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Why Unit Tests Should Never Access Files, Networks, or the Registry

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Every developer has experienced it.

A test passes perfectly on your machine.

It passes again in your local build.

You push your changes.

Minutes later, the CI pipeline fails.

You rerun it.

Now it passes.

Nothing changed.

Welcome to the world of flaky tests.

Flaky tests are one of the biggest productivity killers in modern software development. They waste engineering time, reduce confidence in continuous integration, and eventually train developers to ignore failing builds.

While flaky tests can have many causes, one of the most common is surprisingly simple:

Hidden external dependencies.

Learn about Unit Testing Best Practices


What Is a Unit Test?

The word unit has an important meaning.

A unit test should verify one unit of behavior in complete isolation.

If a test depends on something outside that unit, it becomes harder to reproduce, harder to maintain, and more likely to fail unexpectedly.

A reliable unit test should behave the same way:

  • On every developer’s computer
  • On every CI server
  • Every day
  • Every month
  • Regardless of machine configuration

External dependencies make that much harder.


File System Dependencies

Reading or writing files seems harmless.

Perhaps your test loads a configuration file.

Maybe it writes temporary output.

Or perhaps it reads sample JSON from disk.

The problem isn’t the file itself.

The problem is everything surrounding it.

Questions suddenly appear:

  • Does the file exist?
  • Is the path correct?
  • Is the working directory different on CI?
  • Does another test modify the same file?
  • Does the operating system lock the file?

None of these questions relate to the business logic you’re trying to verify.

Your test has stopped testing your code and started testing your environment.


Network Dependencies

Nothing makes a unit test less predictable than relying on a network.

Even calls to localhost introduce unnecessary risk.

Network-dependent tests can fail because:

  • DNS changes
  • Firewalls
  • VPN connections
  • Internet outages
  • Service latency
  • Authentication failures

The production code may be perfectly correct.

The network simply wasn’t.

Unit tests should never require a network connection unless they are intentionally integration tests.


Registry Access

Windows applications often read configuration from the Registry.

That works perfectly in production.

It usually doesn’t belong in a unit test.

Registry values differ between:

  • Developer machines
  • Build agents
  • Customer environments
  • Operating system versions

Tests that quietly depend on Registry values become extremely difficult to reproduce.


Current Time

Time is one of the most overlooked dependencies.

Consider code like this:

if (DateTime.Now.Hour > 18)

It seems innocent.

Until the test suddenly fails tomorrow.

Or next month.

Or during daylight saving time.

Time-based logic should be isolated so tests can control it.

Otherwise your test suite changes behavior simply because the clock moved forward.


Environment Variables

Environment variables are another hidden dependency.

Perhaps your code reads:

  • API keys
  • Machine names
  • Build configuration
  • User profile
  • Temporary folders

Those values differ everywhere.

Tests that depend on environment variables often pass on one machine and fail on another.


Random Values

Random numbers.

GUID generation.

Temporary file names.

Thread scheduling.

These all introduce uncertainty.

A good unit test should produce the same result every single time it executes.

Anything random makes failures harder to reproduce.


Why These Dependencies Matter

Individually, these dependencies seem small.

Together they create enormous maintenance costs.

Developers begin saying things like:

“Just rerun the build.”

“That test always fails.”

“Ignore that warning.”

Those phrases are warning signs.

Once developers stop trusting the test suite, the value of automated testing begins to disappear.


Detecting Hidden Dependencies

The challenge is that many developers don’t even realize their tests contain these dependencies.

Reading the source code isn’t always enough.

A file access may happen three libraries deep.

A network call may occur through a helper class.

A registry read may happen inside a framework component.

These behaviors only become visible while the test executes.

That’s why runtime analysis is so valuable.

Instead of asking what the code looks like, runtime analysis asks:

What did this test actually do?


Integration Tests Are Different

It’s important to distinguish between unit tests and integration tests.

Integration tests are expected to communicate with databases, web services, queues, file systems, and other external components.

That’s their purpose.

Unit tests have a different goal.

They should isolate business logic from those dependencies.

The problem isn’t accessing external resources.

The problem is doing so unintentionally inside a unit test.


Building Tests Developers Can Trust

Reliable unit tests share several characteristics.

