For a facility that Ukraine has decided would cause $10m in economic damage if hit by a Russian drone, how much should Ukraine spend on interception for that site to not make it worth Russia's expense to target it? Let's assume a Russian drone is $100k (current estimate in news for Geran-5 jet-powered drones). But Ukraine can build their Bars interceptor drone for half the cost at just $50k (low estimate from public rumours/reports). It wouldn't be worth Russia's expense to send more than 100 drones in a salvo at this target. For Ukraine, they should defend the site with $5m worth of Bars interceptor drones, assuming a very favourable 100% success rate (actual reported success rates are reported by media to be just 30%). Russia has now made every $10m facility in Ukraine cost $15m (+50% increase).

The defender is essentially forced to find a 10:1 or better cost advantage for their interceptors vs. incoming drones. This is hard to do when an interceptor and attack drone are substantially the same except for larger fuselage (cheap), larger fuel tank (cheap) and larger engine (possibly cheaper than making a small engine).

And perhaps that is the reason Russia and Ukraine are largely focussed on attack rather than defence in the hope of being first to cripple the other's economy and supply chain for munitions.

1. US+Israel economies have a 100:1 size advantage over Iran's economy.

2. Israel is a very small area to defend, dramatically reducing the cost of anti-ballistic missile defence required. As little as 4 missile defence launch sites may cover the entire country.

Perun's video on the cost effectiveness of missile defence vs. attack is more focussed towards the economics of defence of an expansive country or region such as the United States, Saudi Arabia or the gulf region generally, where many missile defence sites would be required. Israel is a bit of an exception due to the small area needing to be defended.

Another minus specifically for Iran in the Iran/US+Israel war is being forced to build underground factories at much greater expense than an above-ground factory for Israeli interceptor missiles. I don't think analysts have been considering this long term cost in their cost comparison ratios. However, for both Iran and Israel--they're importing a lot of the components and materials from overseas anyway.

Both topics are very relevant to the current conflicts of eastern Europe and the middle east. Perhaps I took away the wrong message but I don't think Perun would propose to stockpile interceptor missiles any time soon.

[1] https://www.youtube.com/watch?v=CpFhNXecrb4&t=2382s

For just one of many examples, see [1] where the Gentoo developer had to submit a patch upstream for a Chromium build toolchain bug that to 99% of experienced Linux users is bordering on unintelligible due to how much specific knowledge is needed to understand the bug.

[1] https://bugs.gentoo.org/956054#c2

I've tried to find similar for Finland. The closest I've found is [1] which maps "old forests" which are defined to be over 100 years old (not actually very old). The three examples [2], [3] and [4] are Google street view imagery of nearby "old forests" and all indicate a much higher biodiversity and natural planting than the site Google has cleared. The site Google has cleared and nearby forests are visible in overhead imagery as having a pattern of tree plantings (distinct rows/grid) and show visible disturbance for forestry trails (ground compaction), whereas the "old forest" sites don't have these patterns. The "old forest" sites also show variance in trunk diameters, greater separation between trunks, more forest floor diversity and more tree variety (both taxon and structure). Plantation forests have trees deliberately planted too close to each other to encourage upright growth habit, lower limbs are deliberately removed and trees thinned after some years, all to maximise the number of sawmill-ready trunks. Revegetation planting would look completely different to what occurred at the Google site.

Revegetation planting and ongoing management is also much more expensive to do correctly. It's a struggle to find success stories. Best examples likely involve aerial seeding and complex seed collection and preparation, as well as ongoing manual weed management and introduced species control for 20+ years. There is often a fair bit of complexity to simulating nature to make seeds viable--for example, refer to Przewalski's Horse case study where hoofed animals were reintroduced and found to be essential for embedding seeds at required depth and reshaping the terrain to support seed germination. More examples are water courses upstream having to be cleared of weeds first, introduced animals need to be excluded/culled, etc, etc.

[1] https://luontovahti.fi/vanhojametsia -- select Pohjois-Pohjanmaa as the area then zoom in near Muhos.

