I like your comment, want to try to expand on it

Comment long but there is a TL;DR at the bottom

My theory is that there are 4 areas to domain knowledge worth taking about here - there may be more but I like 2*2 matrices

1) explicit internal requirements - core of how the how the app should work towards achieving your business objectives - code expresses what should be done and to a pretty large extent, why it should be done - from business unit requirements - we are building a tool to do “X”

2) implicit internal requirements - core of how the how the app should work towards achieving your business parameters and constraints

eg profit = selling price - ( total of costs )

  - code expresses what should be done but really can’t express why. At best it is in the comments 

3) implicit internal requirements - core of how the how the app should work towards achieving your business constraints - code expresses what should be done but really can’t express why. At best it is in the comments

eg if item is “rocket” , shipping = $1m ( we only make rockets in Antarctica and shipping from there is $1m)

4) implicit external requirements - core of how the how the app should work towards achieving your business constraints - code expresses what should be done but really can’t express why. At best it is in the comments

eg if item is “rocket” , add a 3 month gating stage to get approval from government to sell the item and do not collect payment till gating approved - AI can see the code but has no idea why it has to be done

These come from partners, regulation, compliance, auditability etc

So, my theory

AI can be good at the explicit stuff trivially (1, 2) but cannot be good at the implicit stuff (3,4)

It might be able to figure out implicit stuff needs to be done but will probably not be able to figure out why it needs to be done and it will definitely not be able to definitively figure out edge cases for when to do it / not do it

As long as you focus on implicit stuff, you will be fine for a little bit

TL;DR - become good and keep being good at being the person who understands the implicit external drivers of software dev

It is money.

Specifically, right now, petro-dollars. For a while before that, it was pounds

The writer is asking how much longer that will continue to be true that it is petro-dollars.

https://en.wikipedia.org/wiki/World_currency

Formulation matters and is very important.

A1 jet fuel, propane, regular 87 gas and vaseline are four different formulations of some version of mineral oil (petroleum).

Which do you want in a car you are driving? On your parched lips? In your plane engine? Coming into your kitchen stove?

An example - say you have 4 acres of land and have a family of 4.

In the old world, say you needed one acre per person to grow enough food to the next crop harvest. This would be something like corn or potatoes that can keep. So all your land goes to growing food to survive and you cant make any money.

In the new world, with irrigation, you can do much more - say for the sake of argument, 4 times the crop, in the same space. Now, you only need 1/4 of an acre per person or an acre for everyone. So you grow vegetables that sell for 10 times as much on the 3/4s of land you have that you no longer need to use to survive.

Or even better, you grow high veg on the entire piece of land for income and use the cash to buy your corn and potatoes or whatever as you need them.

Just as all other commercial farmers do across the world.

In other words, solar allows them to become small business owners.

https://learn.microsoft.com/en-us/azure/cloud-adoption-frame...

TL;DR version - its about money and business balance sheets, not about technology.

For businesses past a certain size, going to cloud is a decision ALWAYS made by business, not by technology.

From a business perspective, having a "relatively fixed" ongoing cost (which is an operational expense ie OpEx ) even if it is significantly higher than what it would cost to do things with internal buy and build out (which is a capital expense cost ie CapEx), make financial planning, taxes and managing EBITDA much easier.

Note that no one on the business really cares what the tech implications are as long at "tech still sorta runs mostly OK".

It also, via financial wizardry, makes tech cost "much less" on a quarter over quarter and year over year basis.

+ for case of cost: lots of very large companies have prod environments that cost big $$$. Business will not double prod cost for a staging environment mirroring prod. Take an example of any large bank you know. The online banking platform will cost tens if not hundreds of millions of dollars to run. Now consider that the bank will have hundreds of different platforms. It is just not economically feasible.

+ for the case of law: in some sectors, by law, only workers with "need to know" can access data. Any dev environment data cannot, by law, be a copy of prod. It has to be test data, even anonymization prod data is not allowed in dev/test because of de-anonymization risk.