They are:

  • Fast
  • Deterministic
  • Independent
  • Easy to understand
  • Easy to maintain

Removing hidden dependencies is one of the most effective ways to achieve those goals.

Developers spend less time investigating failures.

CI pipelines become more reliable.

Refactoring becomes safer.

Confidence increases.


Runtime Analysis Finds What Static Analysis Can’t

Static analysis can detect many useful issues.

But runtime behavior tells a different story.

By observing tests while they execute, it’s possible to identify hidden dependencies that are invisible from source code alone.

TypeMock Test Review performs this runtime analysis and highlights unexpected behaviors such as:

  • File system access
  • Network communication
  • Registry usage
  • Environment dependencies
  • Time-based behavior

These insights help development teams identify fragile tests before they become flaky builds.


Conclusion

The best unit tests don’t just pass.

They pass consistently.

They don’t depend on your laptop.

They don’t depend on today’s date.

They don’t depend on network connectivity.

They don’t depend on files that happen to exist.

They validate one piece of behavior in isolation.

As automated test suites continue to grow, identifying hidden dependencies becomes increasingly important.

Because the goal isn’t simply writing more tests.

It’s building tests developers can trust.


Continue Reading

Learn More

TypeMock Test Review, included in the TypeMock Isolator 9.5 , helps identify hidden runtime dependencies that make automated tests fragile, unreliable, and difficult to maintain.

The post Why Unit Tests Should Never Access Files, Networks, or the Registry appeared first on Typemock.

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Lessons Learned from CISA’s Recent GitHub Leak

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The Cybersecurity and Infrastructure Security Agency (CISA) has issued a postmortem on a recent data leak in which a contractor published dozens of internal CISA credentials — including AWS Govcloud keys — in a public GitHub repository for almost six months before being notified by KrebsOnSecurity. Experts say the gaps identified in the agency’s initial response provide important lessons that all security teams should absorb.

On May 15, 2026, the security firm GitGuardian asked for help in notifying CISA about the existence of a public GitHub repository called “Private CISA” that included 844 MB of sensitive CISA-related data. One of the exposed files, titled “importantAWStokens,” included the administrative credentials to three Amazon AWS GovCloud servers. Another file — “AWS-Workspace-Firefox-Passwords.csv” — listed plaintext usernames and passwords for dozens of internal CISA systems.

CISA quickly acknowledged our initial alert, but took more than 48 hours to invalidate the AWS keys and many other important secrets leaked in the GitHub repo. In its report on the data leak, CISA said the complexities of the agency’s systems and interconnections with federal and industry partners caused its key rotation to take longer than anticipated.

“Drawing on this experience, CISA encourages others to maintain mature and well-tested key management capabilities,” the report notes.

CISA also admitted it can do better when it comes to responding to security incident notifications from external parties. The postmortem stresses that clear and distinct reporting channels are essential to ensure that incidents affecting the organization itself are handled differently from those involving its products or customers.

“In CISA’s case, these channels were not well defined, leading the security researcher to try multiple avenues – including emailing the contractor, submitting through CISA’s vulnerability disclosure platform (which is intended for vulnerabilities impacting the broader cybersecurity community), and ultimately involving a reporter,” reads the analysis written by Preston Werntz and Brad Libbey, the acting chief information officer and acting chief information security officer at CISA, respectively.

CISA said it is refining its reporting channels to make them easier and faster for researchers. “Additionally, while many researchers rely on the security.txt file, organizations can ensure clarity by publishing reporting instructions in multiple prominent locations,” the CISA authors wrote.

Guillaume Valadon, the GitGuardian researcher who first contacted KrebsOnSecurity about the exposed CISA credentials, said CISA ignored nine automated alerts about the exposed credentials prior to our notification on May 15. Valadon’s company constantly scans public code repositories at GitHub and elsewhere for exposed secrets, automatically alerting the offending accounts of any apparent sensitive data exposures.

“Letting nine notification emails go unanswered is how a one-day incident becomes a six-month exposure,” Valadon wrote in an analysis of CISA’s report. “Make it trivial to report a leak about you, not just about your products. The person reporting a leak to you is not the threat. Publish a security.txt, but do not stop there. Put reporting instructions in several prominent places, and make sure a report about your own infrastructure does not land in a product-bug queue.”

