Astral Codex Ten

Lithuania

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Lithuania

Article

Lithuania is a recurring place in the Astral Codex Ten archive, appearing 9 times across 9 issues between December 11, 2021 and April 01, 2026. The archive places it in contexts such as “Australia and Lithuania, as among the few countries in the world that have both a Land Value Tax”; “un-recognizing Lithuania’s independence”; “If I help Lithuania, it will only benefit a few million people”. It most often appears alongside California, ACX, Australia.

Metadata

  • Category: Places
  • Mention count: 9
  • Issue count: 9
  • First seen: December 11, 2021
  • Last seen: April 01, 2026

Appears In

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

Does Georgism Work, Part 3: Can Unimproved Land Value be Accurately Assessed Separately From Buildings?
December 11, 2021 · Original source
Gwartney says that when he was the assessment commissioner and chief executive officer in British Columbia, he had a staff of 690, and that this number has not changed significantly since then. British Columbia has a population of about 5 million, so that's 1 assessment officer for every 7,250 British Columbians. For context, the IRS has a staff size of 74,454, or about one IRS agent for every 4,425 Americans. I don't have data on how many property tax assessors the USA has in total, but the above slide suggests British Columbia's figure is on the high end. As for how you actually do assessments, sure, you can send out an army of assessors to value each and every property in your jurisdiction by hand. However, not only is that labor-intensive, it's also a recipe for inconsistency. Whatever method you're using to value properties needs to be consistent and standardized across all properties, so you don't have sharp discontinuities on the assessment map that are due solely to differences between Assessor Fred and Assessor Sally's personal methodologies. Thankfully, we're living in the modern age, and we have some fancy new tools at our disposal. 4. Modern Technology Georgists were doing split-rate assessments to allegedly good success long before the rise of the computer, such as J. J. Pastoriza's effort in setting up a Georgist tax regime in Houston, Texas in 1911. Today, we have spreadsheets, property value databases, GIS mapping visualizations, regression analysis, machine learning...the works. According to Gwartney, the Canadian province of British Columbia has revalued all its land and all its property on an annual basis simply by using computers and market analysis, ever since he first helped them set up their system back in 1975. Not every jurisdiction revalues their land this thoroughly and this often, but Gwartney says there is no significant technical or staffing barrier standing in the way. Gwartney has been retired for some time, so his seminar didn't cover all the latest cutting-edge techniques that have come out in the last few years. Let's look at some recent papers and see what new tools assessors have to play with. The first on my list is Land Value Appraisal Using Statistical Methods by Kolbe, Schulz, Wersing, and Werwatz (2019). This is a study on mass appraisal techniques using real estate transaction data from Berlin, Germany. It claims that not only are the results cheaper and faster to generate than those done by conventional property assessment methods, but they are also no less accurate than those done "by hand" by experts. Kolbe et al. assert that, provided you have access to high quality market transaction data, you can perform accurate and efficient mass appraisals of land values. They chose Berlin because it "has a very effective system of property transaction data collection and storage," in contrast to other parts of Germany. They cite some prior work by Almy (2014) studying Canada, the Netherlands, and the United States, suggesting that the assessment cost per property can be brought down to 20 Euros–25 times cheaper than what some other people (Fuest, et al. (2018)) assert. Given an average tax receipt of 2,000 Euros per property, this means that the assessment cost should represent only about 1% of the funds raised. Is that good? Let's take this assertion at face value for the moment and compare it to the cost of the IRS. Federal tax receipts in 2020 were $3.42 trillion, and operation costs for the IRS were $12.3 billion, or 0.36%. However, the IRS outsources most of the labor of tax preparation to the taxpayers themselves, with compliance costs estimated between $200 billion and $400 billion a year, to the delight of Intuit. Add that up and the total cost of federal tax collection to the economy is anywhere between 6-12% of the amount it raises. And what about sales tax? According to a 2006 report by PriceWaterHouseCoopers: The study finds that the national average annual state and local retail sales tax compliance cost in 2003 was 3.09 percent of sales tax collected for all retailers, 13.47 percent for small retailers, 5.20 percent for medium retailers, and 2.17 percent for large retailers So a compliance cost of 1% would be way more efficient in terms of cost collection than the other two most common forms of taxation, and taxpayers don't even have to do anything themselves, other than pay the bill. Alrighty, how about the accuracy? The authors cite two international examples, Australia and Lithuania, as among the few countries in the world that have both a Land Value Tax and statistical methods for mass appraisals. Hefferan and Boyd (2010) assert that objections to assessments from property owners in Australia are less than 1%. I'm willing to buy the improved efficiency claims just by taking a look at some methodologies. It seems reasonable that computerized records and algorithms can cut costs significantly; the real question is if you're trading off accuracy. The other papers I found on the subject are Bencure, et al (2019) in BayBay City, Philippines, Kilić, et al (2019) in Croatia, Yalpir & Unel (2017) in Konya, Turkey, and Raslanas et al. (2014) in Vilnius, Lithuania. Let's dive in and examine some methods. 