The journal articles the chart is based on are referenced in light grey on the side, if you prefer to view the same data in a peer reviewed article format with a more serious font.
> Until someone can reconcile these facts, and they can say distinctively that the rise in temperature we see right now isn't the same reason as before, I'm going to believe that temperature will moderate and cool, just like it did in the past.
Would you indulge me and see if this one chart might change your mind? It includes each of your data points.
My father was a printer his entire life. I went with him to work one day when I was around 12 years old. He loaded up a box of, what seemed to me a random selection of flyers, booklets and other printed goods in his car, and together we drove to the National Archives (in Sweden).
He explained to me that every media artifact of cultural significance would be stored there in three (I believe) copies for future generations to enjoy, or researchers, or historians.
I was given a tour by an archivist there and this became a core memory of mine.
I was always at unease growing up, wondering what would happen to video games when they no longer became popular. Would I be able to enjoy them when I got older? Would my children ever be able to play the games that shaped my teenage years?
The discussion around the matter of Stop Killing Games always devolves to one around free labour or around infringing the rights of the creator, but at some point, when a game, or a film, or a book is no longer monetised, makers of cultural works should be obliged to archive and ensure that our shared cultural heritage and identity is preserved for the future.
Film makers, authors, printers, ad agencies, music producers, and many others are already obliged to do this in many countries.
Why should video game producers be exempt?
It's just better for all of us, and our children, if these works of art are preserved, and that at an insignificant effort and cost, compared to the cost of developing it.
I run a company that have done over 200 similar studies for various NGOs and international organisations.
In the general case, with some exceptions, we have found that two types of interventions stand above all others in terms of long term positive economic impact:
1. Infrastructure projects - like building roads
2. Gender projects - projects furthering women's rights in some way
These projects are long-term sticky and do not rely on continuous funding. A paved road will remain paved even after the funding is gone, and will have a positive impact on the community for many decades. Roads allow children to go to school in neighbouring villages, and people to sell their goods in a market, use a bike or other vehicle where they otherwise would not be able to.
Working with local governments to improving the attitudes towards girls and women often has a major impact on the economic output of a community both because more people can contribute, but also because the types of products and services become more diverse. This type of project is also sticky, once attitudes or structural barriers disappear they don't tend to come back.
Education or sanitation initiatives can be hit or miss, where, once funding dries up, all that is left is a non functioning latrine or empty school building.
As I understand it, from our lawyer, is that this exact wording is automatically enforceable in UK courts and easiest in the event of a dispute. It’s also generally internationally accepted.
Oh and another lesson! Ensuring that each deliverable invoice is small enough that it falls under the simplified claims procedure (in the UK it’s 10,000 pounds) greatly simplifies collection.
It costs something like 80 quid to file for recovery in court and in our experience invoices are immediately paid up when a “Letter before action” is sent.
You burn the relationship, but arguably you probably don’t want it anyway.
We’ve also learned this lesson the hard way. These are now the clauses we require in every project we do:
- Payment is due X days after receipt of invoice, or immediately after the consultant has addressed any quality issues, whichever is sooner
- Late payment shall incur interest at 8% above the BoE base rate and a late fee of 100 GBP as per the UK Late Payment Legislation. Partial payments on invoices shall apply to late fees, interest, and then principal, in that order.
- In the event of a late payment the invoice for the next deliverable shall immediately fall due.
- The consultant shall be entitled to shift deadlines on deliverables in the event of a late payment as a result of any work disruption, without incurring any liability.
- Payment shall be made in X currency, or an exchange rate at X date on Oanda.com shall apply.
- The client is responsible for any bank fees incurred by their, or any intermediary bank. In the event of a SWIFT transaction it shall be made with the OUR payment code.
- The jurisdiction in the event of a conflict shall be England and Wales. Neither party shall be bound by arbitration.
- The client and consultant shall both indemnify the other up to the total value of the contract and shall not under any circumstance be liable beyond X GBP.