[2] https://maps.app.goo.gl/wncKi2JMxuPXC7Bc7

[3] https://maps.app.goo.gl/BX2ta5dQmihSgVAM6

[4] https://maps.app.goo.gl/4W3WvR5tH9wroVna7

Google street view imagery of the site showing logging completed by June 2026 and logs waiting collection: [1]

Sentinel-2 L2A imagery from 2026-10-11 showing the logged area: [2]

[1] https://www.google.com.au/maps/place/91500+Tahvola,+Finland/...

[2] https://browser.dataspace.copernicus.eu/?zoom=15&lat=64.8369...

"the Court finds that Plaintiffs are likely to succeed on the merits of their claim that Defendants violated their First Amendment rights through coercion of Facebook and Apple."

My reference to a press freedom index is due to a lack of "countries by freedom of software development" index. If such index existed, the methodology may have some commonality with a press freedom index.

Placing barriers in front of publishing of books, online content, software, etc all leads to some suppression of speech. The question is whether the benefits of the barriers placed outweigh the risks/negatives. In this case, why should anyone care what the passport number or residential address is of a student interested in a career in software development who is writing their first Android game?

[1] https://www.fire.org/cases/rosado-et-al-v-blanche-et-al

[2] https://www.fire.org/sites/default/files/2026/04/Memorandum%...

[1] https://developer.huawei.com/consumer/en/doc/start/rna-00000...

[2] https://en.wikipedia.org/wiki/World_Press_Freedom_Index#Rank...

- Enrol in Medicare.

- Apply for a passport.

- Find a job in federal government by uploading a resume/CV and having currently advertised jobs deemed most relevant returned.

[1] https://america.gov/coming-soon

Q: "Where can I find a NASA comic book about women astronauts?"

A (summarised): Press release for the "Callie Rodriguez" comic book that now has only dead links / private YouTube video, and a forgotten SoundCloud account that survived the DOGE purge. Dead link provided to the NASA web page that used to exist officially for this comic book.

Indicates training data predates the DOGE .gov website purge?

Q: "What is 2+2" (and similar math problems)

A (summarised): Grudgingly answered as "4" despite most off-topic prompts being refused.

Seemingly it'll do maths homework for free :)

- Many perpetrators are committing crimes with alcohol and drug impairments, mental disabilities, and as "crimes of passion". You could point 1000 cameras at the scene of the crime and many perpetrators are not going to notice nor care about the cameras. Unfortunately for surveillance camera proponents it's typically the crimes most warranting prevention and investigation (e.g. crimes such as assault) that are impacted least by surveillance cameras. Surveillance cameras instead have been found to have some deterrent effect on minor property crimes (e.g. theft of bicycles or cars) but whether the state is going to invest the resources into investigating and convicting someone who stole a <$1000 bicycle is questionable especially when insurance exists and the onus can then be placed back on property owners to do more to secure their property from thieves.

- Perpetrators were being convicted without use of surveillance cameras and can continue to be convicted without use of surveillance cameras. There may be some argument that surveillance cameras help reduce the cost of getting a conviction (e.g. less evidence needing to be captured?) but not much support to the idea that conviction rates are higher. Quite often the courts, corrections system, government (for funding), etc will push back for a number of reasons--lack of funding for courts to hear minor cases, lack of funding for correction systems, correction systems being found in their current state of funding to have adverse impacts on future criminality, etc.

- The cost-benefit of operating surveillance cameras versus the cost-benefit of spending on other areas of policing (or just public spending in areas such as education and welfare) is hotly debated. Total cost of ownership for each surveillance camera is typically a lot more than people may expect, particularly due to the labour cost of reviewing and processing stored surveillance camera footage. There are of course all the other obvious costs too--camera equipment every X years, poles, cabinets, electricity conduit installation, data cable conduit installation, wireless repeater installation including leases, backhaul data costs, data centre hosting costs, labour costs for support/operations staff--none of this is cheap. What if this same funding was instead invested in other programs--would there also be reduced crime levels some years in the future?

[1] https://en.wikipedia.org/wiki/Closed-circuit_television#Crim...

