Technical Blog Writing

Writing technical blog posts that demonstrate depth - paper summaries, project write-ups, tutorials.

Reading time: ~30 min | Interview relevance: High | Roles: All AI/ML roles

The Real Interview Moment

You are sitting across from a staff machine learning engineer at a mid-stage AI startup. The conversation has been going well - you answered the system design question about a recommendation engine, you whiteboarded a reasonable approach to handling class imbalance, and you explained your portfolio project clearly.

Then she says: "I actually read your blog post about quantization-aware training before this interview. Can you walk me through why you chose to benchmark INT8 versus FP16 specifically for the inference latency comparison?"

You freeze. You did not write a blog post about quantization. You have no blog. You have a GitHub with three repositories and a resume with four bullet points about side projects. She is confusing you with another candidate.

But here is the thing - that other candidate just got a massive advantage. Before the interview even started, the interviewer had already formed a positive impression. She already believed that candidate understood quantization deeply, could communicate technical ideas clearly, and was genuinely passionate about the field. The interview was a confirmation exercise, not an evaluation from scratch.

Now imagine the opposite scenario.

You are the candidate with the blog. You published a detailed post three months ago comparing quantization strategies for transformer models. You included benchmark tables, code snippets, and a section on when quantization hurts accuracy more than it helps. The post got 2,400 views on Medium and was shared by two ML engineers on Twitter.

When you walk into the interview, the staff engineer has already read 3,000 words of your technical thinking. She knows how you approach problems. She knows you can write. She knows you care enough about ML to spend a weekend running experiments and documenting them. The conversation is not "prove to me you know things" - it is "let us talk shop as peers."

That is the power of a technical blog. This chapter shows you exactly how to build one.

Why Technical Blogging Matters for AI Careers

Let us be direct about what a blog does for your job search. It is not about becoming an influencer or building a massive audience. It is about creating durable, searchable proof of your technical thinking.

The Recruiter Signal

When a recruiter searches for candidates, they often Google names that appear promising. Here is what they find for most candidates: a LinkedIn profile and maybe an empty GitHub. Here is what they find for candidates with blogs: articles demonstrating deep knowledge, clear communication, and genuine interest.

A 2023 survey by Hired found that candidates with active technical blogs received 40% more recruiter outreach compared to candidates with equivalent resumes but no public writing. The blog does not need to be popular - it needs to exist and demonstrate competence.

The Interview Advantage

Technical blogs create what hiring managers call a pre-interview signal. Before you ever sit in a room together, the interviewer has evidence of:

• Depth of knowledge - You did not just use a tool, you understood it well enough to explain it
• Communication ability - One of the hardest skills to evaluate in a 45-minute interview
• Intellectual curiosity - You explored a topic beyond what was required for your job
• Follow-through - You did not just think about something, you wrote 2,000 words about it

60-Second Answer

A technical blog is the single highest-ROI activity for AI job seekers because it simultaneously builds your knowledge, proves your communication skills, creates recruiter-discoverable content, and gives interviewers a reason to be excited about talking to you - all from a single weekend of writing.

The SEO and Discoverability Factor

When someone Googles "fine-tuning LoRA adapters for domain-specific tasks," your blog post can appear in the results. This means hiring managers, recruiters, and potential collaborators can find you without you actively reaching out to them. This is inbound interest - the most powerful kind.

Technical blog posts have a much longer shelf life than social media posts. A well-written tutorial about deploying ML models with FastAPI will continue generating traffic for 12-24 months. A tweet about the same topic disappears from feeds in 4 hours.

What a Blog Is NOT

Let us clear up misconceptions:

What People Think	What Is Actually True
You need to be an expert to blog	You need to be learning and willing to share what you learned
Posts need to be original research	Explaining existing concepts clearly is extremely valuable
You need thousands of readers	50 readers who include 3 hiring managers is all you need
You need to post weekly	6-8 high-quality posts over 3 months is more than enough
Posts need to be perfect	Clear, accurate, and useful beats polished but empty
You need your own website first	Medium, Substack, or dev.to work perfectly fine

What to Write About

The most common blocker for technical blogging is not knowing what to write. Here is a systematic framework for generating topics that are both useful to readers and valuable for your career.

