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I am frustrated by this article.

1. Why focus on Swifts as opposed to any other species in decline? They state that they are "iconic", so maybe that's the answer? Are they more "iconic" than any other specifies in Scotland?

2. Why are these bricks the best solution? Why not take that money that would be spent on bricks and instead preserve land, or just build them dedicated houses elsewhere?

3. Why does this need to be done via government mandate versus voluntarily asking people to build Swift housing in existing buildings or land?

I'm worried that this is a government policy with great intentions that will result in economic costs with unmeasured benefits and bureaucratic bloat. Hopefully I am proven wrong!

GDP records market transactions. So in your scenario, purchasing the chicken, the chicken feed, the chicken coop, etc would all be measured. The time spent raising the chicken (assuming you did not pay someone to do it), would not be captured.

However, I think part of your criticism is a moralistic and health based argument that raising a chicken is intrinsically better and healthier that Doordashing fast food. When a consumer chooses a $60 delivered meal over a $45 home-cooked alternative, that reveals a preference for whatever they value, such as convenience, time saved, taste, service quality, etc. GDP doesn’t evaluate whether that preference is “good”; it only records the transaction value that arises from it.

So I agree GDP is not a perfect measure, but it's far from useless.

I would not over complicate this. My personal four part plan for this is:

1. Donate a portion of my income to charitable causes I find worthy. I would like to target 10% of my net income.

2. Volunteer at a local organization that you care about. There are usually ample opportunities for committed volunteers if you look for them.

3. Have children. Try to raise them to be responsible, caring citizens. That way I can extend my efforts to the next generation.

4. In the near future, I intend to attempt to get involved with local politics somehow. I am still figuring out the specifics, but I am thinking that running for the local school board would be beneficial.

This is spot on. Obviously there is a lot to dislike about performance reviews, but big companies need some process to determine who gets a raise, who gets promoted, etc. Although flawed, performance reviews are the best process for that.

I think the things that companies can do to make them better are:

1. Have well established career frameworks (aka career ladders) ahead of time. These should be as detailed as possible. https://sijinjoseph.com/programmer-competency-matrix/

2. Have transparency about the ranking system and distribution to all current employees and future employees.

3. Ensure that some amount of accountability is shared at the department level and also at the team level, so you can have somewhat objective conversations about trade-offs between departments and teams.

Seems like an over sight that there is not a photo of the actual diamond. This article has a photo. https://www.nytimes.com/2024/08/22/world/africa/large-diamon...

Has anyone implemented a DSL for a professional use case? I would love anyone to share what was the use case, what alternatives did you consider and how did you measure success?

I have previously implemented one for credit policy decisions, but I am not sure in hindsight if it was the right approach and we did a poor job of measuring success, so I would love to hear from the community.

That's fascinating. Where would you direct someone that is interesting in learning more about these items? Any good sources or websites to read?

I think we should retitle this to reflect the actual article title, "How Leaders Miscommunicate When Tensions Are High".

This is a common practice in other high altitude, high traffic mountains, such as Aconcagua. Rangers actually watch you deposit your excrement bag and fine you if you do not do so or they catch you leaving your waste on the mountain. I'm frankly surprised its just now becoming a policy on Everest.

It sounds gross, but in practice it is not as bad as it seems. For anyone interested in the details, it is stored in multiple bags (one bag per movement, then multiple larger outer bags) and freezes pretty quickly, so there isn't really any smell.

" They could only get hired into new companies where nobody knew any of their past coworkers, because a simple reference check would reveal how difficult they were to work with." --> Any tips on how to effectively conduct reference checks? In my experience, candidates will only provide references from folks that they know will give glowing review, so you end up with generic positive feedback that ends up not being informative.

Would love to hear any strategies others have found successful.

Many universities will have a portal where students and professors can get free or heavily discounted versions of software, such as Mathematica.

I don't think its any less convenient for my partner than if I worked in an actual office. Plus, I still have the added benefit of no commute, which means more time with my partner.

I see the value in this, but I personally have not had success with it. I have found I am most effective remote if during my working hours, I don't do anything I wouldn't do if I was working at the office. For me, that means no laundry during the day, no extended lunches, etc. It keeps my mind in "work mode" and at the end of the day, I shut my office door and transition my brain out of work-mode.

Streamlit and Shiny for Python are both great for cranking out simple UI's for basic use cases, especially where there is a tie-in with something data or machine learning related.

A typical workflow could be for a team to prototype in one of these tools, get feedback, but at some point they eventually get rebuilt as a proper webapp. I would love to see these tools evolve to not require that total rebuild. Maybe they could add the capability to export to React or Angular. Then that could be used as a starting point for converting that into a proper webapp.