I am surprised to learn from both satellite and street view imagery that this site is located just <50m off a public road (in clear line of sight) and within a residential backyard partly behind the house on the block, and protected by a 1.2-1.5m high white picket fence. To be fair, the lack of almost any visible security deterrent may perhaps scare off an attacker because... security surely wouldn't be that non-existent? My only other thought is these sites really do not matter to Starlink because Starlink will have 1000's of them and as they only process encrypted data, almost anyone can run a ground station for a small reimbursement per month if they've got semi-decent fibre connectivity and a bit of power?

1. Possible use of differential privacy[1] techniques to train on private data but prevent the release of statistically underpresented facts/data/words. For example, ACME Inc's private data could frequently include the term 'ACMEwidgetPRO' for an upcoming product that is not publicly revealed anywhere else. It would therefore be a bad day for the AI technology company to output 'ACMEwidgetPRO' from one of their public models. Consider now that a few models could be trained--X for public data only, Y for public and private data of ACME Inc together, Z for private data of ACME Inc. A prompt is provided to model Y but output is cross-checked with model X to double check terms such as 'ACMEwidgetPRO' are known in public. If not--provide a "I don't know" response for the prompt.

2. Possible attempted defences similar to "Oops, our model was fine tuned against a model supplied by Temporary18271 Inc (company that no longer exists) and perhaps their model might have been trained on a non-public document which was accidentally exposed to the Internet" that _might_ work occasionally to fob off concern.

3. What recourse does a small or medium company or government especially in a developing country realistically have? They perhaps can't host their own LLMs locally due to availability and cost, can't individually negotiate their own favourable terms with an AI technology company (who cares that much about a potential customer with $100k budget that has no other options anyway), and perhaps can't remain competitive in their industry without heavy use of LLMs.

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

1. Probing of AIHW's website to try and obtain PBS statistics, as collusion.wiki findings show. The collusion.wiki findings don't indicate anything other than intentionally public data was obtained. Bots appear to be trying to get around Cloudflare geo-blocking implemented on AIHW's public website. I can't think of a reason why geo-blocking may be deemed necessary on that website though?

2. Probing of an outdated Medicare statistics reporting website. (I guess at [2] this could be the recently shut down https://medicarestatistics.humanservices.gov.au or related website that matches timeframes of this story).

I suspect though anything to do with PBS data is more important than Medicare data because of heightened tensions from international pharmaceutical companies that lobby extensively against Australia's public healthcare system and collective purchasing of medication by the federal government.[3] Regardless of whether a course of medication costs AUD$50 or AUD$50k, it's purchased in bulk by the Australian government after negotiating with pharmaceutical companies, and then subsidised down to a maximum of AUD$25 at the time it is sold to a patient at a pharmacy. Perhaps if international pharmaceutical companies had obtained more detailed data on use of each brand of prescription medicines in Australia, they could be advantaged in their price negotiations with the Australian government, or advantaged against their competitors?

Less alarmingly though, perhaps some researcher studying the side effects of a particular medication was just asking an LLM to answer a benign question such as "How often is ACME Inc's FixMeUp medication prescribed in Australia?"

[1] https://www.abc.net.au/news/2026-09-24/openai-agents-plotted...

[2] https://news.ycombinator.com/item?id=49825084

[3] https://www.abc.net.au/news/2025-03-19/australia-defends-pbs...

[1] https://web.archive.org/web/20260811115217/https://medicares...

1. Not use AI technology and fall behind the rest of the world.

2. Use Chinese AI technology, either hosted by Chinese companies or the models self-hosted.

3. Sue US AI companies for damages, but not enough to have any meaningful impact to such companies that it'd impact US national security goals (per US government contribution to NY Times copyright lawsuit).

I'm sceptical though whether use of LLMs would encourage creation of data that doesn't already exist. For example, if you ask an LLM "What are the top 100 most prevalent flora endemic to ACME National Park", this data may not currently exist _at all_, and to collect, would require paying botanists to do an extensive field survey. If no one has done this work yet--why? Is it relevant to the scientific community, to making government decisions, etc, or just an obscure academic curiosity. There are certainly some journal articles on _other_ national parks describing some of their common endemic flora, but perhaps there was a reason for this. Such as a scientist funded by a one-off government program trying to determine how to preserve or even create habitat for a specific endangered species.

