The internet is drowning in shallow AI content. "Top 10 Python libraries for data science" has been written a thousand times. Another version adds no value to the world or your career. What the market desperately lacks is depth — genuine technical exploration that goes beyond the README and into the real-world complexities.
One deeply researched, well-written technical post per month is enough to build a reputation as a thoughtful practitioner. Here's exactly how to write one.
Shallow content competes with millions of other shallow pieces. Deep content competes with almost nothing. When someone searches "how to handle distribution shift in production ML pipelines," there might be 3-5 genuinely useful articles. If yours is one of them, it will rank, get shared, and establish your authority for years.
Deep posts also signal something important to hiring managers and collaborators: this person can think rigorously, communicate clearly, and stay focused long enough to produce something substantial. These are exactly the qualities that differentiate senior practitioners from junior ones.
The best deep posts come from real problems you've solved. Not hypothetical scenarios — actual challenges you faced, researched, and resolved. The closer the topic is to your daily work, the more authentic and detailed the writing will be.
The "I wish this existed" test: Think about the last time you searched for something technical and couldn't find a good answer. That missing article is your next post. You've already done the research — now you just need to write it down for the next person who searches.
Three reliable topic categories: (1) Comparing two approaches you've actually tested, with real data. (2) Explaining a complex concept in simpler terms than existing resources. (3) Documenting a non-obvious solution to a common problem.
Technical posts need structure more than any other content type. Your reader is likely scanning first, then reading deeply if the structure looks worth their time. Here's the format that performs:
Opening (100 words): State the problem clearly. Who has this problem? Why do existing solutions fall short? What will this post cover? No fluff, no lengthy introductions about the history of AI.
Context (200 words): Describe the specific environment or constraints. "We had 2M documents, needed sub-200ms retrieval, and couldn't use a hosted solution due to data residency requirements." This specificity is what separates valuable content from generic advice.
Approach (500-800 words): Walk through what you tried, why, and what happened. Include code snippets, configuration details, and actual metrics. This is the meat of the post — don't rush it.
Results and analysis (300 words): Show the outcome. Tables, charts, or before/after comparisons make this section scan-friendly and shareable.
Lessons and recommendations (200 words): What would you tell someone facing the same problem? What would you do differently? This is what people save and reference later.
Week 1: Choose the topic and create a rough outline. Gather your data, screenshots, and code examples. Don't write prose yet — just collect the raw material.
Week 2: Write the first draft in a single 2-3 hour session. Don't edit as you go — just get everything out. The goal is a complete but rough draft.
Week 3: Edit for clarity. Cut anything that doesn't serve the reader. Add headers, code formatting, and visuals. Have a colleague review it if possible.
Week 4: Publish and promote. Share on LinkedIn (3-4 posts across the week pointing to different sections), post in relevant communities, and submit to newsletters in your niche.
Update your posts every 6 months with new findings or corrections. Add an "Updated [date]" note at the top. This signals ongoing commitment and keeps the post relevant for search engines. A single post updated three times over 18 months will outrank newer content that hasn't been maintained.
Link your deep posts from your LinkedIn profile, GitHub README, and personal site. They become your portfolio — proof of depth that no resume bullet point can match.
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