The major story of the past couple of weeks has been Ukraine’s Kursk offensive and the seizing of over 1100 square kilometres of Russian territory in the past ten days. This has been a stunning change in the direction of the war. At least five Ukrainian brigades, or elements of those brigades, and possibly more have seized the initiative and remained on the move since surprising the Russians in their initial crossing of the border into the Russian Kursk oblast.

Highlights

So the point of the American UI system is not to make it easier to quit a job. But a few economists are now beginning to ask: Should it be?

A safety net program that would encourage more Americans to quit their jobs has generally been seen as a bad thing.

Boosting UI generosity doesn’t affect overall employment rates one way or the other. Instead of loafing around in subsidized unemployment, more generous benefits can support people to quit their jobs in search of better ones, which benefits workers through higher wages and better job satisfaction, and the economy through enhanced productivity as people find better uses for their skills.

The real losers would be lousy jobs, which would struggle to retain workers with a greater cushion to quit and go looking elsewhere.

A major barrier facing lasting reform has been that most people do not care about improving the unemployment system long enough to build the kind of political momentum that gets laws through Congress.

Without financial support, quitting in search of better work just isn’t always a viable option, especially for the more than one in 10 US households that have zero wealth to fall back on.

The unemployment insurance system was established during the Great Depression as part of the Social Security Act in 1935, when the unemployment rate was about 20 percent; helping those workers who still had jobs quit wasn’t exactly a policy priority. About half of American workers were excluded from coverage, including agricultural and domestic workers (many of whom were Black).

The surge of quits during the pandemic and the expansion of unemployment insurance created a unique dataset that caught the attention of economists Zhifeng Cai and Jonathan Heathcote.

After an extra $600 was added to weekly UI checks, along with a major expansion to who is eligible for the benefits, studies found no connection between the boosted UI and laziness or joblessness (echoing findings around unconditional cash transfers more broadly, where giving people cash doesn’t undermine their desire to work).

Economists have historically held equality and efficiency at odds with each other, with higher UI benefits seen as an equality booster that trades off against economic efficiency. But Cai explained in an interview with Vox that “if you give nothing to people who quit, it’s actually not an efficient choice, because there are too few people quitting. Our point is that even from an efficiency perspective, you still want to have some UI going to quitters.”

Highlights

European beech trees more than 1,500 kilometers apart all drop their fruit at the same time in a grand synchronization event now linked to the summer solstice.

From England to Sweden to Italy — across multiple seas, time zones and climates — somehow these trees “know” when to reproduce. But how?

Their analysis of over 60 years’ worth of seeding data suggests that European beech trees time their masting to the summer solstice and peak daylight.

The discovery of the genetic mechanism that governs this solstice-monitoring behavior could bring researchers closer to understanding many other mysteries of tree physiology.

So it’s easy to see why masting trees synchronize their seed production. Understanding how they do it, however, is more complicated. Plants usually synchronize their reproduction by timing it to the same weather signals.

Then the team stumbled across a clue by accident. One summer evening, Bogdziewicz was sitting on his balcony reading a study which found that the timing of leaf senescence — the natural aging process leaves go through each autumn — depends on when the local weather warms relative to the summer solstice. Inspired by this finding, he sent the paper to his research group and called a brainstorming session.

It’s the first time that researchers have identified day length as a cue for masting. While Koenig cautioned that the result is only correlational, he added that “there’s very little out there speculating on how the trees are doing what they’re doing.”

If the solstice is shown to activate a genetic mechanism, it would be a major breakthrough for the field. Currently, there’s little data to explain how trees behave as they do. No one even knows whether trees naturally grow old and die, Vacchiano said. Ecologists struggle just to study trees: From branches to root systems, the parts of a tree say very little about the physiology of the tree as a whole. What experts do know is that discovering how trees sense their environment will help them answer the questions that have been stumping them for decades.

Alice Evans is diving into a new Econ paper.

Ingrid Haegele finds that junior men are more likely to apply for promotions, primarily due to a greater desire for team leadership.

We can best view the method of science as the use of our sophisticated methodological toolbox

Metadata

• Author: OUP Academic
• Category: article
• URL: https://academic.oup.com/pnasnexus/article/3/4/pgae112/7626940?login=false

Highlights

Scientific, medical, and technological knowledge has transformed our world, but we still poorly understand the nature of scientific methodology.

scientific methodology has not been systematically analyzed using large-scale data and scientific methods themselves as it is viewed as not easily amenable to scientific study.

