Rung 8: Original Technical Writing

Month 13 | Phase 6: Writing | Ongoing from Month 4+ Estimated effort: 8–12 hours per post × 3–5 posts = 25–60 hours total, spread across the roadmap

Technical writing is not a soft skill addendum to this portfolio — it is the proof layer for the most difficult claim in the pitch sentence: “explain it to anyone.” A practitioner who can build complex systems but cannot explain them is an incomplete engineer. A practitioner who can both build and explain is rare and valuable. This rung also functions as a force multiplier: your blog posts attract opportunities before you apply for them, because 70–80% of roles are filled via networks before they’re publicly posted, and public technical writing is network-building at scale.

The Standard: What “Technically Rigorous” Means

Technical writing for this portfolio is not:

  • A tutorial that walks through steps someone else designed

  • A summary of a paper you read

  • A list of tools with usage examples

  • A “getting started with X” guide that duplicates the official documentation

Technical writing for this portfolio IS:

  • A piece that advances a reader’s understanding in a specific, non-obvious way

  • A benchmark, experiment, or comparison you ran yourself with your data

  • An explanation of a mechanism that is currently explained poorly or incorrectly in existing resources

  • A synthesis of insights from multiple sources that produces a new perspective

  • A post-mortem or analysis that documents real decisions, real failures, and real lessons

The test is: could this post have been written without doing the work? If yes, it doesn’t count.

The 3-Post Minimum (5-Post Target)

Write at least 3, target 5 posts across the 13-month roadmap. The timing below is recommended, not fixed — write when you have genuine findings to share:

Recommended Timing

Post Type

Sourced From

Month 4

Competition post-mortem

Rung 2

Month 8

Transformer experiment findings

Rung 4

Month 10

LLM fine-tuning: what nobody tells you

Rung 5

Month 12

Production ML failure modes I found

Rung 6

Month 13

Paper reproduction: what the paper didn’t say

Rung 7

These posts are not separate work — they are the writing layer over work you are already doing for other rungs. The discipline is capturing the insights while you have them, not reconstructing them months later.

Post Architecture: The Minimum Structure for a Rigorous Technical Post

Every post must contain these structural elements, in roughly this order:

1. Problem / Motivation (150–300 words)

What question or problem prompted this post? Why does it matter to a practitioner? Do not start with “In this post, I will…” — start with the problem itself. The reader must understand in the first paragraph why they should keep reading.

2. Setup / Methodology (200–400 words)

What did you do? What dataset, what code, what experiment? This section must be specific enough that a reader could reproduce your work. Link to the code repository. If you ran an experiment, state the exact conditions.

3. Findings / Results (the core, no word limit constraint)

What did you find? This is where the post lives. Use:

  • Tables for quantitative comparisons

  • Figures (your own figures, not screenshots of other people’s results)

  • Specific numbers, not “it performed better”

  • At least one finding that is non-obvious or contradicts common assumption

4. Interpretation (200–400 words)

What do the findings mean? Why did you get these results? What are the limitations of your analysis? What would you need to do to be more confident in your conclusions? This section is where analytical thinking is most visible.

5. Conclusion / What’s Next (100–200 words)

One concrete takeaway for the reader. One thing you would do differently. One open question that you couldn’t answer and why.

Optional but High-Signal: “What Didn’t Work”

A section documenting approaches you tried that failed, with hypotheses for why. This is the single highest-signal addition you can make to a technical post — it proves genuine experimentation and intellectual honesty.

Platform Strategy

Primary recommendation: Substack

  • Free, no algorithmic barrier to discovery

  • Email subscription builds a genuine audience (a subscribed reader opted in; an algorithm-fed pageview did not)

  • Clean reading experience, good code block support

  • Your subscribers are an asset you own, unlike platform-dependent follower counts

Secondary recommendation: Medium / Towards Data Science

  • TDS has 700K+ subscribers and active ML readership — discoverability is real

  • Getting published on TDS requires editorial review, which adds credibility

  • Cross-post your Substack to Medium if accepted into TDS — don’t choose one over the other

Personal site (optional but worth it):

  • A personal domain (e.g., yourname.dev) with a simple static site (Astro, Hugo, or Next.js) gives you permanent ownership of your writing independent of platform survival

  • Not required, but having a personal domain linked from your GitHub profile is a minor positive signal

What not to do:

  • Do not publish only on company/team blogs where you don’t own the content

  • Do not publish on LinkedIn articles as your primary platform — formatting is poor and the audience is non-technical