5. Mass Appraisal Methods Here are some of the latest mass appraisal methods cribbed from the research papers listed above. All of these are based on taking market transaction data, plotting them out on a map, and running computations over them to estimate valuations for the properties you don't have known values for. Furthermore, all of these methods are able to value land and building values separately. Multiple Regression Analysis This paper by Yalpir and Unel out of Turkey gives a straightforward example of using Multiple Regression Analysis for land valuation. For those of you who didn't study math, let me explain regression analysis. This is a family of mathematical models where you basically take a data set, ask the question "what mathematical formula would best fit this data," choose a basic equation model, and then have a computer search for a set of coefficients that "best fit" that curve to the data with the least amount of error. The simplest example is using linear regression on a scatterplot of observed data points to fit a trend line. This is a common exercise in freshman physics and statistics classes. You can use more complicated versions of this numerical method to take a big bag of observations (real estate sales) and use "multiple regression" to tease out dependent variables (land value and improvements value) based on the independent variables (size, location, age, number of bedrooms) of your observations. In this case the team identified about a hundred different factors that can affect the price of a property: Then you create an entry for each property, fill in the values for each of those characteristics, and run it through the regressor. Take note of how many of these factors start with the words "proximity to." Each of these can be calculated automatically just by knowing where the property is on a map, and each of them is an independent contributor to the value of the property's location. The next step is to generate individual "index maps" that combine various related features into combined heat maps. Then you run everything through and see if it works. You can get the land share of the final value by combining the contributions of all the individual factors that you associate with "land," such as proximity to important things. In the verification section the authors say: As a result of the analysis, since the significance level (0.000) p <.05, corresponding to the F values in the ANOVA test, indicates that the regression analysis is appropriate and the models are significant. The criteria that make up the model account for about 85% of the market value and 15% cannot be explained for reasons such as economic, non-existent data and unearned income. Unfortunately, they don't say anything about how accurate their model is for assessing land values specifically. Otherwise, this is a pretty good example of using the Multiple Regression method for estimating the individual contributions of various factors to overall property values. Gwartney says Multiple Regression Analysis was a standard method he typically used, of which this specific paper is just one example. Nonparametric kernel regression This will be a method familiar to the programmers in the audience who have any experience with image processing algorithms. Here's an example from this old Gamasutra article: The basic idea here is to take a matrix of numbers, called a "kernel", and run that over every pixel in a source image. The kernel tells you how strongly to weight all of the source pixel's neighbors to compute a final result for that position. A simple "box blur" is a kernel where every value is 1 (meaning it averages the values of all neighboring pixels within a range). The more subtle gaussian blur illustrated above uses a two-dimensional normal distribution of values so that each pixel is most affected by those nearest to it. So let's apply the same principle to land valuations. If you have a map with lots of transaction data of pure land sales–defined as sales of either vacant land or teardown properties (where the building value is essentially zero)–then you can use a special kernel filter to smoothly interpolate land values across the region. So you basically have a smooth curve that mostly favors close-by points, tapers off a bit, and then disregards anything outside a certain distance entirely. The big assumption here is that land values change smoothly and do not change suddenly across very short distances. There are, in fact, locations with sharp jumps in value (any town with an "other side of the tracks," for instance). But for cases where we know a priori that land values change smoothly, this method is appropriate. No other prior restriction is placed on the form of the land value map, however, and this is why it's called "nonparametric." Here's an illustration. The outer box is the entire search distance that the kernel considers, and the circles represent