The report’s authors also emphasized the importance of continuously scanning public code repositories like GitHub for exposed secrets, and said CISA has since rotated all secrets and created an action plan to improve management of developer secrets and to better monitor for them going forward.

The report notes that while CISA had developed a playbook for responding to cybersecurity incidents, that playbook somehow didn’t include what to do in situations involving GitHub or other cloud services. Valadon said the report validates the need to scan continuously — not just quarterly — for exposed secrets.

“The Private-CISA repository sat public for six months,” Valadon wrote. “Continuous monitoring of public GitHub surfaced it. Comprehensive internal scanning could have caught the plaintext passwords and committed backups long before they left the building.”

CISA gave itself passing grades on several areas of security preparedness that it said helped the agency gauge the scope and impact of the exposed secrets, including enhanced logging capabilities, and the adoption of zero-trust principles in both its production and development systems. CISA said those detailed logs allowed it to show that no customer or mission data was exposed, and that the leaked credentials were not used outside of CISA’s environments. The agency said the contractor who exposed the secrets had their system access revoked.

Valadon reckons the biggest takeaway is the CISA postmortem itself, and praised the agency for being transparent about what worked and what didn’t.

“To my knowledge, it is also the first time a national cybersecurity agency has publicly advocated for secrets scanning and for simplifying relations with security researchers,” Valadon wrote. “That is exactly the incident communication we should expect from every organization.”

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White House report says Trump can usher in a "new golden age" of science

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On Tuesday, the White House Office of Science and Technology Policy (OSTP) released a report entitled "Science: A New Golden Age," in which it lays out how it has viewed science, found it lacking, and believes the Trump administration is in the perfect position to fix things. It's a bit unexpected coming from an administration that has been proposing crippling funding cuts to research and trying to enable political appointees to terminate grants awarded based on scientific merit.

The report presents itself as the spiritual successor of "Science, the Endless Frontier," a policy document that laid out the case for government-funded science in the wake of World War II. The New Golden Age (SNGA) says that, while the concepts promoted by the original remain vital, the circumstances have changed such that we need major revisions to how the government is implementing things.

The result is an odd mix. It completely ignores or glosses over many things that the administration is doing to harm scientific progress. In some cases, it identifies issues that have already been discussed as problems within the scientific community. Elsewhere, it's a mixture of political grievances, ideas without a solid intellectual foundation, and an injection of Silicon Valley's perspective on innovation (Michael Kratsios, the director of the OSTP, formerly worked with Peter Thiel). As a result, it's unlikely to have anything like the impact of "Science, the Endless Frontier."

Lacking internal coherence

The report is trying to make the case that US government-funded science has some structural problems, serious enough that they require major changes to the entire enterprise. (It also makes a positive argument, namely that developments in AI necessitate a new approach, which we'll come back to.) Some of those concerns are real and had been discussed within the research community previously.

One of the real problems it discusses is the amount of time researchers need to spend on writing grants and performing administrative tasks, taking away from their time doing research. SNGA promises to relieve this by simplifying regulations and streamlining the grant application process. But it's also remarkably vague, in that its authors do not identify a single regulation that is in need of reform.

The report also seems not to realize that it is being written by the Trump administration. One of the reasons researchers have spent so much time writing grants is a general uncertainty about funding, and that's something that the administration has dramatically increased through the haphazard termination of existing grants, by proposing massive budget cuts for science agencies, and by slowing the release of funds that had been allocated to researchers by Congress.

This lack of self-awareness permeates the document, but it's worth looking at a second example. In several instances, the report highlights the Human Genome Project as an example of the sort of project that government excels at driving. But it seems to be unaware that the project was a major international collaboration, with entire chromosomes being sequenced outside the US. Pride in the completion of the genome seems out of place from an administration that is actively trying to minimize international collaborations involving the researchers it funds.

In the same way, it highlights how the Human Genome Project required the immediate and open release of the data it generated as an example of what the Trump administration considers "gold standard science." But SNGA also decries how making results accessible to all has allowed other countries to develop industries based on advances that occurred within the US.

In short, the report lacks a perspective that's internally coherent, or consistent with the other goals and actions of the Trump administration.