This study reveals that 25% of all discoveries since 1900 did not apply the common scientific method (all three features)—with 6% of discoveries using no observation, 23% using no experimentation, and 17% not testing a hypothesis.

Empirical evidence thus challenges the common view of the scientific method.

This provides a new perspective to the scientific method—embedded in our sophisticated methods and instruments—and suggests that we need to reform and extend the way we view the scientific method and discovery process.

In fact, hundreds of major scientific discoveries did not use “the scientific method”, as defined in science dictionaries as the combined process of “the collection of data through observation and experiment, and the formulation and testing of hypotheses” (1). In other words, it is “The process of observing, asking questions, and seeking answers through tests and experiments” (2, cf. 3).

In general, this universal method is commonly viewed as a unifying method of science and can be traced back at least to Francis Bacon's theory of scientific methodology in 1620 which popularized the concept

Science thus does not always fit the textbook definition.

Comparison across fields provides evidence that the common scientific method was not applied in making about half of all Nobel Prize discoveries in astronomy, economics and social sciences, and a quarter of such discoveries in physics, as highlighted in Fig. 2b. Some discoveries are thus non-experimental and more theoretical in nature, while others are made in an exploratory way, without explicitly formulating and testing a preestablished hypothesis.

We find that one general feature of scientific methodology is applied in making science's major discoveries: the use of sophisticated methods or instruments. These are defined here as scientific methods and instruments that extend our cognitive and sensory abilities—such as statistical methods, lasers, and chromatography methods. They are external resources (material artifacts) that can be shared and used by others—whereas observing, hypothesizing, and experimenting are, in contrast, largely internal (cognitive) abilities that are not material (Fig. 2).

Just as science has evolved, so should the classic scientific method—which is construed in such general terms that it would be better described as a basic method of reasoning used for human activities (non-scientific and scientific).

An experimental research design was not carried out when Einstein developed the law of the photoelectric effect in 1905 or when Franklin, Crick, and Watson discovered the double helix structure of DNA in 1953 using observational images developed by Franklin.

Direct observation was not made when for example Penrose developed the mathematical proof for black holes in 1965 or when Prigogine developed the theory of dissipative structures in thermodynamics in 1969. A hypothesis was not directly tested when Jerne developed the natural-selection theory of antibody formation in 1955 or when Peebles developed the theoretical framework of physical cosmology in 1965.

Sophisticated methods make research more accurate and reliable and enable us to evaluate the quality of research.

Applying observation and a complex method or instrument, together, is decisive in producing nearly all major discoveries at 94%, illustrating the central importance of empirical sciences in driving discovery and science.

Cyber Conflict and Subversion in the Russia-Ukraine War

Metadata

• Author: Default
• Category: article
• URL: https://www.lawfaremedia.org/article/cyber-conflict-in-the-russia-ukraine-war

Highlights

The Russia-Ukraine war is the first case of cyber conflict in a large-scale military conflict involving a major power.

Contrary to cyberwar fears, most cyber operations remained strategically inconsequential, but there are several exceptions: the AcidRain operation, the UKRTelecom disruption, the September 2022 power grid sabotage, and the catastrophic Kyivstar outage of 2023.

These developments suggest hacking groups are increasingly fusing cyber operations with traditional subversive methods to improve effectiveness.

The first exceptional case is AcidRain. This advanced malware knocked out satellite communication provided by Viasat’s K-SAT service across Europe the very moment the invasion commenced. Among the customers of the K-SAT service: Ukraine’s military. The operation that deployed this malware stands out not only because it shows a direct linkage to military goals but also because it could have plausibly produced a clear tactical, potentially strategic, advantage for Russian troops at a decisive moment.

The second exception is a cyber operation in March 2022 that caused a massive outage of UKRTelecom, a major internet provider in Ukraine. It took only a month to prepare yet caused significant damage. It cut off over 80 percent of UKRTelecom’s customers from the internet for close to 24 hours.

Finally, the potentially most severe challenge to the theory of subversion is a power grid sabotage operation in September 2022. The operation stands out not only because it used a novel technique but also because it took very little preparation. According to Mandiant, it required only two months of preparation and used what is called “living off the land” techniques, namely foregoing malware and using only existing functionality.

