More on Technology
Amelia Winger-Bearskin
3 years ago
Reasons Why AI-Generated Images Remind Me of Nightmares
AI images are like funhouse mirrors.
Google's AI Blog introduced the puppy-slug in the summer of 2015.
Puppy-slug isn't a single image or character. "Puppy-slug" refers to Google's DeepDream's unsettling psychedelia. This tool uses convolutional neural networks to train models to recognize dataset entities. If researchers feed the model millions of dog pictures, the network will learn to recognize a dog.
DeepDream used neural networks to analyze and classify image data as well as generate its own images. DeepDream's early examples were created by training a convolutional network on dog images and asking it to add "dog-ness" to other images. The models analyzed images to find dog-like pixels and modified surrounding pixels to highlight them.
Puppy-slugs and other DeepDream images are ugly. Even when they don't trigger my trypophobia, they give me vertigo when my mind tries to reconcile familiar features and forms in unnatural, physically impossible arrangements. I feel like I've been poisoned by a forbidden mushroom or a noxious toad. I'm a Lovecraft character going mad from extradimensional exposure. They're gross!
Is this really how AIs see the world? This is possibly an even more unsettling topic that DeepDream raises than the blatant abjection of the images.
When these photographs originally circulated online, many friends were startled and scandalized. People imagined a computer's imagination would be literal, accurate, and boring. We didn't expect vivid hallucinations and organic-looking formations.
DeepDream's images didn't really show the machines' imaginations, at least not in the way that scared some people. DeepDream displays data visualizations. DeepDream reveals the "black box" of convolutional network training.
Some of these images look scary because the models don't "know" anything, at least not in the way we do.
These images are the result of advanced algorithms and calculators that compare pixel values. They can spot and reproduce trends from training data, but can't interpret it. If so, they'd know dogs have two eyes and one face per head. If machines can think creatively, they're keeping it quiet.
You could be forgiven for thinking otherwise, given OpenAI's Dall-impressive E's results. From a technological perspective, it's incredible.
Arthur C. Clarke once said, "Any sufficiently advanced technology is indistinguishable from magic." Dall-magic E's requires a lot of math, computer science, processing power, and research. OpenAI did a great job, and we should applaud them.
Dall-E and similar tools match words and phrases to image data to train generative models. Matching text to images requires sorting and defining the images. Untold millions of low-wage data entry workers, content creators optimizing images for SEO, and anyone who has used a Captcha to access a website make these decisions. These people could live and die without receiving credit for their work, even though the project wouldn't exist without them.
This technique produces images that are less like paintings and more like mirrors that reflect our own beliefs and ideals back at us, albeit via a very complex prism. Due to the limitations and biases that these models portray, we must exercise caution when viewing these images.
The issue was succinctly articulated by artist Mimi Onuoha in her piece "On Algorithmic Violence":
As we continue to see the rise of algorithms being used for civic, social, and cultural decision-making, it becomes that much more important that we name the reality that we are seeing. Not because it is exceptional, but because it is ubiquitous. Not because it creates new inequities, but because it has the power to cloak and amplify existing ones. Not because it is on the horizon, but because it is already here.
Shalitha Suranga
3 years ago
The Top 5 Mathematical Concepts Every Programmer Needs to Know
Using math to write efficient code in any language
Programmers design, build, test, and maintain software. Employ cases and personal preferences determine the programming languages we use throughout development. Mobile app developers use JavaScript or Dart. Some programmers design performance-first software in C/C++.
A generic source code includes language-specific grammar, pre-implemented function calls, mathematical operators, and control statements. Some mathematical principles assist us enhance our programming and problem-solving skills.
We all use basic mathematical concepts like formulas and relational operators (aka comparison operators) in programming in our daily lives. Beyond these mathematical syntaxes, we'll see discrete math topics. This narrative explains key math topics programmers must know. Master these ideas to produce clean and efficient software code.
Expressions in mathematics and built-in mathematical functions
A source code can only contain a mathematical algorithm or prebuilt API functions. We develop source code between these two ends. If you create code to fetch JSON data from a RESTful service, you'll invoke an HTTP client and won't conduct any math. If you write a function to compute the circle's area, you conduct the math there.
