dhx

To extend on this good point--

DJB is not just a mathematician looking over theoretical equations. He's also an expert in the real world _implementation_ of cryptography where most security failures can be expected to occur.

For some mathematician's brilliant cryptography scheme, how easy would it be for implementers to develop constant time / constant power computer algorithms to avoid side channel leakage? Have these computer algorithms been developed, are they easy to implement securely or are implementers going to continually mess it up?

See [1] and [2] for answers. Summary: Technology is not ready.

[1] https://dl.acm.org/doi/10.1145/3569420

[2] https://dl.acm.org/doi/10.1145/3779208.3785290

At least in my experience for large retail chains or restaurant chains, hours on their websites are very accurate. I think where Google might benefit from calling to verify hours is, for example, where they've scraped https://schema.org/openingHours from the web page for an independent store, but the website has used an invalid/incorrect format, and some manual human check or clean-up is necessary, but this work would be too time consuming for each of 100,000's of independent stores which exist.

There are cases where a brand might have broken opening hours information such as "Mo- Tu - We 09 00 -4 p m". For alltheplaces.xyz scrapers, a human is often able to make sense of the situation and fix the problem through a few lines of extra code. Contacting the business to get the hours confirmed would only be necessary in very rare cases.

Where it would perhaps make the _most_ sense to contact a business to confirm opening hours is:

1. Ahead of public holidays to confirm planned temporary changes to opening hours. Many brands don't bother to update hours on their websites for such temporary changes, or at least don't list those hours until the week prior, which is usually too late for OSM, ATP, offline maps, etc which would ideally want to know these changes to hours up to a year in advance.

2. To confirm actual dates of permanent store/restaurant closures. Many brands in liquidation tend to fire the website developers (or can't pay them to work) and leave an outdated website online until the final shop or restaurant is closed. Commonly it's only some "CLOSING DOWN SALE LAST DAYS EXTENDED TO FRIDAY" post on social media that informs visitors accurately.

See alltheplaces.xyz for continuously updated straight-from-the-primary-source opening hours of chains of shops and restaurants, public facilities such as libraries, etc. This is probably as accurate as it gets AND you have the confidence of knowing exactly where the data came from (down to the URL) and when it was last checked.

Some OSM contributors go brand-by-brand/operator-by-operator in making sure OSM features have the most up-to-date opening hours added to them from matched ATP features. As such, OSM may be fairly accurate for chains too.

For a standalone shop or restaurant the opening hours situation is usually still better with Google Maps rather than OSM. There aren't enough OSM contributors who care enough to check and maintain opening hours for every shop, restaurant, fuel station, etc.

1. This is largely country-dependent with some governments being quite adamant that address data should be in the public domain, and some governments doing the opposite and selling address databases to private companies for some quick cash, where private companies then sell address lookups for some absurd per-lookup price for the next few decades. Most of the world though is probably just not rich enough to compile, publish and accurately maintain a national address database.

OpenAddresses is perhaps the gold standard for open source address data compilation from government datasets. Note for the future that alltheplaces.xyz (project I contribute to) is looking like it may eventually perform the automatic address data download/extraction/compilation that OpenAddresses currently performs. This has the benefit that in backwards countries, alltheplaces.xyz also obtains some addresses through other means--such as advertised location of international restaurant chains. And quite often, being within +/- 100 address numbers on a road is good enough for navigation. Google Maps obviously crawls addresses from all over the Internet AND has quite a high tolerance for errors, hence will perhaps always seem more complete than OSM.

2. Some further ideas for open source mapping applications trying to determine real time traffic situations:

2a. Use GTFS/GTFS-RT feeds for bus networks to detect real time delays but also to compare planned bus route schedules for different times of the day (different traffic conditions) where buses share the road with the public. There's already a few maps out there that overlay nearby GTFS-RT feeds for the city of interest and usefully provide a visual indication of how well public transport vehicles are currently moving.

2b. alltheplaces.xyz extracts public traffic camera feeds which could be presented to users when they plan/commence a journey as an indication of what lies ahead on the route.

I change that slightly to "Good luck getting an explosive payload."

