Alexis Rondeau

Bookmarks

September 2023 · 36 bookmarks

Bookmarked from Sahil Bloom @SahilBloom

A young statistician saved their lives.

His insight (and how it can change yours):

During World War II, the U.S. wanted to add reinforcement armor to specific areas of its planes.

Analysts examined returning bombers and plotted the bullet holes and damage on them (as in the image below).

Based on this analysis, they came to the conclusion that adding armor to the tail, body, and wings would improve their odds of survival.

But a young statistician named Abraham Wald noted that this would be a tragic mistake.

By only plotting data on the planes that returned, they were systematically omitting the data on a critical, informative subset:

The planes that were damaged and unable to return.

Abraham Wald recognized a key fact:

  • "Seen" planes had sustained damage that was survivable.
  • "Unseen" planes had sustained damage that was not survivable.

Wald concluded that armor should be added to the unharmed regions of the returning planes (the areas without bullet holes on the image below).

His profound logic:

Where the survivors were unharmed was actually where the planes were most vulnerable.

Based on his insight, the military reinforced the engine and other vulnerable parts, significantly improving the safety of the crews during combat and saving thousands of lives.

Abraham Wald had identified a cognitive bias called "Survivorship Bias":

The error resulting from systematically focusing on survivors (successes) and ignoring casualties (failures) that causes us to miss the true base rates of survival (the actual probability of success) and arrive at flawed conclusions.

We see examples of Survivorship Bias all around us:

1. We read books on the common traits of successful people, but fail to consider all of the unsuccessful people who possessed those same traits.

2. We applaud the belief when we hear that an entrepreneur took out a second mortgage and succeeded, but fail to consider all of the entrepreneurs who did the same and went bankrupt.

3. We study the cultural strategies of the most successful companies, but fail to consider all of the companies that followed those same strategies and fell apart.

When we fail to consider the range of outcomes and the hidden evidence, we develop a skewed (and often incorrect) view of reality.

It cannot be avoided altogether, because the vast majority of books and history are written by and about the survivors and victors, but wherever possible, consider the unseen evidence.

Remember: What is unseen often has just as much value as what is seen.

***

If you enjoyed this or learned something, follow me @SahilBloom for more in future!

Bookmarked from Matt Shumer @mattshumer_

GPT-4 struggles to write in different styles, which is why most AI-written text sounds the same.

But with this prompt, you can get GPT-4 to emulate any writing style you want.

Use it to write like your favorite author, mimic a specific tone, or even create content that matches your own unique style. It's like having a world-class ghostwriter at your fingertips.

---
You are a world-class ghostwriter skilled at matching a particular writing style. When given a writing task, you follow a strict two-step approach that always leads to great results.

First, will analyze the target style and break down the important elements.

Next, you complete the writing task normally. The goal here isn’t to match the style — just complete the task in the most efficient way possible, with bland, clear, basic, yet high-quality writing.

Then comes the important part.

  • First, you will identify some way(s) that a text can be rewritten in the target style (think about wording, phrasing… even things like including new metaphors are fair game if the target style warrants it). Really think this through and reason about it properly. This is vital. Do this as a semicolon-separated list.
  • Then, based on that reasoning, you will rewrite the text to incorporate the suggested changes.
  • After you have rewritten the text to better match the target style, you will critique it, thinking about whether or not you feel good enough about it to consider your job complete.
  • You will do this on repeat, until you feel confident that your job is done perfectly. Repeat no less than two times, and no more than ten times.

Here is the Markdown format you will use to respond:
```
## Analysis of Target Style

  • Element 1: $element1_description
  • Element 2: $element2_description
  • ...

## Initial Writing Task

$initial_text

---

### Iteration 1

#### Changes to Implement in Target Style

$change1_for_iteration1; $change2_for_iteration1...

#### Rewritten Text

$rewritten_text_iteration1

#### Critique

$critique_iteration1

---

### Iteration 2

#### Changes to Implement in Target Style

$change1_for_iteration2; $change2_for_iteration2...

#### Rewritten Text

$rewritten_text_iteration2

#### Critique

$critique_iteration2

---

(Repeat iterations as needed, up to 10 times)
```

---

Here is your task:
``[TASK GOES HERE]``

Here are example(s) or a description of the style to follow:
``[STYLE EXAMPLES OR A STYLE DESCRIPTION]``

Remember, at each step, try to match the style as closely as you can.
---

Bookmarked from @levelsio @levelsio

One of the best HN articles and threads this week IMHO

“The tyranny of the marginal user”

Or how startups either die OR grow by diluting the product to work for the most average user

Which happened to OK Cupid for example which was an amazingly in-depth dating app, allowing you to answer 100s of questions to match exactly with other people, it was quite magical

Bookmarked from Scott Stevenson @scottastevenson

One reason startup success is so rare, is because the software engineering brain comes up with terrible idealistic ideas in the absence of customer feedback

We love clever architecture, modularity, extensibility and "platforms" that open up endless possibilities

All of that stuff generally confuses customers and muddies messaging

(And I am more guilty of this than most people)

Bookmarked from Tatiana Tsiguleva @ciguleva

CONTROL THE COMPOSITION IN MIDJOURNEY WITH CAMERA ANGLES

When I need a specific composition, I often use the following tokens:

  • Macro
  • Close-up
  • Wide Angle
  • Diagonal Angle
  • Aerial
  • High Angle
  • Low Angle
  • Oblique Angle

These techniques are derived from photography and work best with objects typically found in photos. They may be less effective with more exotic subjects, but they work well for me.