After all, why go through the trouble of finding vulnerabilities in complex networks and develop sophisticated exploits when you can take the easy route via an employee, or even direct network access?

Some article from the past ;)

Why We Love Music

Metadata

• Author: Jill Suttie
• Category: article
• URL: https://greatergood.berkeley.edu/article/item/why_we_love_music

Highlights

Using fMRI technology, they’re discovering why music can inspire such strong feelings and bind us so tightly to other people.

“A single sound tone is not really pleasurable in itself; but if these sounds are organized over time in some sort of arrangement, it’s amazingly powerful.”

There’s another part of the brain that seeps dopamine, specifically just before those peak emotional moments in a song: the caudate nucleus, which is involved in the anticipation of pleasure. Presumably, the anticipatory pleasure comes from familiarity with the song—you have a memory of the song you enjoyed in the past embedded in your brain, and you anticipate the high points that are coming.

During peak emotional moments in the songs identified by the listeners, dopamine was released in the nucleus accumbens, a structure deep within the older part of our human brain.

This finding suggested to her that when people listen to unfamiliar music, their brains process the sounds through memory circuits, searching for recognizable patterns to help them make predictions about where the song is heading. If music is too foreign-sounding, it will be hard to anticipate the song’s structure, and people won’t like it—meaning, no dopamine hit. But, if the music has some recognizable features—maybe a familiar beat or melodic structure—people will more likely be able to anticipate the song’s emotional peaks and enjoy it more. The dopamine hit comes from having their predictions confirmed—or violated slightly, in intriguing ways.

On the other hand, people tend to tire of pop music more readily than they do of jazz, for the same reason—it can become too predictable.

Her findings also explain why people can hear the same song over and over again and still enjoy it. The emotional hit off of a familiar piece of music can be so intense, in fact, that it’s easily re-stimulated even years later.

“Musical rhythms can directly affect your brain rhythms, and brain rhythms are responsible for how you feel at any given moment,” says Large.

“If I’m a performer and you’re a listener, and what I’m playing really moves you, I’ve basically synchronized your brain rhythm with mine,” says Large. “That’s how I communicate with you.”

He points to the work of Erin Hannon at the University of Nevada who found that babies as young as 8 months old already tune into the rhythms of the music from their own cultural environment.

“Liking is so subjective,” he says. “Music may not sound any different to you than to someone else, but you learn to associate it with something you like and you’ll experience a pleasure response.”

Interesting findings

Startling differences between humans and jukeboxes

Metadata

• Author: Adam Mastroianni
• Category: article
• URL: https://www.experimental-history.com/p/startling-differences-between-humans?utm_source=substack&utm_medium=email

Highlights

Similarly, when you survey people about what motivates them at work, they go “Feeling good about myself! Having freedom, the respect of my coworkers, and opportunities to develop my skills, learn things, and succeed!” When you survey people about what motivates others, they go, “Money and job security!” In another survey, people claimed that they value high-level needs (e.g., finding meaning in life) more than other people do.3

I’m saying “people” here as if I wasn’t one of them, but I would have agreed with all of the above. It was only saying it out loud that made me realize how cynical my theory of human motivation was, and that I applied it to everyone but myself. Yikes!

It’s not that hard to give people skills. It’s way harder to give them interests.

The best way to use incentives, then, is to:

1. find the people who already want what you want

2. help them survive

in a department where an internal survey revealed low morale among the graduate students. A town hall was convened to investigate the issue. The students knew that one of the biggest problems was that a handful of professors terrorize and neglect their underlings, and the fastest way to fix this would be to put those faculty members on an ice floe and push it out to sea. This, of course, was difficult to bring up (some of those faculty members were in the room), and so instead we talked about minor bureaucratic reforms like whether there should be some training for advisors, or whether bad advisors should have fewer opportunities to admit students. Nobody could name who these mysterious bad advisors were, of course, so even these piddling suggestions went nowhere.

If you hire someone based on the shininess of their CV and then hope that, somehow, the employee handbook will show them how to also be a good person and not just a prolific paper-producer, you’re going to end up with a department full of sad graduate students.

If you’re writing a constitution or a code of conduct, by all means, do a good job. But if you’re counting on something like “SUBSECTION 3A: Being evil is not allowed” to stop people from being evil, or if you think Roberts Rules of Order are going to turn an insecure despot into an enlightened ruler, well, strap in for some Dark Ages and some bad improv.

discovering your inner motivations takes time and experience, and we gum up the process with lots of strong opinions about what should motivate us.

