The Canadian province of Manitoba has introduced a bill to ban algorithmic price discrimination (also known as surveillance pricing), i.e., the use of personal data to set prices for individual consumers:

New Democrats announced in December they would begin cracking down on what’s known as differential or predatory pricing. That is when retailers charge different amounts for the same products based on the timing of customer purchases, where they live or other personal data. […] The proposed legislation would render the use of “personalized algorithmic pricing,” both online or in store, an unfair business practice.

Okay, I guess there’s a lot of different names for this particular practice. Whatever we call it, I believe bills cracking down on algorithmic price discrimination will be very popular, as it constitutes a very clear example of companies using our data against us to rip us off. The most famous recent exposé of this practice is Groundwork Collaborative’s report on how grocery delivery service Instacart charges users different prices depending on who they are.

Manitoba isn’t the only jurisdiction introducing bills targeting this practice, but I don’t believe anywhere in the US or Canada has actually managed to ban it yet. However, New York has made in mandatory for companies to disclose when they use personal data to set prices.

The Times of Israel journalist Emanuel Fabian is claiming that Polymarket gamblers (sorry, “traders”) have threatened his life over a report he released about an Iranian missile attack on Israel on March 10. According to the rules, this bet resolves as true if Iran strikes Israel using a drone, a missile, or an air strike on this date. At issue here is this specific rule:

Missiles or drones that are intercepted and surface-to-air missile strikes will not be sufficient for a “Yes” resolution, regardless of whether they land on Israeli territory or cause damage.

On March 10, Fabian reported a single missile had hit an open area outside the Israeli city of Beit Shemesh; he included in the report a video of the strike. This would resolve the bet as “Yes”. Evidently, holders of “No” shares would very much like him to change his report to say that the missile was intercepted, which would resolve the bet as “No”, according referenced above. This bet has seen more than 23 million USD in trading volume.

If you look at the vitriol in the comments of the bet on Polymarket, I have no trouble believing people would send threats to a journalist demanding him to change his story, whether out of desperation to change their fortunes or just in an attempt to be edgy.

Economist Scott Cunningham shared an important example of why we should always report the statistical package and version used in our analyses, as he used Claude Code to produce six versions of the exact same analysis using six different packages in R, Python, and Stata. In a difference-in-differences analysis of the mental health hospital closures on homicide using the standard Callaway and Sant’Anna estimator (for DiD with multiple time periods), he got very different results for some model specifications.

Since the specifications and the data were identical between packages, he discovered the divergences occurred due to how the packages handled problems with propensity score weights. Packages were not necessarily transparent about issues with these weights. If you were not running multiple analyses and comparing results across packages, or else carefully checking propensity score diagnostics, you might never have realized how precarious your results were.

Prof. Cunningham closes with the following advice:

The fifth point, and the broader point, is that this kind of cross-package, cross-language audit is exactly what Claude Code should be used for. Why? Because this is a task that is time-intensive, high-value, and brutally easy to get wrong. But just one mismatched diagnostic across languages invalidates the entire comparison, even something as simple as sample size values differing across specifications, would flag it. This is both easy and not easy — but it is not the work humans should be doing by hand given how easy it would be to even get that much wrong.

…

Many people in the Americas would probably be surprised to learn that, in much of the rest of the world, being born in a country does not by itself make you a citizen. In most of the Americas, citizenship is automatically granted on the basis of jus soli (“right of soil”): birth on the territory. Elsewhere, citizenship is more often based on jus sanguinis (“right of blood”): descent. This is the case in most of the EU.

Citizenship in an EU country is considered unusually desirable because of the mobility rights and powerful passport it confers. However, the rules concerning exactly what kind of descent confers citizenship varies widely among member states. Italy used to be considered among the easiest, requiring only that an applicant prove they had an Italian ancestor alive after March 17, 1861, when the Kingdom of Italy was founded. That changed last year, when the country passed a new law significantly tightening the requirements for citizenship, which was recently upheld by the country’s Constitutional Court. The new law brings requirements more in line with norm among EU member states:

Now, only individuals with at least one parent or grandparent born in Italy will automatically qualify for citizenship by descent. The amended law does not affect the 60,000 applications currently pending review. Additionally, dual nationals risk losing their Italian citizenship if they “don’t engage” by paying taxes, voting or renewing their passports.