When your source code gets more mathematical, you'll need to use mathematical functions. Every programming language has a math module and syntactical operators. Good programmers always consider code readability, so we should learn to write readable mathematical expressions.
Linux utilizes clear math expressions.
Inbuilt max and min functions can minimize verbose if statements.
How can we compute the number of pages needed to display known data? In such instances, the ceil function is often utilized.
import math as m
results = 102
items_per_page = 10
pages = m.ceil(results / items_per_page)
print(pages)Learn to write clear, concise math expressions.
Combinatorics in Algorithm Design
Combinatorics theory counts, selects, and arranges numbers or objects. First, consider these programming-related questions. Four-digit PIN security? what options exist? What if the PIN has a prefix? How to locate all decimal number pairs?
Combinatorics questions. Software engineering jobs often require counting items. Combinatorics counts elements without counting them one by one or through other verbose approaches, therefore it enables us to offer minimum and efficient solutions to real-world situations. Combinatorics helps us make reliable decision tests without missing edge cases. Write a program to see if three inputs form a triangle. This is a question I commonly ask in software engineering interviews.
Graph theory is a subfield of combinatorics. Graph theory is used in computerized road maps and social media apps.
Logarithms and Geometry Understanding
Geometry studies shapes, angles, and sizes. Cartesian geometry involves representing geometric objects in multidimensional planes. Geometry is useful for programming. Cartesian geometry is useful for vector graphics, game development, and low-level computer graphics. We can simply work with 2D and 3D arrays as plane axes.
GetWindowRect is a Windows GUI SDK geometric object.
High-level GUI SDKs and libraries use geometric notions like coordinates, dimensions, and forms, therefore knowing geometry speeds up work with computer graphics APIs.
How does exponentiation's inverse function work? Logarithm is exponentiation's inverse function. Logarithm helps programmers find efficient algorithms and solve calculations. Writing efficient code involves finding algorithms with logarithmic temporal complexity. Programmers prefer binary search (O(log n)) over linear search (O(n)). Git source specifies O(log n):
Logarithms aid with programming math. Metas Watchman uses a logarithmic utility function to find the next power of two.
Employing Mathematical Data Structures
Programmers must know data structures to develop clean, efficient code. Stack, queue, and hashmap are computer science basics. Sets and graphs are discrete arithmetic data structures. Most computer languages include a set structure to hold distinct data entries. In most computer languages, graphs can be represented using neighboring lists or objects.
Using sets as deduped lists is powerful because set implementations allow iterators. Instead of a list (or array), store WebSocket connections in a set.
Most interviewers ask graph theory questions, yet current software engineers don't practice algorithms. Graph theory challenges become obligatory in IT firm interviews.
Recognizing Applications of Recursion
A function in programming isolates input(s) and output(s) (s). Programming functions may have originated from mathematical function theories. Programming and math functions are different but similar. Both function types accept input and return value.
Recursion involves calling the same function inside another function. In its implementation, you'll call the Fibonacci sequence. Recursion solves divide-and-conquer software engineering difficulties and avoids code repetition. I recently built the following recursive Dart code to render a Flutter multi-depth expanding list UI:
Recursion is not the natural linear way to solve problems, hence thinking recursively is difficult. Everything becomes clear when a mathematical function definition includes a base case and recursive call.
Conclusion
Every codebase uses arithmetic operators, relational operators, and expressions. To build mathematical expressions, we typically employ log, ceil, floor, min, max, etc. Combinatorics, geometry, data structures, and recursion help implement algorithms. Unless you operate in a pure mathematical domain, you may not use calculus, limits, and other complex math in daily programming (i.e., a game engine). These principles are fundamental for daily programming activities.
Master the above math fundamentals to build clean, efficient code.
Muhammad Rahmatullah
3 years ago
The Pyramid of Coding Principles
A completely operating application requires many processes and technical challenges. Implementing coding standards can make apps right, work, and faster.
With years of experience working in software houses. Many client apps are scarcely maintained.
Why are these programs "barely maintainable"? If we're used to coding concepts, we can probably tell if an app is awful or good from its codebase.
This is how I coded much of my app.
