How Good ML Blogs Are Structured Post AI-Mushiness (According to People Who Avoid Medium)

You know exactly the kind of post I’m talking about. The title promises "Master Transformers in 5 Minutes." The content is a stock photo of a robot shaking hands with a human, three paragraphs of generic history about AI that reads like it was scraped from a 2018 Wikipedia entry, and a "Conclusion" that says "AI is the future."

It’s the "Medium Special." And for serious developers, students, and engineers in 2025, it is the digital equivalent of a wet sock.

Readers are tired. They are tired of paywalls blocking mediocre content. They are tired of "tutorials" that are just glorified ads for a SaaS tool. They are tired of 2,000-word introductions to concepts they already know, just to reach one paragraph of actual implementation.​

The best technical blogs right now are the ones that get bookmarked, shared in Slack channels, and cited in papers, look nothing like the content farms of the early 2020s. 

They are raw, opinionated, messy, and structured to respect your intelligence. If you are building a blog to actually help people (and maybe earn some AdSense revenue without selling your soul), you need to unlearn everything "content gurus" taught you.

Read: How To Start A Blog Using Blogger And What Things You Should Do Before You Start Publishing

The "Anti-BS" Structure

The modern technical reader decides in approximately 3 seconds whether to read your post or close the tab. If you start with "Artificial Intelligence has revolutionized the world," you are already dead. They know. They live with internet access. They probably used an LLM to generate their grocery list this morning.

Great ML blogs today start in the middle of the action.

The "Cold Open" Pattern:
Instead of an intro, start with the problem statement or the end result.

• Bad: "In this tutorial, we will explore the intricacies of Recurrent Neural Networks..."

• Good: "Your RNN is forgetting context after 50 tokens. Here is why the standard LSTM implementation fails on long sequences, and the 3-line change in PyTorch that fixes it."

This respects the reader's intent. They didn't come for a history lesson; they came because their code is broken or their model is stupid.

The "Prerequisite" Checkbox:
Put a small box at the top stating exactly what the reader needs to know. "Requires: Basic Python, familiarity with Hugging Face Trainer." This filters out people who aren't ready and reassures the experts that you won't waste time explaining what a variable is.

Code That Actually Runs (The Bare Minimum)

Nothing destroys credibility faster than a code snippet that doesn't import its dependencies. Or worse, code that is obviously hallucinatory pseudo-code generated by an older LLM.

The best blogs use "Copy-Paste-Run" blocks. Every snippet should be self-contained or part of a clearly linked repo. 

If I copy your code into a Colab notebook and it errors out because you forgot import numpy as np, I am closing your tab and never coming back, sorry but I know how to use AI too.

The "Why This Line Matters" Annotation:
Don't just dump a 50-line block. Break it up. Show the 3 lines that do the heavy lifting. Explain why you chose learning_rate=3e-4 instead of 1e-3. That specific insight—the heuristic, the "I tried X and it failed"—is the value. The code itself is a commodity; your experience with the code is the product.

Read: How To Make Money Blogging in 2020-2021

Visuals That Aren't Stock Photos

Stop using the glowing blue brain image. Please. I beg you.

The most effective ML blogs use "Napkin Diagrams." Hand-drawn (or stylistically simple) sketches that explain data flow are superior to polished corporate graphics. 

Why? Because they show you understand the architecture well enough to strip it down to boxes and arrows.

If you are explaining Attention mechanisms, don't copy the diagram from the "Attention Is All You Need" paper. We've all seen it. Draw a diagram showing what happens to one specific vector as it moves through the layers. Show the shapes: (Batch, Seq, Dim) -> (Batch, Heads, Seq, Dim/Heads). 

That shape transformation info is what developers actually look for when debugging dimension mismatch errors.

The "It Didn't Work" Section

This is the secret sauce. This is what separates a tutorial from a journey.

In 2026, or post 2024, every "official" tutorial works perfectly. You follow the steps, you get the result. But in the real world, nothing works perfectly. Libraries have version conflicts. GPUs run out of memory. Gradients explode.

Include a section titled "Where I Screwed Up" or "Pitfalls to Avoid."

• "I tried using float16 here, but the loss went to NaN. Stick to bfloat16 for this specific layer."

• "This approach failed completely on M1 Macs because of the MPS fallback."

This vulnerability builds massive trust. It tells the reader, "I actually built this. I suffered so you don't have to." It transforms you from a faceless author into a fellow engineer in the trenches.

Opinionated Tech Stacks

Generic advice is useless. "Use the right tool for the job" is a cop-out.

Good blogs pick a side. "We are using Polars instead of Pandas for this because the memory overhead on the 5GB dataset crashed my Colab instance." You don't have to be objectively right, but you have to be decisive. Readers are looking for guidance, not a menu of options.

If you hate a specific library, say it (professionally). "I avoided LangChain for this agent because the abstraction overhead made debugging the loops impossible." That is a valuable, citable opinion. It sparks discussion. It makes your content memorable.

Read: Top 10 Common Mistakes Every Blogger Makes + Infographic

The "So What?" Conclusion

Don't summarize what you just wrote. They just read it.

Use the conclusion to look forward or sideways.

• "This architecture works for text, but could we adapt it for time-series data?"

• "The cost of running this in production would be roughly $0.50 per hour. Is it worth it for a hobby project? Probably not."

Give them a next step that isn't just "subscribe to my newsletter." Give them a repository to star, a paper to read, or a challenge to try.

Structuring for Scannability (The F-Pattern)

People don't read; they scan. Your structure must support this.

• Descriptive H2s: Not "Step 1", but "Step 1: Quantizing the Model to 4-bit".

• Bold Key Concepts: Highlight the variables or concepts that matter.

• TL;DR at the Bottom: Paradoxically, putting a summary at the end often helps people who jumped there first to decide if they should scroll back up and read the details.

Final Thoughts: Be Human

The "Medium fatigue" comes from a lack of humanity. The articles feel generated, polished to a dull shine, and devoid of soul.​

Your blog is yours. If you think a specific paper is garbage, say it. If you found a hack that is ugly but works, share it. Write like you are explaining it to a friend over a beer (or a very caffeinated energy drink). 

That connection is what brings people back. It’s what makes them click your AdSense ads not because they were tricked, but because they want to support you.

Keep it real. Keep it broken. Keep it useful.

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