the falloff of the curve itself. The size of the box is called the "bandwidth" and is set by the user. Everything outside of it will have zero influence on the kernel's output at any given location. This method operates on the same basic logic that I used when I hand-estimated the land value of that San Francisco house in Part I based on the value of the empty lot next door. However, it makes the whole procedure systematic. It can easily and accurately estimate the land value of a property with a big fat building on it simply by smoothly interpolating the known values of the nearby parking lots. Of course, it has limitations. First and foremost, it's a highly local operation, so if you have properties you're trying to value that don't have nearby pure land sales data, you can't really do much with this. Also, most people assume that city centers have less market transactions for undeveloped land than the countryside, as did I until I read that paper by Albouy in Part I. But in any case, this is just one method in your toolbox and might not be sufficient by itself. Its key advantage is that it works directly from true market data for land and doesn't need or want any other subjective data. In the end, basic kernel estimation just fills in the land value of unmeasured locations with a local weighted average of known locations. Nonparametric adaptive regression Kolbe, et al. build on the kernel regression method with a technique called Adaptive Weights Smoothing (AWS), which runs in several iterations and adds additional weight to any observed data points that are sufficiently close to the point being estimated. I'm not 100% sure about what all the math means, but it seems like it's basically a "smarter" version of the basic kernel method. Left: Nonparametric kernel regression, Right: Adaptive Weights Smoothing. I think the authors goofed and printed the same figure twice with different headings because they're identical if you overlay them in Photoshop. Semiparametric regression Now, the above two methods assume you have plenty of "pure" land sale records to work with. But if you're trying to work out prices in the city center, you've probably mostly got land and buildings mixed together. To do this effectively, we need more data, and this is where the "parameter" in "semiparametric" comes in. The model described in Kolbe et al. seems like a flavor of multiple regression analysis that takes the price, the location, and various characteristics of the building and feeds it into a regressor. But we've got "semi" parametric here. What does that mean? Well, if you already know how certain relationships between the data work a priori, it's better to enforce those relationships yourself rather than leave it to the computer. Here, we enforce the assumption that if two properties are right next to each other, then the value due to location is going to be essentially identical. This algorithm starts by ordering things geographically and then working out the differences in observed price by regressing on the difference between remaining property characteristics. In this method, the power of "location, location, location" is not something we're leaving to the regressor to discover by itself. Results of the Semiparametric regression method, we can see some significant differences from the simple kernel-based model. As you can see above, this gives you more detailed and likely more accurate results, and you're better able to assess the values of properties with buildings on them, even in the absence of pure land sales. This technique is more complicated and bakes in assumptions about the power of location, but otherwise doesn't assign subjective human weights to the various property characteristics. The chief human bias comes in the form of deciding which property characteristics are measured and made legible to the model in the first place. Okay great, but how accurate are the above three methods? Their main point of comparison is this thing called the "Bodenrichtwerte," or BRW. I think that means "ground-level-values" in English, and it's an expert-assessed map of land values for Berlin done the traditional way. The nonparametric kernel regression method has a correlation of 0.704 with the traditional method and has the added disadvantage that it's not able to produce estimates for the city center, only the outlying areas. Furthermore, the BRW map does show sharp discontinuities, which is another knock against the kernel method, at least for the city center. What about the iterative method? Kolbe et al. find that "the agreement between [Adaptive Weights Smoothing] land value estimates and, both, land prices and BRW land values is fairly good for all values of λ." Doing some quick checks, their values seem to be within about 85% of the BRW values. A different Kolbe et al. paper called Identifying Berlin's land value map using adaptive weights smoothing goes into more detail and claims to give "similar" values to that of the BRW. For the semiparametric method, they "found a strong positive correlation of 0.845" between their numbers and a previously expert-assessed set done using the traditional method. That sounds pretty good. It seems their margin for error is about plus or minus 15% compared to the traditional expert method. I'd like to see more direct comparisons against market transactions themselves, though, because if the prior expert assessments are wrong, then the main achievement here is improved efficiency, not accuracy. However, this method