The probability that belief A is true, given new evidence B is equal to the probability of B given A times the probability of A (regardless of B) divided by the probability of B (regardless of A).

For example, suppose you are getting a medical test to find out if you have a disease. The disease only appears in 1 out of 100 people and the test is 99% accurate. What is the likelihood you have the disease if you receive a positive test result?

Plugging it into the equation:

P(A|B) The probability of having the disease given a positive test result (what we want to find out).

P(B|A) = 99% Probability that the test is accurate if you actually have the disease.

P(A) = 1% Probability of having the disease regardless of the test.

P(B) = (1% x 99%) + (1% x 99%) The probability the test is accurate means summing a positive test result and the probability of a false negative test result. To get the false negatives if you have a population where 100 people have the disease and take the test which is 99% accurate, you get 1 false negative.

Result There’s a 50% probability you have the disease given a positive test result

(99% x 1%) / ((1% x 99%) + (1% x 99%)) = 50%

A traditional rhythm which is a variation of the Chaabi found in Northern Africa. It’s characterized by low beats (like a base drum) 5 and 10 with ‘high’ hits (like a snare drum) on 3 and 8 in two bars of 6/8. This creates a unique groove that, for western listeners, can be difficult to find beat 1 (which is not important in the Moroccan Chaabi rhythm).

Conventional ways people (and law enforcement) use to spot a liar is pseudoscience. Research studies have found that markers like fidgeting, looking away, and emotional response are not science-based and no better than guessing. This is problematic in the justice system and other law enforcement like the TSA because they are convicting people based on appearance of guilt that is baseless.

Alternatives that are found to be effective and science-based include withholding evidence to catch contradictions and sketching interviews which aids in recall (liars are less detailed).

Read the article

See also:

• This is an example of structural racism e.g. cultural differences about eye contact will result in racial profiling
• Interrogators may believe spotting liars is tacit knowledge, but that was also found to be incorrect

Being all consumed by engineering (writing blogs, contributing to open source, giving away your time) is not good because it leads to burnout and perpetuates more people to do the same. Open source for example, is co-opted by large corporations to exploit passionate developers that provide high quality code for free and putting the training burden on the person rather than corporation.

A counter argument to that is most effective people care a lot—a subtle difference, but you can be both dispassionate and care a lot.

Read the blog post

See also:

• Open source and ‘passionate developers’ have greatly increased the productivity of the field and contributes to Baumol’s cost disease (managers did not get 10x more efficient in their field, but salaries have gone up nonetheless)

In Bayes' Theorem, the probability of something occurring is based on probabilities of other parameters of the problem. Put simply, using the theorem builds on prior knowledge of the problem domain to update a prediction. This became very popular because, in the real world, there is much uncertainty and Bayes Theorem provides a way of modeling that uncertainty through probability (e.g. machine learning).

A survey by Microsoft found that 41% of employees are considering leaving their current job and 46% say they are likely to move to work remotely. This would result in a major change in the percentage of remote workers at companies compared to before the pandemic. A survey by McKinsey about remote work found that 20-25% of workforces in advanced economies could work from home after the pandemic.

See also:

• Two-thirds of remote workers want to continue to work remotely
• Topeka Kansas estimates a 10x return on relocating remote workers to their city

Like software bugs, strategy bugs are a failure of understanding of how the real world works and the value your product creates. They also have varying degrees of severity—some which should be solved right away and some which can slowly accumulate without significant harm.

Company and product strategy doesn’t tend to have the same rigor as more concrete practices (like software development), but one could imagine a ‘strategy bug’ tracker or a ‘bug bash’ where you try to rapidly iterate or gather evidence about an issue.

See also:

• 7 Powers lists macro company strategies that could be used to evaluate progress or file ‘bugs’.
• Metacognition and epistemology are a potential corollary for being more rigorous about strategy and spotting bugs (how do we know what we know and does that match reality).
• Gödel Incompleteness For Startups states we are probably wrong about because formal systems (acquiring knowledge about the market) can not also be complete systems

Thousands of Ice Age paintings were discovered in the Amazon rainforest in Colombia which earned the nickname ‘the Sistine Chapel of the ancients’. Researchers date the artwork in part because of the depictions of long extinct animals (including mammoths) and plants around the time of the Ice Age.

Read the article

See also:

• Brewarrina Fish Traps is another example of a long lasting artifact from 40,000 years ago

The Pyramid Principle by Barbara Minto.

Overview

Always give the summarizing idea first before the individual ideas that are being summarized. Summarizing is explaining the links between the ideas.

Readers can only take sentences one at a time. The right sequencing is vital.

Vertical relationships are formed by making a statement and then answering the next logical question. E.g. pigs are great -> why, reasons a b c. You shouldn’t answer a question the reader won’t ask.

Horizontal relationships are formed by an inductive or deductive argument. A deductive argument is a statement about a situation and the second idea comments on the subject and predicate of the first idea and the third idea states the implications e.g. a is b, c is a, therefore c is b. An inductive argument is where all the ideas can be described with one plural noun e.g. reasons, steps, problems.