The Topic Selection Matrix

Topic Categories That Work

1. "What I Learned" Posts (Best for beginners and career changers)

These are the most natural posts to write because you are documenting your own learning journey. They are also extremely popular because other people learning the same thing will find them useful.

Examples:

• "What I Learned Implementing a RAG Pipeline from Scratch"
• "5 Mistakes I Made When Fine-Tuning My First LLM"
• "What Surprised Me About ML System Design in Production"

2. Tutorial and How-To Posts (Best for demonstrating practical skills)

Step-by-step guides that solve a specific problem. These generate the most consistent traffic and are the most useful for job searches because they prove you can actually build things.

Examples:

• "How to Deploy a Transformer Model with Sub-100ms Latency on AWS"
• "Building a Custom Named Entity Recognition System with spaCy and Active Learning"
• "Setting Up MLflow Experiment Tracking for a Small Team"

3. Deep-Dive Explanations (Best for research-oriented roles)

Take a single concept and explain it thoroughly. These posts demonstrate depth and are especially valuable if you are targeting research scientist or research engineer roles.

Examples:

• "Understanding Flash Attention: Why It Works and When to Use It"
• "A Visual Guide to Mixture of Experts Architectures"
• "How RLHF Actually Works: From Reward Modeling to PPO"

4. Comparison Posts (Best for engineering roles)

Compare two or more tools, approaches, or frameworks. These are extremely popular in search results because people frequently search for "X vs Y."

Examples:

• "LangChain vs LlamaIndex: Which RAG Framework Should You Use in 2025?"
• "PyTorch Lightning vs Hugging Face Trainer: A Practical Comparison"
• "FAISS vs Pinecone vs Weaviate: Vector Database Benchmarks for 1M Embeddings"

5. Paper Summaries and Breakdowns (Best for research roles and demonstrating depth)

Take an important paper and explain it in accessible language. Add your own perspective on implications, limitations, or applications.

Examples:

• "Breaking Down the Mamba Paper: Is the Era of Transformers Ending?"
• "What the Scaling Laws Paper Actually Tells Us (and What It Does Not)"
• "A Practical Reading of 'Retrieval-Augmented Generation for Knowledge-Intensive Tasks'"

6. Opinion and Analysis Posts (Best for senior roles)

Share a perspective on where the field is heading, what practices are overrated, or what lessons you have learned. These work best when backed by concrete experience.

Examples:

• "Why Most ML Teams Should Not Fine-Tune Their Own Models"
• "The Uncomfortable Truth About AI Engineering Job Interviews"
• "Three Lessons from Shipping ML Products That Nobody Talks About"

Common Trap

Do not write posts that are just a list of tools or links with no original analysis. "10 Best Python Libraries for NLP" without any comparison, experience, or unique perspective is content mill filler. It will not help your career and may actually hurt your credibility.

How to Generate Specific Topics

If you are staring at a blank screen, use these prompts:

1. The "I just figured this out" prompt: What did you spend hours debugging this week? Write about the solution.
2. The "I wish someone told me" prompt: What do you know now that you wish you knew when you started?
3. The "explain it to a colleague" prompt: What concept did you recently explain to someone? Write that explanation down.
4. The "compare what I tried" prompt: Did you evaluate two tools or approaches recently? Document your comparison.
5. The "paper that excited me" prompt: Did you read a paper that changed how you think about something? Summarize it.
6. The "production vs. tutorial" prompt: What is different about doing X in production versus how tutorials teach it?

Blog Post Structures: Templates for Every Type

Template 1: The Tutorial Post

This is the workhorse of technical blogging. A tutorial that solves a real problem will generate traffic for months.

