Inside Automattic’s remote hiring process

The CEO of Automattic on Holding “Auditions” to Build a Strong Team

Reprint: R1404A Mullenweg founded Automattic, the company behind WordPress, in 2005, and began hiring in the traditional way: using résumé screening, reference checks, and interviews. He focused on the experience candidates had and paid special attention to what other start-ups they’d worked for. He and his colleagues invested a lot of energy in the process and believed that they were being as rigorous as they could. But when some hires didn’t work out, they began to examine their approach in light of Automattic’s unconventional philosophy: Work where you want and when you want; you’ll be measured by outputs, not by time spent at a desk in an office. They realized that being well-spoken or charming in an interview often had little bearing on how a candidate would perform the job in question. So they introduced tryouts. After an initial screening, promising candidates are required to work with the company for three to eight weeks (with pay), performing real tasks that are closely related to the jobs they’re applying for, and working alongside the people who will be their colleagues if they’re hired. They can size up the company as it evaluates and provides feedback to them, benefiting all concerned.

We Hire the Best, Just Like Everyone Else

One of the most common pieces of advice you’ll get as a startup is this: Only hire the best. The quality of the people that work at your company will be one of the biggest factors in your success – or failure. I’ve heard this advice over and over

The One Method I've Used to Eliminate Bad Tech Hires - Mattermark

Let’s be real. Interviews are a terrible way to hire tech candidates. Here’s why paying candidates to solve problems works.

Interviews with Failed & Successful Startup Founders

+200 interviews with the brains behind shut down and active startups. Learn from their wins and their mistakes and become a better founder.

Making your first startup ops hire – what founders should look for - PostHog

Most founders I talk to approach their first ops hire in a totally sensible but wrong way. Their typical approach is to look at the long list of admin…

Why Founders Market Their Products Too Late

Founders predictably underestimate the importance of hiring marketers. Here’s why many startups don’t get round to marketing their product…

Startups Target the Tricky Task of Making AI Seem More Human

Simulated human behavior companies raise millions

Companies That Tried to Save Money With AI Are Now Spending a Fortune Hiring People to Fix Its Mistakes

Companies that rushed to replace human labor with AI are now shelling out to have IRL workers to fix the technology's screwups.

How to Hire

Most companies approach hiring with faulty assumptions and poor practices. They believe talent is fixed rather than contextual. They fail to create real partnerships between internal recruiters and hiring managers. And they rely too much on salary surveys and rigid compensation formulas. The author shares what she learned about making and keeping great hires during her 14 years as the chief talent officer at Netflix. The process requires probing beneath the surface of people and their résumés; engaging managers in every aspect of hiring; ensuring that recruiters deeply understand the business and are not viewed as support staff; adopting a mindset in which you’re always recruiting; and coming up with compensation that suits the performance you need and the future you aspire to. These lessons may be especially relevant to fast-growing tech-based firms, whose rapid innovation means a continual need for new talent. But organizations of all types can benefit from taking a fresh look at their hiring and compensation practices.

How Bad Entrepreneurs Reveal Themselves in the First 5 Seconds of a Pitch

A few simple words is all it takes to figure out a founder’s odds of startup success.

For the love of God, stop calling your AI a co-worker | TechCrunch

A growing number of startups are anthropomorphizing AI to build trust fast -- and soften its threat to human jobs.

The Stress of Remote Working | HackerNoon

In software engineering, remote working makes a lot of sense since, most of the time, you only need a computer and an internet connection to perform your duties. So there are fewer reasons to force people to sit in a predefined office everyday. Consequently, it has become an important feature of a lot of IT jobs, even in Belgium, while it is definitely not the most forward looking job market. That said, most of the time, the remote working offer only apply to a part of the week (1 or 2 days a week seems to be the most frequent proposition here). Nevertheless, it definitely is getting a hold in most companies.

nxthompson on Twitter / X

AI is mucking up the job market. Everyone uses AI to write similar cover letters; resumes are all perfect. People apply for zillions of jobs and cheat in Zoom calls. Hiring is now basically AI talking to AI while humans wait to hear nothing back. https://t.co/kyqOotxeAl— nxthompson (@nxthompson) June 2, 2026

How to Write Good Job Descriptions that Attract Great Candidates

Here at Trakstar, we occasionally have clients ask us why they’re not getting the volume of applicants they’d anticipated for a certain opening. There can be any number of reasons–maybe the opening isn’t on certain job boards and should be; maybe it’s a hot job in an ultra-competitive market (say, iOS developer in San Francisco). But sometimes it comes down to the job description.

CEO-Bench: Can Agents Play the Long Game?

Language model agents are becoming proficient executors at isolated, short-horizon tasks such as software engineering and customer service. Yet real-world challenges require a combination of sophisticated skills that remain largely untested in agents: (1) navigating long horizons amid uncertainty; (2) acquiring information in noisy environments; (3) adapting to a changing world; (4) orchestrating multiple moving parts toward a coherent goal. We introduce CEO-Bench, which evaluates these capabilities together by simulating a representative real-world task: operating a startup for 500 days. An agent manages pricing, marketing, budgeting, and many other aspects of a fictional company through a programmable Python interface, operating in the same environment and facing the same challenges as a human CEO. Success demands analyzing noisy, interconnected business databases, translating signals into sound strategy, and coordinating many decisions with programming. The strongest agents write sophisticated code that simulates customer cohorts to forecast future cash and mines negotiation history to uncover hidden customer preferences. Even so, most state-of-the-art models struggle in this environment. Only Claude Opus 4.8 and GPT-5.5 finish above the $1M starting balance, and neither consistently turns a profit. CEO-Bench takes a first step toward measuring the intelligence required to drive sustained, adaptive progress over time.