Given this, consider a platform / app that is multi-tenant (and therefore data driven ) eg a SaaS app in a legally regulated industry such as banking or health care. Or even something like Shopify or GMail for corporate where the app hosts multiple organizations and the org to be used is picked based on data (user login credentials).

The app in this scenario is driven by data parameterization - the client site and content are data driven e.g. when clientXYZ logs on, the site becomes https://clientXYZ.yourAppName.com and all data, config etc are "clientXYZ" specific. And you have hundreds or thousands of clentsAAA through clientZZZ on this platform.

In such a world, dev & test environments can never be matched with prod. Further, the behaviour of the client specific sites could be different even with the same code because data parameters drive app behaviour.

Long story short, mirroring staging and prod is just not feasible in large corporate tech

sees: "an uncursed food ration"

This is wild; I gotta start playing text based games.

The author actually realizes this but did not nail this idea to the church door as part of his manifesto.

      >Being valued, on the other hand, means that you are brought into
      >more conversations, not just to execute, but to help shape the
      > direction. This comes with opportunities to grow and contribute
      > in ways that are meaningful to you and the business.

The second part - "opportunities to grow and contribute in ways that are meaningful to you and the business." - that is being 'valued strategically'

      > Being useful means that you are good at getting things done in a
      > specific area, so that people above you can delegate that
      > completely. You are reliable, efficient, maybe even
      > indispensable in the short term. But you are seen primarily as a gap-filler,
      > someone who delivers on tasks that have to be done but are not
      > necessarily a core component of the company strategy. “Take care
      > of that and don’t screw up” is your mission, and the fewer
      > headaches you create for your leadership chain, the bigger the rewards.

The second part, "Take care of that and don’t screw up” is your mission, and the fewer headaches you create for your leadership chain, the bigger the rewards." is being 'valued tactically'.

So, the theory is every member of staff is dropped BOTH a 'useful' and 'valued' bucket for tactical work and for strategic work.

ie: - one can be useful or not useful for strategic or tactical work or both - one can be valued or not valued for strategic or tactical work or both

A couple of counterpoints:

1. You can,unfortunately, be useful strategically and not be valued. Think about the hachet man every leader of a large organization has - the guy who does the layoffs. That slot is useful strategically but can be filled by almost anyone - it is not valued by the org.

2. You can, fortunately, be useful tactically, useless strategically, and be be very very valued in an organization. Best examples of this are folks who are very very good at running operations. Think about a good truck dispatcher, or a 911 operator or an air traffic controller. 90% of their job is effective tactical execution - dealing with this emerging situation right now effectively and efficiently. That is highly valuable to organizations.

Also note that every org needs strategy people and tactical people for long and short term.

One is not better than the other. They are just different.

And there are lots of very highly paid tactical roles, sometimes better paid, that are more challenging and more interesting than any strategy role.

These tend to be "do this or fix this thing right now efficiently and effectively" jobs.

For example, almost any practicing medial role is a tactical one - ER doctor (fix this sick person right now) or controllers for real time stuff - concert and live TV producers (make this thing look good right now), air traffic controllers (keep these planes safe right now) etc etc.

So, net net, pick you spot - tactical vs strategic or both, useful vs. valuable or both - get good at it and then may the odds always be in your favor.

They can certainly be created at test run time but that would slow things down a lot - you would essentially be creating a ton of objects every time you run the test which means having a setup to test if they exist or not, take them down if they do or fix them if they don't match spec ( e.g. column and data type changes etc etc )

The more I think about this, the more complicated I realize it would be to manage this dynamically:

You essentially have to build a test harness enviroment that figures out your testing elements dynamically from your data environment (with some kind of parameterization engine and data set to tell it what to look for so as to "make functors and run them" (e.g. all PKs of FKs or all columns starting with a certain prefix or all columns of a certain data type etc etc), gets the most up to date definitions of those elements from system tables and uses that data to create or update or drop functor objects ... wow, ok, this is getting complicated, I am going to stop now before I see the void.

