Anand Gopal

First Round [ @firstround ] asked founders behind Mercury, Gusto, Stedi and others for their best hiring takes. The six worth reading:

Hello, world. Meet our new office. Indiranagar, Bangalore.

"Underperformers want ease. Midperformers want credit. Winners want pressure."

HackerRank AI Add-on x.com/i/broadcasts/1…

Claude Sonnet 4.5 is now available in our AI-Assisted IDE for tests and interviews. Assess candidates in an environment that mirrors their actual workflow.

Just wrapped up an AI-assisted interview for a Frontend role on @HackerRank. For everyone who keeps saying these don’t test problem-solving skills and that only DSA rounds matter - F u all. - 1 / x

HackerRank’s AI Circle starts soon at our Bangalore office 🙌🏼 Live updates 🧵

This quote from Steve Jobs lives rent free in my head...

Which AI model is the best for coding? We put it to the test. 👇🏼 HackerRank ASTRA pushes AI beyond single prompts, challenging them with real multi-file projects and complex software tasks. Checkout the full leaderboard and how we ranked them here: hackerrank.com/ai/astra

Ripple animation when toggling between light and dark modes in @hackerrank → designspells.com/spells/ripple-…

Faced with the challenge of keeping their engineers up-to-date on the latest technologies, Deliveroo turned to HackerRank SkillUp to bridge the skills gap. Hear from Adam Coleman, Senior Recruiting Manager, as he shares how this initiative transformed their engineering team. Read more : bit.ly/3AZeATA

we've officially reached AGI

Earlier this week on Spaces, someone asked me about how poker has informed my view of business risk. In short, profoundly. Poker is a fundamentally defensive game when played at an elite level. A defensive game doesn’t mean you can’t generate huge profits. In fact, poker can yield enormous profits but the way it happens is unintuitive to most. Maximum profits in poker, and other defensive games for that matter, occur when your error rate is less than your opponent’s error rate. So their errors - your errors = your profit. If you minimize your errors, you maximize your potential profit. This simple formula forces you to learn that a lot of the time, the biggest enemy of your success is you. By managing yourself in a predictable, reliable way, you give yourself time for your opponent to self-own themselves. This is true in poker, but it is even more true in business. As an example, suppose you have an R&D budget and you’re trying to build a product. Once you have some initial product market fit, the most important thing to do is to allocate your remaining resources in a thoughtful way. You should have many small bets that extend the product area. If any one of these fail, it won’t be life-threatening and you will have learned something that will reduce your future error rate. These small bets can then ladder into a few medium-sized bets which ultimately lead to a few large bets. In such a process, you’ve not only taken many bets, of various sizes, you’ve also done this over a long quantum of time. In that same time, a less organized competitor will eventually do something wrong/stupid/both. Said differently, you’ve de-risked your error rate in a thoughtful methodical way and have evidence that things are working while giving your competitor enough time to flail and eventually fail. In so many companies that I’ve invested in and companies that I’ve worked for, I’ve seen enormous bets being made too early, and mostly out of ego. These bets are rarely rooted in data and most have eventually been rolled back. The second thing to understand in poker is that when you make many small bets, you can play more hands - and some of these can lead to huge pots. Some of the biggest pots I’ve won have been with 2-2 and 8-6 suited while some of the biggest pots I’ve lost are with A-A! In business, as in poker, you have to make unconventional bets if you want to win huge pots. And the no-brainer bets are rarely big winners and can sometimes come back and sting you. So as an investor, by keeping my bets small I keep my errors small while giving myself a chance to win big by doubling and tripling down at the right time.

this is the most interesting year in human history, except for all future years

How to Do Great Work by @paulg The average PG essay contains condensed wisdom packed into 500-1500 words. So when he dropped this 10k word essay in the summer of 2023 on how ambitious people could do great work, it was—as they say—Christmas in July. The essay reminds me of Hamming's "You and Your Research" but written for technologists in the 21st century. Here's my mindmap of the essay but of course go read the whole thing for yourself (link in following comment) My biggest takeaway was the importance of following your curiosity and pursuing projects you find personally meaningful. In some ways I've been inclined towards that approach my whole career. Yet I didn't always have the confidence to double down on my curiosity when others doubted. And as I transition from Youth to Age, I need to truly internalize this wisdom.

This is one of the most beautifully written tributes I’ve read. Fitting ode to a ridiculously deep relationship.

Say hello to Sora, our next generation text to video model created by @OpenAI 🤯 openai.com/sora

RT @TheTennisLetter: Rohan Bopanna falls on his back after winning his first Grand Slam title at the age of 43. You’re looking at the old…

Why do analytics teams fail? When analytics is harmoniously integrated into product development and design, it serves as a revenue driver, not a cost center. When I see analytics teams thrive, it's generally because the team -- and the infrastructure it builds and maintains -- is viewed as fundamentally vital to the company's product vision. But analytics teams often fail. And when they do, they are viewed as cost centers and are generally treated as service organizations (or, worse, task-oriented support). The three most common reasons that I see analytics teams fail are: Lack of agency and authority. Instead of having an active role in determining which features are prioritized and developed through analysis and testing, the analytics function is forced into a “reactive” role with respect to product development: new features are added to the product or a new product is launched, and the analytics team's efforts are narrowed to the ex-post measurement and support of those changes. This often results in the analytics team being inundated with ad hoc requests for reports. This type of work is what I call "first-order" analysis: derivation of trivia that is interesting but not generally commercially valuable and answers "what" questions (eg. "what percentage of the last cohort made a social connection?"). Analytics teams deliver value through second-order analysis -- "why" and "how" questions, and ad hoc reporting requests often eat up so much time as to preclude that from getting done. Lack of investment into infrastructure. Somewhat as a corollary: when analytics teams aren’t given the resources to build a robust infrastructure that can be interfaced with by the entire organization to conduct first-order analysis (ie. answering the “what” questions), then that analysis falls to them and can occupy a significant portion of their time. Simple trivia should be retrievable from some sort of analytics interface, be it a dashboard or an off-the-shelf analytics tool. When this isn’t possible, the analytics team is tasked with the type of menial work that is better suited for machines: simple counts, averages, sums, etc. Humans are better equipped than machines to manage creative tasks and critical thinking, and so organizations should leverage an analytics team for that kind of work. Improper placement in the organization. The product and analytics teams need to work closely but independently; the analytics team should be able to receive fully-formed requests from the product team and prioritize them (or reject them) at its discretion. This is often accomplished with a configuration that sees analysts embedded with the product teams but reporting to a Head of Analytics. It can be awkward to have the Head of Analytics report to, for instance, the CFO because that person may have trouble evaluating the importance of the analytics team’s work (and thus is less likely to create political cover for the team in disputes).

I spent an afternoon with the director of the longest running study on happiness. 3 powerful learnings (everyone needs to hear):