Make It Work
Before adopting any concept, make sure the apps are completely functional. Why have a fully maintained codebase if the app can't be used?
The user doesn't care if the app is created on a super server or uses the greatest coding practices. The user just cares if the program helps them.
After the application is working, we may implement coding principles.
You Aren’t Gonna Need It
As a junior software engineer, I kept unneeded code, components, comments, etc., thinking I'd need them later.
In reality, I never use that code for weeks or months.
First, we must remove useless code from our primary codebase. If you insist on keeping it because "you'll need it later," employ version control.
If we remove code from our codebase, we can quickly roll back or copy-paste the previous code without preserving it permanently.
The larger the codebase, the more maintenance required.
Keep It Simple Stupid
Indeed. Keep things simple.
Why complicate something if we can make it simpler?
Our code improvements should lessen the server load and be manageable by others.
If our code didn't pass those benchmarks, it's too convoluted and needs restructuring. Using an open-source code critic or code smell library, we can quickly rewrite the code.
Simpler codebases and processes utilize fewer server resources.
Don't Repeat Yourself
Have you ever needed an action or process before every action, such as ensuring the user is logged in before accessing user pages?
As you can see from the above code, I try to call is user login? in every controller action, and it should be optimized, because if we need to rename the method or change the logic, etc. We can improve this method's efficiency.
We can write a constructor/middleware/before action that calls is_user_login?
The code is more maintainable and readable after refactoring.
Each programming language or framework handles this issue differently, so be adaptable.
Clean Code
Clean code is a broad notion that you've probably heard of before.
When creating a function, method, module, or variable name, the first rule of clean code is to be precise and simple.
The name should express its value or logic as a whole, and follow code rules because every programming language is distinct.
If you want to learn more about this topic, I recommend reading https://www.amazon.com/Clean-Code-Handbook-Software-Craftsmanship/dp/0132350882.
Standing On The Shoulder of Giants
Use industry standards and mature technologies, not your own(s).
There are several resources that explain how to build boilerplate code with tools, how to code with best practices, etc.
I propose following current conventions, best practices, and standardization since we shouldn't innovate on top of them until it gives us a competitive edge.
Boy Scout Rule
What reduces programmers' productivity?
When we have to maintain or build a project with messy code, our productivity decreases.
Having to cope with sloppy code will slow us down (shame of us).
How to cope? Uncle Bob's book says, "Always leave the campground cleaner than you found it."
When developing new features or maintaining current ones, we must improve our codebase. We can fix minor issues too. Renaming variables, deleting whitespace, standardizing indentation, etc.
Make It Fast
After making our code more maintainable, efficient, and understandable, we can speed up our app.
Whether it's database indexing, architecture, caching, etc.
A smart craftsman understands that refactoring takes time and it's preferable to balance all the principles simultaneously. Don't YAGNI phase 1.
Using these ideas in each iteration/milestone, while giving the bottom items less time/care.
You can check one of my articles for further information. https://medium.com/life-at-mekari/why-does-my-website-run-very-slowly-and-how-do-i-optimize-it-for-free-b21f8a2f0162
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Vitalik
3 years ago
Fairness alternatives to selling below market clearing prices (or community sentiment, or fun)
When a seller has a limited supply of an item in high (or uncertain and possibly high) demand, they frequently set a price far below what "the market will bear." As a result, the item sells out quickly, with lucky buyers being those who tried to buy first. This has happened in the Ethereum ecosystem, particularly with NFT sales and token sales/ICOs. But this phenomenon is much older; concerts and restaurants frequently make similar choices, resulting in fast sell-outs or long lines.
Why do sellers do this? Economists have long wondered. A seller should sell at the market-clearing price if the amount buyers are willing to buy exactly equals the amount the seller has to sell. If the seller is unsure of the market-clearing price, they should sell at auction and let the market decide. So, if you want to sell something below market value, don't do it. It will hurt your sales and it will hurt your customers. The competitions created by non-price-based allocation mechanisms can sometimes have negative externalities that harm third parties, as we will see.
However, the prevalence of below-market-clearing pricing suggests that sellers do it for good reason. And indeed, as decades of research into this topic has shown, there often are. So, is it possible to achieve the same goals with less unfairness, inefficiency, and harm?