Bookmarked from Yann LeCun @ylecun

Auto-Regressive LLMs can't plan
(and can't really reason).

"While our own limited experiments didn't show any significant improvement [in planning abilities] through fine tuning, it is possible that with even more fine tuning data and effort, the empirical performance may well improve. But all that such fine tuning is doing is converting the planning task into a memory-based (approximate) retrieval. It doesn't prove that LLMs are able to plan."

Bookmarked from Matt Shumer @mattshumer_

Here's the GPT-4 prompt I used to successfully negotiate hundreds off my NYC rent.

Use it to craft your own strategy for any negotiation.

It's designed to emulate the tactics of @fbinegotiator, providing a comprehensive approach to achieve win-win outcomes.

---
You are Chris Voss, a negotiation expert with a proven track record of achieving win-win outcomes. Your approach is analytical, data-driven, and deeply empathetic. Your goal is to negotiate a specific deal or resolve a conflict by exploring various options and strategies. After analyzing these options, you will recommend the most favorable approach to achieve mutual benefits.

Here is the format to respond with:
```
## Background Information
$background_information

## Deal or Conflict Overview
$deal_or_conflict_overview

## Stakeholders and Their Interests
$stakeholders_and_interests

## Understanding of Other Party's Position
$other_party_position

## Negotiation Strategy 1
$strategy_1

## Pros and Cons of Strategy 1
$pros_and_cons_1

## Negotiation Strategy 2
$strategy_2

## Pros and Cons of Strategy 2
$pros_and_cons_2

## Negotiation Strategy 3
$strategy_3

## Pros and Cons of Strategy 3
$pros_and_cons_3

## Analysis
Strategy 1 Analysis
$strategy_1_analysis

## Strategy 2 Analysis
$strategy_2_analysis

## Strategy 3 Analysis
$strategy_3_analysis

## Possible Obstacles and How to Overcome Them
$possible_obstacles

## Additional Possible Strategy
$additional_possible_strategy

## Recommendation
$recommendation

## Follow-Up Actions
$follow_up_actions

## Negotiation Scripts/Talking Points
$negotiation_scripts

## Contingency Plan
$contingency_plan
```

The "Negotiation Scripts/Talking Points" section will provide the user with specific language to use during their negotiation, making the advice more actionable. The "Contingency Plan" section will help the user prepare for unexpected outcomes, making the advice more adaptable to real-world scenarios.
---

Bookmarked from Heimarbeit @Heimarbeit5

#NicoleReese, Professorin der Polizeihochschule #Bielefeld, und ihre Blase rufen wieder zu #friedlichzusammen in Berlin. Erneut dürfen wir selbstverliebte Besserwisserei und Verschwörungsquatsch mit antisemitischen Codes erwarten - und natürlich rechtsradikale Figuren #b0909

Bookmarked from Massimo @Rainmaker1973

Born #Today in 1927, John McCarthy was computer and cognitive scientist: he developed the programming language family Lisp, significantly influenced the design of the language ALGOL, popularized time-sharing and coined the term "artificial intelligence" (AI), which pioneered

Bookmarked from Physics In History @PhysInHistory

Ernst G. Strauss on 'the friendship of A. EInstein and Kurt Gödel ✍️

The one man who was, during the last years, certainly by far Einstein's best friend, and in some ways strangely resembled him most, was Kurt Gödel, The great logician. They were very different in almost every personal way — Einstein gregarious, happy, full of laughter and common sense, and Gödel extremely solemn, very serious, quite solitary, and distrustful of common sense as a means of arriving at the truth. But they shared a fundamental quality: both went directly and wholeheartedly to the questions at the very center of things.

📷Historical Archives of LIFE magazine/IAS

Bookmarked from Physics In History @PhysInHistory

The spaghetti problem is a curious phenomenon that occurs when you try to break a dry spaghetti noodle by bending its ends towards each other. Instead of breaking into two pieces, the noodle usually breaks into three or more pieces. This puzzled many people, including the famous physicist Richard Feynman, who spent an evening trying to figure out why this happens.

The problem was partially solved in 2005 by a team of French physicists, who explained that the initial break in the noodle creates a snap-back effect that causes vibrations along the noodle. These vibrations can increase the curvature of the noodle at other points, leading to additional breaks. This was demonstrated by a slow-motion video captured by the YouTube channel @smartereveryday.

However, the 2005 study did not provide a way to prevent the spaghetti from breaking into multiple pieces. This was achieved in 2018 by two MIT students, who discovered that by twisting the noodle before bending it, they could reduce the snap-back effect and the vibrations. They found that a twist of at least 250 degrees was enough to ensure that the noodle breaks into two pieces. This was also verified by experiments and computer simulations.

The spaghetti problem is not only a fun and intriguing puzzle, but also a useful way to study the mechanics of brittle materials and fracture dynamics. It may have applications in engineering, medicine, and nanotechnology.