doesn't seem to be dramatically less accurate than the old way of doing things. The last three models came from the Berlin case study, where you have excellent market transaction data in an extremely wealthy and high-trust society. But what if you're trying to assess land in a developing nation with poor market transaction records, weak institutions, and widespread poverty? Innovative Land Valuation Model (iLVM) This is the particular name of the method described in Development of an Innovative Land Valuation Model (iLVM) for Mass Appraisal Application in Sub-Urban Areas Using AHP: An Integration of Theoretical and Practical Approaches by Bencure, Tripathi, Miyazaki, Ninsawat, and Kim. They used BayBay City, Philippines as their case study. Whereas the previous models are very "hands-off" and let the computer work out the relationships between prices and property characteristics, here you get expert human opinion directly involved in building the model, baking in weights that directly embody judgments like "properties next to major roads are more valuable." These judgments are based on expert opinions that presumably come from observed experience but are a priori judgments nonetheless. Here, look at this big complicated flowchart. The "Analytic Hierarchy Process" in the box on the left is a particular kind of method for getting experts to set weights. The authors give this reason for using it: Despite criticism pinpointed by other scholars, the AHP remains the commonly used in many research fields and practical applications. This is because the AHP: (1) overcomes human difficulty in making simultaneous judgment among factors to be considered in the model; (2) is relatively simple as compared to other MCDA [multi-criteria decision analysis] methods; (3) is flexible to be integrated in various techniques such as programming, fuzzy logic, etc.; and (4) has the ability to check consistency in judgment After identifying a list of "factors" that can affect land value, they group them into taxonomical buckets: Note that certain factors like "Coastline" appear in multiple buckets; this captures the various influences a characteristic can have. For instance, land on the coast tends to be more economically valuable because of tourism, shipping, fishing, etc., so that goes under "economic." But land that's next to the coast is also more likely to flood, so it also goes under "environmental." And then there are various land use restrictions that apply specifically to coastal areas, so it goes under "legal" as well. In this way, a single factor like "the property is on the coastline" can have both positive and negative effects on land value (e.g., it's more economically valuable but it also might flood, and there are certain things you aren't allowed to do there). The next step is to set down some rules for how sensitive each factor is to location and distance. So here we can see that the economic benefit of being on the coast is most strongly felt if you're within half a kilometer of the ocean, but the environmental effect (e.g., risk of flooding) is most strongly felt when you're within 0.03 kilometers. And so on and so forth. Your experts help you work out all these rules. Note that for a few of these factors (such as land use and slope), you use metrics other than distance (e.g. land use classification and grade). Then you take all that stuff and assign everything a value between 0 and 5. Your team of experts then uses this table to come up with a set of weights for everything. What essentially comes out of this is a big linear equation with a bunch of coefficients for every one of your factors, which is then broadly fit to the observed market prices. When you're done, you can take any property on your list, multiply each of its characteristics by its respective weight, run that through your equation, and calculate the predicted price of the land. So how accurate is it? The authors compare it to standard Multiple Regression Analysis and claim it fares better. The Root Mean Square Error is quite a bit less than MRA. In addition, I think it's also saying that the MRA algorithm decided that only four of the factors were significant and basically ignored all the rest. By contrast, iLVM was able to maintain contributions from all the factors, because it doesn't leave that decision to the computer. I'm not 100% sure; it's not clear from the paper. The authors claim that about 67% of the variability is explained by their model, but they note that there are some areas where the model can be off by more than a factor of 1.0 in either the positive or negative direction. One thing that's kind of fun about this model is that you can make neat graphs like this that show the individual contribution of each factor: The main downside to this model is that it relies on a whole lot of subjective expert opinion and can be questioned on that basis. That said, it can be cheaply deployed in a transparent and consistent way across a large area. You can see why that's attractive for a developing nation with weak institutions and poor market transaction records; the argument is that this is a significant improvement over the former status quo. I wonder how well this model performs when you feed it better market transaction data, and how that would compare against all the others methods under identical conditions. More research is needed. Rather than drag you through a bunch more research papers, I'll just leave these others I found cited in the above studies: Killić et al. (2019) - Fuzzy expert system for land valuation in land consolidation processes