I think Netezza, SQL Server and Oracle are all cursor-based processing "by default" so this makes a lot of sense. I suspect that they all have bulk operation capability but can't immediately think of how I would have worked bulk processing in a way that maps to this article - maybe something like analytic functions like windowing, partitioning etc. that is definitely not row by row.

Having said that, the examples I see for actual testing in the article are DQL / DML so would be multiple row processing by default .. yes, the functor definition / creation is a DDL process but it is a "do once and reuse the definition" thing (like, the author correctly observes, a view, which is the point of functors) and the functor in use would just be DML. In which case, functors go back to looking like stored procedures...

I also understood composability as being built in for SQL - for example, in Oracle, packages allow composability of stored procedures, triggers, sequences etc allow composability of DML and views allow composability of queries and tables - which the author points out in the article.

With functors, DDL, DML, DQL, DCL, TCL would still be the only command tools available unless a new command language was invented for SQL for testing - let call that something like DTL (Data Test Language), with a whole new bunch of associated new SQL keywords, capability and functionality that are built right into the core of the DB engine that are optimized for what functors are trying to achieve.

I do have a question though - while I understand how functors can help make the problem easier to tackle, I am not sure I fully understand how functors are different from a similar existing tool - stored procedures.

Some DB flavors:

- can take tables as arguments to stored procedures - can return tables - also offer the additional benefit of being able to run almost all flavors of SQL commands ( DDL, DML, DQL, DCL, TCL) in those stored procedures

Netezza stored procedures, for example, can do what you describe here:

https://www.ibm.com/docs/en/netezza?topic=nsg-return-result-...

As can SQL Server & Oracle (which both return cursors, which are just ordered tables):

https://learn.microsoft.com/en-us/sql/relational-databases/s...

https://docs.oracle.com/cd/B28359_01/appdev.111/b28843/tdddg...

Am I missing something fundamental here? How are functors different from stored procedures? To me, they seem to be just a sub-class / sub-type of stored procedures.

Passengers are paying the drivers directly ( and the drivers then passing on the apps share on to them - the 30% in commission and booking fee ) because when the passengers pay outside the rideshare app, the app portion becomes payable to the app.

Passengers pay outside the app because mobile money is ubiquitous in Kenya (mobile phone to mobile phone micropayments called MPESA): it is used even more frequently than cash

They have to keep taking an 'L' specifically because there is extreme efficiency in price information discoverability in a commodity market.

Explaining:

Some background info: - almost all drivers are signed up with all services [ ie i) Lyft, Uber - which are global & ii) Faras, Bolt which are international but Africa only I think ]

- riders have all apps and will price shop on every single app before booking a trip because it will be the same freaking vehicle and driver (ie literally the same service because of item above)

- ride prices are pretty expensive relative to local earnings: take for example, that first item (Minimum to and from JKIA, which is the main airport in Nairobi). It is 1000kshs which is about $8 - not a big amount in the west but is a bit of coin in Kenya - a day;. If I can get the same ride for $6, it is kind of a big deal because it means I can maybe also get a meal from the ride savings - estimates say apps takes 30% (20% commission and 10% booking fee) and this is off the top of the payment

So, the rides are a fungible commodity. It is the same ride, all you are choosing is whom to buy it from

Consider Bob looking for a ride to the airport: he looks at all 4 apps ( Uber, Lyft, Faras and Bolt), finds the cheapest one (Alice, on say the app HNewsRides) at Kshs 800 (~$6) and books that (since it is the same freaking car and driver as all drivers all use all the apps! Note that they can also sorta see - based on proximity and time to arrival for pickup - that the same ride with Alice would cost Kshs 900 on AppA, Kshs 1000 on AppB, Kshs 950 on AppD etc )

Even worse, after Bob has taken a couple of rides with Alice, and they know each other a little better, they will establish a relationship and exchange phone numbers so that Bob will just reach out to Alice directly and cut out the middle man ( apps ) because Alice will charge them the app amount less say 25% (because there is no cut for the app now). Bob gets a 10%-20% discount on the ride and Alice (the driver ) gets 20% more for the ride. This means that apps have to cut prices again to compete ad infinatum till every ride is free (or the apps are paying riders to get market share and then jack prices, which they have done )

So, the real problem is extreme information availability and a multitude of players. If there were just one or two apps, price management is easier but with 4+ players, price competition is the name of the game.

