A significant body of work has investigated the effects of acute exercise, defined as a single bout of physical activity, on mood and cognitive functions in humans. Several excellent recent reviews have summarized these findings; however, the neurobiological basis of these results has received less attention. In this review, we will first briefly summarize the cognitive and behavioral changes that occur with acute exercise in humans. We will then review the results from both human and animal model studies documenting the wide range of neurophysiological and neurochemical alterations that occur after a single bout of exercise. Finally, we will discuss the strengths, weaknesses, and missing elements in the current literature, as well as offer an acute exercise standardization protocol and provide possible goals for future research.

As we age, cognitive decline, though not inevitable, is a common occurrence resulting from the process of neurodegeneration. In some instances, neurodegeneration results in mild cognitive impairment or more severe forms of dementia including Alzheimer’s, Parkinson’s, or Huntington’s disease. Because of the role of exercise in enhancing neurogenesis and brain plasticity, physical activity may serve as a potential therapeutic tool to prevent, delay, or treat cognitive decline. Indeed, studies in both rodents and humans have shown that long-term exercise is helpful in both delaying the onset of cognitive decline and dementia as well as improving symptoms in patients with an already existing diagnosis

Design Engineers tend to work on products and systems that involve adapting and using complex scientific and mathematical techniques. In essence, they work on high technical challenges that requires strong design consideration

tech has a tendency to consolidate roles. Interaction Designers, UX Designers, UI Designers, and Visual Designers became Product Designers. Graphic Designers, Illustrators, Communications Designers, and Marketing Designers became Brand Designers.

Design Engineers have deep knowledge in technological systems while scaling interface quality. They naturally fit in design crit or reviewing code with engineering.

three core areas Design Engineering is the top candidate for leading: product architecture, design infrastructure, and 0-1 R&D.

Design Engineers have the right skills to explore aspects such as information architecture of a product and also understand the technical logic of how such things function.

contributing to component libraries, building internal tools to increase efficiency, or prototyping new patterns and interactions to inform the future of their software.

When you’re dealing with an interaction with a Language Server Protocol (LSP) or layout engine for a site builder, you have to build the prototype in code.

Exploring new product directions can be a need for an early stage company needing to pivot in direction or an established company looking for new growth opportunities.

It’s uncertain if every company needs a Design Engineer. However, I am confident authoring environments or work with complex interactions/ data need Design Engineers. Snap, Retool, Replit, Uber, Square, Open AI, and The Browser Company are a few examples of companies that have Design Engineers on their software teams.

Collective harmony and hierarchy are strongly idealised across East Asia. Communication is thus implicit and indirect. Conflict aversion and emotional suppression make it harder to learn what someone else really thinks. So what’s the solution?Alcohol reduces people’s inhibitions. This promotes social bonding and information-sharing. As argued in Edward Slingerland’s book “Drunk”, it benefits businesses! But this exact same cognitive shift also elevates risks of sexual abuse. Women may prefer to leave early. By doing so, they miss out on homosocial boozing and schmoozing.

We want to know what the other is thinking. We want to know what our whole species thinks (written language) and has learned (school). And yet, minds are not directly observable. We have to talk about them. We have a seemingly endless interest in stories, because there is information there we crave—how to be. Sharing stories of events and people, whether real or fictional, synchronizes our values, provides (perceived) control over this insane world via meaning and causal explanations, and creates—not reinforces, but creates—the basic, primal social bond humans have as we, as listeners, all tune into to one point of attention.

a good listener is actually someone who is good at talking.

the really good advice, the secrets that will make you much better at listening to a degree that your relationships are significantly more successful, peaceful, gratifying, intimate, and trusting, have to do with what you say.

Some simple and powerful phrases to use when someone is feeling feels: “I hear you.” “I bet it is hard.” “That makes sense.” Ones to strike from your vocabulary: “You have no reason to feel that way.” “Don’t be silly!” “I’m sad/mad/whatever too!” (see #5).

Don’t allow lies you want to correct, or generalizations you want to protest, or insults you want to decry, or any angry words to manipulate you into engaging. This is not a real conversation. Real conversations and problem solving don’t happen in yells or insults.

Replace all of the shocking, mean, hateful, incorrect, ignorant, offensive, cruel things coming out of this person’s mouth with “I’m hurt! I’m hurt! I’m hurt! I’m hurt. I’m hurrrrt.” Summon your best pity, then disengage. END this moment with “I’ll be up for talking another time about this if you want.” Don’t say “…when you are less angry.” It will make the person angrier.

Don’t let “what do you want to be when you grow up?” be the first thing you say to a child. It reinforces the message that children, in the eyes of all the adults they meet, have no real value until they grow up. Ask instead what the child is interested in now—favorite books, hobbies, subjects in school, etc. If it’s a female child, be aware of avoiding remarks on only her appearance or clothing. If you only heard compliments on your hair or dress or whatever from everyone you met, you’d start to think your looks are your most important feature, too. Maybe your only important feature.

