Gig work is great for easily defined, repeatable tasks. Uber is going from point A to point B. This is something done for a long point of human history, and can be easily optimized through maps and routing algorithms. General software development can be more murky, because usually:
1. The person writing the requirements doesn't fully understand what's needed from an eng perspective
2. They need someone to describe tradeoffs of eng approaches
3. They'll probably need custom changes due to shifting business or client needs
4. They might need someone to guide them on a better framework or eng setup for their project
Being able to actually complete a task well, on time or faster will get you a premium (it can be really hard to find). Being able to assist on a consulting side on top of that is very valuable and rare. I think the people who are really good on Upwork often get hired outright by contractors with more money or get higher paying side gigs (or they are outsourcing their work to other devs).
Basically, you work on upwork for company X full-time but are paid on hourly basis and without any overtime or benefits.
This is what the article alludes to. So called "gig" workers at uber are constrained, the flexibility is an illusion. You are forced to comply and work a hard schedule or you will be punished by the algorithms.
So as a developer you are more likely to be average than exceptional, because the field has been commodified by the learn2code movement and bootcamps and therefore you are at the whim of the employer and dont have any flexibility. They can replace you in a heartbeat.
no, i'm thinking more the like of toptal who i am seeing increasingly more and more.
i don't know their business model exactly but what i do know is that companies are increasingly seeing web dev as a commodity or to use the expression "code monkey" work.
something to be outsourced, get people to work unpaid overtime.
The difference between a good web developer (80th percentile?) and a median one is much greater than the difference between a good driver and a median one. And the inputs and outputs can be fuzzier. And bad output in tech lasts longer than the negative effects of a bad ride. That’s why it’s harder to hire anonymously like via an app.
I agree but just like uber it's not really "gig" work. You're essentially working full-time and even more (unpaid overtime) but you're classified as an independent contractor so you don't have any benefits or security under the false guise of "flexibility".
The loss of the wash sale is added to the basis of the next purchase. You would still be taxed on a gain of $100 in that scenario.
The nasty case is if you buy X at $100, sell at $1000, buy at $1000, wash sell at $100, then buy at $100.
You have $900 in gains according to the wash sale rule but no actual gains. Your basis in X is $1000, so you would have no gain if you sold at the current price, but if you hold it until the end of the tax year, then you owe taxes on $900 despite not making anything.
Would it be safer to close out out potential wash-sale positions by end of November, instead of end of year?
I imagine a situation where you sell everything of Stock A on Dec 20 realizing all losses, and then mistakenly rebuy back on Jan 10. Now all your loss for previous year is disallowed and you have no way to fix it? Even if you immediately sell the loss would count for the current tax year, and not previous tax year, right?
This is not how it works under the wash sale rule. In your example, the $900 loss would be added to the cost base when doing the second purchase. Because of that, the sell at $1100 will have a cost base of $1000.
Why is “data driven” policing bad when we’re pretty much striving for “data driven” everything else in government?
The EFF argues that the methods here are pseudo scientific, but they seem more rigorous than many of the other “data driven” methods governments are implementing in other contexts.
Usually it comes down to statistics being extremely difficult.
If you base your model on historical data you are likely to have correlating factors with low economic status and race. You haven't actually abstracted out these concepts but rather baked them into the model. Latent variables are extremely difficult to remove from the system and as far as I'm aware no one has (afaik no one has done even a remotely good job at this, bordering/sometimes bad faith).
We should strive more for data driven solutions, but we have a bad human element that will use data as a crutch rather than a resource. Given how we know the data often fails, this makes it difficult to put into use without amplifying those effects. (there's plenty of easily googleable/ddg-able sources you can find on this. Decades of material actually)
While we're going data driven in many areas, you may notice that most of these areas don't have as much of a direct impact on a person's life as policing does. That gives more room for error. It sucks, but it isn't that big of a deal if you pay more than your neighbor for that flight to NYC. Move fast and break things doesn't work so well when "breaking things" results in "broken homes" and "broken lives". Maybe we need a different approach.
