Jade Rubick explaining why we should strive to maximise collaboration within teams and minimise communication between teams.

To the maximum extent possible, teams should have what they need to succeed within the borders of their team. And where that is not true, you need some structure to ensure the team can get what it needs in a way that will scale with the organization’s growth.

As companies grow, communication and dependencies proliferate. Companies start out with many-to-many communication. As they grow, the communication patterns within the company must necessarily switch to being segmented and defined. Otherwise, the communication burden on teams will grow at an exponential rate, and the increasing complexity will degrade the effectiveness of the company.

Andi Roberts explaining the difference between pilots, probes and experiments. He then goes on share guidance on how to manage each of them.

Pilots: A pilot is a small, time-bound test to determine whether a product, process, or service can work in practice. It is used when an idea appears promising but remains unproven.

Think of a bank that tries a new mobile feature in one city before a national launch. The trial exposes what could not be seen on paper: user confusion, system issues, or compliance gaps. The value of a pilot lies in its realism. It bridges the space between concept and operation, letting leaders see what truly happens when an idea meets the world. From this, they can refine and strengthen the design before committing at scale.

Probes: A probe begins with curiosity, not certainty. Drawn from complexity thinking, probes are small, safe-to-fail actions that explore what might work when the path ahead is unclear.

A city struggling with congestion might try three different approaches: a cycling subsidy, staggered work hours in one district, and AI-driven traffic lights. None is guaranteed to succeed, and that is the point. Each test offers a glimpse of how the system responds. The power of a probe is its capacity to uncover patterns that analysis alone cannot reveal.

Experiments: An experiment is a structured test designed around a hypothesis. It is used when a leader wants clear evidence about cause and effect.

An online retailer, for example, may compare two website layouts to see which converts more visitors into buyers. Experiments are precise, controlled, and measurable. They do not explore the unknown in the same way that probes do, but they provide reliable evidence where outcomes can be quantified. Their strength lies in giving leaders grounded answers to specific questions.

Steve Francia talking about why engineers can’t be rational about choosing a programming language. He ends his post by highlighting that choosing a programming language should be reframed to an economic debate. What is programming language going to cost us?

Instead of asking “which language is best?” we need to ask “what is this language going to cost us?” Not just in salaries, but in velocity, in technical debt, in hiring difficulty, in operational complexity, in every dimension that actually determines whether you survive.

Reframe it from a technical debate to an economic one. And unlike identity, economics can be measured, compared, and decided without anyone’s ego being threatened.

Choosing a programming language is the single most expensive economic decision your company will make. It will define your culture, constrain your budget, determine your hiring pipeline, set your operational costs, and ultimately dictate whether you can move fast enough to win your market.

John Steele highlighting the irony of how farming sea food strips the sea itself.

In the cold waters of the Pacific, the anchoveta once shimmered in swarms so vast that sailors described them as turning the sea into a river of quicksilver. They were small, unassuming fish, yet the abundance of the ocean rested upon their delicate bones. Seabirds wheeled overhead in their millions, sea lions and whales dove into their depths, and predatory fish rose through the blue to feed on them. In those shoals lived the vitality of the sea itself. But in our age, the anchoveta, along with sardines and menhaden, have been transformed from living threads in an ancient web into bags of meal and casks of oil. Ninety percent of the forage fish caught by human hands are not eaten by us but ground down to feed salmon being raised in the cold fjords of Norway and shrimp and fish in the tropical ponds of Southeast Asia.

It is one of the great ironies of our time. To farm the sea, we strip the sea. We take from the ocean’s foundation to build its surface anew, and in the process we imperil both.

But all is not lost. There are some innovative solutions in the horizon.

This comment by th explains how DEI is essentially an outreach program. This is on the news of Python Software Foundations’ decision to withdraw from $1.5 million proposal for US government grant program.

It seems like a number of the “DEI is anti-merit discrimination” messages in this thread are overlooking how DEI work usually works.

A relevant tweet from 2016 (https://x.com/jessicamckellar/status/737299461563502595):

> Hello from your @PyCon Diversity Chair. % PyCon talks by women: (2011: 1%), (2012: 7%), (2013: 15%), (2014/15: 33%), (2016: 40%). #pycon2016

Increased diversity in communities usually comes from active outreach work. PyCon’s talk selection process starts blinded.