The difficulty for an attacker is the explosive payload, not the delivery mechanism. If it were easy for an attacker to get an explosive payload there would be car bombs going off every day as an easier delivery mechanism than use of quadcopters.

Thankfully it seems to be relatively easy to prevent people from making explosives, at least outside of warzones in countries with strict border control, because random people don't have a valid reason to buy industrial chemicals or equipment, and especially not chemicals identified as precursors for making explosive compounds. And for plants where such industrial chemicals are legitimately necessary to use, inputs and outputs can be measured, and detection taggants[1] used, and many other security measures, to prevent accidental or deliberate loss of control over such chemicals.

[1] https://en.wikipedia.org/wiki/Taggant#Explosive_taggants

"Fix this code" should ideally solve entire vulnerability classes, not just spot fix buffer overflows one by one. Thus it may be possible to design an LLM which can solve entire vulnerability classes and remain useful to users, but refuses to reason about specific buffer overflow vulnerabilities or specific race conditions, etc.

For example, "fix this code" on an ageing monolithic C codebase that accepts media files as input and outputs them visually to a display server could:

1. Recreate the software using a modular and loosely coupled architecture rather than monolithic and tightly coupled software architecture. For example, command line argument parser is a separate process, file format parser is a separate process and display server output is a separate process. If new features are added in the future (such as filters for manipulating output) then the architecture supports such additions with ease.

2. Use operating system sandboxing features to restrict what each modular component of the software architecture is permitted to do. Now that the parsers are separate processes, it's easy to pass an open file handle to the file format parser and only permit the process to read the file handle (not write to the file, not open any other file, not read the system clock, not open a new network socket, etc). The worst case impact of a parser bug is now significantly reduced.

3. Convert at least critical components to "safe" programming languages (Rust, Ada, SPARK, etc) which can be used to remove entire classes of bugs--read/write out of bounds, division by zero, numeric overflows, etc. For cryptography code--use a formal mathematical proof language. With a modular and loosely coupled architecture, different programming languages can be used depending on the use case--for example, assembly for video decoding where performance matters most and sandboxing can provide the security guarantee, Rust for implementing multi-threaded servers where race conditions must be avoided and Python for low-criticality user-adjustable code/plugins where ease of use and maintainability is most important.

4. Ensure software components are reproducible during their build.

5. ...etc

However, a prompt of "Are there any buffer overflow bugs in this codebase?" or "Fix the integer overflow vulnerability in add_numbers(x, y)" would be rejected. In the later case, telling the LLM to fix some specific bug in each of function1 through function9999 would force an LLM to reveal whether it thinks a bug exists or not. Responses of "Silly human, that bug doesn't exist in function596" or "Good find human, I've fixed that bug in function596 for you" allows a human to quickly narrow down where the LLM thinks a bug worthy of manual human detection can be found.

I like that it's non-serious and contains jokes throughout the user interfaces. Reminds me a little of Airline Tycoon's non-serious/joking nature, and Theme Hospital if going further back in time. It makes the game fun rather than a boring simulator some city planner would use for work.

Contributor for an open source scraper -- https://alltheplaces.xyz/ -- which currently has about 4700 scrapers extracting 40-someting million points of interest.

The project aims to scrape location data and other general information about shops (main use case), postal addresses, restaurants, schools, weather stations, marine buoys, traffic cameras, street trees, whatever someone may decide is worthwhile to add to OpenStreetMap, or use as location data in other projects.

There's a decent community of contributors keeping the scrapers maintained and further expanding the number of points of interest which are extracted.

The opportunity I'd like to work more on (time permitting) is the data scraped by this project I feel is heavily underutilised in other open source/community projects. For example, there's a great opportunity to generate a location map once a week of current locations of international restaurant chains, and upload to Wikimedia Commons where every language Wikipedia would gain a high quality infographic of current locations of a chain's restaurants, etc. As an additional example, time-series data from ATP could be used to update a graph hosted on Wikimedia Commons each week where the graph plots the rise and fall of retail or restaurant chains.

There's an example at [1] of a prompt for a HTML mockup operating system where 3 applications are requested to be "white hat tools" that show diagnostic system information. Claude Fable 5 is shown and said in the video to switch back to Opus 4.8 as a "safety" feature.