About the "it will be the same driver and vehicle" thing: this is a quirk of the Kenyan market. If an ride is showing as less than 5 mins away from you on all apps, it is likely to be a situation where it is just one specific car and driver across all apps. Nairobi has horrendous all day traffic. Really bad. Think your worst nightmare traffic and multiply by two. Two cars separated by as little as 1 mile could have as much as a 10 min difference in estimated arrival times because of traffic. So, if a vehicle says "5 mins away" in 4 different apps, you can relatively safely assume it is the same vehicle listed on across all apps.

> 1. Online prices are not the same as in store prices This depends but mostly they match ie online grocer prices in Canada are the same as in-store prices

This is because most grocers have use online prices as flyers / ads / sales pricing for stores.

Also, in Canada, because of price match policies from the grocers themselves, most grocers have to match their own online prices and, in many cases, competitor prices - this includes competitor online prices!!! including sales and flyer offers

https://blog.flipp.com/7-canadian-grocery-stores-that-price-...

Further, there is - pricing law in Canada around charging more than advertised pricing

https://competition-bureau.canada.ca/deceptive-marketing-pra...

- a pricing code of conduct: if an item is more expensive than the listed price, the consumer gets that first item for free. This means that grocers go out of their way to show the lowest correct possible price all the time everywhere.

https://competition-bureau.canada.ca/deceptive-marketing-pra...

The only exception I can think of is delivery prices where in-store pricing will not match the online pricing. But delivery is usually differentiated by completely different branding from the actual store eg Sobey's has the "Voila" brand for delivery and Metro has "metro.ca" as the delivery brand which is distinct from Metro.

> 2. No one, other than random people in messages forums, is alleging collusion or price fixing. No one at any university, not the government, not the media."

This is factually incorrect: grocer bread price fixing / collusion went to court and there was a $500m judgement

https://globalnews.ca/news/10642801/loblaw-bread-price-fixin...

> 3. The government’s investigation, which many of the conspiracy theorists site as evidence, shows a lack of competition led to 1 or 2 % increase in price over 12 years, and the vast majority of the price increases was due to things like war in Ukraine, Covid restrictions, fuel prices due to energy crisis.

Price fixing preceded all these events. In cases being investigated e.g. bread which is now resolved, the period was as far as 15 years back.

https://globalnews.ca/news/10642801/loblaw-bread-price-fixin...

Makes perfect sense now.

That is a really clever joke.

I can't help but think that I am missing a really cool inside joke of some kind here.

I get that it is an electronics project using a piece of transit authority electronic equipment so "transistor" make a ton of sense from that perspective but also get it that it is easy to go "transistor" when you mean "transit" especially if you work a lot with electronics.

Can anyone help me out with a hint ( and not an answer ) if I am indeed missing something key all together.

Either way, both article title and article content are dope.

… and this analysis generalizes all types of AIs based on characteristics of just one type of AI - LLMs - that select the “next best word “ (my layman’s understanding of what they do). With so many other types of AI out there, we will eventually definitely get new approaches to AI where the arguments presented won’t make sense

About throwing punches in the air in a tantrum - that was a funny comment. So funny that I wanted to see if someone made art of it. Found something and sharing a link here that is pretty short and to the point, sweet and cool

(I hate mystery links so some info - it’s an performance art piece called “Plastic Bag” on YouTube, 5 mins long, about 60k views, where someone throws punches in the air at a plastic bag)

https://youtu.be/-W6rn2cWs2g