Does it scale? Who cares! · Jacques Mattheij

The Value of Getting Closer to the Work

Scaling The core problem of scaling a team is that not everyone can know everything. Scaling is the work of systematizing that – so that things are understandable, knowable. It’s turnin…

Andreas Kling on Twitter / X

Approximate browser engine team sizes:- Chromium: 1000- WebKit: 100- Ladybird: 10I don't have a point to make, just fascinated by the massive difference in scale.— Andreas Kling (@awesomekling) January 9, 2025

Absolute scale corrupts absolutely

The Internet has gotten too big. Growing up, I, like many computery people of my generation, was an idealist. I believed that better, faste...

Donald MacKenzie · AI’s Scale

AI’s scale doesn’t matter just to specialists. The rest of us are being taken on a ride along the logarithmic curve...

How To Scale a Development Team

As hackers, we’re familiar with the need to scale web servers, databases, and other software systems. An equally important challenge in a growing business is scaling your development team.

Scaling trust on the web

The Task Force for a Trustworthy Future Web's report on gaps and opportunities for how the next generation of online spaces will be built.

alphaXiv on Twitter / X

"Small-Scale Experiments: Are We There Yet?"Scaling laws show up even in tiny 4M parameter models, but only when their hyperparameters are tuned well enough.This Meta paper found that small models are much more hyperparameter-sensitive. As scale increases, the hyperparameter… pic.twitter.com/vQfFS1TpMA— alphaXiv (@askalphaxiv) August 15, 2026

The web can be weaponised – and we can't count on big tech to stop it | Tim Berners-Lee

It’s dangerous having a handful of companies control how ideas and opinions are shared. A regulator may be needed

Neural scaling law

In machine learning, a neural scaling law is an empirical scaling law that describes how neural network performance changes as key factors are scaled up or down. These factors typically include the number of parameters, training dataset size, and training cost. Some models also exhibit performance gains by scaling inference through increased test-time compute (TTC), extending neural scaling laws beyond training to the deployment phase.

Scaling Engineering Teams: Lessons from Google, Facebook, and Netflix

After spending over a decade in engineering leadership roles at some of the world’s most chaotic innovation factories—Google, Facebook, and Netflix—I’ve learned one universal truth: scaling e…

How to Scale a System from 0 to 10 million+ Users

Scaling is a complex topic, but after working at big tech on services handling millions of requests and scaling my own startup (AlgoMaster.io) from scratch, I’ve realized that most systems evolve through a surprisingly similar set of stages as they grow.

An Update On How I Use AI To Help With Techdirt (It’s Still Not Writing Articles)

Over in our Insider Chat, someone recently pointed out a tiered “REAL Rating” five-tier scale designed to replace the fairly blunt and misleading question of “did you use AI on th…

An Update On How I Use AI To Help With Techdirt (It’s Still Not Writing Articles)

Over in our Insider Chat, someone recently pointed out a tiered “REAL Rating” five-tier scale designed to replace the fairly blunt and misleading question of “did you use AI on th…

The Metric Is Not The Mission: The Finished Internet

The Metric Is Not the Mission is a ten-part examination of how Big Tech moved from building and expanding the open internet to increasingly shaping it around its own metrics, incentives and as…