I frequently see debates about whether it's better to be a cog at a giant semi-monopoly, or to take investment money in the hopes of one day growing to be head cog at a giant semi-monopoly.

Role models matter. So I made a list of small companies that I admire. Neither giants nor startups - just people making a living writing software on their own terms.

Le cabinet d’Élisabeth Borne a décidé de repousser à la fin de l’année la publication d’une étude inédite sur les violences et cyberviolences de genre « chez les jeunes de 11 à 18 ans », qui touchent pas moins de « 43 % » des élèves. Mediapart en publie les principales conclusions.

How Duolingo works: 14 years of big learnings in one little handbook.

A culture like Duolingo’s doesn’t come from some corporate playbook - it had to be built from scratch.

We started with a few dozen nerds above a Pittsburgh sports bar, fueled by a wild dream to make the world’s best education accessible to everyone.

Today, we're a ~$16B* company with 800+ employees, but we've held on to what makes us special: a truly quirky culture, endless experiments, an obsession with thinking long-term, and our slightly unhinged sense of humor.

After 14 years of figuring out what works (and what doesn't), we decided to write it all down. The Duolingo Handbook captures our core principles through stories of wins, failures, and plenty of surprises along the way. It's not a blueprint – it's a look inside a culture that's helped us build something unique in tech.

What's in our handbook?

At the center of this book are 5 principles. These aren’t aspirational—they’re lessons we've learned through experience.

• Take the Long View: If it helps in the short-term but hurts Duolingo in the long-term, it’s not right.
• Raise the Bar: To change how the world learns, we must do world-class work.
• Ship It!: For a good idea to become reality, we need to push experiments with a sense of urgency. So go, go, go!
• Show Don’t Tell: We use clear, concise communication that is grounded in data and real impact.
• Make It Fun: We bring a sense of humor, joy, and imagination to everything we do.

Plus, we unpack The Green Machine, our simple approach to building things: gather excellent people, let them experiment, and double down on what works.

Want the full scoop?

The Duolingo Handbook is packed with insights, stories, and principles that you can use for your own work or team. Whether you’re a founder, creative, aspiring CEO, or just someone who loves learning about how companies work – this handbook is for you.

We are destroying software by no longer taking complexity into account when adding features or optimizing some dimension.

We are destroying software with complex build systems.

We are destroying software with an absurd chain of dependencies, making everything bloated and fragile.

We are destroying software telling new programmers: “Don’t reinvent the wheel!”. But, reinventing the wheel is how you learn how things work, and is the first step to make new, different wheels.

We are destroying software by no longer caring about backward APIs compatibility.

We are destroying software pushing for rewrites of things that work.

We are destroying software by jumping on every new language, paradigm, and framework.

We are destroying software by always underestimating how hard it is to work with existing complex libraries VS creating our stuff.

We are destroying software by always thinking that the de-facto standard for XYZ is better than what we can do, tailored specifically for our use case.

We are destroying software claiming that code comments are useless.

We are destroying software mistaking it for a purely engineering discipline.

We are destroying software by making systems that no longer scale down: simple things should be simple to accomplish, in any system.

We are destroying software trying to produce code as fast as possible, not as well designed as possible.

We are destroying software, and what will be left will no longer give us the joy of hacking.

In computer-based language recognition, ANTLR (pronounced antler), or ANother Tool for Language Recognition, is a parser generator that uses a LL(*) algorithm for parsing. ANTLR is the successor to the Purdue Compiler Construction Tool Set (PCCTS), first developed in 1989, and is under active development. Its maintainer is Professor Terence Parr of the University of San Francisco.[citation needed]

L'Internet était une promesse utopique, il est devenu notre cauchemar contemporain, s'alarme le pionnier du Web français Bruno Walther. Hypnotisés par nos écrans, nous ne cherchons plus à changer le monde mais à le fuir. Et si l'on se reconnectait au réel ?

None of what I write in this newsletter is about sowing doubt or "hating," but a sober evaluation of where we are today and where we may end up on the current path. I believe that the artificial intelligence boom — which would be better described as a generative AI boom — is (as I've said before) unsustainable, and will ultimately collapse. I also fear that said collapse could be ruinous to big tech, deeply damaging to the startup ecosystem, and will further sour public support for the tech industry.

“Beware: Silicon Valley’s cultists want to turn you into a disruptive deviant.” “Tech’s cult of the founder bounces back.” “Silicon Valley’s Strange, Apocalyptic Cults.” “How the cult of personality and tech-bro culture is killing technology.” “Company or cult?” “Is your corporate culture cultish?” “The Cult of Company Culture Is Back. But Do Tech Workers Even Want Perks Anymore?” “10 tech gadgets with a cult following on Amazon—and why they’re worth it.” “13 steps to developing a cult-like company culture.”

The evolution of software development over the past decade has been very frustrating. Little of it seems to makes sense, even to those of us who are right in the middle of it.

We usually only notice trends and popular frameworks and libraries after they’ve exploded in popularity. By that time they’re often so far removed from their original context that their initial technical merit is a hard-to-distinguish quiet signal in the overwhelming noise of hype, grift, and false promises.

If you do take the time to trace these trends back to their early beginnings, you often discover that, no, it wasn’t just somebody’s con or VC startup pitch that happened to get traction. Usually there was something there at the beginning, however slight that “something” might have been.

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