We also no longer share downloadable links of our deliverables until they are paid up. They get a view/comment only link for reports/data etc.
We’ve found that clients that aren’t willing to accept these terms won’t pay you either way.
We determine the net days on the invoice based on the credit rating of the client. Ironically, the good clients pay within 2-3 days normally, and the difficult ones are very “long tail”. About 1% of contracts tend to fully or partially default on their payments.
We’re in a particularly credit poor industry but our average delay due to late payment is 23 days. Those clients where we stop delivery pay on average 11 days sooner than those contracts where we don’t stop delivery.
This is based on around 2,000 invoices sent over the last 5 years.
AI has led us into a deep spaghetti hole in one product where it was allowed free rein. But when applied to localised contexts. Sort of a class at a time it’s really excellent and productivity explodes.
I mostly use it to type out implementations of individual methods after it has suggested interfaces that I modify by hand. Then it writes the tests for me too very quickly.
As soon as you let it do more though, it will invariably tie itself into a knot - all the while confidently ascertaining that it knows what it’s doing.
We had this debate at my company and the end result was a ban on “random” service names.
So we ended up with “auth service” instead of something like “Galactus”. The problem of course is that “auth service” isn’t searchable in our monorepo and it was a nightmare to find or discuss any info or references to the service itself. Now imagine if docker was called “container manager”. Good luck googling that and disentangling it from all the search results.
The value of a name doesn’t come from it being self-explanatory but rather from it being a pseudo-unique identifier. The small cognitive tax of remembering it serves as a shared bookmark between people that you can refer to when discussing or speaking to others about it - whether we’re talking about docker, Linux, or another person.
Is there any real drawback to just never giving your real name or address to service providers to minimise the chance of identity theft? Most likely it’s against terms of service, but other than account suspension are you likely to suffer any legal consequences?
> This is the part I disagree with. It hasn't been true for years. Anyone with the free version of ChatGPT can pass a hacker rank today.
It certainly still is true today. Anybody who is sufficiently motivated to cheat can pass it. It was true prior to ChatGPT, and it still remains true today. And yet they don’t. Most people completely fail these screens
> It does for mine, because we've hired all of the good developers that get through the process you're describing and it isn't enough.
Then your industry is atypical in the type of applicants that you are getting. So to accommodate you’ve had to increase your false positives to reduce false negatives. That’s completely fine if it’s what you need to do, but it’s not the typical experience for a tech company.
We also do a pair screen after the code test and we still reject around 80% who make it to that stage. How do you scale interviewing everyone if you don’t pre screen?
It only appears that HackerRank/Leetcode isn’t good at filtering because you’re viewing it from your perspective, and not the perspective of the entire population that is tested. To you, the predictive power at the top tail end of the distribution is low, because you’re thinking of two strong developers Alice and Bob. Alice happens to know algorithm X and would pass the test, whereas Bob does not. But that’s not the population we’re testing. Think more along the lines of Alice and Bob and your grandmother were the test population. It’s absolutely fantastic at filtering the lower 95% of applicants because they will _never_ be able to pass. Yes, inadvertently 2.5% of “good developers” are filtered too, but that doesn’t matter to the outcome of your company. They just want someone competent, and they don’t care if it’s Alice or Bob.
The same logic sort of applies to Tim and his performance. The bias of having an imperfect metric is probably much better than the bias of letting an army of middle managers go with their cut. Besides, it doesn’t have to be a hard filtering function at this stage, but a metric to indicate that we need to look a little closer at Tim
Pretty much _any_ EV car is better both cost wise and emissions wise than _any_ PHEV.
The same holds comparing PHEV with ICE.
Not to mention that EVs are just so much more fun and satisfying to drive. Even a mediocre EV these days will do 0-60 in 5s.
At this point buying anything but an EV for the typical driver is just objectively the wrong choice.