Selling at below market-clearing prices has large inefficiencies and negative externalities
An item that is sold at market value or at an auction allows someone who really wants it to pay the high price or bid high in the auction. So, if a seller sells an item below market value, some people will get it and others won't. But the mechanism deciding who gets the item isn't random, and it's not always well correlated with participant desire. It's not always about being the fastest at clicking buttons. Sometimes it means waking up at 2 a.m. (but 11 p.m. or even 2 p.m. elsewhere). Sometimes it's just a "auction by other means" that's more chaotic, less efficient, and has far more negative externalities.
There are many examples of this in the Ethereum ecosystem. Let's start with the 2017 ICO craze. For example, an ICO project would set the price of the token and a hard maximum for how many tokens they are willing to sell, and the sale would start automatically at some point in time. The sale ends when the cap is reached.
So what? In practice, these sales often ended in 30 seconds or less. Everyone would start sending transactions in as soon as (or just before) the sale started, offering higher and higher fees to encourage miners to include their transaction first. Instead of the token seller receiving revenue, miners receive it, and the sale prices out all other applications on-chain.
The most expensive transaction in the BAT sale set a fee of 580,000 gwei, paying a fee of $6,600 to get included in the sale.
Many ICOs after that tried various strategies to avoid these gas price auctions; one ICO notably had a smart contract that checked the transaction's gasprice and rejected it if it exceeded 50 gwei. But that didn't solve the issue. Buyers hoping to game the system sent many transactions hoping one would get through. An auction by another name, clogging the chain even more.
ICOs have recently lost popularity, but NFTs and NFT sales have risen in popularity. But the NFT space didn't learn from 2017; they do fixed-quantity sales just like ICOs (eg. see the mint function on lines 97-108 of this contract here). So what?
That's not the worst; some NFT sales have caused gas price spikes of up to 2000 gwei.
High gas prices from users fighting to get in first by sending higher and higher transaction fees. An auction renamed, pricing out all other applications on-chain for 15 minutes.
So why do sellers sometimes sell below market price?
Selling below market value is nothing new, and many articles, papers, and podcasts have written (and sometimes bitterly complained) about the unwillingness to use auctions or set prices to market-clearing levels.
Many of the arguments are the same for both blockchain (NFTs and ICOs) and non-blockchain examples (popular restaurants and concerts). Fairness and the desire not to exclude the poor, lose fans or create tension by being perceived as greedy are major concerns. The 1986 paper by Kahneman, Knetsch, and Thaler explains how fairness and greed can influence these decisions. I recall that the desire to avoid perceptions of greed was also a major factor in discouraging the use of auction-like mechanisms in 2017.
Aside from fairness concerns, there is the argument that selling out and long lines create a sense of popularity and prestige, making the product more appealing to others. Long lines should have the same effect as high prices in a rational actor model, but this is not the case in reality. This applies to ICOs and NFTs as well as restaurants. Aside from increasing marketing value, some people find the game of grabbing a limited set of opportunities first before everyone else is quite entertaining.
But there are some blockchain-specific factors. One argument for selling ICO tokens below market value (and one that persuaded the OmiseGo team to adopt their capped sale strategy) is community dynamics. The first rule of community sentiment management is to encourage price increases. People are happy if they are "in the green." If the price drops below what the community members paid, they are unhappy and start calling you a scammer, possibly causing a social media cascade where everyone calls you a scammer.
This effect can only be avoided by pricing low enough that post-launch market prices will almost certainly be higher. But how do you do this without creating a rush for the gates that leads to an auction?
Interesting solutions
It's 2021. We have a blockchain. The blockchain is home to a powerful decentralized finance ecosystem, as well as a rapidly expanding set of non-financial tools. The blockchain also allows us to reset social norms. Where decades of economists yelling about "efficiency" failed, blockchains may be able to legitimize new uses of mechanism design. If we could use our more advanced tools to create an approach that more directly solves the problems, with fewer side effects, wouldn't that be better than fiddling with a coarse-grained one-dimensional strategy space of selling at market price versus below market price?