If 300 people submit talks and 294 are men, then 98% of talks will likely be from men.

If 500 people submit talks and 394 are men, then ~79% will likely be by men.

Outreach to encourage folks to apply/join/run/etc. can make a big difference in the makeup of applicants and the makeup of the end results. Bucking the trend even during just one year can start a snowball effect that moves the needle further in future years.

The world doesn’t run on merit. Who you know, whether you’ve been invited in to the club, and whether you feel you belong all affect where you end up. So unusually homogenous communities (which feel hard for outsiders to break into) can arise even without deliberate discrimination.

Organizations like the PSF could choose to say “let’s avoid outreach work and simply accept the status quo forever”, but I would much rather see the Python community become more diverse and welcoming over time.

There’s a spirited discussion on the research paper A Definition of AGI on Hacker News. This comment by fnordpiglet caught my attention.

Try this exercise. Do not think and let your mind clear. Ideas will surface. By what process did they surface? Or clear your mind entirely then try to perform some complex task. You will be able to. How did you do this without thought? We’ve all had sudden insights without deliberation or thought. Where did these come from? By what process did you arrive at them? Most of the things we do or think are not deliberative and definitely not structured with language. This process is unobservable and not measurable, and the only way we have to do so is through imperfect verbalizations that hint out some vague outline of a subconscious mind. But without being able to train a model on that subconscious process, one that can’t be expressed in language with any meaningful sufficiency, how will language models demonstrate it? Their very nature of autoregressive inference prohibits such a process from emerging at any scale. We might very well be able to fake it to an extent that it fools us, but awareness isn’t there – and I’d assert that awareness is all you need.

François Chollet explaining the concept of understanding.

To really understand a concept, you have to “invent” it yourself in some capacity. Understanding doesn’t come from passive content consumption. It is always self-built. It is an active, high-agency, self-directed process of creating and debugging your own mental models.

Shray Chandra and Deepak Shenoy’s discussion on how we treat gold, land very differently from equities.

Shray Chandra: But when I’m looking at my portfolio, if I’m going to put them all in equity, I’m this super demanding and difficult person saying this doesn’t deserve to be in my portfolio, it’s not doing well, I’ve lost faith in management, this sector is completely screwed.

But if 5% of that money is in gold, and gold doesn’t do well, I’m like, oh, this is fine, it’s meant to be hedge. It’s almost like I treat it completely differently. So when it goes up, I said, see, I was right.

And when it doesn’t do well, I said, yeah, I know, because it’ll do well at the other times. So how does one deal with this sort of like, is this a rational way of thinking about it? Is it just managing emotional expectations or is it like insurance? It won’t do well for many years and some years it will do well.

Deepak Shenoy: That’s a brilliant philosophical thought process difference. And I think real assets have this advantage. You buy land, you have a similar thing.

Land has this advantage that people don’t re-value it. And even if they do re-value it, they say, we’ll wait. People tell me that they want to wait for the metro in Bangalore before they sell their apartment.

I’m like, that’s at least three, four years away where I am. They’re like, huh, it’s okay. I’m like, nobody will tell me.

I will wait three, four years for Nifty to go back up. It’s very rare to find that, right? I have to try really hard to…

Listen, it’s not down too much. It’s only been a year. So just hang on for a few more years and it’ll be fine over the long term, right?

You have to give any investment at least a few years before it gets in. But it’s so much easier to tell that about land or about gold than it is to tell them about stock A or portfolio A or mutual fund Y and so on. So I feel that maybe it has become a second part of nature for us. It says, well, if this mutual fund thing doesn’t work out for me in a year, I’m done. But the land bit, I want to, I don’t mind. That is actually long term.

Shray Chandra: We’re able to bring in our long term thinking properly when it comes to or the diversification thinking when it comes to say assets like land, real assets. We struggle when it comes to financial assets where the job is, you’re supposed to make me rich. What are you doing?

Steve Blank explaining how science works. He then shares this simple table explaining the difference between theorists and experimentalists.