Consider for the prompt of: "What are the top 100 most prevalent flora endemic to ACME National Park"

An LLM may reply: "I couldn't find any journal article or other prior work that may answer this question. Typically such survey field work may cost $X to complete, require expert botanists, and take 6-12 months to complete. Let me know if you want further information on how to find and select a company to conduct such a botanical field survey."

Would this type of LLM response grow the industry of botanical field surveys, or do nothing, perhaps because anyone likely to fund botanical field surveys is already doing so regardless of whatever is happening with AI.

It'd be great to see a description of even just a subset of training datasets. It feels very much under-reported how much expense is worth investing in preparing and selecting training datasets versus just using masses of random quality unprepared training data. This dashboard appears to be good though in showing the limits quickly reached when throwing parameters and compute at the problem.

For example, if they were to train on Wikipedia dumps, do they consider every article to be the same quality across each language, or have they done more work beyond Wikipedia's own article quality ratings to make training decisions such as "Ignore cebwiki it's machine-generated spam" and "Treat dewiki articles with coordinates within Germany as being higher quality (weight it higher) than their equivalent enwiki articles".

And let's say one of the datasets is all the source code of packages in the Gentoo package repository. Not every software package is a good example of how to write code. You perhaps wouldn't want to train your LLM on 1990s era PHP web application source code as an example of how to write code in 2026. Instead, you'd possibly want to use such PHP web application source code as a negative training example of what _not_ to write. But when training an LLM to detect software bugs, maybe outdated PHP source code is good for training.

Similarly for translation, perhaps UN treaty documents translated into 4+ languages are good translation examples because of high accuracy needed, professional translators being used, and bigger budgets. However this training data would perhaps be a negative training example towards translating chat messages, movie subtitles, etc because it doesn't use everyday slang and could result in output of nonsense such as "Pending Your Excellency's response, please accept, Your Excellency, my sincere greetings." for a prompt asking to write a birthday card for a child.

Preparing training data and deciding how to best use it for training I assume would be the largest expense (cost of labour -- mostly expert labour too) and also the greatest opportunity in the future for LLMs to improve. It seems to me somewhat irrelevant if the dashboard indicates a compute expense of $1m or $5m if good training datasets (prepared by experts in their fields) cost $10m/y to maintain. For example, hiring expert software developers to tag 1000's of open source software packages according to their quality, on different metrics, such as human readability, performance optimisation with choice of algorithms, reasonable trade-off between coherence and coupling in the software architecture, currency with state of the art programming trends/preferred dependencies/operating system APIs, etc. And keeping that metadata continually updated rather than a rapidly obsolete once off tagging project completed in 2005.

* Sending a delegation representing all major political parties to the US to argue for the release of Assange. Imagine picking the Republican and Democrat politician LEAST likely to want to cooperate on anything, and those two would have been Australia's equivalent representatives in this delegation. Reports afterwards of the meeting at DOJ HQ indicated it wasn't the type of meeting where the Australians would have brought Tim Tams to share around the room.[2]

* The Australian parliament voted publicly 2:1 on a motion for Assange's release.

* Repeated petitioning through ambassadors in the UK and US, official visits of Australian politicians, etc. Not in private either, as is typically the case for diplomatic affairs.

* Australian politicians attending UK extradition hearings.

* After getting agreement to a plea deal, flying Australian ambassadors for the UK and US to the court of a one-pub-town in the middle of the Pacific Ocean no one has heard of (Northern Mariana Islands) in support of Assange, then all of them flying back to the Australian prime minister's aircraft terminal for a welcome home bevvy.

This was all at a time too where "Free Assange" posters and graffiti was _widely_ distributed across Australian cities.

[1] https://en.wikipedia.org/wiki/Julian_Assange#Plea_bargain_an...

[2] https://www.abc.net.au/news/2024-06-27/inside-the-us-austral...