If you believe that people need to be treated like jukeboxes or secret criminals, you are accepting the behaviorist premise that we need to put people inside a giant [operant conditioning chamber](https://en.wikipedia.org/wiki/Operant_conditioning_chamber#:~:text=An%20operant%20conditioning%20chamber%20(also,graduate%20student%20at%20Harvard%20University.) that dispenses food pellets for good behavior and electric shocks for bad behavior.

The habits of effective remote teams

Metadata

• Author: PostHog
• Category: email
• URL: mailto:reader-forwarded-email/1c3a1c8d6e553790a7809f3edb62d8f3

Highlights

Remote work doesn't work without a strong writing culture.

If a decision is made on a call and nobody writes it down, was a decision even made?

Writing is thinking, so it helps you think through both what you’re trying to achieve and how you want to communicate it, making everyone more intentional.

But the manager's schedule is the enemy of remote work. Successful remote teams avoid it entirely, which makes them incredibly good at getting shit done.

We optimize for productivity, not presence.

The biggest (and stupidest) argument against remote work? It makes people less productive. This is demonstrably untrue.

So, if productivity isn't the problem, what is it? It’s trust. If you trust people to do their very best work, and create a culture that values transparency, they'll deliver.

We trust people to do their best work and, in return, they deliver.

Simply put, when something goes wrong, the investigation of the root cause is, as the company puts it, blameless. This encourages everyone to speak up without fear of punishment. People are trusted to make mistakes and, as a result, problems that do arise are easier to solve.

Successful remote companies build everything out in the open. This gives everyone the context they need, and eliminates the political squabbles that plague less transparent companies.

As Buffer co-founder and CEO Joel Gascoigne wrote almost a decade ago, “transparency breeds trust, and trust is is the foundation of great teamwork.” It’s still true today.

Would be nice ;)

cross-posted from: https://group.lt/post/1926151

Don't read if you don't want to ruin your day.

The One About The Web Developer Job Market

Metadata

• Author: Baldur Bjarnason
• Category: rss
• URL: https://www.baldurbjarnason.com/2024/the-one-about-the-web-developer-job-market/

Highlights

Many organisations are also resorting to employee-hostile strategies to increase employee churn, such as forced Return-To-Office policies.

Finding a non-bullshit job is likely only going to get harder.

• Finding effective documentation, information, and training is likely to get harder, especially in specialised topics where LLMs are even less effective than normal.

as soon as you start to try to predict the second or third order consequences things very quickly get remarkably difficult.

In short, futurists are largely con artists.

you need to do something Note: ah.. the famous "you need to do something" This is not a one-off event but has turned into a stock market driven movement towards reducing the overall headcount of the tech industry.

What this means is that when the bubble ends, as all bubbles must, the job market is likely to collapse even further.

The stock market loves job cuts

Activist investors see it as an opportunity to lower developer compensation

Management believes they can replace most of these employees with LLM-based automation

Discovering whether it’s true or not is actually quite complicated as it, counter-intuitively, doesn’t depend on the degree of LLM functionality but instead depends entirely on what organisations, managers, and executives are using software projects for.

Since, even in the best case scenario of the most optimistic prediction of LLM power, you’re still going to need to structure a plan for the code and review it, the time spent on code won’t drop to zero. But if you believe in the best-case prediction, a 20-40% improvement in long term productivity sounds reasonable, if a bit conservative.

The alternate world-view, one that I think is much more common among modern management, is that the purpose of software development is churn.

None of these require that the software be free or even low on defects. The project doesn’t need to be accessible or even functional for a majority of the users. It just needs to look good when managers, buyers, and sales people poke at it.

The alternate world-view, which I think I can demonstrate is dominant in web development at least, is that software quality does not matter. Production not productivity is what counts. Up until now, the only way to get production and churn has been to focus on short-term developer experience, often at the expense of the long term health of the project, but the innovation of LLMs is that now you can get more churn, more production, with fewer developers.

This means that it doesn’t matter who is correct or not in their estimate of how well these tools work.

You aren’t going to notice the issue as an end-user. From your perspective the system is working perfectly.