The role of innovation

Another area of concern in the SNGA is that of innovation, both within science and in the translation of scientific findings to commercialization. The former has been recognized as a potential issue by the scientific community, as some measures indicate that more of the research we're doing in recent decades is incremental, and there are fewer large advances. The challenge is that these measures are controversial; it's hard to reach consensus on what counts as innovative, and some impacts of research may take longer to become apparent than most studies of the issue consider.

Yet the SNGA treats this as settled and uncontroversial. It talks a lot about how we need to promote more innovative science, and suggests a wide range of different funding models that might promote more innovation. It also says agencies will need to evaluate whether grant funding is accomplishing what we intend it to. But it never describes how agencies should objectively measure innovativeness, so it's unclear how they can perform that evaluation, or whether the scientific community will consider their results valid.

On the flipside, this is one of the cases where SNGA's proposed solutions align with Trump administration actions, specifically its call to reduce the importance of peer review. In the new document, peer review is presented as part of the problem: "Review panels often gatekeep proposals by consensus, disincentivizing transformative ideas." And many of the new funding mechanisms it proposes include reviews by single individuals who may or may not be trained scientists. While this is likely to broaden the scope of ideas that get funded, it also seems likely that it will increase the funding of fringe ideas, something that the OSTP does not seem to consider.

(I'll note that this is consistent with Trump administration actions and not ideas; peer review remains part of what the administration is calling gold standard science, which SNGA also endorses.)

Aside from innovations within science, SNGA also is concerned with how science gets translated into products and processes by commercial interests. Here, the report decries what it terms a linear model, one where government funds basic science, which the market then takes a one way journey to commercialization. Again, this is a case where people in science would likely agree that things are often considerably more complex. The report favors a model where commercialization is more of a conversation, as technology developments allow new scientific work that in turn leads to new or enhanced opportunities for further commercialization.

There have definitely been instances of this. A great example is the real-time RT-PCR tests that were initially used during the pandemic. Commercialization of both reverse transcriptase (the RT) and PCR allowed scientists to develop the real-time monitoring of reaction progress for their own research. Companies then commercialized hardware that simplified the process, and still other companies then developed diagnostic tests using it.

But SNGA puts all of its eggs in that basket, which is just as incomplete as the linear model. Quantum mechanics stayed trapped in physics departments for roughly 50 years before it got commercialized via lasers and semiconductors. And it's hard to imagine the timeline in which we'll end up commercializing something like the detection of gravitational waves.

The whole idea of government funding for basic science was the recognition that it's not possible to predict in advance which scientific findings will have commercial applications, so companies, for the most part, weren't going to do it. While SNGA is right that we should encourage those cycles of science-technology innovation when we find them, it offers no suggestions for how we identify one before it takes off and government funding becomes irrelevant. Because that is the only type of technology development it acknowledges exists, it has little to offer for any others.

Innovation in action?

Perhaps the strangest thing about SNGA is that it seems upset that scientists are highly trained specialists (although that's in keeping with the Trump budget proposals, which radically slash funds for graduate students). At its most extreme, it presents people in the field as out-of-touch elitists. "Engineering students study the theory of combustion, but few can disassemble and rebuild a combustion engine," its authors complain. "Graduate programs reward theoretical contributions measured in citation counts, but not practical applications measured in jobs and dollars."

Here, its solution is grand in scope. It views the maker community, which is mostly interested in scratching personal itches, as a sign that the US public wants to make things again. The SNGA authors figure that they would be perfectly happy making things for science. "Establish national fellowships for skilled craftspeople," they suggest, "practitioner-in-residence programs embedding machinists and technicians alongside Ph.D. researchers, and portable industry-recognized credentials in advanced manufacturing and lab techniques."

It's unclear what these machinists will be doing with the researchers. But the results will be glorious: "By rebuilding the link between science and hands-on craft, federal leadership can ensure that the economic returns of discovery, including the jobs, supplier networks, and process knowledge encoded in the hands of workers, accrue to Americans."

Its example is Detroit in its heyday, although that seems to have been a period of engineering refinements that lacked a notable scientific component. And it's unclear what scientific research center the SNGA associates with that time.

Are there some fields that can be revolutionized by things like a 3D printer and a bit of time with the maker community? Robotics seems like an obvious choice; developmental biology does not.