What an utterly useless model if it refuses to work on something as benign as basic system diagnostic utilities (nmap or whatever).

[1] https://youtu.be/9GLYsrMpprs?t=305

Also have a look at Inform 7[1] (domain specific language for interactive fiction), specifically the "Stone" example section[2] which explains edible food and the effects different foods may have on the adventurer.

It's similar concept to OPM with some modelling basics already built on top for player movement throughout a world, player interaction with objects (looking at, lifting, moving), and many other primitives needed to write interactive fiction. And relevant to this thread, Inform 7 of course has modelling basics for a player eating food, drinking potions, etc.

[1] https://en.wikipedia.org/wiki/Inform#Inform_7_programming_la...

[2] https://ganelson.github.io/inform-website/book/RB_9_1.html

Agreed. My comment was to highlight that if a recipe for a soup just says "1kg squash", that could mean anything from "Cucurbita maxima subsp. maxima var. Jarrahdale"[1] through to "Cucurbita pepo subsp. pepo var. recticollis"[2], with vastly different outcomes for the soup.

The model under the hood should probably have ingredients as parts of a taxon, then have common names mapped (many:many) to these parts of taxons. Then it's necessary to have abstract classifications such as "pumpkin seed" which could be defined as the seed of multiple different taxons, which for some recipes, may not matter which one of 5 Cucurbita subspecies is used. That way if someone types "squash" or "pumpkin seed" they get asked to clarify what they mean, which will change quite a bit depending on locality of the person being asked.

[1] https://en.wikipedia.org/wiki/Jarrahdale_pumpkin

[2] https://en.wikipedia.org/wiki/Straightneck_squash

This will perhaps be overkill for what you're trying to achieve, but there is Object Process Methodology (OPM)[1] which may be inspiration for modelling recipes. It looks similar to the methodology you're using, and as an example, expects the following type of modelling:

"peeling carrot" (process) consumes "washed carrot" (object)

"peeling carrot" (process) yields "peeled carrot" (object)

"peeling carrot" (process) yields "carrot peel" (object)

"finely dicing carrot" (process) consumes "peeled carrot" (object)

"finely dicing carrot" (process) yields "finely diced carrot" (object)

"prepare mirepoix" (process) consumes "finely diced carrot" (object)

"prepare mirepoix" (process) consumes "finely diced celery" (object)

"prepare mirepoix" (process) consumes "finely diced onion" (object)

"prepare mirepoix" (process) consumes "butter" (object)

"prepare mirepoix" (process) yields "mirepoix" (object)

The advantage of OPM is alignment of graphical and textual representations.

The downside of such approach is you soon discover how many millions of objects may exist in recipes--unwashed carrot, washed carrot, orange carrot, purple carrot, yellow carrot, white carrot, peeled carrot, coarsely diced carrot, finely diced carrot, julienned carrot... and purple julienned carrot vs. yellow julienned carrot. And that's just basic preparation complexity well before any contemplation of cooking or plating up elements. To go further you then discover a lack of useful labels such as "mirepoix" or "soffrito", if for example, you wanted to substitute sweet potato in place of carrot in the recipe.

Then there is SysML 2[2] which is kind of like OPM if you ever wanted to write a recipe in 35,000 lines of code, including possibly all the complexity of mathematical modelling of the Maillard reaction for purple carrots vs. yellow carrots using either extra virgin olive oil or butter. Probably best suited for the largest food processing companies such as Nestle, Unilever, Modelez, etc and even then, inherent complexity of their food products rarely would reach the level of a fine dining dish prepared by a chef.

[1] https://en.wikipedia.org/wiki/Object_Process_Methodology

[2] https://www.omg.org/spec/SysML/2.0/Language/PDF

See [1] for a demo, seemingly of an older iteration of what this paper describes. I was curious what ingredients the demo had selected (1032 available vs 1790 this paper selects) so I tried some obscure ingredients from "Organum: Nature, Texture, Intensity, Purity" by Peter Gilmore[2] (known for Quay restaurant in Sydney, Australia).

