Turning research into understanding.
Hi, I’m exploring the frontier of AI, machine learning, and data science — one experiment at a time. NexByteLab is where research, code, and curiosity meet: tutorials you can run, papers explained in plain English, and honest write-ups on the tools reshaping how we build.
About NexByteLab.
NexByteLab started as a simple idea: most AI and data science content either oversimplifies to the point of being useless, or buries the point in jargon. I wanted something in between — writing that respects the reader’s intelligence without assuming they already have a PhD.
Everything published here follows the same rule: if I haven’t run it, tested it, or verified it myself, it doesn’t get published as fact. Tutorials are things you can actually execute. Research breakdowns come with the caveats intact. When something doesn’t work as advertised, that goes in the write-up too.
Whoever you are, there’s something here for you. Researchers get papers translated into plain language without losing the rigor. Industry practitioners get tools and techniques evaluated against real-world use, not just benchmarks. Students and academics get concepts built up from first principles, with the reasoning shown, not just the result. And if you’re simply curious and want the frontier of AI explained without the hype, that’s exactly what this is for.
This is a one-person lab, which means it moves slowly by design — but everything here is something I’d stand behind.
Ideas you can verify.
Infinite Curiosity
Our Approach. Good research is reproducible and honest at the same time. I don’t just chase benchmarks — I explain the why behind the results. Because understanding wins. Every time.
More than tutorials and takeaways.
Topics
Whether you’re just getting started with AI, building your first ML project, or diving into research papers — NexByteLab covers the full journey from fundamentals to the frontier.
Good content isn’t just about being current. It’s about being clear, correct, and useful. That’s what NexByteLab aims for.
Real experiments, real results.
Latest Articles
From beginner tutorials to research deep-dives — here’s what’s fresh on NexByteLab.
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Reproducing Double Descent: Why 300 Features Beat 39 on the Same 40 Data Points
A minimal least-squares experiment reproduces double descent: test error peaks near the interpolation threshold, then drops 28x as parameters keep growing.
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RAG | Experiments | Failures
Honest Write-Up: Why “Just Retrieve More Chunks” Doesn’t Scale in RAG
A real top-k retrieval experiment: precision stays near-perfect up to a point, then collapses — the exact failure the “retrieve more for safety” advice hides.
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Honest Write-Up: The Law of Large Numbers Doesn’t Always Save You
A real Cauchy-distribution experiment shows the Law of Large Numbers and Central Limit Theorem can both fail — the sample mean never converges, no matter how much data you add.
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INT8 Quantization Keeps Accuracy Intact, But Doesn’t Automatically Save Energy
A 10-seed benchmark shows INT8 weight quantization cuts memory 8x with zero accuracy loss, but current GPU studies show it often doesn’t cut energy use.
No hype. No hand-waving.
Method
Whether it’s a tutorial, an experiment, or a research breakdown, here’s how each piece comes together:
1. Research &
Scope
I start by digging into the papers, docs, and existing work — figuring out what’s worth explaining and what’s already been covered elsewhere.
2. Build &
Test
Before writing anything, I build it — running the code, training the model, benchmarking the claims against reality.
3. Write &
Explain
This is where the writing happens. I explain the how and the why, with real examples, code, and honest caveats.
4. Publish &
Revisit
Once it’s live, I keep it that way — updating posts as tools change, models improve, and I learn something new.
1. Research &
Scope
I start by digging into the papers, docs, and existing work — figuring out what’s worth explaining and what’s already been covered elsewhere.
2. Build &
Test
Before writing anything, I build it — running the code, training the model, benchmarking the claims against reality.
3. Write &
Explain
This is where the writing happens. I explain the how and the why, with real examples, code, and honest caveats.
4. Publish &
Revisit
Once it’s live, I keep it that way — updating posts as tools change, models improve, and I learn something new.
Want to see the latest experiment in action?
Get In Touch.
Contact
Got a topic suggestion, spotted an error, or just want to say hi? I’d love to hear from you.
I typically respond within a few days (unless I’m deep in a research rabbit hole). Let’s chat.
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