Structure:

1. The Problem (100-150 words)
   - What we are building/solving
   - Why this matters
   - What the reader will have by the end

2. Prerequisites (50-100 words)
   - Required knowledge
   - Required tools/packages

3. Architecture Overview (100-200 words + diagram)
   - High-level diagram of what we are building
   - Key components and how they connect

4. Step-by-Step Implementation (800-1500 words)
   - Step 1: Setup and configuration (with code)
   - Step 2: Core implementation (with code)
   - Step 3: Integration/deployment (with code)
   - Each step: what, why, and common pitfalls

5. Results and Evaluation (200-300 words)
   - Show it working
   - Performance metrics
   - Comparison to alternatives if relevant

6. Common Issues and Troubleshooting (100-200 words)
   - Top 3-5 issues readers will encounter
   - Solutions for each

7. Next Steps and Extensions (100 words)
   - How to extend this
   - Links to related resources

Example opening for a tutorial post:

Building a Production-Ready RAG Pipeline with LangChain and Pinecone

Most RAG tutorials show you how to stuff documents into a vector store and query them. They work great on the demo dataset. They fall apart the moment you try them on real documents with messy formatting, ambiguous queries, and users who expect accurate answers.

In this post, I will walk through building a RAG pipeline that actually works in production. We will cover chunking strategies that preserve context, embedding model selection based on your domain, retrieval with re-ranking, and evaluation metrics that tell you if your system is improving. By the end, you will have a working pipeline and the knowledge to debug it when things go wrong.

Template 2: The Deep-Dive Explanation

Structure:

1. Why This Matters (100-150 words)
   - Contextualize the concept
   - Who needs to understand this and why

2. The Core Intuition (200-300 words)
   - Explain the concept without math first
   - Use an analogy if helpful
   - Diagrams showing the core idea

3. How It Actually Works (500-800 words)
   - Step through the mechanism
   - Include equations if relevant (with explanations)
   - Visual diagrams for each key step

4. Practical Implications (200-300 words)
   - When to use this in practice
   - When NOT to use it
   - Performance characteristics

5. Code Example (200-400 words)
   - Minimal working example
   - Key parameters to tune

6. Common Misconceptions (100-200 words)
   - What people get wrong
   - Corrections with evidence

7. Further Reading (50-100 words)
   - Original paper
   - Best follow-up resources

Template 3: The Comparison Post

Structure:

1. The Decision Context (100-150 words)
   - Why someone would need to choose between these options
   - The criteria that matter

2. Quick Summary Table (100 words)
   - Side-by-side comparison on key dimensions
   - Clear "winner" per dimension

3. Detailed Comparison (600-1000 words)
   - Category 1: Setup and learning curve
   - Category 2: Performance/speed
   - Category 3: Features and flexibility
   - Category 4: Community and ecosystem
   - Category 5: Cost and scaling
   - Each category: concrete evidence, not opinions

4. Benchmark Results (200-300 words)
   - If you ran benchmarks, show them
   - Tables and charts
   - Methodology transparency

5. Recommendation Matrix (100-200 words)
   - "Use X if you need..."
   - "Use Y if you need..."
   - "Avoid Z if..."

6. My Experience (100-200 words)
   - What you actually chose and why
   - What surprised you

Template 4: The Paper Summary

Structure:

1. Paper Context (100-150 words)
   - What problem does this paper address?
   - Why should practitioners care?
   - Where does it fit in the field's progression?

2. Key Contributions (150-200 words)
   - 3-5 bullet points summarizing what is new
   - Why each contribution matters

3. How It Works (400-600 words)
   - Core method explained accessibly
   - Diagrams re-created or annotated
   - Key equations with plain-language explanations

4. Results That Matter (200-300 words)
   - Most important experimental results
   - Tables or charts from the paper (with attribution)
   - What the numbers mean practically

5. Limitations and Open Questions (100-200 words)
   - What the authors acknowledge
   - What you noticed they did not address
   - Implications for practitioners

6. My Take (100-200 words)
   - How this changes your work or thinking
   - Whether the claims are convincing
   - Practical applications you see

Template 5: The Opinion/Analysis Post

Structure:

1. The Claim (100-150 words)
   - State your thesis clearly and directly
   - Why this is a contrarian or underappreciated view

2. The Evidence (400-600 words)
   - Data points supporting your argument
   - Examples from your experience
   - Industry trends that support your view

3. The Counterargument (200-300 words)
   - Steel-man the opposing view
   - Explain why people reasonably disagree
   - Where the opposing view has merit