It's got some adventurous ingredients such as juniper berry, macadamia nut, nigella seed, orange blossom water and lemon verbena. It even separates sesame oil and toasted sesame oil. Even though the ingredients list only has "rice", "black rice", "brown rice" and "glutinous rice", when you select "rice" as an ingredient, the recipes it generates are smart enough to advise of chilling cooked jasmine rice before using in a fried rice, and smart enough to soak and rinse Basmati rice before using in a pilaf. If selecting "lamb" as an ingredient, the recipes it generates will choose the cut as shoulder or shank if you select vegetables normally associated with braising.

It doesn't know of grapeseed oil, orzo, mangosteen, lemon myrtle, and of course anything that only Peter Gilmore might use in a recipe and most chefs would have never heard of (karkalla as an example). I don't see this being too much of a limitation because such ingredients are quite localised or speciality. It knows of "pumpkin seeds" but not "pumpkin"--that is "squash", so there are some localisation improvements which could be made to improve British and American English use. I tried pairing "lamb" and "avocado" together in the hope it'd generate a recipe with a salad, but this failed. I then realised the ingredients list doesn't include lettuce or rocket, but has "salad greens" instead (American English) and no matter what I tried (other salad ingredients, chicken or no protein), it would not give me a salad. It kept generating wannabe-fancy dishes of a chunk of protein surrounded by tomato gel (agar agar) and a smear of avocado, or similar.

[1] https://epicure.kaikaku.ai/

[2] https://en.wikipedia.org/wiki/Peter_Gilmore_(chef)

I dug into how software such as LS-DYNA could have been modified. Take for example the EOS_JWL equation at [1] (vendor website, public manual) which is implemented by LS-DYNA. This equation seemingly could be used, alongside other equations implemented within LS-DYNA, to answer questions such as how long it'd take for a detonator in a missile warhead to detonate a primary explosive substance to cause a particular pressure wave at 20m distance. Working backwards from this result may provide a required fuze timing. Equations and parameters used with LS-DYNA are derived from scientific research, such as [2], which is US government research from the 1980's providing experimental results for high explosive substances. One such example from [2] is experimentation to determine the friction an explosive substance has against different materials which may enclose it. Given the software has equations purposely designed for explosives modelling, it'd be fairly easy to just target those equations in ways which will just slightly frustrate a scientist/engineer into thinking they've got a problem with the manufacturing quality of steel, rather than suspect the software is deliberately adding +/-20% noise to a friction coefficient.

The modern equivalent may be something like {insert adversarial country name here} downloading a pirated version of Ansys Autodyn 2026 R1 shortly after official release from a Chinese cracking group on a Chinese bulletin board forum, where just a handful of seeders sit behind Russian ISPs. And then {insert adversarial country name here} later notice during experimentation that the software calculations never quite match experimental results, and maybe then suspecting the pirated copy was deliberately tampered with and distributed. However, this situation may be fairly easily solved by {insert adversarial country name here} by just grabbing a copy of the software they want off a hacked network of a random university or engineering consulting firm in the aerospace and defence sector. Plus it may be naive to assume {insert adversarial country name here} in 2026 couldn't develop their own software from scratch (and/or perform calculations manually), or just rely on experiments, to achieve whatever outcome some other nation state group of hackers is trying to avoid. {insert adversarial country name here} would have to have experimentation equipment and skills regardless to verify manufacturing quality. Simulation software mostly reduces costs and timeframes by reducing the number of mockups and physical experiments needed. For example, it's cheap to run 1000 simulations of an artillery shell hitting vehicle armor plates as shown in [3], and more expensive and time consuming to do the same repetitive thing in the real world.

[1] https://ftp.lstc.com/anonymous/outgoing/jday/manuals/LS-DYNA...

[2] https://www.osti.gov/servlets/purl/6530310

[3] https://www.youtube.com/watch?v=_dv2PecKUBM

For more, there is also Umbra SAR open data at [1]. Most of this imagery is also copied to Wikimedia Commons where if someone has got around to doing so, much better metadata may have been added, and smaller images of key features within a larger scene extracted.

[1] https://registry.opendata.aws/umbra-open-data/

Nothing ready-to-go that I'm aware of. ATP will just observe in the next weekly crawl that a shop is no longer returned by the storefinder API call or sitemap crawl, and that shop will simply not be present in the next weekly dataset generated.

