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Ask HN: What are the best engineering blogs with real-world depth?

474 points by nishilpatel ↗ HN
I’m looking for examples of high-quality engineering blog posts—especially from tech company blogs, that go beyond surface-level explanations.

Specifically interested in posts that: 1. Explain technical concepts clearly and concisely 2. Show real implementation details, trade-offs, and failures 3. Are well-structured and readable 4. Tie engineering decisions back to business or product outcomes

Any standout blogs, posts, or platforms you regularly learn from?

74 comments

[ 2.3 ms ] story [ 82.1 ms ] thread
yrand
Encountered one specific example about a month ago here on HackerNews - All about automotive lidar. https://news.ycombinator.com/item?id=46110395

Blog posts where I find quality really shows are usually about something I know next to nothing about how it works. A badly written article usually either goes really shallow or skips some facts when going into depth and requires catchup elsewhere to actually understand it. The lidar article from Main Street Autonomy goes beyond basics and explained everything from the ground up in such a connected way that it was a real pleasure reading it.

qznc
Sounds like you look for an intersection of academic papers (1.), tech blogs (2.), text books (3.), and confidential business strategies (4.)? A very high ambition.
nchmy
You're probably looking for something that is more focused on specific software decisions/implementations, but https://infrequently.org is the best web development blog out there.

It's not "technical" so much as it just educates you on how to be a good web developer/run a team. There's zero fluff and considerable detail (footnotes are practically blog posts themselves).

gethly
There are no such blogs. Usually companies, or individuals, will write these after they implement some feature into their products. Which makes them inherently little pieces of information scattered all over the internet and there is no one blog that is just about this.
nishilpatel
That’s true. This kind of writing usually shows up as post-implementation retrospectives, so it’s inherently fragmented.

I’m trying to surface and study those scattered examples—especially the ones that explain why decisions were made, not just what was built.

pella
> especially from tech company blogs,

https://engineering.fb.com/

https://netflixtechblog.com/

https://stripe.com/blog/engineering

https://eng.uber.com

https://engineering.linkedin.com/

https://engineering.atspotify.com/

https://tailscale.com/blog

https://careersatdoordash.com/engineering-blog/

https://dropbox.tech/

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Aggregators:( https://engineering.fyi/ ; https://diff.blog/ )

+ https://hn.algolia.com/?query=engineering%20blog

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create a public engineering-blog SKILL.md. ( ~ collect the writing patterns that work on HN )

javcasas
> https://engineering.fyi/

Ugh. That looks like AI this, LLM that, Agent this.

Where are the databases, the distributed systems, where is the software verification?

rldjbpin
i wish the aggregators supported rss feeds.
mitjam
Maybe it's just because I'm LLMing a bit too much, recently, but this question sounds to me like a prompt.
ludicity
I'm a huge fan of https://eblog.fly.dev/index.html. The author, Efron, very graciously advises me on a lot of little things around my engineering practice, and I've learned a huge amount about weird holes in my practice from industry dysfunction in a very short period of time from him.
Joel_Mckay
These should be read at least once in your life if interested in building industrial grade electrical, mechanical, and or software.

1. https://nepp.nasa.gov/whisker/

2. https://standards.nasa.gov/standard/NASA/NASA-STD-87394

3. https://standards.nasa.gov/NASA-Technical-Standards

4. https://sma.nasa.gov/sma-disciplines/workmanship

5. https://www.stroustrup.com/JSF-AV-rules.pdf

6. https://en.wikipedia.org/wiki/The_Power_of_10:_Rules_for_Dev...

7. https://www.nist.gov/pml/owm/laboratory-metrology/metrology-...

8. https://www.mitutoyo.com/training-education/

9. "Memoirs of extraordinary popular delusions and the madness of crowds" (Charles Mackay, 1852, https://www.gutenberg.org/files/24518/24518-h/24518-h.htm )

The artifacts are usually beautiful from good Workmanship Standards, Design For Manufacturability, and systematic Metrology. Dragging us all into the future one project at a time.

Note that training an ML model with such data would be pointless, as statistical saliency forms a paradox with consumer product design compromises. Note, there are _always_ tradeoffs in every problem domain.

'What it actually means to be "AI Generated"' ( https://www.youtube.com/watch?v=ERiXDhLHxmo )

https://www.youtube.com/watch?v=iXbzktx1KfU

Have a nice day, and note >52% of the web is LLM slop now. YMMV =3