4. The Nuance (200-300 words)
   - When your claim does NOT apply
   - Edge cases and exceptions
   - How context changes the answer

5. Practical Implications (100-200 words)
   - What should people do differently based on this
   - Specific, actionable recommendations

The Writing Process: From Idea to Published Post

Step 1: Capture Ideas Continuously

Do not sit down to write without a topic. Keep a running list of ideas. Every time you:

• Debug something non-obvious, add it to the list
• Read a paper that changes your thinking, add it
• Compare two tools, add it
• Explain something to a colleague, add it
• See a question asked repeatedly on Stack Overflow or Reddit, add it

Use whatever tool you already use for notes - a text file, Notion, Apple Notes, a Slack channel with just yourself. The tool does not matter. The habit of capturing ideas matters.

Step 2: Outline Before Writing

Spend 15-20 minutes on an outline before writing a single paragraph. A good outline includes:

• The one-sentence thesis (what is the reader's takeaway?)
• 5-8 section headers
• 2-3 bullet points under each section (key points to cover)
• A note about what code/diagrams you will need

The outline prevents the most common writing failure: a post that wanders without a clear point.

Step 3: Write the Ugly First Draft

Write the entire post in one sitting if possible. Do not edit as you go. Do not worry about perfect sentences, correct grammar, or elegant transitions. Just get the content out.

For most people, a 2,000-word post takes 2-3 hours to draft. If you are spending 8 hours on a draft, you are editing while writing. Stop. Write ugly, edit later.

60-Second Answer

The most productive writing workflow: Monday evening (30 min) outline the post. Tuesday/Wednesday (2 hours) write the ugly first draft. Thursday (1 hour) edit and add code/diagrams. Friday (30 min) final proofread and publish. Total: 4 hours across a week.

Step 4: Add Code, Diagrams, and Examples

After the draft exists, go back and add:

• Code snippets - Working, tested code. Never publish code you have not run. Include imports and setup. Readers should be able to copy-paste and run.
• Diagrams - Architecture diagrams, flow charts, comparison visuals. Tools: Excalidraw (for sketchy style), draw.io (for clean diagrams), Mermaid (for diagrams in markdown).
• Tables - Comparison tables, benchmark results, feature matrices.
• Screenshots - Terminal output, UI screenshots, dashboard views.

Step 5: Edit Ruthlessly

Editing is where good posts become great posts. Focus on:

1. Cut the throat-clearing. Delete the first 1-3 paragraphs if they are just preamble. Start where the useful content begins.
2. Cut the qualifiers. "I think maybe it could be somewhat useful" becomes "It is useful."
3. Add specific numbers. "It was faster" becomes "Inference latency dropped from 340ms to 45ms."
4. Check every code snippet. Run it. If it does not work, fix it or remove it.
5. Read it aloud. If a sentence is hard to say, it is hard to read. Simplify.

Step 6: Proofread and Format

• Run spell check
• Check all links
• Verify code formatting renders correctly
• Add a clear title and meta description
• Add relevant tags/categories
• Check that images load

Step 7: Publish and Promote

Publishing is not the end. See the promotion section below for how to get your post in front of the right people.

Where to Publish

The platform you choose matters less than actually publishing. But each platform has different strengths:

Platform Comparison

Platform	Best For	Audience	SEO	Monetization	Ownership
Medium	Maximum reach for tutorials	General tech audience	Good (Medium ranks well)	Medium Partner Program	Medium owns distribution
Substack	Building an email list	Subscribers you own	Moderate	Paid subscriptions	You own the list
Dev.to	Developer community engagement	Developers	Good	None	You keep content
Hashnode	Custom domain + community	Developers	Excellent (custom domain)	None built-in	Full ownership
Personal site (Hugo, Jekyll, Next.js)	Complete control, long-term brand	Depends on your promotion	Excellent (your domain)	Whatever you want	Full ownership
LinkedIn Articles	Professional network visibility	Recruiters and hiring managers	Poor	None	LinkedIn owns it

The Best Strategy for Job Seekers

If you are writing primarily for career purposes, here is the optimal approach:

1. Start with Medium or dev.to - Zero setup, built-in audience, you can publish today
2. Cross-post to LinkedIn - Every post should also appear as a LinkedIn article or long-form post
3. Migrate to a personal site later - Once you have 8-10 posts, set up a personal blog and import everything

Company Variation

Interviewers at research-focused companies (Google DeepMind, Meta FAIR, Anthropic) respond more strongly to paper summaries and deep-dive explanations. Interviewers at product-focused companies (Airbnb, DoorDash, Stripe) respond more to tutorials and production experience posts. Write for your target audience.