Introductions

An introduction sets expectations of what the reader knows or will know and the initial key question that it raises which will be answered in the document. This is the root node for the rest of the document. Situation, complication, question, answer SCQA.

Never write about categories, only ideas. For example: a section called ‘background’ is not helpful.

Introductions remind rather than inform. The length should be based on the needs of the reader—history, background, prior works—interwoven in narrative form.

Intros as narratives build credibility and say up front what is in the supporting body.

Sometimes the S, C, and A are in a different order, but they are always present. Sometimes the question is implied.

There should only ever be one question (stated or implied) the document answers.

Sections

To transition between sections or chapters, use backward references. Restate the main idea from the section and state the next idea which raises the question the next section answers. You don’t need a full narrative to connect them because that was already done in the intro.

Long sections should be summarized as a form of good manners.

Conclusions are not necessary, but if you do add them do not just summarize, leave the reader with an appropriate emotion (e.g. the implication or philosophical point) or next steps that are not controversial.

When structuring the connections of key lines use inductive arguments rather than deductive otherwise there can be a large separation between the problem and the answer. Deductive arguments are best when the arguments are close together e.g. in the same paragraph.

Inductive arguments that have a single supporting argument should be deductive arguments otherwise it’s an implied connection and unclear.

Headings

Headings should also follow a pyramid pattern. There should always be more than one subheading and they should form an inductive argument supporting the parent heading. Sections (between major headings) have an ‘invisible’ fence that separates them for the reader so emphasize parallel ideas not between groups of subsections.

Headings should be concise and a reminder of the idea—not dominate. Headings are more for the eye than the mind so don’t use regard them as text–the doc should flow smoothly even if you removed the headings. The opening sentence should connect the ideas between sections and not rely on the heading.

Always introduce subheadings otherwise it’s up to the reader to figure out how they connect. Subsections should never begin immediately after the heading.

A good test of headings is putting them in the table of contents to see if it fits together as an overview of the contents.

There are 4 rules to reviewing writing: you can question the order of ideas, the source in your problem-solving, summary statements, and prose used to express ideas.

Impose a structure on the order of ideas to determine if it makes sense (main point is supported, lists are in a logical order e.g. structure, process or ranking, are mutually exclusive and collectively exhausted).

Problem-solving

A problem is either a result you don’t like or a result you can’t explain.

Problem-solving is composed of:

• What is the problem
• Where does it lie
• Why does it exist
• What could we do about it
• What should we do about it

You must identify the gap between where you are and where you want to be, the situation that gives rise to the gap, the underlying processes, alternatives, and a recommendation.

You’ll know when you have researched enough about where the problem lies when you have identified all the parts in the system, can arrange them in sequential order, and you know the inputs and outputs. This means you understand the relationships between all of the things in the system.

What could we do about it enumerates all the ways to solve the problem.

Very often you are writing for a reader who doesn’t know the problem so it must be explained in detail.

Use Logic Trees to organize the analyses that must be performed. Typical logic trees are financial structure (ROI), task structure (increase earnings), choice structure (bifurcate by activity), and sequential structure (combines activity and sequence).

Abduction is the process of problem-solving.

Summary statements

An inductive argument summary is either the effect of the supporting actions or inference from the supporting situation statements. For example, in a list of steps the heading is the effect of each cause listed below.

Deductive argument summaries are the conclusion of the deductive statements.

Action ideas can not be grouped by similarity only by effect. Situation ideas can be grouped by similarity.

Sometimes ideas are actions disguised as statements. In that case, think about why you chose those points and what brings them together. Often it’s an indication that it’s a set of actions that lead to an effect.

Putting it into readable words

Use mental imagery to help the reader call to mind an image as they read it. This helps with recall and enjoyment. Think of it as a skeletal model you are building in the reader’s mind. To do that, visualize the relationships between ideas then use visual sounding pros e.g. “feed information”, “deploy people”.

Problem-Solving in Structureless situations

In analytical reasoning, deduction, induction, and abduction all have a case, followed by a result, followed by a rule, repeating as needed. Where you start in this cycle determines the form of analytical thinking (e.g. deduction starts with a rule, induction starts with a case, abduction starts with a result).

In scientific reasoning, it’s abduction but we don’t know the underlying structure that produces the result. To find that, we create hypotheses and run experiments to prove them until the result is sufficiently explained by the rule and the underlying structure it reveals.

Using plant-based actuators, experiments have shown that we can achieve a limited form of soft robots. In a recent paper An on-demand plant-based actuator created using conformable electrodes, they were able to use conformable electrodes to move Venus flytrap with a 1.3 second latency that can even be remotely controlled via an ESP8266 board.

Soft robots made from plants could be grown (maybe even from existing native species) and tailored to specific factory work tasks or farming. Imagine farming done by plant-based robots?

D3 is not a visualization library as it is described, but an API to svg and canvas elements. In order to create visualizations, one must set all of the svg objects and attributes in a pipeline of operations managed by D3. This allows the user to script over it much easier to create complicated visualizations, but I would describe the API as low-level.