Begin with the goals. We'll try to cover ICOs, NFTs, and conference tickets (really a type of NFT) all at the same time.
1. Fairness: don't completely exclude low-income people from participation; give them a chance. The goal of token sales is to avoid high initial wealth concentration and have a larger and more diverse initial token holder community.
2. Don’t create races: Avoid situations where many people rush to do the same thing and only a few get in (this is the type of situation that leads to the horrible auctions-by-another-name that we saw above).
3. Don't require precise market knowledge: the mechanism should work even if the seller has no idea how much demand exists.
4. Fun: The process of participating in the sale should be fun and game-like, but not frustrating.
5. Give buyers positive expected returns: in the case of a token (or an NFT), buyers should expect price increases rather than decreases. This requires selling below market value.
Let's start with (1). From Ethereum's perspective, there is a simple solution. Use a tool designed for the job: proof of personhood protocols! Here's one quick idea:
Mechanism 1 Each participant (verified by ID) can buy up to ‘’X’’ tokens at price P, with the option to buy more at an auction.
With the per-person mechanism, buyers can get positive expected returns for the portion sold through the per-person mechanism, and the auction part does not require sellers to understand demand levels. Is it race-free? The number of participants buying through the per-person pool appears to be high. But what if the per-person pool isn't big enough to accommodate everyone?
Make the per-person allocation amount dynamic.
Mechanism 2 Each participant can deposit up to X tokens into a smart contract to declare interest. Last but not least, each buyer receives min(X, N / buyers) tokens, where N is the total sold through the per-person pool (some other amount can also be sold by auction). The buyer gets their deposit back if it exceeds the amount needed to buy their allocation.
No longer is there a race condition based on the number of buyers per person. No matter how high the demand, it's always better to join sooner rather than later.
Here's another idea if you like clever game mechanics with fancy quadratic formulas.
Mechanism 3 Each participant can buy X units at a price P X 2 up to a maximum of C tokens per buyer. C starts low and gradually increases until enough units are sold.
The quantity allocated to each buyer is theoretically optimal, though post-sale transfers will degrade this optimality over time. Mechanisms 2 and 3 appear to meet all of the above objectives. They're not perfect, but they're good starting points.
One more issue. For fixed and limited supply NFTs, the equilibrium purchased quantity per participant may be fractional (in mechanism 2, number of buyers > N, and in mechanism 3, setting C = 1 may already lead to over-subscription). With fractional sales, you can offer lottery tickets: if there are N items available, you have a chance of N/number of buyers of getting the item, otherwise you get a refund. For a conference, groups could bundle their lottery tickets to guarantee a win or a loss. The certainty of getting the item can be auctioned.
The bottom tier of "sponsorships" can be used to sell conference tickets at market rate. You may end up with a sponsor board full of people's faces, but is that okay? After all, John Lilic was on EthCC's sponsor board!
Simply put, if you want to be reliably fair to people, you need an input that explicitly measures people. Authentication protocols do this (and if desired can be combined with zero knowledge proofs to ensure privacy). So we should combine the efficiency of market and auction-based pricing with the equality of proof of personhood mechanics.
Answers to possible questions
Q: Won't people who don't care about your project buy the item and immediately resell it?
A: Not at first. Meta-games take time to appear in practice. If they do, making them untradeable for a while may help mitigate the damage. Using your face to claim that your previous account was hacked and that your identity, including everything in it, should be moved to another account works because proof-of-personhood identities are untradeable.
Q: What if I want to make my item available to a specific community?
A: Instead of ID, use proof of participation tokens linked to community events. Another option, also serving egalitarian and gamification purposes, is to encrypt items within publicly available puzzle solutions.
Q: How do we know they'll accept? Strange new mechanisms have previously been resisted.
A: Having economists write screeds about how they "should" accept a new mechanism that they find strange is difficult (or even "equity"). However, abrupt changes in context effectively reset people's expectations. So the blockchain space is the best place to try this. You could wait for the "metaverse", but it's possible that the best version will run on Ethereum anyway, so start now.
Mickey Mellen
2 years ago
Shifting from Obsidian to Tana?
I relocated my notes database from Roam Research to Obsidian earlier this year expecting to stay there for a long. Obsidian is a terrific tool, and I explained my move in that post.
