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What applications do you think this opens up?

One of the first ones I can think of is pairing it up with Browser extensions to increase productivity of knowledge workers. Think of sales people quickly assessing the viability of leads, or simply reducing noise on social media as a B2C app.

What I found interesting was the fifth section that describes the strategies to improve RAG performance. Basically:

1. Quality control. What documents to include?

2. Timing. When to query?

3. Pre & post processing. Improve LLM outputs based on retrieved data.

4. End to end training. Expensive, and data intensive but possibly the best long-term approach.

5. Controller. An interesting idea with similarities to Reinforcement Learning.

I wonder what are your thoughts. Which one is most promising? What has been your experience when building RAG apps? Also, is RAG the leading architecture for building applications?

Curiously, Mario's inception was a result of Nintendo's expansion to America, and the need to provide arcade games with a relatable narrative.

Interesting, you should publish your results.

And it does makes sense, in a way it would be similar to letting children (developing persons) make their own decisions.

The interesting question would be what is then, the best form of government for developing nations. Or even better, what are the required skill-set for leaders to bring development?

The article does present a very objective evaluation of Bukele as a person, and statesman.

One has to deeply wonder about Political Theory after reading stories like this.

> getting promoted again requires someone else quitting.

I can imagine this being true for 80% of high-mamager positions. But there's also a good portion of leadership positions that get created with new lines of business.

That is blue ocean thinking. Generally, it will give you more ownership about the outcome of your career.

That's the appeal: is lightweight.

And as long as robots are where they are. There a lot of jobs in hospitality, and healthcare available. In average, life quality will improve.

Yes. The distribution of information is indeed a complex problem, as graph theory shows us.

Regarding science is about one standard deviation further than software development in complexity. It's not necessarily a trade off. On the contrary, software applies scientific breakthroughs.

Folks see it wrong. Is not about human vs machine software development.

Is about allocating the brightest humans to the most productive activities.

Brilliant! As if a solid feature roadmap doesn't require inputs from sales, marketing or customer support.

By definition, semantic search works best by similarity. Thus, the interface you are looking for, is one that facilitates selecting one or multiple objects (i.e. video games).

Conclusion:

> The problem of excess genius is one of the most important questions I can imagine, but very little progress has been made. It surprises me that essentially no scholarly effort has been directed towards it. I warmly solicit any suggestions from readers that may help me to clarify my own confusion and uncertainty regarding this.

Excess tends to have a negative connotation, but not as used in the article. It also mentions that "the great minds in each of these societies tended to hang out together" which resonates as true based on reading biographies from accomplished philosophers. I'm curious to read your thoughts:

A good option is to search for a short-term contract at a cloud-first start-up. Look for something challenging that requires a scalable solution from the ground-up. Even before that you can consider getting some certifications. Unfortunately, it's difficult to learn, and earn on the same job. So if you need to earn a lot on a short period of time, your best chances are in a mid to large company related to your niche. As you said, due to the job market conditions, this will not be easy.

However, if your SaaS product already provides a developer salary, possibly your best chance is to look for sources of growth. Search how you can reposition it based on market conditions for a larger customer base, get professional advice, and even invest in sales, and marketing. Getting a job today might be the easy solution but a few years down the road you might regret it: getting the first customers is always the most difficult part. Finally, consider that if you've been developing for 10 years independently it could be challenging, or unrewarding to lose that freedom.

Congratulations on the launch. The website looks great, and the product seems well thought.

But I do wonder, what is the plan to achieve VC type returns. The market seems fairly small, and not particularly growing. I would think this is a great indie, profitable business but have a difficult time imagining how it scale to become a meaningful player in the start-up ecosystem.

Could you please offer me some insights?

> What should we measure to measure productivity of journalists?

Visits, clicks, read time.

> Novel writers?

Sales, reviews.

> Pop song composers?

Hits.

> "Management" needs to stop thinking that software is an assembly line and that they are managing "workers"

I can't avoid to find funny the thought of telling management, as an engineer, what they should do.

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Good software engineers should embrace good productivity metrics (like the ones presented) as a meritocratic path to success.

I find there are 2 distinct set of metrics for engineers: ability to delivery, and to create value. Both important, both difficult to measure, both possible to approximate, and both benefit the business.

I've been curious to learn theories where only a subset of scenarios beget alternative world, and many of these ultimately merge into more probable branches of reality. Keeping the total number of worlds growing at less than O(n*2) with respect to time.

I agree with the problem (getting distribution is not easy), but not necessarily with the solution (calling out anti-competitive practices).

If the problem is serious enough, the capitalist solution would be to develop a platform that solves the problem for independent artists. However, as others have pointed out, the real customers for Spotify are not indie artists but rather paying users, and established artists.

While it is tempting to blame platforms for commercial failure. One has two recognize the truth of the business: making music follows a long-tailed distribution, and ultimately the listeners decide who rips those benefits.

Acquired. Particularly, episodes on Nvidia, Qualcomm, Benchmark, Enron, and Walmart.

From my perspective, ontologies serve 2 main purposes: 1. Providing an overview of the structure of a large body of content (e.g. a wiki). 2. Measuring, and understanding the change in the same corpus.

There used to be fancier applications to ontologies, like question answering, but I agree with the author that LLM could replace most of them. The more interesting question is how to auto-generate ontologies?

I feel that while the article makes some accurate remarks linking Victorian ethos to our current technological prowess, it's too simplistic to assume that today's scientific achievements are not instead part of a multicultural advancement continuing from millennia.

I did found very interesting the anecdote around 1820 with the shift from the "old guard" on the British Royal Society. But it led to me to wonder why such an importance is given to specialized knowledge. It's like reducing every problem to be solved by a depth-first search algorithm.

What about digital? Also, why not prioritize the spectator experience over space?

i.e. storage is cheap, attention expensive