Setting Up a Personal Blog (Quick Start)

If you want your own site, the fastest options:

Option 1: Hugo + GitHub Pages (Free, 30 minutes)

# Install Hugo
brew install hugo   # macOS
# Create a new site
hugo new site my-blog
cd my-blog
# Add a theme
git init
git submodule add https://github.com/CaiJimmy/hugo-theme-stack themes/stack
# Create your first post
hugo new post/my-first-post.md
# Run locally
hugo server -D

Option 2: Next.js + Vercel (Free, 1 hour)

npx create-next-app my-blog --example blog-starter
cd my-blog
npm run dev
# Deploy
npx vercel

Option 3: Docusaurus (Free, 30 minutes)

npx create-docusaurus@latest my-blog classic
cd my-blog
npm start

For most job seekers, starting with Medium and moving to a personal site after you have a collection of posts is the right approach. Do not spend two weeks setting up a personal site and then never write anything.

SEO for Technical Posts

Search Engine Optimization for technical blog posts is different from SEO for marketing content. Here is what matters:

Keyword Research for Technical Topics

You do not need fancy SEO tools. Use these free approaches:

1. Google autocomplete - Type the beginning of your topic and see what Google suggests. "Fine-tuning LLMs" might autocomplete to "fine-tuning LLMs with LoRA," "fine-tuning LLMs cost," "fine-tuning LLMs vs prompting." Each of these is a topic people are searching for.

2. Stack Overflow / Reddit - Search for your topic on these platforms. The questions people ask are the queries people Google. If 50 people asked "How to handle class imbalance in NLP" on Stack Overflow, that is a proven topic.

3. Google Trends - Compare search interest between related terms. "LangChain tutorial" vs "LlamaIndex tutorial" might reveal which has more demand.

On-Page SEO for Technical Posts

Element	Best Practice	Example
Title	Include the primary keyword naturally, under 60 characters	"Fine-Tuning LoRA Adapters: A Practical Guide with Code"
URL slug	Short, keyword-rich, hyphenated	/fine-tuning-lora-adapters-guide
Meta description	150-160 characters summarizing the post	"Step-by-step tutorial for fine-tuning LLMs with LoRA, including code, benchmarks, and hyperparameter recommendations."
H2 headings	Include secondary keywords	"Setting Up Your Training Environment," "Choosing the Rank Parameter"
First paragraph	Include primary keyword in the first 100 words	"In this guide, I will walk through fine-tuning LoRA adapters for..."
Images	Use descriptive alt text	alt="Comparison of training loss curves for LoRA ranks 4, 8, and 16"
Internal links	Link to your other posts	"As I explained in my post about [quantization strategies]..."
Code blocks	Use proper syntax highlighting	Always specify the language: ```python

Content-Level SEO

The most important SEO strategy for technical posts is simply to be comprehensive. Google rewards content that thoroughly answers a query. A 2,500-word tutorial that covers setup, implementation, troubleshooting, and benchmarks will outrank a 500-word tutorial that only covers setup.

Other content-level factors:

• Answer the question in the first paragraph - Google features this as a snippet
• Use numbered steps - Google can display these as a rich result
• Include a table of contents - Helps both readers and search engines understand structure
• Update old posts - A post updated with "Updated for 2025" in the title gets a ranking boost

Common Trap

Do not stuff keywords artificially. "In this LLM fine-tuning LoRA adapters tutorial for fine-tuning LLMs with LoRA adapters..." reads like spam and Google will penalize it. Write naturally. Include the keyword 3-5 times across a 2,000-word post, always in context.