Elsewhere, the report returns to the familiar claim that regulations are also stifling the potential for science-driven commercial innovation. The only specific area that's cited, however, is nuclear power: "nuclear energy stalled in America not because the physics failed, but because regulatory choices over the past half-century made building uneconomical." That is not a realistic reading of the history, as there is little indication that regulations are the primary cause of the massive delays and cost overruns that plague nuclear plant construction.

Political grievance and Silicon Valley

The complaints about regulations are one of the many cases where SNGA descends into political and cultural grievance. The most obvious case is where it detours to grumble about the role of the scientific community in the arguments over COVID school closures, but there are many additional ones.

It is once again dismissive of diversity, equity, and inclusion (DEI), while saying that all Americans with aptitude need to have access to scientific training—exactly what DEI programs were meant to ensure. It also complains that foreign students were taking slots in PhD programs from deserving Americans, while ignoring the reality that the administration's attacks on science funding have caused a number of schools to cut the number of students they admit. The fact that those foreign students often want to stay in the US to contribute to either scientific or commercial endeavors is also ignored.

Grant overheads allow the institutions that host federally funded research to pay for the upkeep of the facilities where science happens. But they've been targeted by the administration, so the SNGA takes time to complain about them as well.

Most strikingly, the administration that terminated grants wholesale due to political disagreements with the subjects they were funding had the audacity to argue that it would lead the charge to "ensure that selection [of grants] rests purely on merit, not the political fashions of the day."

Beyond the political grievances, the report seems to have been shaped by OSTP head Kratsios' time as a venture capitalist. Venture capital is presented as a separate source of scientific funding from government and commercial. SNGA presents it as potentially more rigorous, since companies will fail if they're based on faulty scientific ideas. Left out is a consideration of the fact that most venture-backed companies do in fact fold; this is a track record that would be considered problematic by the authors of this report if it involved grant-backed projects.

This attitude pervades the report's approach to AI, which largely buys into the biggest hype imaginable. The reality is that we're still in the process of understanding what types of AI developments will have an impact in which fields. But SNGA envisions a future that, well, deserves to be read in full:

These pieces lay the foundation for a continuous, market-mediated, agent-based scientific economy. Imagine a funder posting a million-dollar bounty for the first validated therapeutic target for a rare disease. An agent working on adjacent problems notices a promising lead and posts a smaller bounty for replicating the finding. Other agents assess whether the problem falls within their competence, bid for the work, and contract an autonomous laboratory accessible through the internet, which runs the experiment and returns cryptographically signed results.

I guess we all have our dreams.

But it's not clear what dream the people who wrote the new report are pursuing. The Endless Frontier was a serious effort to make the case that it was in the US's national interest to fund basic science, even if there wasn't a clear commercial or national security endpoint to the work. Its audience was the policymakers that could turn that into a reality.

A New Golden Age, in contrast, is a grab bag of political grievances, half-thought-through justifications for policies the Trump administration was already pursuing, and insinuations that scientific experts are kind of annoying and should learn how to fix car engines. It's unclear who the report's audience is. Certainly not the policymakers in the current administration, since it repeatedly indicates that the decisions they are making are great already. And it's certainly not the scientists, who are currently suffering from the impact of those decisions.

A similar ambiguity exists about the goals of the document. There are lots of specific suggestions: get new funding mechanisms; don't pay attention to peer review; hire some mechanics; get industry more involved; support AI, as it will change everything. But if those added up to a coherent whole, I was not able to identify it.

"A decade from now, American researchers should look back at our work and say: 'The vital questions I could not pursue then, I am free to pursue now,'" Kratsios wrote about the document, in what seems to be the clearest indication of a goal. But A New Golden Age provides no reason to expect that they will.

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HarlandCorbin
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One trip and fall and this misadministration could massively help scientific progress. And the environment. And world relations.

Confusion swirls on source of diarrhea outbreak, but it’s still Taylor Farms

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Federal officials on Monday reaffirmed that iceberg lettuce from Taylor Farms still appears to be the source of Cyclospora, the microscopic parasite behind a multi-state surge in explosive diarrhea cases.

The assertion was intended to clear up confusion that began swirling over the weekend around a false positive test from the Food and Drug Administration—which Taylor Farms celebrated, sparking confusion. Still, many details of the outbreak remain murky, largely due to the vague recall information Taylor Farms has publicly provided.