If it’s a male child, try an unguided, open-ended invitation like “what’s on your mind today, buddy?” What a different world we’d live in if more boys felt safe sharing feelings, in their own way, right from the start.

Empathy is not “hey that happened to me too!” or “I also know what you are talking about—in fact I know a lot more than you do!” This is more like someone has just brought out his bowling pins to juggle but you grab them and juggle obliviously away from him. Not empathy. Empathy is just the opposite: turning away from your ego, for just a minute (don’t worry, you can have it back soon!) in order to imagine, really imagine, what it’s like for someone else to be alive.

Ask “What happened?” “What kind of place was that?” “When did you first…?” or other non yes-or-no questions.

Asking why can lead to defensiveness, and a sort of shallow string of quick justifications for behavior that aren’t actually insightful or productive. You can sit for days and discuss whys without any real benefit or helpful solutions.

Questions say, “I’m interested. You are valuable.” And they are my go-to solution whenever I have no idea what to say.

The term "meme" was coined by biologist Richard Dawkins in his 1976 book The Selfish Gene,[1] to illustrate the principle that he later called "Universal Darwinism".

He gave as examples, tunes, catchphrases, fashions, and technologies. Like genes, memes are selfish replicators and have causal efficacy; in other words, their properties influence their chances of being copied and passed on.

Just as genes can work together to form co-adapted gene complexes, so groups of memes acting together form co-adapted meme complexes or memeplexes.

Criticisms of memetics include claims that memes do not exist, that the analogy with genes is false, that the units cannot be specified, that culture does not evolve through imitation, and that the sources of variation are intelligently designed rather than random.

The “hard tech” in AI are the LLMs available for rent from OpenAI, Anthropic, Cohere, and others, or available as open source with Llama, Bloom, Mistral and others. The hard-tech is a level playing field; startups do not have an advantage over incumbents.

There can be differentiation in prompt engineering, problem break-down, use of vector databases, and more. However, this isn’t something where startups have an edge, such as being willing to take more risks or be more creative. At best, it is neutral; certainly not an advantage.

This doesn’t mean it’s impossible for a startup to succeed; surely many will. It means that you need a strategy that creates differentiation and distribution, even more quickly and dramatically than is normally required

Whether you’re training existing models, developing models from scratch, or simply testing theories, high-quality data is crucial. Incumbents have the data because they have the customers. They can immediately leverage customers’ data to train models and tune algorithms, so long as they maintain secrecy and privacy.

Intercom’s AI strategy is built on the foundation of hundreds of millions of customer interactions. This gives them an advantage over a newcomer developing a chatbot from scratch. Similarly, Google has an advantage in AI video because they own the entire YouTube library. GitHub has an advantage with Copilot because they trained their AI on their vast code repository (including changes, with human-written explanations of the changes).

While there will always be individuals preferring the startup environment, the allure of working on AI at an incumbent is equally strong for many, especially pure computer and data scientsts who, more than anything else, want to work on interesting AI projects. They get to work in the code, with a large budget, with all the data, with above-market compensation, and a built-in large customer base that will enjoy the fruits of their labor, all without having to do sales, marketing, tech support, accounting, raising money, or anything else that isn’t the pure joy of writing interesting code. This is heaven for many.

A chatbot is in the chatbot market, and an SEO tool is in the SEO market. Adding AI to those tools is obviously a good idea; indeed companies who fail to add AI will likely become irrelevant in the long run. Thus we see that “AI” is a new tool for developing within existing markets, not itself a new market (except for actual hard-tech AI companies).

AI is in the solution-space, not the problem-space, as we say in product management. The customer problem you’re solving is still the same as ever. The problem a chatbot is solving is the same as ever: Talk to customers 24/7 in any language. AI enables completely new solutions that none of us were imagining a few years ago; that’s what’s so exciting and truly transformative. However, the customer problems remain the same, even though the solutions are different

Companies will pay more for chatbots where the AI is excellent, more support contacts are deferred from reaching a human, more languages are supported, and more kinds of questions can be answered, so existing chatbot customers might pay more, which grows the market. Furthermore, some companies who previously (rightly) saw chatbots as a terrible customer experience, will change their mind with sufficiently good AI, and will enter the chatbot market, which again grows that market.

the right way to analyze this is not to say “the AI market is big and growing” but rather: “Here is how AI will transform this existing market.” And then: “Here’s how we fit into that growth.”

Wikipedia's community of editors and volunteers demand citations for any information added to Wiki pages, which keeps some degree of accountability behind the massive community responsible for operating Wikipedia. While it should never be used as a primary source, Wikipedia tends to be at least an excellent place to start researching those topics due to those citation requirements.