"...rather you get a bunch of people xgboosting their way up your leaderboard" -->

I don't see that as a universally bad thing. I feel this is actually very representative of the majority of ML projects that most organizations encounter. Most organizations don't have petabytes of data and a huge compute budget to train a DNN. They typically have megabytes to gigabytes of somewhat crappy data and need something that can be developed and deployed relatively quickly for low cost.

"Shares of the company were down 0.5% in afternoon trading on Tuesday." --> If that was their goal, it seems like in this case it was not successful.

I've had a similar thought. I read a LOT of these various blogs and I frequently ask myself whether its valuable and how much time I should invest in these. I've concluded its probably valuable and a few hours a week is probably the right amount of time.

I think where the value lies is in when I am asked to give input into product or technology strategy. Having a broader understand of the technology ecosystem is a useful foundation for making recommendations to my specific scenario.

Interesting paper analyzing patent and research article data to measure productivity and innovation.

Yeah I totally agree, but I do think there are times when starting with the absolute most basic implementation that meets the business needs is the way to go. That could be a simple bash script, or a python script without Django, etc.

In my experience, the key is making those tradeoff decisions in an educated manner. Ideally, product and engineering and in sync and have a somewhat informed view of the future, so appropriate tradeoffs on velocity (simple bash script) versus extensibility (Python with Django) can be discussed.

Pinecone is in a tough spot. A huge number of database vendors are implementing vector functionality into their products, which risks the niche that Pinecone is attempting to carve out for their business. They are probably trying to gain adoption of their database by developing an open source RAG framework that leverages their database and gives companies a solution to quickly implement RAG in their specific applications.

Regarding whether RAG will still be required in the future, this is a good question. Increasing context window sizes may render this moot or much less important. However, there are still cost and performance implications of a RAG architecture for many use cases.

This may be nitpicking, but technologies being described as "cool" versus "enterprise" or "new" versus "old" I find meaningless. I don't necessarily want to have the "coolest" or "newest" tech stack; I want to have the tech stack that solves reasonably and reliably solves my business problems. If that means leveraging "old" or "enterprise" technologies and practices, that could be totally fine.

Taking a lunch break. I used to work through lunch at my desk and then found that my focus and energy would really crash around 2-3. I have found that even taking a 30 minute break away from my desk keeps my energy high for the rest of the day.

Can you elaborate why you think that?

Yeah I see your point. A few thoughts:

1. This assumes that that a someone can/will usually accurately scope 4 stories, with proper requirements, and clear peer review standards. In practice, this can be hard to do and inconsistent.

2. If someone falls behind on their assigned story quota, how would a manager know whether that is because the story didn't meet the criteria in item 1, or because the person isn't putting in sufficient effort because of OE or other commitments? How I typically see this play out is most people assume that its not related to effort and try to work with the employee to push back deadlines and better refine the story.

As long as they perform the tasks of their jobs, why shouldn't they be "getting away with it"?

Curious how you would measure whether they are "performing the tasks for their jobs". I think most folks would say that measuring SE output is very difficult to do and as such, most managers give their team the benefit of the doubt that they are working at a reasonable but sustainable pace.

I am not sure that I personally would want to work at a place that places huge emphasis on measuring my output, even if that would free me up for potential OE. It seems like this thinking leads to a commoditization of software engineering work and promotes a very transactional relationship with an employer.

Yeah it actually gave me comments summarizing fields from related tables using shortened more readable table names

Agreed. For example, I recently had an auto-generated SQL query that I needed to debug. It was formatted on one line and was impossible to read. I asked GPT-4 to clean up the query and it gave me something that was much easier to consume. I definitely could have written some script for this or tried to find another tool, but it took me 20 seconds to just ask GPT-4 and get a reasonable result.

I do not know of a coach, but two resources I would encourage you to check out are:

1. Silicon Valley Product Group Blog: They are thought leaders in the field and have a very strong opinion on how product management SHOULD be done. I do not agree with everything they recommend, but they are a great resource to help get up to speed with product management. https://www.svpg.com/articles/

2. Northwestern Kellogg Product Strategy Online Course: While expensive, this is an excellent and rigorous resource for learning product management basics. https://www.kellogg.northwestern.edu/executive-education/ind...

I know this isn't quite what you were looking for, but I hope these are some useful resources to get you started. I'd love to hear what you ultimately end up doing and whether you found it valuable or not.

Basic web scraping!

I have basic knowledge of browser automation via Selenium and Python. Often I can crank out a quick script to automate some tedious or repetitive task and wow my coworkers who either don't have that knowledge or just haven't taken the time. My other use case is using some basic webscraping to collect data from various disparate sources and then combine it together with some basic Pandas code to create a very useful data set.

I am by no means an expert on this, but I am curious:

1. What makes you think Jerome Powell is raising rates to prop up short-term stock gains? Don't higher rates typically decrease short-term stock gains?

2. Even if you disagree with his strategy, what makes you think he's doing something nefarious or illegal?