On Friday, the FDA, along with the Centers for Disease Control and Prevention, announced that the FDA's traceback investigations for the outbreak converged on Taylor Farms. Traceback investigations work by tracking backward from sick people and the foods they ate to where those foods came from through the supply chain, down to individual farms or production facilities. When the FDA said their investigation converged on Taylor Farms, the agency means that, over and over again, tracebacks led them to shredded iceberg lettuce grown in Central Mexico and supplied by Taylor Farms—which also goes by Taylor Fresh Foods.

The convergence of tracebacks was the basis for the FDA and CDC publicly identifying Taylor Farms on Friday. It was also the basis for Taylor Farms issuing a sweeping recall the same day for all its iceberg lettuce sourced from Central Mexico between June 29 and July 16.

At the time, the FDA's traceback investigation specifically focused on tracing back shredded iceberg lettuce that people had eaten from Taco Bell locations. Thus, the messaging at the time suggested that the contaminated lettuce may be limited to Taco Bell. A CDC press release Friday warned consumers not to eat lettuce at Taco Bell locations in five states (Indiana, Kentucky, Michigan, Ohio, and West Virginia). It noted that "Shredded iceberg lettuce sold in grocery stores or served in other restaurants is not affected."

Mixed messages

Then things changed over the weekend. First, on Saturday, the FDA announced that a sample of Taylor Farms lettuce tested positive for Cyclospora. The result confirmed the traceback investigation findings—and also potentially extended the list of contaminated products. But, then on Sunday, the FDA deleted the information from its website, explaining that on further review, the result was a false positive.

In a press briefing Monday, FDA officials explained that the test result was from lettuce sampled at the border that was not included in Taylor Farms' recall. Given that new illnesses are still being reported and the tested lettuce was not part of the outbreak recall, the FDA rushed to publicly disclose the information in hopes of preventing more illnesses, the officials said. But, upon further testing, it became clear that the genetic test for the parasite had returned a false result. Officials would not go into detail on what caused the faulty result, but contamination is a common source of false positives.

Meanwhile Taylor Farms seemed to celebrate the rescinded test result over the weekend, sparking confusion.

"Today, FDA apologized to us," Taylor Farms wrote at the start of a statement Sunday.

"Today, we were informed that FDA made a mistake, and this was a false positive. To be clear, at this moment, FDA has not identified a single positive product test result for Cyclospora," the statement read (emphasis theirs).

This response was followed by headlines that suggested the FDA may have blamed Taylor Farms in error. The Wall Street Journal, for instance, reported the FDA "walked back" an earlier detection, to which the FDA directly responded.

"To clarify, this false-positive lab sample DOES NOT change the basis for FDA’s ongoing outbreak investigation or the overwhelming epidemiological data supporting the current voluntary recall by Taylor Farms," the agency said on social media.

In the press briefing Monday, FDA officials also flatly denied issuing an apology to Taylor Farms. They further explained that the false result didn't change anything about the situation, and outbreaks involving short-lived produce often lack confirmatory positive tests.

"I also want to be clear that our weekend communications around a false positive test result do not change the basis for FDA's ongoing outbreak investigation or the epidemiological data supporting the current voluntary recall by Taylor Farms," Acting FDA Commissioner Kyle Diamantas emphasized at the outset of the briefing.

Vague recall

Although the testing turned out to be false, additional information over the weekend did seem to expand the scope of the outbreak—at least the publicly visible part of it. The New York Times reported that restaurants and retailers other than Taco Bell were removing iceberg lettuce they received from Taylor Farms. In many cases, the lettuce was sold in large bags for restaurants or service operations, and sometimes blended with romaine.

Walmart removed lettuces sold under its Marketside brand—which were included in the FDA's notices but not in Taylor Farms' recall list. Sysco, the nation’s largest food distributor, told the Times that it was removing lettuce products it had from Taylor Farms. So did US Foods, another large food distributor. Jack in the Box restaurants also said they were affected by the recall.

The Times noted that Taylor Farms' recall notice is vague—it doesn't list these distributors or retailers affected, only product codes and dates. The FDA said on its website Saturday morning that Taylor Farms "has not publicly provided distribution information or a list of customers who received the product that was voluntarily removed from the market." The FDA later removed that statement, but the recall list remains vague.