To set up archives of shop-specific pages (e.g. record of opening hours, address, etc at a point in time), one could monitor the latest builds of https://alltheplaces.xyz/builds.html and when a new build completes, take the new build and 2nd oldest build to compare differences. Then for any feature whose attributes have changed (address, phone number, opening hours, etc) archive the `website` and/or `source_uri` attribute pages again to ensure the latest snapshot is captured. Any new feature would get the same treatment so the page for the newly observed shop/feature is archived for the first time.

I'm also aware ArchiveTeam projects tend to commence once the impending collapse of a retail chain is known and someone realises there is a website not archived which would be useful to preserve. Monitoring of ATP feature counts for brands across time may give some hint of how a brand is performing and whether it is growing or shrinking without having to find press releases and financial statements of the brand. Even if a brand suddenly announces bankruptcy (it happens all the time), generally the website will remain online for at least a few months whilst a new buyer is sought or whilst each retail location has a fire sale to get rid of remaining merchandise. It's also worthwhile to be aware of acquisitions of retail chains as this often results in the new parent company changing websites soon after acquisition closes, possibly removing useful content that once existed. Websites also change "just because" and this could be observed after-the-fact by seeing when ATP spiders break and get replaced/fixed.

The situation with retail chains is improving thanks to projects such as https://alltheplaces.xyz/ (disclaimer: I'm a contributor) and efforts of some OSM contributors to focus their contributions towards comparing OSM and ATP features to add missing shops, remove closed shops, update opening hours, etc. For one such example, see https://matkoniecz.codeberg.page/improving_openstreetmap_usi... for a tool (created by https://news.ycombinator.com/user?id=matkoniecz) which is used to match and compare OSM and ATP features.

This work has been slow to take off though as the OSM community has traditionally been stuck on time wasting debates about whether opening hours displayed on the wall of a shop are copyrighted (just the raw data, not a photo of their presentation), and debating the merits and pitfalls of armchair mapping vs. on-the-ground mapping. At least these historical roadblocks seem to now be mostly resolved.

For OsmAnd, you might be able to use the OBF import feature (see https://www.osmand.net/docs/user/personal/import-export/) to add the raw ATP dataset, or potentially other open data such as Overture Maps if that is more to your liking. Data is mostly sourced direct from brand websites, APIs, etc (as if you were using a storefinder map on their website).

Last account activity was 2024-07-08 to push changes to the personal website andrewjdillon.com.[1][2]

Last account activity to contribute to any to any repository was 2021-11-28 to comment on the hopding/pdf-lib repository.[2]

It's clearly now an unmaintained repository with 4+ years of inactivity, and likely now also a mostly unused GitHub account in general.

[1] https://github.com/Hopding/andrewjdillon.com/commit/0657c690...

[2] https://play.clickhouse.com/play?user=play#U0VMRUNUICogRlJPT...

The SANs associated with https://crt.sh/?q=andrewjdillon.com are extremely suspicious. They reminded me straight away of https://ourbigbook.com/cirosantilli/cia-2010-covert-communic...

There appears to be no obvious plausible link between the SANs other than very obvious lack of plausibility to each website. They're mostly pretend (or knock-off) business websites in random countries (everywhere from Trinidad and Tobago, Germany, mainland USA, Hawaii...) in various languages and all the ones I checked have no verifiable substance to them. For example, one domain is a supposed USA shipping/logistics company whose website states they have 1949 customers and have only delivered 7126 packages, and claims a head office as a house in Renton WA, an office at a different house in Stockbridge GA and a supposed warehouse at a third house in Portland OR. Most domains don't include any valid contact or business information, even a supposed restaurant where you'd want people to find your location easily!

