So. Ich habe gerade mein Google-Ads-Konto hierhin gekappt und bin damit nach gut 13 Jahren wieder komplett frei von Werbung.
Was im Jahr 2012 mit etwas Werbung über damals Stilanzeigen in der Sidebar begonnen und mir ein paar Jahre später mitunter wahnsinnig viel Geld eingebracht hatte, endet damit. Jetzt. Hier. Die Diskussionen damals; Puh…
Weil Werbung eh allen auf den Saque geht (mir auch), für mich aber über einige Jahre doch sehr erträglich war und jetzt nicht mehr ist, fliegt sie raus. Ich muss dafür noch einige Code-Fetzen entfernen und anpassen, aber es sollte hier, nachdem ihr euren Cache geleert habt, schon jetzt deutlich angenehmer aussehen. Guckt mal.
Ich nehme jetzt die letzten 100 Euro von Google Ads und verabschiede mich dann von der Monetarisierung im Blog. Ciao, Kakao! Es war eine wirklich wilde Zeit, die mich nach New York, nach Budapest, nach Montenegro, nach Sotschi und nach London brachte. (Alle Fotos nach gefühlt 138 Abmahnungen und einer damit verbundenen Privat-Insolvenz gelöscht.) Wenn die Zeit meines Lebens es zulassen sollte, werde ich mal irgendwann noch ein Buch darüber schreiben. Glaubt ihr ohne alles gar nicht.
Und wenn Ihr jetzt denken solltet, dass ihr jetzt deshalb Euren Steady-Support einstellen könntet, damit Ihr keine Werbung mehr seht: bitte nicht. Schließt eher einen neuen Deal ab. Für nichts, außer für das hier. Auf jene, die jetzt ob der nicht mehr vorhandenen Werbung kündigen, werde ich ein bisschen sauer sein. Und das ganz zu Recht auch.
A few weeks ago, I came across a wild post on Reddit’s r/DHExchange, a subreddit for trading large datasets: “I hoarded a large database of something valuable, just not what’s [sic] you expect…150k stools images.”
The post, made by a user called Ill_Car_7351, was advertising exactly what it sounds like: A database of poop images, collected from an AI poop analyzing app that he had launched several years ago. Basically, 25,000 people had been taking images of their poop and uploading them to his app. He’d been collecting, analyzing, and annotating these images and now wanted to sell access to them: “I’ve got 150k+ labeled and classified images of 💩 from roughly 25K different people. Jokes aside, I know there’s a lot of value in it (hard to obtain, useful for ML [machine learning] training, cancer studies etc) but not sure on how to move about it. Feels like I’m sitting on a pile of shi..ny coins but can’t find who wants them.” The poster added that “the images are extremely rare,” and that he was trying to figure out how much money he could sell them for.
The comments were from people who were mostly horrified: “When I was 5 the teacher taught me how to read. I now regret that happened,” one read. “What in the fuck,” another read. “How to delete someone else’s post,” a third said.
I messaged the poster and told him I was interested in obtaining the database. Thus began my journey into the Internet of Shit and, by extension, the unpleasant world of the underground sale of highly sensitive, app-collected user data for AI training.
The poop database comes from an app called PoopCheck, an app made by a company called Soft All Things that purports to use AI to analyze images of one’s stool in order to give you a “daily gut health score.”
“Our AI analyzes your poop using the Bristol Stool Scale and advanced pattern recognition. Get insights on consistency, color, shape, and what they mean for your digestive health,” the app advertises. The Bristol Stool Scale classifies stools into one of seven types ranging from “separate hard lumps, like little pebbles” to “watery with no solid pieces.”
The app also features a “community,” of 151,317 “shared stools” at the time of this writing and a “leaderboard,” where people can share images of their poop for commentary from other users and earn points for participating. I found the posts in the community a bit hard to stomach, with titles “like play dough,” “Concerned,” and “Dealing with this on and off for the past 3 weeks.” Pictures are not automatically shared to the community; when you take a photo it asks if you want to share it.