Inline links: J. J. Pastoriza's effort in setting up a Georgist tax regime in Houston, Texas in 1911, Land Value Appraisal Using Statistical Methods, Almy (2014), Fuest, et al. (2018), $12.3 billion, $200 billion, $400 billion, delight of Intuit, 2006 report by PriceWaterHouseCoopers, Hefferan and Boyd (2010), Bencure, et al (2019), Kilić, et al (2019), Yalpir & Unel (2017), Raslanas et al. (2014), https://substackcdn.com/image/fetch/$s_!stkG!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fef4c64b9-38bc-43c8-aa83-05eae3576e03_923x600.png, https://substackcdn.com/image/fetch/$s_!_9z0!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fdd98e114-7eb2-4566-a979-1f2f2dd27c22_701x867.png, https://substackcdn.com/image/fetch/$s_!8HN7!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Ff1232522-51e0-4438-998a-b0be4615df6b_534x806.png, https://substackcdn.com/image/fetch/$s_!jFqw!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fd5836457-7642-4235-9410-00906f043428_662x357.png, Gamasutra article, https://substackcdn.com/image/fetch/$s_!foLQ!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Ff74d378c-39b7-49f9-9655-8cbbf7c89ff5_592x270.png, https://substackcdn.com/image/fetch/$s_!AjnN!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F458458cc-614e-4ab3-a57b-3f28b70db6c3_458x317.png, https://substackcdn.com/image/fetch/$s_!lVcf!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F77ea4f72-0e51-4c63-b14b-15c603ac2500_901x418.png, https://substackcdn.com/image/fetch/$s_!zoSx!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fed78916c-12c5-42ca-b581-7b59aa25bbd5_757x718.png, https://substackcdn.com/image/fetch/$s_!7Wm9!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F8fe15dbb-181f-46ae-bdde-840bdd6a2064_752x735.png, https://substackcdn.com/image/fetch/$s_!zig5!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F958762f1-a425-4017-86cf-058cb3eb4d59_713x389.png, https://substackcdn.com/image/fetch/$s_!8ZGT!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Ff37696ef-7a6a-48ae-8169-734b875b0b57_800x319.png, Identifying Berlin's land value map using adaptive weights smoothing, https://substackcdn.com/image/fetch/$s_!3CR3!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F0489e086-69ae-4840-b658-59fee6b3af44_2000x1672.png, https://substackcdn.com/image/fetch/$s_!fA0K!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F5d39769d-46e2-4891-92aa-cb3766068204_2000x978.png, https://substackcdn.com/image/fetch/$s_!phFK!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F5d1a519c-93d7-4bed-9577-7478fb239bca_1968x3548.png, https://substackcdn.com/image/fetch/$s_!XtLN!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Ff59cb148-e0da-456b-b205-973e04239be7_587x647.png, https://substackcdn.com/image/fetch/$s_!My3b!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fee7b484b-3be8-4363-bcb8-1cb4fb4a7c01_661x655.png, https://substackcdn.com/image/fetch/$s_!SqQA!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F8a84b431-1250-427e-a67a-b3e2b8a3c0dd_896x623.png, https://substackcdn.com/image/fetch/$s_!qPOz!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fb71526d0-736c-45d7-ad14-36e5670f78ab_1153x881.png
Links For June
July 01, 2022 · Original source
31: In response to Russia debating un-recognizing Lithuania’s independence, the Kiev City Council has rescinded the 1147 AD decree by the Grand Prince of Kievan Rus founding the city of Moscow. Moscow now officially has not been founded; please conduct yourselves accordingly. [Update: Likely fake]
Inline links: has rescinded, Likely fake
Book Review: What We Owe The Future
August 23, 2022 · Original source
Imagine you are a wise counselor, and you have the opportunity to spend your life advising one country. Whichever country you advise will become much richer and happier (and for whatever reason, you can’t choose your own homeland). You might think: “If I help Andorra, it will only benefit a few thousand people. If I help Lithuania, it will only benefit a few million people. But if I help India, it will benefit over a billion people. So I will devote my life to helping India.” Then you learn about the future, a country with 50 quadrillion people. Seems like a big deal.
Meetups Everywhere 2023: Times & Places
August 25, 2023 · Original source
RIGA, LATVIA Contact: Artūrs and Anastasia Contact Info: effectivealtruismlatvia[at]gmail[dot]com Time: Wednesday, September 13th, 6:30 PM Location: Gravity Hall, 11 Puskina iela, Riga Coordinates: https://plus.codes/9G86W4RC+PF Lithuania VILNIUS, LITHUANIA Contact: Tom Contact Info: acx[dot]vilnius[at]gmail[dot]com Time: Saturday, September 16th, 3:00 PM Location: Vinco Kudirkos square (Vinco Kudirkos aikštė). I will be in front of the central statue with an ACX MEETUP sign. Coordinates: https://plus.codes/9G67M7QJ+26 Notes: RSVP via LessWrong or email (acx.vilnius@gmail.com) preferred, but not required. Don't have any big plans, anyone who wants to join is welcome.
Inline links: https://plus.codes/9G86W4RC+PF, https://plus.codes/9G67M7QJ+26
VILNIUS, LITHUANIA Contact: Tom Contact Info: acx[dot]vilnius[at]gmail[dot]com Time: Saturday, September 16th, 3:00 PM Location: Vinco Kudirkos square (Vinco Kudirkos aikštė). I will be in front of the central statue with an ACX MEETUP sign. Coordinates: https://plus.codes/9G67M7QJ+26 Notes: RSVP via LessWrong or email (acx.vilnius@gmail.com) preferred, but not required. Don't have any big plans, anyone who wants to join is welcome.