There does appear to be heavy use of Google Firebase, and many of the sites share the same IP address(es) for hosting. A reverse IP lookup of domains hosted at those IP addresses reveals more random suspicious domains beyond just those just listed at https://crt.sh/?q=andrewjdillon.com

I contribute to ATP and can confirm that the author of the wildberries spider was deliberately trying to collect https://wiki.openstreetmap.org/wiki/Tag:shop%3Doutpost (online order pickup locations). It's not a common occurrence within the current set of ATP spiders to capture such features. A quick search indicates that OSM doesn't appear to have tags designed to capture pickup/dropoff partnerships between retail brands, for example, an agreement from a pet supply shop to allow collection of parcels from select fuel stations of a partner brand. Thus I think the author of the wildberries spider has used shop=outpost as the closest tag available in OSM, and Overture Map's filters wouldn't be able to omit these features from their dataset unless Overture Maps adds wildberries to their exclusion list.

Ideally ATP's "located_in" and "located_in:wikidata" fields would be populated for these wildberries pickup locations, making it clear the pickup location is part of a parent feature (e.g. fuel station, supermarket). These fields are specific to ATP and are not OSM fields. OSM would expect features to be merged and a hypothetical field such as "pickup_brands:wikidata=Q1;Q2;Q3" be used instead on the parent feature.

ATP has a much more inclusive set of features it can extract than what Overture Maps, TomTom et al care about. As Overture Maps is more opinionated on what they aggregate they will filter out ATP extracted features such as individual power poles, park bench seats, local government managed street and park trees, stormwater drain manholes, cemetery plots, weather stations, tsunami buoys, etc. I think there might be some exceptions if it helps TomTom et al with their products such as speed camera locations, national postal provider drop-off/pick-up locations within other branded retail shops, etc.

BitLocker encrypts data on a disk using what it calls a Full Volume Encryption Key (FVEK).[1][2] This FVEK is encrypted with a separate key which it calls a Volume Management Key (VMK) and the VMK-encrypted FVEK is stored in one to three (for redundancy) metadata blocks on the disk.[1][2] The VMK is then encrypted with one or more times with a key which is derived/stored using one or more methods which are identified with VolumeKeyProtectorID.[2][3] These methods include what I think would now be the default for modern Windows installations of 3 "Numerical password" (128-bit recovery key formatted with checksums) and 4 "TPM And PIN". Previously instead of 4 "TPM And PIN" most Windows installations (without TPMs forced to be used) would probably be using just 8 "Passphrase". Unless things have changed recently, in mode 4 "TPM And PIN", the TPM stores a partial key, and the PIN supplied by the user is the other partial key, and both partial keys are combined together to produce the key used to decrypt the VMK.

Seemingly once you've installed Windows and given the Microsoft your BitLocker keys in escrow, you could then use Remove-BitLockerKeyProtector to delete the VMK which is protected with mode 3 "Numerical password" (recovery key).[4] It appears that the escrow process (possibly the same as used by BackupToAAD-BitLockerKeyProtector) might only send the numerical key, rather than the VMK itself.[5][6] I couldn't find from a quick Internet search someone who has reverse engineered fveskybackup.dll to confirm this is the case though. If Microsoft are sending the VMK _and_ the numerical key, then they have everything needed to decrypt a disk. If Microsoft are only sending the numerical key, and all numerical key protected VMKs are later securely erased from the disk, the numerical key they hold in escrow wouldn't be useful later on.

Someone did however ask the same question I first had. What if I had, for example, a billion BitLocker recovery keys I wanted to ensure were backed up for my protection, safety and peace of mind? This curious person did however already know the limit was 200 recovery keys per device, and found out re-encryption would fail if this limit had been reached, then realised Microsoft had fixed this bug by adding a mechanism to automatically delete stale recovery keys in escrow, then reverse engineered fveskybackup.dll and an undocumented Microsoft Graph API call used to delete (or "delete") escrowed BitLocker recovery keys in batches of 16.[7]

It also appears you might only be able to encrypt 10000 disks per day or change your mind on your disk's BitLocker recovery keys 10000 times per day.[8] That might sound like a lot for particularly an individual, but the API also perhaps applies a limit of 150 disks being encrypted every 15 minutes for an entire organisation/tenancy. It doesn't look like anyone has written up an investigation into the limits that might apply for personal Microsoft accounts, or if limits differ if the MS-Organization-Access certificate is presented, or what happens to a Windows installation if a limit is encountered (does it skip BitLocker and continue the installation with it disabled?).