Promotion Strategy: Getting Your Posts Read

Writing a great post and not promoting it is like building a product and not telling anyone. Here is a systematic promotion plan for every post you publish.

The Post-Publication Checklist

Day of publication:

• Share on LinkedIn with a 3-5 sentence summary of the key insight (not just "I wrote a blog post")
• Share on Twitter/X with the most interesting finding or a provocative question
• Post in 2-3 relevant subreddits (r/MachineLearning, r/learnmachinelearning, r/datascience) with a genuine comment, not a drive-by link drop
• Share in relevant Discord or Slack communities

Week after publication:

• Comment on related posts/discussions with a link to your post (only when genuinely relevant)
• Send to 2-3 people who might find it useful (direct message, not mass email)
• If it is a tutorial, answer a related Stack Overflow question and link to your post for more detail

Ongoing:

• Link to the post from your GitHub README
• Add it to your LinkedIn featured section
• Reference it in future blog posts (internal linking)
• Mention it in relevant Twitter/X threads

Platform-Specific Promotion Tactics

LinkedIn:

Do not just share a link. Write a post that provides value on its own, then mention the blog post for more depth.

Bad: "I wrote a blog post about RAG pipelines! Check it out: [link]"

Good: "Most RAG tutorials skip the hardest part - chunking strategy. After testing 4 different approaches on a 50K-document corpus, I found that recursive character splitting with 200-token overlap outperformed everything else by 15% on retrieval accuracy. I wrote up the full comparison with code and benchmarks: [link]"

Twitter/X:

Turn your post into a thread of 5-8 tweets covering the highlights. The thread format works much better than a single tweet with a link.

Reddit:

Reddit hates self-promotion. The rule of thumb: only 10% of your Reddit activity should be sharing your own content. The other 90% should be genuine participation. When you do share, write a paragraph explaining why it is relevant to the subreddit, and use the post as a starting point for discussion, not a promotional dump.

Building an Audience Over Time

Most blogs take 3-6 months and 6-10 posts before you see consistent traffic. Do not measure success after one post. Measure it after six.

Examples of Great AI Blogs to Study

Study these blogs not to copy them, but to understand what makes technical writing effective:

Individual Blogs

Blog	Author	Why It Works
Lil'Log	Lilian Weng	Deep, visual explanations of ML concepts with consistent quality
Jay Alammar's Blog	Jay Alammar	Unmatched visual explanations of transformers, attention, and embeddings
Chip Huyen's Blog	Chip Huyen	Combines ML engineering depth with practical industry experience
Sebastian Raschka's Blog	Sebastian Raschka	Thorough, code-heavy tutorials with strong pedagogical structure
Eugene Yan's Blog	Eugene Yan	Applied ML at scale with clear frameworks and real production examples
Hamel Husain's Blog	Hamel Husain	Practical LLM application development with no-nonsense engineering

What These Blogs Have in Common

1. Consistent format - Readers know what to expect
2. Depth over breadth - Each post covers one topic thoroughly
3. Original perspective - Not just summarizing, but adding analysis
4. Visual explanations - Diagrams, charts, and code outputs
5. Practical applicability - Readers can apply what they learn

Common Patterns in Successful Posts

Posts that get widely shared tend to have these characteristics:

• Specific, promise-making titles: "How We Reduced Inference Latency by 85% Using Model Distillation" (not "Thoughts on Model Distillation")
• A clear before/after: Show the problem state and the solved state
• Working code: Tested, copy-pasteable, with all imports included
• Benchmark data: Numbers, tables, charts - not just claims
• Honest limitations: "This approach does not work well when..." builds credibility
• A strong conclusion: What should the reader do next?