The data usually has clear biases present against certain ethnic groups and economic classes. Also you have to look into which laws are broken and feed into the data (again, reflects back on the first sentence). If jaywalking and other minor crimes go into the prediction algorithms, are those crimes treated equally throughout the area and population? Is it really the case that there's no jaywalking in the middle class neighborhoods or is it just that the police only apply it in the poor neighborhoods? This creates a bias in patrols where they step up in areas with more charges, which makes sense on the surface until you examine which areas those are and why they have more charges in that area or amongst that population.
That is sort of like asking "If having a sexual relationship is perfectly fine what is wrong with a boss dating their direct subordinate?" - the power dynamic changes things via coercion. This isn't like A/B testing two apparently nearly equally valid curriculums on classes.
It is a self-fuffilling prophecy in the case of policing - they will skew where they find crime more where they focus their efforts. And that is assuming honest mistakes instead of outright bias laundering operations.
This does the opposite actually, it forced the federal government to de-prioritize areas with worse service in favor of areas with good but not as good as 100/100 service.
There are parts of the US that don't even have access to 15/3. This reclassification would put them in the same group as people with 75 up and down.
> This crisis in Texas is also a good argument for why price gouging during a shortage doesn’t work the way anti-regulatory advocates said it would.
There's no way to make that judgement until we see some actual metrics instead of anecdotes. It's possible this could have been worse without increased prices.
The things that have seen large price increases are things that aren't scalable i.e. they still require the same amount of human input labor to produce
Childcare is constrained by the fact that each childcare worker by law (for better or worse) can only look after so many children at once. This ratio varies by state but it's usually around 4 children per childcare worker. The result is that it has to be expensive because (short of having robot caretakers) it always takes 1/4 of a person's labor.
Education is similar, but has gotten worse in the past few decades w.r.t. labor requirements. While class sizes have remained relatively constant, there are more administrators, special needs educators, counselors, etc. servicing the same group of students.
This isn't to say they shouldn't be regulated, or that there isn't room for improvement. College tuition and textbooks could certainly use some downward pressure.
Textbooks are now distributable by e-books, easily scalable. Yet somehow capitalism has utterly failed to deliver it's supposed benefits.
College tuition is now distributable by remote learning, theoretically massively reducing costs. Again, capitalism has utterly failed to deliver it's supposed benefits.
Housing has massively advanced, with cheaper construction, high rises, etc. but laws have been crafted to ensure that doesn't happen to protect rentiers. No taxes on unoccupied land, restrictions on where you can build, massive consolidation of available land without any desire to actually build anything but expensive condos. Again, capitalism has utterly failed to deliver it's supposed benefits.
The cost of textbooks is not primarily in the distribution method, it's the labor of content creation.
The cost of tuition is not primarily in where or how people attend classes, it's the labor of developing curriculum, mentorship, and research.
The cost of housing is not primarily in construction, it's the scarcity of land. High density construction does address this, but you even mention that laws are obstructing this. How is this capitalism's fault when democratic governments enact laws that are literally stopping it from functioning freely?
"Capitalism" completely solved the textbook problem, it simply ignored universities and degrees and whatnot altogether. (How come coding bootcamps don't have expensive textbooks?)
I put the quotes there because (arguably obviously) it's also thanks to capitalism that intellectual property is extended so obscenely.
Anyway, no university (or higher-ed institution) is forced to use textbooks. They do it because they can. Because there's no market force pushing prices down, because captive audience, because the signaling value of degrees is still high (because nobody got fired for hiring the candidate with more degrees - that also happens to have a wealthier background, and maybe even also happens to be white). But it's changing (due to market forces).
Similarly, housing is not a market problem. It's about "preserving the character of the neighborhood", and most neighborhoods happen to be favoring those who happen to be wealthy and against building.
I find it difficult to believe that the increase in university costs is significantly driven by increases in labor cost for the jobs actually related to delivering a university education. If anything I suspect there have been a significant decrease in the number of university employee-hours per student-credit-hour.
I'm not sure why it's difficult to believe. Universities are more than just faculty. In the past few decades, universities have become full service institutions that provide more than just education and have added an army of counselors and administrators.
