Few books I'd recommend for anyone starting up with AI engineering. These books have the best of explanation in simplest terms and written keeping in mind first principles basics.
@chipro@JayAlammar@MaartenGr@rasbt
18k ⭐️ on a GitHub repo. Organically.
My friend @ghumare64 is a great example of building in public for the community. The guy is genuinely passionate about what he does and builds open-source projects that get serious traction.
His latest project, agentmemory, a persistent memory layer for AI coding agents based on real-world benchmarks, and hit the number 1 trending spot. Incredible milestone.
Check it out here: https://t.co/LRiUtwoWdq
API Design Playbook
Giveaway Alert!!!
• Core API fundamentals.
• Clean & scalable design principles.
• Popular patterns used in real-world systems.
• Practical concepts for interviews & building projects.
(24 hours only.)
To get it for free:
1 Follow @systemdesignone [MUST]
2 Like & Retweet to get DM
3 Reply "Playbook"
Then I'll DM you the details.
Docker 101: Container Lifecycle 🐳
There is more to Docker than "docker run" followed by Ctrl+C. You can create, start, pause, unpause, stop, restart, kill, and remove containers. Learning these operations is the key to efficient usage of containers.
https://t.co/VDlZOTUEy7
New video: Need to install Google Tag Manager on Squarespace? In this quick guide, you'll learn how to install GTM properly, connect it with GA4, and test your setup.
https://t.co/OMSjmxClXW
#gtm#googletagmanager
AI isn't replacing teams, it's amplifying them. GTM engineering is a force multiplier, aiming to double or triple a person's output, not replace them. Think scaling capacity, not reducing headcount. #AI#GTM#FutureOfWork
MS&E 435 with @alighodsi - the most insight dense conversation I've had this year.
We cover the death of software, AI's jagged frontier, AGI implications, open source AI vs closed, advice for students as they navigate education and careers!
If you're just getting started with SYSTEM DESIGN, learn these 16 concepts (not joking):
1 The Computer Science Stack, Simply Explained
→ https://t.co/qfZnlyCSN5
2 Modular Monolith Architecture
→ https://t.co/VVV6v3KGHJ
3 How RPC Works
→ https://t.co/yeIgcmAxQx
4 How JWT Works
→ https://t.co/SZXXrlBsWH
5 Capacity Planning
→ https://t.co/umTNhM2dVY
6 How Bloom Filters Work
→ https://t.co/ntZXq7LxVn
7 How Consistent Hashing Works
→ https://t.co/7d6EipPcKF
8 How Service Discovery Works
→ https://t.co/BcL3tgxx1u
9 API Versioning - A Deep Dive
→ https://t.co/OHAtKSUgVN
10 Concurrency Is Not Parallelism
→ https://t.co/BwRHeuJ5AF
11 How Idempotent API Works
→ https://t.co/afe7ACuSYE
12 Saga Design Pattern
→ https://t.co/2CffTodOHL
13 How Databases Keep Passwords Securely
→ https://t.co/KSfIhpAT2j
14 API Design Best Practices
→ https://t.co/I2ejJ0kbYq
15 How Apache Kafka Works
→ https://t.co/8rOy9KgCMo
16 Distributed Systems 101
→ https://t.co/yi0K5K5RIE
What else should make this list?
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👋 PS - Want my System Design Playbook (for free)?
Join my newsletter with 200K+ software engineers now:
→ https://t.co/ByOFTtOihX
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5 BEST GitHub Repositories to Learn AI Engineering in 2026:
1. Awesome Machine Learning. https://t.co/E9EFe6Lz6F…
2. Full Stack Deep Learning. https://t.co/tEDhJiBTcf…
3. LangChain. https://t.co/FOKqdQ9qGm…
4. LlamaIndex. https://t.co/aASAj7qY6A…
5. Hugging Face Transformers. https://t.co/l6YlQyZ9ZS…
👇Comment "Git" if you find this helpful.
Repost so others can benefit.
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“RAG = vectors” is outdated.
A new shift is happening: Vectorless RAG.
Instead of embeddings + vector DBs, it uses:
• BM25 / keyword search
• SQL queries
• Knowledge graphs
• Direct context injection
→ No embeddings
→ No re-indexing
→ Lower infra cost
Why this matters:
Vector RAG has problems:
• Chunking breaks context
• Embedding drift
• Expensive infra
• Misses exact matches
Vectorless RAG works best when:
• Data is structured
• You need exact answers
• Context fits in window
But here’s the truth:
It’s not vector vs vectorless.
The future is hybrid retrieval 👇
• Keywords → precision
• Vectors → semantics
• Graphs → relationships
• SQL → exact data
One query.
Multiple retrievers.
Best result wins.
💡 Real insight:
RAG is no longer retrieval.
It’s routing.
Save this 💾
Follow for AI engineering insights 🚀
"The models have to touch reality for you to know where things break."
@jonsid Founder & CEO @turingcom says:
“In consumer, models like ChatGPT and Gemini have tight feedback loops.”
“In enterprise, that loop barely exists. Adoption is still near zero.”
“There’s massive headroom, but the data just isn’t there yet.”
“That’s why we focus on real human data and real operational data from deployed systems.”
“We’re even resurrecting ‘dead’ companies — their code, Slack, and email — to preserve that intelligence.”
Whether you're an ML engineer building LLMs or an AI engineer building with them, understanding how to evaluate LLMs and their applications is a skill you need to have.
This is the best overview of LLM benchmarks I've read.
Source by @cwolferesearch: https://t.co/QtzUxAsXpy