Experienced developers will edit out the issues without thinking about it, focusing on the time-saving benefit of generating the rest. Inexperienced developers won’t notice the issues and think they’ve just saved a lot of time, not realising they’ve left a ticking time bomb in the code base.

The training data favours specific languages such as Python and JavaScript.

• The same tool that enhances their productivity by 20-30% might also be outright harmful to a junior developer’s productivity, once you take the inevitable and eventual corrections and fixes into account.

From the job market perspective, all that improved and safe LLM-based coding tools would mean is more job losses.

Because manager world-views are more important than LLM innovation.

If the technology is what’s promised, the churn world-view managers will just get more production, more churn, with even fewer developers. The job market for developers will decline.

they will still use the tech to increase production, with fewer developers, because software quality and software project success isn’t what they’re looking for in software development. The job market for developers will decline.

The more progress you see in the automation, the fewer of us they’ll need. It isn’t a question of the nature of the improvement, but of the attitudes of management.

Even if that weren’t true, technical innovations in programming generally don’t improve project or business outcomes.

The odds of a project’s success are dictated by user research, design, process, and strategy, not the individual technological innovations in programming. Rapid-Application-Development tools, for example, didn’t shift outcomes in meaningful ways.

What matters is whether the final product works and improves the business it was made for. Business value isn’t solely a function of code defects. Technical improvements that address code defects are necessary, but not sufficient.

tech industry management is firmly convinced that less is more when it comes to employing either.

Whether you’re a bear or a bull on LLMs, we as developers are going to get screwed either way, especially if we’re web developers.

Most web projects shipped by businesses today are broken, but businesses rarely seem to care.

Most websites perform so badly that they don’t even finish loading on low-end devices, even when business outcomes directly correlate with website performance, such as in ecommerce or ad-supported web media.

The current state of web development is as if most Windows apps released every year simply failed to launch on 20-40% of all supported Windows installs.

If being plausible is all that matters, then that’s the literal, genuine, core strength of an LLM.

This is a problem for the job market because if all that matters to these organisations is being seen plausibly chasing cutting-edge technology – that the actual business outcomes don’t matter – then the magic of LLMs mean that you don’t actually need that many developers to do that for much, much less money.

Web media is a major employer, both directly and indirectly, of web developers. If a big part of the web media industry is collapsing, then that’s an entire sector that isn’t hiring any of the developers laid off by Google, Microsoft, or the rest. And the people they aren’t hiring will still be on the job market competing with everybody else who wouldn’t have even applied to work in web media.

The scale of LLM-enabled spam production outstrips the ability of Bing or Google to counter it.

But it gets even worse as every major search engine provider on the market is all-in on replacing regular keyword search with chatbots and LLM-generated summaries that don’t drive any traffic at all to their sources.

It’s reasonable to expect that the job market is unlikely to ever fully bounce back, due to the collapse of web media alone.

Experience in Node or React is not a reliable signifier of an ability to work on successful Node or React projects because most Node or React projects aren’t even close to being successful from a business perspective. Lack of experience in Node or React – such as a background in other frameworks or vanilla JS – conversely isn’t a reliable signifier that the developer won’t be a successful hire.

Lower pay, combined with the information asymmetry about employer dysfunction, would then lead to more capable workers leaving the sector, either to run their own businesses – a generally dysfunctional web development sector is likely to have open market opportunities – or leave the industry altogether. This would exacerbate the job market’s dysfunctions even further, deepening the cycle.

The first thing to note is that, historically, whenever management adopts an adversarial attitude towards labour, the only recourse labour has is to unionise.

As employees, we have nothing to lose from unionising. That’s the first consequence of management deciding that labour is disposable.

Diversifying your skills has always been a good idea for a software developer. Learning a new language gives you insight into the craft of programming that is applicable beyond that language specifically.

But the market for developer training in general has collapsed.

Some of it is down to the job market. Why invest in training if tech cos aren’t hiring you anyway? Why invest in training your staff if you’re planning on replacing them with LLM tools anyway?

After all, as Amy Hoy wrote in 2016:

Running a biz is a lot less risky than having a job, because 1000 customers is a lot less fragile than 1 employer.

The tech industry has “innovated” itself into a crisis, but because the executives aren’t the ones out looking for jobs, they see the innovations as a success.

The rest of us might disagree, but our opinions don’t count for much.

But what we can’t do is pretend things are fine.

Because they are not.

Thoughts?