapter 10 – Clustering
Unsupervised grouping techniques
Code: https://lnkd.in/2488v1g4

Chapter 11 – Outlier & Novelty Detection
Anomaly detection in real data
Code: https://lnkd.in/231xisfj

Chapter 12 – Cross-Validation & Evaluation
Correct model validation patterns
Code: https://lnkd.in/1xo5u8ti

Chapter 13 – Production Deployment
Scalable, maintainable ML systems
Code: https://lnkd.in/25bfqrz0

Full runnable notebooks + 80+ ML best-practice recipes are in the book.
Book: scikit-learn Cookbook – Third Edition by John Sukup

If you’ve ever shipped a model and thought “this worked in my notebook…”, this is written for you.

#MachineLearning #scikitlearn #DataScience #ML #Python #MLOps #FeatureEngineering

🚨Most “agent memory” today = dumping chat history into context and praying it works.

That’s not memory.
That’s token waste.

Real agents run for weeks or months, not single chats.

But current setups:

• Stuff entire conversations into prompts
• Re-reason over old context every turn
• Burn tokens, latency, and money
• Still forget important facts

Chat history ≠ lifelong memory.

✅ Solution : 𝗦𝗶𝗺𝗽𝗹𝗲𝗠𝗲𝗺 fixes this at the memory layer, not the UI.

It’s a lifelong memory system for LLM agents that:

• Compresses interactions into clean, atomic facts
• Consolidates related memories hierarchically
• Retrieves only what’s relevant, when needed

No context stuffing.
No brute-force retrieval.

⚙️ 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 

🔹 Semantic Memory Compression
Turns messy dialogue into self-contained facts (noise removed).

🔹 Recursive Memory Consolidation
Related facts get merged into higher-level abstractions over time.

🔹 Adaptive Retrieval
Shallow queries → shallow memory
Complex queries → deeper recall
(No fixed “top-k nonsense”.)

🔹 Token-Efficient by Design
Up to 30× fewer tokens than naïve history-based agents.

🔹 Model-Agnostic
Works with GPT-class models and open-source LLMs.

🔹 MCP Server Support
Memory as a service for agents and tools.

🔗 GitHub link in comments
⭐ Star it if you care about real agent memory

Data centers are often framed as an environmental liability.

But the math tells a very different story.  
(And it’s worth slowing down to look at the numbers.)

After reading Andrew Ng’s analysis, I realized most opposition to data centers isn’t data-driven.  
It’s intuition-driven.

Let’s quantify the debate.

📊 Three claims. Three datasets…

👉 Carbon emissions: efficiency beats intuition  
• Data centers ≈ 1% of global CO₂ emissions  
• Typical enterprise on-prem PUE: 1.5–1.8  
• Leading hyperscaler PUE: ~1.2 or lower  

Lower PUE = less wasted energy per unit of compute.  
Centralizing compute in hyperscalers is mathematically cleaner than distributing it across inefficient local facilities.

👉 Carbon per task: compute vs physical alternatives  
• Google search: ~0.2g CO₂  
• Gemini LLM query: ~0.03g CO₂  
• Driving to a library to check a fact: ~400g CO₂  

AI’s environmental impact is dominated by volume, not inefficiency.  
Per-query emissions are already extremely low.

👉 Electricity prices: shared fixed costs matter  
Lawrence Berkeley National Lab found that state-level load growth from data centers often reduces average retail electricity prices.  
Why?  
Because grid infrastructure has high fixed costs.  
Adding large, stable industrial loads spreads those costs across more users.

👉 Water usage: scale needs context  
• U.S. data centers: ~17B gallons/year  
• U.S. golf courses: ~500B gallons/year  

Yes, local impacts can be significant and must be planned for.  
But at a national scale, data centers are not a dominant water consumer.

📌 The conclusion isn’t “data centers have zero cost.”  
It’s this:

From an efficiency-per-unit-of-output perspective, data centers are one of the most optimized industrial systems we have.

Their footprint exists because demand for computation exists.  
If society wants AI, cloud software, and digital services, the lowest-emission path is more efficient data centers, not fewer of them.

♻️ The environmental question isn’t whether we compute.  
It’s how efficiently we do it.

Are we making policy decisions based on intuition…  
or on the math?

If You want to read this article link in the comment…🔗

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