Moving everything to Tana faster than intended. Tana? Why?
Tana is just another note-taking app, but it does it differently. Three note-taking apps existed before Tana:
simple note-taking programs like Apple Notes and Google Keep.
Roam Research and Obsidian are two graph-style applications that assisted connect your notes.
You can create effective tables and charts with data-focused tools like Notion and Airtable.
Tana is the first great software I've encountered that combines graph and data notes. Google Keep will certainly remain my rapid notes app of preference. This Shu Omi video gives a good overview:
Tana handles everything I did in Obsidian with books, people, and blog entries, plus more. I can find book quotes, log my workouts, and connect my thoughts more easily. It should make writing blog entries notes easier, so we'll see.
Tana is now invite-only, but if you're interested, visit their site and sign up. As Shu noted in the video above, the product hasn't been published yet but seems quite polished.
Whether I stay with Tana or not, I'm excited to see where these apps are going and how they can benefit us all.
Hannah Elliott
3 years ago
Pebble Beach Auto Auctions Set $469M Record
The world's most prestigious vintage vehicle show included amazing autos and record-breaking sums.
This 1932 Duesenberg J Figoni Sports Torpedo earned Best of Show in 2022.
David Paul Morris (DPM)/Bloomberg
2022 Pebble Beach Concours d'Elegance winner was a pre-war roadster.
Lee Anderson's 1932 Duesenberg J Figoni Sports Torpedo won Best of Show at Pebble Beach Golf Links near Carmel, Calif., on Sunday. First American win since 2013.
Sandra Button, chairperson of the annual concours, said the car, whose chassis and body had been separated for years, "marries American force with European style." "Its resurrection story is passionate."
Pebble Beach Concours d'Elegance Auction
Since 1950, the Pebble Beach Concours d'Elegance has welcomed the world's most costly collectable vehicles for a week of parties, auctions, rallies, and high-roller meetings. The cold, dreary weather highlighted the automobiles' stunning lines and hues.
DPM/Bloomberg
A visitor photographs a 1948 Ferrari 166 MM Touring Barchetta. This is one of 25 Ferraris manufactured in the years after World War II. First shown at the 1948 Turin Salon. Others finished Mille Miglia and Le Mans, which set the tone for Ferrari racing for years.
DPM/Bloomberg
This year's frontrunners were ultra-rare pre-war and post-war automobiles with long and difficult titles, such a 1937 Talbot-Lago T150C-SS Figoni & Falaschi Teardrop Coupe and a 1951 Talbot-Lago T26 Grand Sport Stabilimenti Farina Cabriolet.
The hefty, enormous coaches inspire visions of golden pasts when mysterious saloons swept over the road with otherworldly style, speed, and grace. Only the richest and most powerful people, like Indian maharaja and Hollywood stars, owned such vehicles.
Antonio Chopitea, a Peruvian sugar tycoon, ordered a new Duesenberg in Paris. Hemmings says the two-tone blue beauty was moved to the US and dismantled in the 1960s. Body and chassis were sold separately and rejoined decades later in a three-year, prize-winning restoration.
The concours is the highlight of Monterey Car Week, a five-day Super Bowl for car enthusiasts. Early events included Porsche and Ferrari displays, antique automobile races, and new-vehicle debuts. Many auto executives call Monterey Car Week the "new auto show."
Many visitors were drawn to the record-breaking auctions.
A 1969 Porsche 908/02 auctioned for $4.185 million. Flat-eight air-cooled engine, 90.6-inch wheelbase, 1,320-pound weight. Vic Elford, Richard Attwood, Rudi Lins, Gérard Larrousse, Kurt Ahrens Jr., Masten Gregory, and Pedro Rodriguez drove it, according to Gooding.
DPM/Bloomberg
The 1931 Bentley Eight Liter Sports Tourer doesn't meet its reserve. Gooding & Co., the official auction house of the concours, made more than $105 million and had an 82% sell-through rate. This powerful open-top tourer is one of W.O. Bentley's 100 automobiles. Only 80 remain.