  • Do not start a YouTube channel instead of writing — video takes 10× longer to produce per unit of insight delivered, and text is indexable

Audience and Engagement: What Actually Counts

Vanity metrics (total views, total impressions) are not the engagement signal. The signal is:

Meaningful engagement indicators:

  • ≥ 3 substantive comments from practitioners (people who are doing the work themselves)

  • Being linked to by another technical blog or newsletter

  • Being asked follow-up questions by readers that require substantive answers

  • Receiving peer review feedback from someone in the field

  • Your post appearing in a curated ML newsletter (The Batch, Import AI, ML News)

What you can do to generate meaningful engagement:

  • Share in specific communities: ML Twitter/X, r/MachineLearning, relevant Discord servers (Eleuther, HuggingFace), LinkedIn with a technical framing (not “I’m excited to share”)

  • Send to 3–5 practitioners you respect and ask for feedback (not for likes)

  • Reference other practitioners’ work and notify them — genuine intellectual engagement creates genuine response

  • Post consistently, not just when you have a “big” piece — smaller observations published regularly build audience faster than infrequent long-form posts

Audience Growth From Zero

The 13-month timeline means you start with zero readers. That is not a problem — it is a documented starting state that will look impressive in retrospect when you have 200+ subscribers at Month 13.

The growth strategy is simple and slow and works:

  1. Write 1 post per month (you have the material from your rungs)

  2. Share each post in 3–5 specific communities where the content is relevant

  3. Respond to every comment within 24 hours

  4. Link each post from your GitHub README for the relevant rung

  5. Mention your writing in your LinkedIn profile with links

Do not optimize for virality. A post that gets 15 readers who are working ML practitioners and leave substantive comments is worth more to your career than a post that gets 10,000 views from a Reddit upvote cycle and zero practitioners.

Acceptance Criteria

  • ≥ 3 posts published (5 target), each on a public platform with a stable URL

  • Each post meets the minimum structure: problem framing, methodology, findings with quantitative evidence, interpretation, conclusion

  • At least one post contains original data or experiments — something you ran, not something you summarized

  • Evidence of practitioner engagement: at minimum, document ≥ 3 substantive comments, external links, or newsletter citations across your posts

  • All posts are linked from your GitHub profile README and from the relevant rung’s GitHub repository

  • At least one post documents a failure or negative finding — not just success stories

  • A writing/ section exists on your portfolio landing page or GitHub profile README listing all posts with titles, publication dates, and platform links

Engagement baseline to meet by Month 13:

  • Total subscriber count: ≥ 50 (a small but real audience)

  • Total substantive interactions (comments, replies, DMs from practitioners): ≥ 15

  • At least one post shared outside your own network by someone else

These thresholds are modest by design. The goal is establishing the habit and the public record, not building a media company in 13 months.

Writing Quality Checklist (Per Post)

Before publishing, check all of these:

  • Does the opening paragraph make the reader want to continue without explaining what the post will cover?

  • Is every claim supported by either: (a) your own data, (b) cited source, or (c) explicit acknowledgment that it is your opinion?

  • Are all figures your own? Is the figure source labeled?

  • Is the code in code blocks with syntax highlighting?

  • Are all specific numbers written out (e.g., “0.847 F1” not “high F1”)?

  • Is the post free of filler phrases: “In today’s rapidly evolving landscape,” “it is important to note that,” “in conclusion”?

  • Have you had at least one other person read it before publishing? (Even a non-ML friend reading for clarity is valuable)

  • Does the post title describe what the reader will learn, not just what it’s about? (“Why Your RAG System Fails on Long Documents” > “Notes on RAG”)

What Weakens This Rung

  • Tutorial posts: “How to fine-tune BERT in 10 lines” with no original findings is content marketing, not technical writing. The test: could the post have been written without doing the work yourself? If yes, it doesn’t qualify.

  • Paper summaries without original contribution: summarizing a paper is useful practice but does not constitute original technical writing. Add your own experiment, your own comparison, or your own critique.

  • Vanity metrics as engagement evidence: “my post got 5,000 views on Medium” with zero substantive comments means 5,000 people scrolled past it. Views are not engagement.

  • Inconsistent publishing: one excellent post followed by 4 months of silence is not a writing practice. The signal is sustained output, not a single artifact.

  • Private posts “available on request”: technical writing must be public, indexed by search engines, linkable, and findable by someone who doesn’t know you exist.

  • No quantitative evidence in any post: if none of your posts contain a table, a figure from your own analysis, or a specific number from an experiment you ran, the “technically rigorous” standard is not met.

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