Inline links: https://plus.codes/9G67M7QJ+26
Meetups Everywhere 2024: Times & Places
August 29, 2024 · Original source
Contact: Anastasia Contact Info: riga[dot]acx[a t]gmail[d ot]com Time: Wednesday, October 09th, 07:00 PM Location: MiiT Coffee, Lāčplēša iela 10, Centra rajons, Rīga, LV-1010 Coordinates: https://plus.codes/9G86X44C+M5 Group Link: https://www.lesswrong.com/groups/fE7wFrbHoAKAvw5bw Lithuania VILNIUS, LITHUANIA Contact: Linas Contact Info: linaskondrackis[at]gmail[dot]com Time: Sunday, September 22nd, 03:00 PM Location: We'll be in Lukiškių Aikštė. Look for a small group and a guy holding an ACX sign. Coordinates: https://plus.codes/9G67M7QC+Q9 Group Link: https://discord.gg/udTt5QSX Notes: Please feel free to come even if you feel awkward about it, even if you’re not ‘the typical ACX reader’, even if you’re worried people won’t like you, etc. Bonus points if you tag yourself on LessWrong so we know you're coming / thinking about it.
Inline links: https://plus.codes/9G86X44C+M5, https://www.lesswrong.com/groups/fE7wFrbHoAKAvw5bw, https://plus.codes/9G67M7QC+Q9, https://discord.gg/udTt5QSX
Meetups Everywhere Spring 2025: Times & Places
March 25, 2025 · Original source
Contact: Anastasia Contact Info: riga[period]acx[a t]gmail[period]com Time: Friday, April 11th, 6:30 PM Location: MiiT Coffee, Lāčplēša iela 10, Rīga Coordinates: https://plus.codes/9G86X44C+M5 Group Link: https://www.lesswrong.com/groups/fE7wFrbHoAKAvw5bw Notes: Please RSVP on LessWrong for reservation purposes. If you feel awkward about coming, please do anyway! Reach out by email if some social worry is preventing you from dropping by. Lithuania VILNIUS Contact: Tom Contact Info: acx[period]vilnius[a t]gmail[period]com Time: Saturday, April 12th, 4:00 PM Location: Lukiškių aikštė (Lukiškės square), Vilnius I'll be somewhere in the center with an ACX sign. Coordinates: https://plus.codes/9G67M7QC+R7 Group Link: https://discord.gg/MrB [remove this bit] xnNBKbA Notes: Anyone interested is welcome! We'll be gathering at the center of the square, then probably move on to a cafe or something nearby. RSVPs preferred, but not required.
Inline links: https://plus.codes/9G86X44C+M5, https://www.lesswrong.com/groups/fE7wFrbHoAKAvw5bw, https://plus.codes/9G67M7QC+R7
Meetups Everywhere 2025: Times and Places
August 29, 2025 · Original source
Contact: Anastasia Contact Info: riga[period]acx[a t]gmail[period]com Time: Friday, September 19th, 6:30 PM Location: MiiT Coordinates: https://plus.codes/9G86X44C+M5 Group Link: https://www.lesswrong.com/groups/fE7wFrbHoAKAvw5bw Notes: If possible, please RSVP on LessWrong for reservation purposes, and please do come even if you're shy/scared - we are very welcoming. LITHUANIA VILNIUS Contact: Linas Contact Info: linas[dot]ko[at]pm[dot]me Time: Sunday, September 21st, 4:00 PM Location: I'll be wearing a purple "Roboflow" hat. Coordinates: https://plus.codes/9G67M7QC+R7 Group Link: https://discord.gg/jqxuBM [remove this bit] eHaw Additional Notes: The latest event details can be found in: https://discord.gg/y9KNuzRb?event=1412003421908766800
Inline links: https://plus.codes/9G86X44C+M5, https://www.lesswrong.com/groups/fE7wFrbHoAKAvw5bw, https://plus.codes/9G67M7QC+R7, https://discord.gg/y9KNuzRb?event=1412003421908766800
Links For December 2025
December 10, 2025 · Original source
The other good news is that somehow they don’t charge a subscription, which makes them a way to get usually-subscription-only AI models for free. How is this possible? “[The most likely hypothesis is that] Witpaw is an adorable piece of spyware and he’s selling my data to the CCP”. 36: This month’s anti-people-named-Sacks content: NYT on Trump AI czar David Sacks’ conflicts of interest; New Yorker on whether neurologist Oliver Sacks used his case studies to work through his own issues rather than presenting them accurately. [EDITED TO ADD: I originally framed it this way as a joke, but on further research I think David and Oliver are related. Wikipedia says that Oliver was first cousins with Israel statesman Abba Eban, and that Abba Eban was born to Lithuanian Jewish parents in Cape Town. David Sacks’ bio says he was born to Jewish parents in Cape Town, and this article specifies that they were Lithuanian. I doubt there were too many Lithuanian Jewish families named Sacks in mid-1900s Cape Town, so sure, related!) 