Writing Quality Checklist

Before publishing, check every post against these criteria:

Content Quality

• The post has a clear thesis or takeaway - can you state it in one sentence?
• Every section supports the thesis - if a section does not, cut it
• Code snippets are tested and runnable
• Claims are supported by evidence (benchmarks, citations, examples)
• The post answers a question someone would actually search for
• You have included something a reader cannot get from the official documentation

Writing Quality

• The first paragraph hooks the reader and explains what they will get
• Paragraphs are 3-5 sentences maximum
• Technical jargon is defined on first use
• Sentences average 15-20 words (not 40-word monsters)
• You eliminated weasel words ("very," "really," "somewhat," "quite")
• You used active voice (not "the model was trained by us" but "we trained the model")

Formatting Quality

• Headers create a scannable structure
• Code blocks use proper syntax highlighting
• Images have alt text
• Lists break up dense paragraphs
• The post looks good on mobile (check before publishing)
• Tables are used for comparisons (not buried in paragraphs)

Practice Exercises

These exercises are designed to get you from zero to published within two weeks.

Exercise 1: The 30-Minute Blog Post (Do This Today)

Write a post titled "One Thing I Learned About [Topic] This Week." It should be 300-500 words. Publish it on Medium or dev.to. The goal is not quality - it is breaking the publication barrier. You can delete it later.

Exercise 2: The Tutorial Post (Week 1)

Pick something you built or learned in the last month. Write a tutorial following Template 1 from this chapter. Target 1,500-2,000 words. Include at least one code snippet and one diagram.

Exercise 3: The Comparison Post (Week 2)

Pick two tools you have used (or are willing to test). Write a comparison following Template 3. Run actual benchmarks, even simple ones. A post titled "FastAPI vs Flask for ML Model Serving: My Benchmark Results" with real data will outperform any number of hypothetical posts.

Exercise 4: The Paper Summary (Ongoing)

Pick one paper per month from the ArXiv highlights. Write a 1,000-word summary following Template 4. This becomes a habit that builds your research reading and communication skills simultaneously.

Exercise 5: The Blog Portfolio Target

Set a goal: 6 posts in 3 months. After six posts, you will have:

• A body of work to reference in interviews
• At least 1-2 posts generating organic search traffic
• Material for your LinkedIn featured section
• Talking points for networking conversations
• Evidence of communication ability for any application

Dealing with Common Objections

"I do not know enough to write"

If you are learning something, you know more than someone who has not started. The best technical writing often comes from people who recently figured something out, because they remember the confusion and can explain the solution clearly. Experts often skip over fundamentals because they have internalized them.

"What if I am wrong?"

Include caveats. "Based on my testing with X dataset..." is honest and appropriate. The AI/ML community generally appreciates people sharing findings with appropriate caveats. If you make a mistake, someone will likely comment with a correction, and you can update the post. This is how learning works.

"Nobody will read it"

You do not need nobody. You need the right 10 people. If 3 hiring managers at your target companies read your post, that is a success. If 50 people find it via Google while debugging the same issue, that is ongoing value. Volume is not the goal.

"I do not have time"

A 1,500-word tutorial takes approximately 4 hours to write, including code testing and editing. That is one evening or two lunch breaks. If you are applying to AI roles, those 4 hours provide more return on investment than applying to 20 more jobs via automated applications.

Instant Rejection

Never plagiarize or heavily paraphrase someone else's blog post and publish it as your own. The AI/ML community is small. People notice. A single plagiarism incident can permanently damage your reputation in the field. Always cite your sources and add your own original analysis.

Interview Cheat Sheet

Question	What They Want to Hear
"Do you blog or write about ML?"	"Yes, I write about [topic area]. My most recent post was about [specific topic] - I ran benchmarks comparing [X vs Y] and found [specific result]."
"How do you stay current with the field?"	"I read papers regularly and write summaries on my blog. It forces me to understand concepts deeply enough to explain them."
"Can you share something you learned recently?"	Reference a specific blog post: "I recently wrote about [topic]. The most surprising finding was [specific detail]."
"How do you communicate technical concepts to non-technical stakeholders?"	"I practice this regularly through my blog. Writing for a broad audience has taught me to lead with the practical impact before diving into the method."
"What is your approach to learning new tools?"	"I learn by doing and then documenting. For example, when I learned [tool], I built [project] and wrote a tutorial about the process."

Next Steps

Now that you understand how to create discoverable, career-enhancing content through blogging, the next chapter covers LinkedIn for AI Roles - optimizing your LinkedIn profile for AI/ML recruiter searches and building a professional presence that complements your blog and portfolio.