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Canada’s banking sector is legendarily stable. However, this stability comes at the cost of innovation. Canada lags behind peers such as the EU, UK, US, and Australia in an area I care a lot about: open banking.

The premise of open banking is that consumers should be free to share their financial data with the third parties of their choosing, such as a budgeting app.. I have been following open banking in Canada for years now, ever since I started closing tracking my own finances. For a long time, I have been looking for a better way to export transactions than logging into my bank’s website and manually downloading a CSV file representing a certain time range.

Over the years, people have tried to solve this problem by writing third-party packages to retrieve data from specific banks. However, these packages were fragile and prone to breaking, and they usually relied on you providing your full account credentials, granting them to ability to impersonate a login to your account. Shockingly, this is actually the default security model for Canadian fintech companies: even a humble budget app must be given your username, password, and (implicitly) the ability to take any action on your behalf. Needless to say, this is at best a grey zone for liability, since you are willingly handing over the keys to the kingdom to a third party.

David Oks had a great post yesterday on the classic parable of how the adoption of ATMs did not lead to the predicted job losses among bank tellers. In fact, the opposite occurred: the number of bank tellers rose. I heard this story recounted several times in early discussions I had about the anticipated effect of AI on labour. I think I first heard it from Ryan Khurana. More recently it has been trotted out by US Vice President JD Vance.

The problem with this story is that the key statistic quoted alongside it, namely that there are more bank tellers than ever before, is no longer true. The famous graph supporting this assertion stops in 2010, and with good reason: the number of bank tellers has sharply fallen since then.

I think I had come across this fact before, this second half of the famous ATM–bank teller story, but it wasn’t until I read David Oks’s post that I understood the reason behind it. Quite simply, mobile banking ate physical banks. The ATM didn’t reduce the demand for bank tellers because it simply changed the kind of labour they did inside the bank. The iPhone made it so we didn’t need to go to the bank at all. It changed the paradigm. Explained this way, it seems obvious. Many new banks (including my own) do not have physical locations and never did.

Back in September 2025, US President Donald Trump and Health and Human Services Secretary Robert F. Kennedy, Jr. held a White House briefing linking Tylenol (acetaminophen, or paracetamol to Europeans) use in pregnancy to autism. A new study in The Lancet looks at what happened to acetaminophen prescriptions during emergency room encounters for pregnant females aged 15–44. They used data from a large database covering over 1,633 hospitals and 37,000 clinics.

Here is panel A from the figure in the study, with the vertical dashed line marking the date of the White House briefing (September 22, 2025) and the other dashed lined showing the expected prescribing rates (compared to the observed ones).

This provocatively titled piece in the The Hub (“Why the world needs even more Canadian coal”) made me realize I know very little about one of Canada’s most important exports: coal.

Coal is often villainized because it is incredibly dirty way of generating power. I vaguely recall an article from maybe 20 years ago claiming something along the lines of “if everyone in Canada replaced their incandescent bulbs with energy-efficient ones, the greenhouse gas savings would be cancelled out by a single coal plant that China is building every [some shockingly short amount of time]”. Although, China’s dependence on coal for power has been falling for the past two decades.

It turns out LLM-assisted search is fantastic for finding these half-remembered quotes. Here is the exact article and quote I was remembering, from a 2008 Macleans magazine article (I was pretty close):

Even if every household in the U.S. screwed in an energy-efficient light bulb today, the savings in greenhouse gas emissions would be wiped out by fewer than two medium-sized coal plants - the kind of plant that is being built in China at a rate of one a week.

But coal is also used to make most of the world’s steel (“metallurgical coal”), and this is the kind of coal that Canada (or specifically, British Columbia) overwhelmingly exports. The article goes on to claim that Canada’s production of metallurgical coal is among the cleanest (by greenhouse gas emissions) in the world.

In Canada, any person or corporation in the country can make a request for general records to any agency of the federal government through the Access to Information Act (the equivalent in the United States is the Freedom of Information Act). The government provides a searchable database of completed requests, but includes only a summary of the request and the number of pages of responsive material. The actual documents turned over are not included. However, completed request packages may be informally re-requested, and should you do so, someone from the relevant agency will (usually) send them to you eventually.