Overall, Taylor Farms said it distributed the potentially contaminated lettuce to 27 states. But it's unclear if food distributors delivered the food to additional states. For instance, New York and California have reported cyclosporiasis cases, but are not included in the recall list.

According to the CDC data, at least 34 states have reported cases. The true number of cases is in the thousands, clearly exceeding the country's standard range of between 2,000 and 5,000 in recent years. In Michigan alone, health officials are reporting 6,571 cases and 102 hospitalizations as of July 21.

Traceability

Frustrating the situation is that the FDA does not currently require food producers to provide information on all its customers. A new FDA rule, the Food Traceability Rule, was lined up to bolster traceability record requirements for producers of various foods, including leafy greens. The rule would "allow for faster identification and rapid removal of potentially contaminated food from the market, resulting in fewer foodborne illnesses and/or deaths."

It was set to take effect January 20, 2026, but last year the Trump administration pushed back the compliance deadline by 30 months, to July 20, 2028.

With Taylor Farms now in the spotlight, online scrutiny had raised speculation the company may have played a role in the delay. The Trump administration's decision to delay the rule was announced on March 20, 2025. Matthew Cortland, a lawyer focusing on health and disability policy at the think tank Data for Progress, noticed that on March 26, 2025—just five days after the decision—Taylor Fresh Foods (aka Taylor Farms) donated $1 million to the Trump-backing super PAC MAGA, Inc (PDF, page 42).

Taylor Farms did not immediately respond to a request for comment.

In a response posted on social media, an account for the Department of Health and Human Services called the speculation "FAKE NEWS," and claimed nothing influences decisions of the administration "except science and the safety of the American people."

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Judge Approves $1.5 Billion Anthropic Settlement Over Pirated Books Used To Train Claude

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A federal judge has approved Anthropic's $1.5 billion copyright settlement over pirated books used to train its Claude chatbot, with authors and publishers set to receive about $3,000 per book. The case produced a mixed ruling for the AI industry: training on copyrighted books was found not to be illegal, but Anthropic's use of pirated copies from shadow libraries was. The Associated Press reports: District Judge Araceli Martinez-Olguin said in a Monday ruling that the class-action settlement provides "meaningful relief" to affected authors and publishers. About 91% of the more than 482,000 books covered by the ruling have been claimed by authors or publishers who are now due payment. Plaintiff attorney Justin Nelson said in a statement that the settlement was "the largest known copyright recovery in history. We look forward to making distributions to the Class as promptly as possible."

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AI Companies Are Buying Tons of Old Books Because They're Free of AI Slop

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An anonymous reader quotes a report from 404 Media: As AI companies search for more training data to improve their models, one company is offering old, printed books as an ideal source because they are guaranteed to be free of the very AI slop AI companies are producing. "The world's best AI training data is sitting on a shelf," ISBNdb, a company that produces what it claims is "the world's largest book database," and that offers high-volume book acquisition services for AI companies, says on its site. "Books represent curated, peer-reviewed, domain-specific human knowledge, structured in a way no web crawl can replicate. Dense, edited, authoritative." In one article on its site, ISBNdb explains that printed books published before 2022 are ideal for AI training data because they don't include AI generated text. As the article correctly notes, much of the data that AI companies can scrape from the internet today is likely to include AI generated text, which could result in "model collapse," a process by which AI models that are trained on AI generated data results in worse models that are more prone to errors. The article also notes that book authors who object to their writing being scraped for training purposes can now easily poison AI models by producing writing designed to manipulate and sabotage the resulting AI models. "Print books from the pre-LLM era are structurally guaranteed to be free of this contamination. That alone is a significant advantage [...] "Physical books published before this date [pre-2022] are structurally clean of modern poisoning tools." [...] ISBNdb advertises that it can keep the identity of AI companies secret. "Strict NDA [non-disclosure agreement] on every engagement," ISBNdb's site says. "Every project begins with a legally binding non-disclosure agreement. Your identity, strategy, and acquisition targets are never disclosed." ISBNdb notes that AI companies may not want to be caught destroying printed books during the scanning process. "The optics problem is real," ISBNdb's site says. "'AI company destroys two million books' is not a headline that generates sympathy."

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