37: Orca Sciences: There Has To Be A Better Way To Make Titanium. Titanium is a great metal - strong, light, and tough. If we had cheap titanium, it could revolutionize manufacturing the way cheap steel and aluminum did in previous eras. So why don’t we? Not because titanium is rare: it’s “the 9th most common element in the earth’s crust”. Rather, it’s very complicated and expensive to extract from its ore. Some kind of breakthrough in titanium extraction processes always seems tantalizingly close, but has never quite materialized. Is there any hope? 38: If Asians Are Lactose Intolerant, Why All The Milk Tea? Lactose intolerance has confused me for a long time - 23andMe tells me that I’m lactose intolerant, but I drink milk regularly without problems, so what’s up? This post’s answer: lactose-intolerant people who don’t usually drink milk will get sick if they start suddenly. Lactose-intolerant people who drink milk regularly since childhood develop gut microbiota that can digest milk, but which demand an expensive “tax” in calories. Lactose-tolerant people will always be able to digest milk and absorb all the calories themselves. 39: How do different majors change college students’ political beliefs? No surprise that the humanities and social sciences shift people left; no surprise that business and economics shift them right. I was a little surprised that engineering shifts people right a little, and that Education of all things shifts people right (albeit only slightly). How is that even possible? Are these people coming in as Mao Zedong and leaving as “only” Leon Trotsky? Also, Political Science is exactly neutral, lol. [EDIT: I misunderstood, they’re using natural sciences as a zero point, this is a reasonable choice but slightly changes the interpretation] 40: Kindkristin: Language models improved my mental health. 41: More floor employment, from the WSJ (h/t @LaocoonofTroy): Big Paychecks Can’t Woo Enough Sailors For America’s Commercial Fleet: “Straight out of college, graduates from the country’s maritime academies can earn more than $200,000 as a commercial sailor, with free food and private accommodations... Despite the pay and perks, maritime jobs go begging, and it is raising national-security concerns.” Other selling points include “six months vacation, live wherever you want, and you’re serving the nation” and onboard “gyms, connectivity, and cuisine”. The catch is that you have to be at sea for months at a time. 42: Study (h/t @KierkegaardEmil): there was minimal “learning loss” from COVID school closures, best estimate is “0.02 standard deviations per 100 days of school closure”. I correctly predicted this back in 2021, but I also wrote in March of this year about how there’s been a general decline in NAEP scores since then. It seems like maybe a student having their specific school closed for longer than other schools didn’t hurt them, but some sort of general cultural change, maybe related to COVID, did hurt. 43: Sam Bankman-Fried’s mother on why she thinks his trial was unfair. SBF is appealing his conviction and will probably be making some of these same points in court. Can’t find a prediction market directly on the appeal, but this one says only 15% chance he serves under 10 years, this one says 15% chance of a Trump pardon, so it doesn’t seem like there’s much room for him to be freed (or get a significantly shorter sentence) on appeal. And Wired says that only 5-10% of appeals like these succeed. 44: Related: Trump pardons Juan Orlando Hernandez, former Honduran president extradited to the US for narco-corruption. Some sources are trying to find a Prospera angle - Prospera and other ZEDEs were approved under JOH’s administration, and the Prosperans seem to have good MAGAworld connections - but I don’t think this is their top priority, and I don’t know if it requires much explanation for Trump to be pro-right-wing Latin American politicians convicted by the Biden administration. More interesting is that apparently JOH and SBF were cellmates (X), “SBF spent extensive time helping JOH with trial prep” and SBF told an interviewer that “Juan Orlando is the most innocent prisoner I’ve met, myself included.” ChatGPT is not impressed with the Trump/SBF case for JOH’s innocence. Related: JOH’s conservative party on track to win this month’s extremely-close Honduran elections, great news for Prospera if it happens. 