This re-request process has its limits. It can takes weeks or months for the documents to be sent, and the database itself only goes back to January 2020 (they used to delete records older than two years, but stopped doing this some time after 2020). Occasionally, they will never send the documents at all, and all you can do is either re-request them again or open a formal access to information request (which will cost you $5).

Making it easier to access completed access to information requests is why the Investigative Journalism Foundation built Open By Default, “the biggest database of internal government documents never before made publicly accessible”. It includes documents from completed access to information requests obtained using both automated (presumably the re-request form) and manual processes (donations from trusted partners, particularly of documents from before the online re-request form was available). The files are cleaned and OCRed into one beautiful, searchable database.

Time zones are hard. As a well-known Computerphile video so eloquently puts it:

What you learn after dealing with time zones, is that what you do is you put away your code, you don’t try and write anything to deal with this. You look at the people who have been there before you. You look at the first people, the people who have dealt with this before, the people who have built the spaghetti code, and you thank them very much for making it open source, and you give them credit, and you take what they have made and you put it in your program, and you never ever look at it again. Because that way lies madness.

The Canadian province of British Columbia recently decided to switch to permanent daylight time. I wanted to see if this update made it to the IANA Time Zone Database yet. Luckily, we can now view updates to this database as commits on GitHub. And there it was in the news file!

I’ve perused the tz repository before, and I always learn something interesting. For example, during WWII Britain adopted double summer time, adding two hours to the clock in the summer and one hour in the winter. The bulk of the comments in the database are dedicated to documenting this extensive history of time zone changes across the world.

Today, I wanted to share this piece from economist Scott Cunningham (Baylor University), who wrote about how AI is widening the gap between research and publishing. Or, in economics terms (emphasis mine):

But what happens when the same productivity shock hits a system where the bottleneck was never really production in the first place, but rather was a hierarchical journal structure that depended immensely on editor time, skill, discretion and voluntary workers with the same talents called referees for screening quality deemed sufficient for publication?

The post mentions the Autonomous Policy Evaluation project—the end-to-end AI paper pipeline I wrote about a few weeks ago—and discusses the likely consequences of this flood of AI-generated papers. Assuming the number of publication slots in reputable journals is relatively fixed, AI-generated papers should add a very large amount of mass to the left side of the paper quality distribution. Acceptance rates will plummet and journals may rely on other signals of quality (name recognition, pedigree, institution) to thin the herd before actually reviewing content. As always, the rich get richer!

But this is imperfect, not to mention unfair, and so desk rejection gets noisier: good papers get killed by tired editors and marginally lower quality papers slip through to referees. It’s a cascading failure: volume breaks editors, broken editing wastes referees, wasted referees slow science.

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As mentioned last week, I’ve been meaning to test out a personal agent from the Claw-like ecosystem. I settled on testing out Zeroclaw, a popular and lightweight OpenClaw alternative that should run well on my Raspberry Pi 4 4GB.

I wanted to harden my setup as much as possible and opted to running everything in Docker. I started with the official Docker compose file and added my OpenRouter key. I brought up the pre-built container image and tried sending the basic “Hello” message to the agent using the CLI. However, I got error because the automatically generated config file defaulted to a version of Claude Sonnet 4 that wasn’t available on OpenRouter. I switched to claude-sonnet-4.6 and then gpt-oss-20b (for much cheaper testing).

The Zeroclaw web gateway was a bit of a mess. Of the features I tried, only memory management and the basic status dashboard worked. Trying to talk to the agent through the web interface would give me a black screen (here’s someone complaining about the same error). I’m still being charged for the tokens, though! The cost tracker always displayed zero, even as I sent CLI and Telegram messages (more on that soon). The configuration editor gave me an error and so did the diagnostics tool.

The project docs/wiki were helpful for figuring things out, but development is running so far ahead of releases that a bunch of the features referred to aren’t available in the current stable version (v0.1.7, from last week). This includes getting and setting specific config options from the CLI and resetting the gateway pairing token. To use these features, you have to compile yourself.

It’s been one month since my first post on Big Muddy. There were a few factors driving my decision to start this project:

• I wanted to get into the habit of writing every day.
• My “random interesting links” folder was overflowing, but I wasn’t doing anything with these links.
• My admiration for Simon Willison’s work and his suggestion for everyone to start a blog to share what they learn.

