Most technical blog posts are landfill. They’re written by people who either forgot what it’s like to learn something hard, or worse, by people who never actually built anything with the technology they’re explaining. The result is a swamp of hand-wavy abstractions, recycled tutorials, and sentences that sound smart but collapse under the weight of a single Why?

I’m Kai Lindström. I’ve been writing technical content for over a decade—first as a developer documenting my own projects, then as an engineer who got tired of reading garbage and decided to fix the signal-to-noise ratio. This isn’t a style guide. It’s a set of principles, backed by concrete examples, for writing posts that respect the reader’s intelligence and actually transfer understanding.

Engineer writing on a whiteboard full of diagrams and code snippets

Start with the Problem, Not the Tool

Engineers don’t wake up thinking, “I really need to learn about Redis Streams today.” They wake up with a queue that’s dropping messages under load, or a database that’s buckling, or a notification system that’s turning into a distributed mess. The technology is the answer; the problem is the entry point.

Bad posts introduce the tool first: “Redis Streams is a powerful data structure for log-like data.” This is technically accurate and completely useless. It doesn’t tell me why I should care. A better opening:

We had a Celery task queue that was silently losing 2% of jobs during peak traffic. After ruling out memory, network, and worker count, the culprit was the broker itself: Redis lists aren’t designed for consumer groups with at-least-once semantics across multiple workers. Here’s how we migrated to Redis Streams and eliminated message loss entirely.

Now I’m invested. I’ve either experienced that pain, or I’m about to build something that could hit it. The technology serves the story, not the other way around.

Code Examples Are Arguments, Not Decorations

If your code snippet doesn’t prove a point, cut it. Every block of code in your post should answer a specific question or demonstrate a non-obvious behavior. The worst posts treat code like wallpaper—a pretty syntax-highlighted rectangle that says “look, I know how to type.”

Compare these two approaches. First, the wallpaper:

// Bad: this shows syntax, not insight
const result = await fetch('/api/users');
const users = await result.json();
console.log(users);

I didn’t need to see that. You just taught me how to copy-paste from MDN. Here’s a code example that earns its space:

// This demonstrates a specific footgun: fetch doesn't reject on 4xx
async function getUser(id) {
  const res = await fetch(`/api/users/${id}`);
  if (!res.ok) {
    // Without this check, you're silently parsing error HTML as JSON
    throw new Error(`HTTP ${res.status}: ${await res.text()}`);
  }
  return res.json();
}

That snippet makes an argument: “You’re probably handling fetch errors wrong. Here’s exactly where it breaks and how to fix it.” It’s not decorative. It’s the payload.

Close-up of code on a monitor with a debugger breakpoint highlighted

Show Failure Before Success

Every engineer has been burned by a library that works perfectly in the happy-path README example and then explodes in production. Your post should anticipate that bitterness. If you’re explaining a pattern, start with the naive implementation and break it deliberately. Show the stack trace. Show the corrupted state. Then show the fix.

This isn’t pessimism—it’s pedagogy. The brain remembers a near-miss better than a smooth landing. I once wrote about connection pooling in Go by starting with a program that leaked 200 file descriptors in under a minute. The post opened with lsof output and a too many open files panic. That post got shared more than any sanitized “best practices” article I’ve ever written.

Kill the Jargon, Keep the Precision

“We implemented a horizontally scalable, event-driven microservices architecture using cloud-native patterns.” This sentence tells me nothing. It’s a collection of buzzwords that could describe a hundred different systems. Engineers read it and immediately translate it to: “I don’t know how to explain what we built.”

Precision means using specific terms when they’re the right terms, not when they sound impressive. If you used Kafka, say Kafka. If you used a mutex, say mutex. But if you’re tempted to write “utilize,” stop and ask: what concrete thing are you actually doing? “We use” is almost always better. “We use Kafka to buffer events before writing to S3” tells me everything. “We utilize a distributed streaming platform to ensure reliable data ingestion” tells me nothing.

The “Explain It to a Colleague” Test

Before publishing, read your post out loud. If you wouldn’t say it to a respected coworker over coffee, rewrite it. This kills two problems at once: it eliminates bloated corporate-speak, and it catches sentences that are technically correct but emotionally dishonest. “We chose Rust for its memory safety guarantees” might be true, but “We chose Rust because our C++ codebase was a use-after-free nightmare and we were tired of 3 AM pages” is true and credible.

