emanuer

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As a layperson, I find this an approachable way to get an overview of a topic. However, I am only interested in a few select topics, and I was not able to find a way to subscribe to specific ones, such as #insulin-resistance (topic request ;-)

Another thing I really value in science YouTubers (e.g., youtube.com/@Physionic) is the deep dives they offer into the research—highlighting conflicting results, paying special attention to meta-analyses, etc. That would be amazing, although I realize it may be too much to ask.

    Location: Europe
    Remote: Yes
    Willing to relocate: No (travel up to 2x/month)
    Technologies: Jira, CSS, JavaScript, TypeScript, Java, PostgreSQL, MongoDB, OpenAI/Anthropic/Google LLM APIs, WEKA, Deeplearning4j, Figma, Shopify
    Résumé/CV: https://drive.google.com/file/d/1WXOCz93eXYKDjAuVp0clZ3T8dyMp7OoV/view?usp=sharing
    Email: [email protected]
    Work hours: CET to EST/CST

What I do:

- Turn ambiguous SME workflows into shipped AI/LLM products (0-1 -> scale)

- Double conversion (+100%), launch to millions of users, and lead cross-functional teams (6–12; coord. ~50)

- Build pragmatic delivery: discovery with real users, thin slices, instrumented loops

About me:

I’m empathetic with users and exacting about results: I’ll sit with customers to find what they need (not just what they ask), then drive an executable roadmap and delivery process. However I’m not strong at outbound prospecting. My strengths are inbound discovery, executive alignment, and durable partnerships. Roles I fit into: AI Product Manager / Product Lead / Head of Product (AI/LLM)

You are correct, it was just a random example.

And I share your observation, if there is no clear answer to your question, the idea must be disregarded.

Here is the perspective of a serial founder, exploring fields which I might be able to disrupt:

- The regulatory moat is immediately intimidating.

- The data moat, often, is quite surmountable as long as LLMs can generate high-quality synthetic data (e.g., user preferences). On this I disagree with the author, to some extend.

- The "distribution moat" is another significant barrier. Even if I have a superior product, if the marketing and sales demands are so high that neither I nor an army of bots can manage it alone, the business becomes nonviable (e.g., enterprise sales).

- "Switching costs" form the next moat. The higher these costs, the greater the value per dollar I must offer over the incumbents (e.g., software for dentists).

- Another key barrier is the “business rules” moat. Achieving 80% of the required features may be easy, but as customers demand 90% or 95%, the complexity and cost of reverse engineering grow exponentially. The more mature the market, the higher these demands (e.g., Jira).

With the power of LLMs at my disposal, I have reaffirmed two core beliefs:

1. I must focus on a niche small enough, so that I am the only provider. (e.g., accounting software for gym owners in the north of France)

2. I must offer a value proposition different from that of the incumbents, where competing with me, would harm their business. (e.g., image editing app where you pay per hour used)

So my search continues…

As being part of a software development team working on RPS (runway performance) I can tell you, it is stories like this which keep us up at night.

The amount of business logic involved is staggering, but miraculously there are experts who truly seem to understand almost all of it. Understanding “almost all of it” is not enough, so there are several different experts who in their entirety hopefully capture 99.99…% But you don’t know what you don’t know…

One of the “calming” facts is, that in the tens of thousands of test cases we compared our results to existing solutions, we did discover errors in their calculations. The “calming” aspect is, due to the many safe-guards these errors seemingly never surfaced.

Coming from a machine-learning background I quickly understood that there is little appetite for AI solutions, as any optimization which would reduce fuel consumption, is easily offset by all the safety margins. In my mind the goal of the software is to make everything explainable and proof that you truly did think of every eventuality.

I am very curious to learn what major f*-up lead to planes being grounded.

What an excellent point. If it is addressed in the book, I don't remember.

If we actually accept that expansion increase in entropy, I do not know what could possibly compete with this.

To anyone curious about the intimated interplay of entropy and life, I want to highly recommend the book:

The Romance of Reality: How the Universe Organizes Itself to Create Life, Consciousness, and Cosmic Complexity by Bobby Azarian

> When you empty a bathtub, why does a swirl form? → Because it is the most efficient way to increase entropy.

> The author argues in that life is very likely because it is the most efficient way to increase entropy in the universe.

I fear my words don't do the book justice, I found it very long, but just as illuminating, highly recommended.

I am asking this question out of true intellectual curiosity: Could someone please link me a study which shows strong evidence for micro-plastics being harmful to health, that goes beyond finding "potential" harm? – Thank you!

Seriously, I am looking. So far every study I have found — linking micro-plastics to physical harm done to multicellular organisms — had the phrase "potential" (or a derivative) in it.

I dislike plastic as much as the next guy, but I slowly get the feeling: the harmful effects of plastic have the same level of scientific proof as harm caused by radiation of mobile phones — inconclusive to likely _none_.