Antikythera Mechanism is a mechanical model of the positions of celestial bodies. What’s remarkable is the complexity of the device and it’s ability to predict astronomical events—it’s essentially a very old analog computer.

It was built around 100 BC and re-discovered in 1901. Fragments were missing, which made reconstruction difficult. The back was recreated, but the front gears held significantly fewer clues. A recent paper published in Nature reveals an accurate recreation.

See also:

• The Antikythera Mechanism is an example of a deep time model, capable of showing future events that would be difficult to conceive.

Vega is a high level charting and visualization library that uses a declarative grammar (a data structure) that is translated into a D3 rendered chart.

See also:

• It’s a very data-oriented API similar to how you might build things with Clojure. The advantage is using functions to transform the data (grammar).

We tend to think of supply and demand as curves that companies neatly fit into and move proportionally. However, post-Keynesianism economics shows that effective demand is a better way to understand how supply and demand works in reality.

For example, supply can be constrained (e.g. by labor to make the product) and excess demand spills over elsewhere (e.g. maybe they buy something else). Similarly, when demand is in deficit (i.e. less than supply) it causes companies to lay people off resulting in unemployment which decreases demand in other markets.

See also:

• 4.3 million people quit their job in August 2021

In the early days of a pre-product startup there is little to measure. There’s no sales, there’s no product KPIs, no press releases, and so on.

A good indicator at this stage is product and hiring velocity. How quickly is the product being made into something valuable for customers and how fast is the team growing. It’s a simple heuristic, but potentially useful—without one or the other, there’s a problem (e.g. hiring without product could mean papering over a bad product, slow hiring indicates poor scalability and attracting talent).

Read the blog post

See also:

• Slowness begets more slowness
• Speed is undervalued

An expression for Bayesian thinking where you think in ranges of outcomes based on prior information. When new information arrives, to get a more accurate range of solutions you ‘update your priors’.

See also:

• Rigorously updating priors can prevent path dependence and reduce priming and implicit bias
• Product work is a pursuit of facts about the user, market, and their problems, as the facts, user or market changes so should the product.

The likelihood that something will continue to be done the same way it was done from the outset. This can be readily observed in technology. For instance, the width of train rails is the width of a horse pulled cart (or “two horses asses”) and led to the width of rockets on the space shuttle being set to a size not based on what is optimal, but based on what can be transported via train.

See also:

• The significance of persistence in predicting economic outcomes
• Update your priors and Bayesian thinking could counteract this somewhat

Scientific research can sometimes suffer from a form of path dependence where a single study can be cited repeatedly for many years even when it is found out to be incorrect. For example, in 2015 a literature review found that 900 peer-reviewed studies used a cell line derived from a breast cancer patient in 1976 that was found to actually be skin cancer. For eight years (and maybe more) studies kept citing it even though it was incorrect.

See also:

• The Unstoppable Momentum of Outdated Science (what this note is derived from)
• Is this another side of convenience is king? Scientists are people too and it’s easier to cite a source that’s previously cited rather than re-check the findings.

Common advice from the YC crowd (Paul Graham et al) is to build an initial product that a small group of users love. You’ll know that they love it because they tell all of their friends. The logic goes, unless you have built something someone loves, you will eventually fail.

From Startup Playbook by Sam Altman

A form of music characterized by being made using tracker software such as the original Ultimate Soundtracker on Amiga or more modern Renoise. In a tracker, notes are laid out vertically with each column as a track and using letters/numbers to represent a note and parameters. Each point in time is a discrete row so playback continuously scrolls vertically.

See also:

• A distant cousin of this approach to music sequencing is a programmable music environment

Counter to the common advice given by people in Silicon Valley (Paul Graham ‘Startup Mistakes’ for instance), solo founders are more likely to build companies that survive and generate more revenue than multiple founders.

This is thought to be because of the lack of co-founder drama (a leading cause of premature startup death) and fast, consolidated decisions making.

The study referenced here used Kickstarter funded companies to draw these conclusions, but similar insights can be found in Crunchbase data (more than half of startups that had a single founder).

A large amount of the cost (time) of maintaining code is keeping up with breaking changes and incompatability. This is multiplied by the entire ecosystem of developers and libraries.

If compatibility were better understood and library and platform authors made compatibility a higher priority goal the result would be a massive amount of leverage from the time previously spent on this kind of maintenance burden.

See also:

• Lockfiles let you deal with breaking changes on your own terms
• The fundamental theorem of software engineering might indicate that we are over abstracting or that we need another layer of abstraction to deal with compatibility (often the case!)

California passed legislation that makes it significantly harder to classify workers as independent contractors. The ‘ABC’ test is used to determine whether someone meets the exception that they are an independent contractor. This test is significantly stricter and has led to companies going under as a result of misclassification lawsuits.

More States are following California’s lead—New York and New Jersey are considering similar legislation.

See also:

• Back office

A theoretical tool that checks a piece of business writing against a set of rules from The Minto Pyramid Principle. Like a code linter, this would serve as a ratchet for improving the output of others—in this case, those sharing business writing like strategy memos, 1 pagers, and project briefs.

See also:

• This is an example of an ‘organizational linter’