DPM/Bloomberg
The final auction on Aug. 21 brought in $456.1 million, breaking the previous high of $394.48 million established in 2015 in Monterey. “The week put an exclamation point on what has been an exceptional year for the collector automobile market,” Hagerty analyst John Wiley said.
Many cars that go unsold at public auction are sold privately in the days after. After-sales pushed the week's haul to $469 million on Aug. 22, up 18.9% from 2015's record.
In today's currencies, 2015's record sales amount to $490 million, Wiley noted. The dollar is degrading faster than old autos.
Still, 113 million-dollar automobiles sold. The average car sale price was $583,211, up from $446,042 last year, while multimillion-dollar hammer prices made up around 75% of total sales.
Industry insiders and market gurus expected that stock market volatility, the crisis in Ukraine, and the dollar-euro exchange rate wouldn't influence the world's biggest spenders.
Classic.com's CEO said there's no hint of a recession in an e-mail. Big sales and crowds.
Ticket-holders wore huge hats, flowery skirts, and other Kentucky Derby-esque attire. Coffee, beverages, and food are extra.
DPM/Bloomberg
Mercedes-Benz 300 SL Gullwing, 1955. Mercedes produced the two-seat gullwing coupe from 1954–1957 and the roadster from 1957–1963. It was once West Germany's fastest and most powerful automobile. You'd be hard-pressed to locate one for less $1 million.
DPM/Bloomberg
1955 Ferrari 410 Sport sold for $22 million at RM Sotheby's. It sold a 1937 Mercedes-Benz 540K Sindelfingen Roadster for $9.9 million and a 1924 Hispano-Suiza H6C Transformable Torpedo for $9.245 million. The family-run mansion sold $221.7 million with a 90% sell-through rate, up from $147 million in 2021. This year, RM Sotheby's cars averaged $1.3 million.
Not everyone saw such great benefits.
Gooding & Co., the official auction house of the concours, made more than $105 million and had an 82% sell-through rate. 1937 Bugatti Type 57SC Atalante, 1990 Ferrari F40, and 1994 Bugatti EB110 Super Sport were top sellers.
The 1969 Autobianchi A112 Bertone. This idea two-seater became a Hot Wheels toy but was never produced. It has a four-speed manual drive and an inline-four mid-engine arrangement like the Lamborghini Miura.
DPM/Bloomberg
1956 Porsche 356 A Speedster at Gooding & Co. The Porsche 356 is a lightweight, rear-engine, rear-wheel drive vehicle that lacks driving power but is loved for its rounded, Beetle-like hardtop coupé and open-top versions.
DPM/Bloomberg
Mecum sold $50.8 million with a 64% sell-through rate, down from $53.8 million and 77% in 2021. Its top lot, a 1958 Ferrari 250 GT 'Tour de France' Alloy Coupe, sold for $2.86 million, but its average price was $174,016.
Bonhams had $27.8 million in sales with an 88% sell-through rate. The same sell-through generated $35.9 million in 2021.
Gooding & Co. and RM Sotheby's posted all 10 top sales, leaving Bonhams, Mecum, and Hagerty-owned Broad Arrow fighting for leftovers. Six of the top 10 sellers were Ferraris, which remain the gold standard for collectable automobiles. Their prices have grown over decades.
Classic.com's Calle claimed RM Sotheby's "stole the show," but "BroadArrow will be a force to reckon with."
Although pre-war cars were hot, '80s and '90s cars showed the most appreciation and attention. Generational transition and new buyer profile."
2022 Pebble Beach Concours d'Elegance judges inspect 1953 Siata 208. The rounded coupe was introduced at the 1952 Turin Auto Show in Italy and is one of 18 ever produced. It sports a 120hp Fiat engine, five-speed manual transmission, and alloy drum brakes. Owners liked their style, but not their reliability.
DPM/Bloomberg
The Czinger 21 CV Max at Pebble Beach. Monterey Car Week concentrates on historic and classic automobiles, but modern versions like this Czinger hypercar also showed.
DPM/Bloomberg
The 1932 Duesenberg J Figoni Sports Torpedo won Best in Show in 2022. Lee and Penny Anderson of Naples, Fla., own the once-separate-chassis-from-body automobile.
DPM/Bloomberg