Building something valuable is no longer about validating a novel idea as fast as possible. Instead, the modern MVP exercise is about building a version of an idea that is different from and better than what exists today. Most of us aren’t building for a net-new market. Rather, we’re finding opportunities to improve existing categories. We need an MVP concept that helps founders and product leaders iterate on their early ideas to compete in an existing market.

It’s not good enough to be first with an idea. You have to out-execute from day 1.

The MVP as a practice of building a hacky product as quickly and cheaply as possible to validate the product does no longer work. Many product categories are already saturated with a variety of alternatives, and to truly test the viability of any new idea you need to build something that is substantially better.

Airbnb wanted to build a service that relied on people being comfortable spending the night at a stranger’s house. When they started in 2009, it wasn’t obvious if people were ready for this. Today, it’s obvious that it works, so they wouldn’t need to validate the idea. A similar analogy works for Lyft when they started exploring ridesharing as a concept.

Today, the MVP is no longer about validating a novel idea as quickly as possible. Rather, its aim is to create a compelling product that draws in the early users in order to gather feedback that you then use to sharpen the product into the best version of many.

If you look at successful companies that have IPO'd in the recent years–Zoom, Slack, TikTok, Snowflake, Robinhood–you see examples not of novel ideas, but of these highly-refined ideas.Since many of us are building in a crowded market, the bar for a competitive, public-ready MVP is much higher than the MVP for a novel idea, since users have options. To get to this high bar, we have to spend more time refining the initial version.

The original MVP idea can still work if you’re in the fortunate position of creating a wholly new category of product or work with new technology platforms, but that becomes rarer and rarer as time goes on.

Let’s jump over the regular startup journey that you might take today when building a new product:You start with the idea on how you want to improve on existing products in a category.You build your first prototype.You iterate with your vision and based on feedback from early users.You get an inkling of product market fit and traction.Optional: You start fundraising (with demonstrable traction).Optional: You scale your team, improve the product, and go to market.

In today’s landscape, you’re likely competing against many other products. To win, you have to build a product that provides more value to your users than your competition does.To be able to do this with limited resources, you must scope down your audience (and thus your ambitions) as much as possible to make competing easier, and aim to solve the problems of specific people.

When we started Linear, our vision was to become the standard of how software is built. This is not really something you can expect to do during your early startup journey, let alone in an MVP. But you should demonstrate you have the ability to achieve your bigger vision via your early bets. We chose to do this by focusing on IC’s at small startups. We started with the smallest atomic unit of work they actually needed help with: issue tracking.

We knew we wanted our product to demonstrate three values:It should be as fast as possible (local data storage, no page reloads, available offline).It should be modern (keyboard shortcuts, command menu, contextual menus).It should be multiplayer (real-time sync and teammates presence).

Remember, you’re likely not building a revolutionary or novel product. You’re unlikely to go viral with your announcement, so you need a network of people who understand the “why” behind your product to help spread the word to drive people to sign up. Any product category has many people who are frustrated with the existing tools or ways of working. Ideally you find and are able to reach out to those people.

Once you have a bunch of people on your waitlist, you need to invite the right users at each stage of your iteration. You want to invite people who are likely to be happy with the limited set of features you’ve built so far. Otherwise, they’ll churn straight away and you’ll learn nothing.

To recap:Narrow down your initial audience and build for them: Figure out who you're building the product for and make the target audience as small as possible before expanding.Build and leverage your waitlist: The waitlist is the grinding stone with which you can sharpen your idea into something truly valuable that will succeed at market, so use it effectively.Trust your gut and validate demand with your users: Talk, talk, talk to your users and find out how invested in the product they are (and if they’d be willing to pay)

The New Yorker contains empty frames that can be filled by whatever a series of unknown adtech companies decide is the best fit for me based on the slice of my browsing history they collect, like little spies with snippets of information. If it were a direct partnership to share advertising slots, at least we could imply that a reader of both may see them as similarly trustworthy organizations, given that they read both. But this is not a decision between the New Yorker and the Times. There may be a dozen other companies involved in selecting the ad, most of which a typical user has never heard of. How much do you, reader, trust Adara, Dataxu, GumGum, MadHive, Operative, SRAX, Strossle, TelMar, or Vertoz?

As important as it is for users to confirm who is collecting their data and for what purpose, it is more important that there are limits on the use and distribution of collected information. This sea of data is simply too much to keep track of.

Bloomberg reporters are evaluated and receive bonuses tied to reporting market-moving news. They’re incentivized financially to make mountains out of molehills, and craters out of divots, to maximize the immediate effect of their reporting on stock prices. And Bloomberg appends these stock price movements right there in their reports, to drive home the notion that Bloomberg publishes market-moving news, so maybe you too should spend over $2,000 per month on a Bloomberg Terminal so that you can receive news reports from Bloomberg minutes before the general public, and buy, sell, and short stocks based on that news