As the saying goes, writing is thinking. Instead of allowing interesting articles, tools, and bits of knowledge to languish in a “temporary” bookmarks folder, I could actually engage with and learn from the material by writing something about each item and make it easier to re-find later. This also forces me to curate the links and ideas that are actually worth saving, since writing a post, even a short one, takes a lot more effort than just throwing a link into a folder.

I figured I might as well share the results with the world, since someone else might find this information useful. And I’m helping to write my ideas and preferences into the next generations of LLMs, I guess.

I’ve made exactly one post per day since starting this blog, which was my goal when I set out. A handful of these posts are pre-written the day before (if I know I won’t have the opportunity to write a post the next day), but most are written the day of. Most are short (Bash tells me just over 190 words on average, though this is slightly inflated by Markdown formatting). Some are very perfunctory, just a link with a few words, when I really needed to get a post out for the day. At the start of this project, I cut my “temporary” bookmarks folder to zero. It has now been replaced with a backlog of links I want to write about on this blog.

Twitter’s “Today’s News” section is a mix of real news, very minor stories (usually discussion of a random AI-related post), nonsense trends, and barely disguised marketing.

The algorithm behind it seems pretty easy to manipulate.

This trending topic revolves around an explosive DM warning of imminent 25% layoffs at a FAANG company:

Here is the original post, which comes from an account called Tech Layoff Tracker (@TechLayoffLover):

There is no reason to believe this post is real. The account, created this month (February 2026), made its first post 7 hours ago. The post in question was made 5 hours ago, or 2 hours after the account’s very first post. Of course, the account carries an utterly meaningless blue “verified” checkmark.

But despite all this, the news summary puts “Tech Layoff Tracker” right in the headline, as if it’s a known reliable source and not an account (most likely) created the same day as the summary itself!

I recently came across the following policy on the submission page of an academic journal:

Use of Artificial Intelligence (AI) tools: One of the goals of Spectrum is to stimulate critical thinking and skill development among authors and reviewers alike. Spectrum discourages the submission of content generated by artificial intelligence (AI)-assisted technologies (such as chatGPT and similar tools). This includes tools that generate text, data, images, figures, or other materials, as well as tools that are used to summarize and synthesize sources. Authors should be aware that such tools are vulnerable to factual inaccuracies, biases, and logical fallacies, and may pose risks to privacy, confidentiality, and copyright.

If authors choose to submit work created with the assistance of AI tools, such use must be disclosed and described in the submission. The disclosure must include: 1) what system was used, 2) who used it, 3) the time/date of the use, 4) the prompt(s) used to generate the content, and 5) the content in the submission that resulted from use of AI tools. The output from the AI system should also be submitted as supplementary material. Authors must accept full responsibility for the accuracy and integrity of the submission. AI systems do not meet the criteria for authorship, and should not be listed as a co-author.

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Chrys Bader has created ClawCharts to track the popularity and growth of OpenClaw and its growing number of competitors.

I have an unused Raspberry Pi 4 4GB that I’ve been meaning to test one of these Claw-like personal agents on (locked down to prevent the security nightmare scenarios we’ve seen play out since OpenClaw took off).

OpenClaw is a bit of a resource hog (which is why so many people are running out to buy Mac Minis), so I’ve been looking at the list of lightweight competitors. There is no obvious reason to prefer one over the other, so I’ll probably go with the fast-growing ZeroClaw.

ZeroClaw offers OAuth connectors for OpenAI and Anthropic subscription plans, but presently neither company is clear on whether this usage is permissible or not. Anthropic recently blew up the OpenClaw community by updating their docs to specifically ban using OAuth outside of Claude Code. An Anthropic employee partially walked this back on Twitter, but there is still no clear statement whether this use case is permitted. Regarding the use of OAuth from OpenAI for OpenClaw (specifically, GPT Codex), Peter Steinberger, creator of OpenClaw, stated on Twitter: “that already works, OAI publicly said that”. No one can seem to find this public statement, but it’s worth noting that Steinberger himself is now an OpenAI employee. So, will you get banned for using your ChatGPT Plus/Pro or Claude Pro/Max subscriptions with OpenClaw? Nobody knows.