[1] https://learn.microsoft.com/en-us/purview/office-365-bitlock...

[2] https://itm4n.github.io/tpm-based-bitlocker/

[3] https://learn.microsoft.com/en-us/windows/win32/secprov/getk...

[4] https://learn.microsoft.com/en-us/powershell/module/bitlocke...

[5] https://learn.microsoft.com/en-us/graph/api/bitlockerrecover...

[6] https://learn.microsoft.com/en-us/powershell/module/bitlocke...

[7] https://patchmypc.com/blog/bitlocker-recovery-key-cleanup/

[8] https://learn.microsoft.com/en-us/graph/throttling-limits#in...

Not really, but it's quite complex for Linux because there are so many ways one can manage the configuration of a Linux environment. For something high security, I'd recommend something like Gentoo or NixOS because they have several huge advantages:

- They're easy to setup and maintain immutable and reproducible builds.

- You only install the software you need, and even within each software item, you only build/install the specific features you need. For example, if you are building a server that will sit in a datacentre, you don't need to build software with Bluetooth support, and by extension, you won't need to install Bluetooth utilities and libraries.

- Both have a monolithic Git repository for packages, which is advantageous because you gain the benefit of a giant distributed Merkle tree for verifying you have the same packages everyone else has. As observed with xz-utils, you want a supply chain attacker to be forced to infect as many people as possible so more people are likely to detect it.

- Sandboxing is used to minimise the lines of code during build/install which need to have any sort of privileges. Most packages are built and configured as "nobody" in an isolated sandbox, then a privileged process outside of the sandbox peeks inside to copy out whatever the package ended up installing. Obviously the outside process also performs checks such as preventing cool-new-free-game from overwriting /usr/bin/sudo.

- The time between a patch hitting an upstream repository and that patch being part of a package installed in these distributions is fast. This is important at the moment because there are many efforts underway to replace and rewrite old insecure software with modern secure equivalents, so you want to be using software with a modern design, not just 5 year old long-term-support software. E.g. glycin is a relatively new library used by GNOME applications for loading of untrusted images. You don't want to be waiting 3 years for a new long-support-support release of your distribution for this software.

No matter which distribution you use, you'll get some common benefits such as:

- Ability to deploy user applications using something like Flatpak which ensures they are used within a sandbox.

- Ability to deploy system applications using something like systemd which ensures they are used within a sandbox.

Microsoft have long underinvested in Windows (particularly the kernel), and have made numerous poor and failed attempts to introduce secure application packaging/sandboxing over the years. Windows is now akin to the horse and buggy when compared to the flying cars of open source Linux, iOS, Android and HarmonyOS (v5+ in particular which uses the HongMeng kernel that is even EAL6+, ASIL D and SIL 3 rated).

If you want more up-to-date/accurate data on NZ transmission infrastructure there is also the following "Point" geometry tagged per OSM conventions:

[1] Transpower pylons: https://alltheplaces-data.openaddresses.io/map.html?show=htt...

[2] Transpower substations: https://alltheplaces-data.openaddresses.io/map.html?show=htt...

The public source of this data (ArcGIS Feature Server account of Transpower) shows data last modified by Transpower in October 2025 for pylons and February 2025 for substations. At the rate of development of NZ, you wouldn't expect major changes to any of this data unless it's a major transmission upgrade project identified years in advance in hundreds of public announcements and documents.

For distribution, the largest distributor in NZ (Vector) provides "Line" geometry at https://www.arcgis.com/apps/mapviewer/index.html?url=https:/... (note: not included in AllThePlaces due to ATP not currently collecting geometry other than points)

Australia is miniscule by global standards and Alice Springs is miniscule by Australian standards. Alice Springs isn't connected to the grid servicing most of Australia's population crammed up along the East coast and doesn't have much in the way of heavy industrial users nearby. The difficulty for OSM mappers is the low-capacity above-ground power lines in Alice Springs have no more pixels as the trunk of any 20 year old tree so at satellite imagery resolutions of >30cm you may need to find an image taken at sunrise or sunset where the long shadow of a pole is visible on the ground. I also think it is preferred in remote locations such as Alice Springs to run lines underground (particularly along roads) due to decreased total cost of ownership of not having to worry about bushfire and flood damage to infrastructure.

