On the Death of Amazon’s Writing Culture
How AI has turned what was once a superpower into a liability.
How AI has turned what was once a superpower into a liability.

How do you review code at the volume agents produce it? On the Claude Code team, once we had high coverage and deep reviews, we started optimizing for the minimum amount of human review time that doesn't raise reverts or incidents.

Why block flow works, why American yards lost it, and how to get it back
Our latest Homepage & Category Navigation UX benchmark reveals that the performance for up to 64% of leading US and European sites is “mediocre” to “poor”. Here are 10 UX best practices.
Dispatching, finite-capacity scheduling, MES, APC and simulation each attack a different share of the time wafers spend waiting, and all of them depend on accurate WIP and equipment-state data.

Four in five air freight quotes on cargo.one now go out without human review. Here are the six patterns that build forwarder trust in AI quoting.
We have run enough of these deployments to see the same six patterns emerge.
Deep system design curriculum — 18 modules, 200 units, 118 architecture diagrams, from-scratch algorithm builds, and curated video walkthroughs for senior engineers.

Ask a team what they shipped this year and you’ll get a list. Ask what they killed and the room goes quiet.

We integrated the OpenTelemetry Ruby logs SDK in a Rails app to export logs directly to a vendor, without running a Collector.

Click through for our favourite writing from the New York Times
Splitting a finished branch into small PRs used to be the expensive part, so we decided the boundaries before the code existed. That cost collapsed. Now the code can tell you where to cut it.

NASA invented systems engineering. Skunk Works perfected it.
In this article, we will cover three essential NumPy tricks to optimize your code: vectorization and broadcasting, in-place operations, and leveraging memory views instead of copies.
To optimize, you must write code using native universal functions (ufuncs) and broadcasting.
When working with very large arrays (e.g. millions of entries), allocating and garbage-collecting these temporary intermediate arrays creates substantial overhead.
A lot of people seem convinced that the point of AI coding is to write low-quality code as fast as possible. Spew out barely-passable slop, open massive PRs, and merge them unvetted. Ship it! But t…
A single CLAUDE.md file to improve Claude Code behavior, derived from Andrej Karpathy's observations on LLM coding pitfalls. - multica-ai/andrej-karpathy-skills

Learn how to put Large Language Model-based applications into production safely and efficiently.
In this article, we will explore five fundamental concepts that every Python developer should have in their toolkit.

Hyrum's law, Conway’s law, Zawinski's law, and 10 others.
['Programmer', 'Computer nerd', 'Unix enthusiast'];

A collection of principles and patterns that shape software systems, teams, and decisions.

Five git commands that tell you where a codebase hurts before you open a single file. Churn hotspots, bus factor, bug clusters, and crisis patterns.
The 20 most-changed files in the last year.
Every contributor ranked by commit count.

Thirteen years ago, Mike Neighbors dreamed up an idea to help others: He started writing down his mistakes.
For context, I work in a Django/React TS/React Native codebase on a 20-person team at MorphMarkethttps://www.morphmarket.com/. I have 17 years experience wit...

Safely Deploying Machine Learning Models to Production: Four Controlled Strategies (A/B, Canary, Interleaved, Shadow Testing)
Note: This bookmark, **[Safely Deploying ML Models to Production: Four Controlled Strategies](#rid:1652757915)**, is an article from MarkTechPost that outlines how to release machine learning models into production while minimizing risk. The article details four key deployment strategies: * **A/B Testing**: Randomly splits a small percentage of total traffic to the new model while the rest stays on the…

40 Google features to find exactly what you need, the alternative search engines that do things Google won't, and the reference desk framework underneath all of it.

My Claude was craving for better tooling.

After 50+ engagements, I've learned the first week isn't about reading the code. It's about reading the signals.

AI writes the code now. The skill that matters is controlling what it builds.
Every time you accept that output without checking, you’re running up a tab. Call it trust debt: the accumulated cost of all the assumptions you never audited. Trust debt doesn’t announce itself. It compounds quietly until Future You has to pay it all back at once, usually at the worst possible…

Use Claude Code to speed up data science. Master data cleaning, visualization, and model prototyping with Python, pandas, and scikit-learn. Get actionable power tips.
𝗖𝗹𝗮𝘂𝗱𝗲 𝗦𝗸𝗶𝗹𝗹𝘀 𝗶𝘀 𝗽𝗿𝗼𝗯𝗮𝗯𝗹𝘆 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝘂𝗻𝗱𝗲𝗿𝗿𝗮𝘁𝗲𝗱 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝗖𝗹𝗮𝘂𝗱𝗲 𝗵𝗮𝘀 𝗯𝘂𝗶𝗹𝘁. If you’re still writing the same prompts in Claude every day, you’re doing extra work. Skills fix that. They package your best instructions once, then you reuse them on demand. Result: less repetition, more…

Lessons learned from 14 years of engineering at Google, focusing on what truly matters beyond just writing great code.

Von Maur has made a different play than several other retailers. Is it working?

Here's how to overcome entrepreneurial doldrums, when a business is seemingly stuck in no growth or worse.

Many Amazon ad delivery issues have little to do with bids. Find out what really stops ads from running – and how to prevent it.
the problem is rarely ad copy or targeting
tightly tied to your listings, inventory, and account health.

Master the art of onboarding with proactive strategies. Discover how to quickly adapt and make an impact in your new tech role.

In this article, I'll take you through the complete LLM tech stack you should know to develop & deploy real-world LLM applications.
I’ve started writing more Python code lately (because of… AI, you know). In this post, I share the tools, libraries, configs, and other integrations I use for building production-grade Python applications following a frontend-backend architecture.

A CS leader who has built the function from the ground up shares where founders should start — from how to hire to the most important metrics to track.

We analyze the home pages of 10 marketplaces to spot common merchandising traits.

Learn to leverage Python patterns like a professional.

Superhuman became known for its unique approach to onboarding: a highly-personalized, white-glove, human-led experience. Superhuman's Gaurav Vohra, startup advisor and growth leader, gives us a detailed look at how he built and scaled the program.
Google’s Engineering Practices documentation
What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers? - humanlayer/12-factor-agents
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Levels founder & CEO Sam Corcos shares his detailed time-tracking data across 5 years of startup growth

People have different styles when they cook. Some people are neat and tidy. After taking out and using an ingredient they don’t need anymore, they return it to the fridge or cabinet. After chopping up vegetables, they clean up the scraps left behind. When a dish has been…
A short introduction to Interval Tree Clocks, a causality tracking mechanism, based on a lightening talk I gave at dotScale 2017.

Goodhart's Law is useless. It tells you about a phenomenon, but it doesn't tell you how to solve it. We look at how organisations actually prevent Goodhart's Law, and illustrate this with Amazon's Weekly Business Review as an example.