Structure for Skimmers and Deep Divers

Engineers don’t read linearly. They scan for relevance, then dive deep when they hit something valuable. Your post must serve both modes. This means:

  • Headings that tell a story: Not “Implementation Details” but “Why the Naive Retry Loop Doubles Your Latency.”
  • Bold lead sentences: The first sentence of each paragraph should carry the thesis of that paragraph. Someone skimming just the bold text should get a rough outline of the argument.
  • Code annotations, not just comments: Use comments to explain why, not what. The code already says what.

Here’s a simple litmus test: open your post, scroll through it at high speed, reading only headings and bold text. Does a coherent argument emerge? If not, restructure until it does.

Person reading a technical article on a tablet, highlighting sections with a stylus

Cite Sources, Not Authorities

Linking to a tweet from a famous developer isn’t evidence. Linking to a GitHub issue where the bug was discussed, a commit that fixed it, or a spec that defines the behavior—that’s evidence. Engineers trust primary sources. Secondary sources (“as Martin Fowler said”) are fine for context, but they don’t prove your claim.

If you’re making a performance claim, show a benchmark and link to the benchmark code. If you’re describing a language quirk, link to the relevant section of the spec or the compiler source. This isn’t pedantry—it’s the difference between a blog post and a rumor. I’ve had posts challenged in comments, and being able to reply with a link to the exact line of CPython that explains the behavior ends the argument cleanly.

Edit Like You’re Debugging

Writing and debugging are the same mental muscle. In both, you’re hunting for mismatches between intent and reality. When you edit, ask:

  • Does this paragraph deliver on the promise of its heading? Or did I wander into a tangent about my personal setup?
  • Is every claim backed? If I say “this pattern scales poorly,” did I show the scaling numbers or at least describe the bottleneck?
  • Can I make this sentence 30% shorter without losing meaning? Usually, yes.

I delete roughly a third of what I write in the first draft. Not because it’s bad—some of it is decent—but because it doesn’t pull its weight. A technical post is not a diary. It’s a tool for transferring understanding. If a paragraph doesn’t sharpen that tool, it’s dulling it.

Respect the Reader’s Time

The single best compliment I ever got on a post was: “I fixed a production bug in 20 minutes because of your article. I’d been stuck for two days.” That reader didn’t care about my narrative arc or my clever metaphors. They cared that I compressed two days of suffering into 20 minutes of clarity.

Every sentence you write should be pulling a reader closer to that outcome. If you’re adding words to hit a word count, or because you think a post “needs” an introduction, you’re wasting someone’s time. Engineers have a finely tuned bullshit detector. It activates the moment they sense filler. Don’t trigger it.

FAQ

How long should a technical blog post be?

Exactly as long as it needs to be, and no longer. A post explaining a single-line shell trick might be 300 words. A deep dive into database isolation levels might be 3,000. The length is a side effect of the depth, not a target. If you find yourself padding to reach some arbitrary number, stop. Publish it short. Your readers will thank you.

Should I write for beginners or experienced engineers?

Pick one and commit. The worst posts try to serve both and end up serving neither. Beginners get lost in unexplained jargon; experts get annoyed by hand-holding. If you must address mixed audiences, use clearly labeled sections or call-out boxes so readers can skip what they already know. But ideally, write the post you wish you’d had when you were learning this topic. That’s usually a specific audience.

What if I’m not an expert on the topic?

That’s fine—as long as you’re honest about it. Frame the post as a learning journey or an investigation, not a tutorial. “I tried to build X with Y, here’s what broke” is a legitimate and often highly valuable format. What’s not legitimate is pretending you’ve been using a tool in production for years when you just finished the quickstart. Engineers can tell the difference.

How do I handle corrections or updates?

Add a dated note at the top of the post explaining what changed and why. Don’t silently edit the article and hope nobody notices—someone will have bookmarked or referenced the original version. A transparent changelog builds trust. If a reader points out an error in the comments, thank them and fix it promptly. Nothing erodes credibility faster than leaving a factual error visible with no acknowledgment.