The ACT government provides ~10cm aerial imagery of Canberra and surrounds a few times a year and from this imagery, unless a minor power pole is obscured by trees or a building, it is generally easy to identify most poles. Evoenergy (distribution operator for the ACT) also publicly provide detailed maps of poles and lines no matter how minor they are. The reason this detail won't be mapped in OSM is lack of interest and availability of mappers to micro-map every minor power pole from aerial imagery, and OSM's very conservative approach to importing datasets, particularly from a licensing perspective (e.g. attempting to apply European database directive concerns in countries like Australia which don't have equivalent laws, and even have opposing case law precedents).

Australia is one of the most open countries when it comes to supplying electrical grid data. Even underground conduit locations are available publicly for most distributors, as well as designed summer/winter constraints for each transmission line (e.g. maximum kA per line). See [1] for some links to maps and other data that is made publicly available.

[1] https://query.wikidata.org/#SELECT%20%3Foperator%20%3Foperat...

I was looking for @marklit (Mark Litwintschik / https://tech.marksblogg.com) in the list as he's a geospatial-focussed blogger I've seen regularly on HN with interesting blog posts where he finds and presents open source datasets I'd never thought about, walks through some basic processing/querying steps, and provides some examples of what can be produced with the data. Many of the blog posts have left me thinking about possibilities to setup bots for uploading maps to Wikimedia Commons (for embedding within Wikipedias etc) based on these interesting datasets, or at least automating via scripts the development/upload of maps on a country-by-country basis (or other criteria) for static once-off datasets.

Unfortunately he doesn't show in the top 100. Also unfortunately, there is no blogger described in the top 100 as having a geospatial interest/focus.

Compare at the same scale:

Vantor Legion-2 image of the BYD plant in Zhengzhou as captured on 18 January 2025: https://livingatlas.arcgis.com/wayback/#mapCenter=113.9361%2...

Vantor WorldView-3 image of the Tesla plant in Austin as captured on 31 January 2024: https://livingatlas.arcgis.com/wayback/#mapCenter=-97.6189%2...

I don't understand the pretend secrecy. Why not just state openly they're rebuilding or expanding the bunker? Wouldn't it be obvious from the high number of trucks coming and going from the area (even if disguised within a shed) that significant excavation is occurring, or that a significant amount of steel and concrete is then being added?

It didn't take long for people to link a growing mound of spoil on a nearby golf course (East Potomac Golf Links) to excavations occurring at the White House.[1] A few simple volume calculations, and/or truck movement counts, is all one would seemingly need to estimate the size of the new bunker.

USD$300m also doesn't buy much of anything underground, especially at top secret military bunker pricing. Look at the cost of typical rail tunneling projects (much simpler and more efficient construction) for rough comparison.

[1] https://golf.com/news/white-house-carting-dirt-golf-course-p...

Are you referring to the 'GovPort' website episode of Utopia, season 3, episode 3 'Nation Shapers'[1] or a different episode?

[1] https://www.youtube.com/watch?v=_otJbx-PVOw

It might have a minor beneficial impact to tourism in Saint Barthelemy and Norfolk Island from geeks wanting a trendy new account registered in a territory with fewer than 1000 IPv4 addresses allocated.[1]

A more useful addition would be a contributions calendar similar to GitHub's [2] but focused on which time zones the user is active within, and importantly, the latency of the user's replies. It's trivial to fake geographic location observed through source IP addresses (or even RTT multilateration) but much harder to fake time zones a user is active within, particularly if monitoring latency of replies.

edit: To further clarify, I don't think a contributions calendar would be beneficial to HN either. I've never cared to think about the country a commenter resides in, and don't care about username/real name either unless the commenter is appealing to their own authority (e.g. "I am the author of this software"). Even then, the usefulness of an appeal to own authority is often limited to the ability to reverse lookup the user's personal website (which itself is proven to be notable from other sources) for a link back to their HN profile.

[1] https://impliedchaos.github.io/ip-alloc/

[2] https://docs.github.com/en/account-and-profile/concepts/cont...