45: The “100 Above The Park” building in St Louis (h/t Bobby Fijan on X): 46: The death toll of the ongoing Sudan genocide has risen to about 150,000. Nicholas Kristof writes that the world has once again failed to prevent atrocities, and argues that the most important point of leverage is pressure on the United Arab Emirates, which is arming the genociders. Sam Kriss also writes about the situation in The World’s First Matcha Labubu Genocide, but is unimpressed with Kristof’s take: Sudan is passed over in a deeply uncomfortable silence. The absolute most you can do is blame the Emiratis. From what I’ve seen, more people seem to be appalled at the UAE for its frankly marginal role in arming the RSF than at the RSF itself. This is the approved way of understanding any inscrutably indigenous foreign conflict: you just worm out any third-party involvement and then act like you’ve solved the whole thing. I side with Kristof here, for reasons that Sam himself touches on later in his piece, in a section comparing Darfur with Gaza. It would be very easy to make people care about Darfur again. All it would take is a loud, vocal contingent of RSF apologists in the Western media. I agree, but would frame it less cynically: the reason Westerners pay attention to Gaza is that there’s a lever to push: not only does America support Israel, but many of their friends support Israel, so they can imagine convincing America or at least their friends to stop, and at least feel like there is some remote chance of making a small difference (and in fact, Trump getting mad at Israel and deciding to pressure them was decisive in effecting the cease-fire). On the other hand, we don’t have many levers to affect ethnic Baggara in the Rapid Support Forces of Sudan, so it doesn’t really feel useful to write blog posts arguing that they should stop; obviously they should stop, nobody disagrees with this, and it goes without saying - so nobody says it. But the US does support the UAE, and many of our friends like the UAE or at least go there on vacation, so maybe it’s possible to have make some small difference by embarrassing them. 4D chess take is that Sam Kriss agrees with all of this, but “loudly” and “vocally” argued against it to give people like me a hook to write about this genocide with, in which case I thank him for his sacrifice. It would also be nice to be able to donate, but I don’t know who to trust in the region - other than Doctors Without Borders, who are usually pretty good. 47: The AI Futures Project (group of AI-will-be-fast intellectuals) and the AI As A Normal Technology team (group of AI-will-be-slow intellectuals) wrote an adversarial collaboration in Asterisk explaining what they agree on, for example: That there’s an important distinction between existing AI and “strong AGI”
Inline links: Trump AI czar David Sacks’, neurologist Oliver Sacks, says, Abba Eban was, bio, this article, There Has To Be A Better Way To Make Titanium, If Asians Are Lactose Intolerant, Why All The Milk Tea?, How do different majors change college students’ political beliefs?, https://substackcdn.com/image/fetch/$s_!xR53!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa36d404d-9574-4e25-93bb-7ef4d2ca9f9a_940x606.jpeg, as a zero point, Language models improved my mental health, floor employment, @LaocoonofTroy, Big Paychecks Can’t Woo Enough Sailors For America’s Commercial Fleet, Study, @KierkegaardEmil, correctly predicted this, wrote in March of this year, Sam Bankman-Fried’s mother on why she thinks his trial was unfair, appealing his conviction, this one, this one, Trump pardons Juan Orlando Hernandez, Some sources, JOH and SBF were cellmates (X), is not impressed, on track to win, Bobby Fijan on X, https://substackcdn.com/image/fetch/$s_!Gj0x!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd354a169-6127-4815-85b5-940414c632eb_603x900.jpeg, Nicholas Kristof writes, The World’s First Matcha Labubu Genocide, and in fact, Trump getting mad at Israel and deciding to pressure them was decisive in effecting the cease-fire, Doctors Without Borders, an adversarial collaboration in Asterisk explaining what they agree on
Meetups Everywhere Spring 2026: Times & Places
April 01, 2026 · Original source
Contact: Kirils Surovovs Contact Info: kirils[.]surovovs[@]gmail[.]com Time: Monday, April 13th, 7:00 PM Location: Baznīcas iela 15, Rīga Coordinates: https://plus.codes/9G86X449+JH7 Group Link: https://www.lesswrong.com/groups/fE7wFrbHoAKAvw5bw , https://www.facebook.com/EALatvia Notes: Please RSVP on LessWrong or Facebook Lithuania VILNIUS Contact: Lynn Contact Info: acx[.]vilnius[@]gmail[.]com Time: Saturday, April 11th, 3:00 PM Location: Lukiškių aikštė (Lukiškių square) Coordinates: https://plus.codes/9G67M7QC+V8 Group Link: https://discord.gg/R8E [remove this bit] bg2bVaM Notes: Anyone interested is welcome. RSVPs not required.
Inline links: https://plus.codes/9G86X449+JH7, https://www.lesswrong.com/groups/fE7wFrbHoAKAvw5bw, https://www.facebook.com/EALatvia, https://plus.codes/9G67M7QC+V8