“Popular” posts on the app include people speculating as to whether their fellow community members have parasites or colon cancer; in the comments section of a few posts I saw people recommending ivermectin to the original poster.
Though users have the option to share their poops with other users, the app provides mixed messages about the fact that the data uploaded to the app will be analyzed, annotated, and packaged with other poops into a commercial database to be sold to AI companies.
On the App Store page for PoopCheck, it says “The developer does not collect any data from this app.” The link to the privacy policy from within the App Store download page does not mention anything about selling or sharing the data and says “your health data is encrypted in transit and at rest. Photos are processed securely. We implement industry-standard security measures to protect your data.”
The PoopCheck website’s About page states “Privacy First.” And “Health data is sensitive. That’s why privacy isn’t a feature, it’s our foundation. Your photos are encrypted. You can delete everything at any time. We built PoopCheck the way we’d want our own health apps built.” The FAQ also notes “your privacy is our priority.”
This is completely different from the “Service Agreement” and “Terms and Conditions” people agree to when they actually open the app and make an account. The Service Agreement states that “by uploading stool images or any health-related data to the App, you grant Soft All Things LLC a worldwide, irrevocable, perpetual, unconditional, royalty-free, fully-paid, transferable, sub licensable license to use, reproduce, modify, adapt, distribute, sell, license, and create derivative works from such content for any lawful purpose, including but not limited to research, commercial exploitation, product development, and third party licensing. You acknowledge that your images and data may be used to create, train, improve, and commercialize AI technologies and machine learning models, and that such models and any outputs derived from your data may be licensed or sold to third parties, including medical organizations, research institutions, and commercial partners.”
It adds that “your data may be irreversibly incorporated into AI models and aggregated datasets. Deletion of your account will remove your personal profile data but does not require the removal of anonymized, aggregated, or derivative data already processed or incorporated into AI models.” Under a section called “Sharing of Information,” it adds that the company reserves the right to share or sell the data “for any business purpose,” including “AI and Data Licensing.”
On Reddit, I messaged Ill_Car_7351 and said “Hi - am interested in this database you posted about. Can you share any more info about what you're looking for / details about the app where it was collected? also any chance there's like, a sample of what the data looks like etc?” They responded quickly and said “Hey! The db was gathered by real users, we had 25k users over the last couple years, since we launched the app. It’s called PoopCheck btw if you wanna see it. Let’s maybe talk via email? I’ll be happy to share a sample of the data if that interests you.”
I sent an email to someone named “Marco” at Soft All Things, who identified himself as one of the founders of PoopCheck. I said I had reached out on Reddit and was interested in a sample of the data. I used my real email address and real name.
“We can surely send you a sampling of the dataset, would a Google Drive link containing an image folder and JSON data work? We can also figure out other ways if you prefer,” Marco said. “In terms of the actual dataset you need, what would be the size of it for your needs? And what would you be using it for? Just so we can make sure it’s actually a good fit for your use case.”
I told Marco that I wanted 10,000 pieces of data and said I would use it for AI training. I asked him for pricing and what type of data was included.
Marco responded:
“You'll find a folder with images and JSON metadata covering the key fields we capture per entry. Let us know if you have any questions about it.
To give you a better idea of the dataset and pricing options: we currently have over 150,000 images validated by AI. Around 5,000 of these have also been manually reviewed by a member of our team, who verified the AI output and labeling, making this portion more valuable and priced accordingly. It's also worth noting that certain types on the Bristol Stool Scale are rarer than others, so availability may vary depending on your specific needs.
With that in mind, here there is an estimation of pricing options:
• 5,000 fully human-reviewed & annotated (on top of AI validation) — $4,000
• 5,000 reviewed + 5,000 unreviewed — $5,000
It would be great to have a quick call to take this further as there are a few things about the dataset's structure and coverage that are easier to walk through live.”
The sample dataset Marco sent me included 20 images of poop from four specific users (five poops each). Each image was tied to a series of user-reported data points as well as AI analyses of each image. AI-analyzed datapoints included the time the poop was taken, the Bristol Type of each poop, whether it was “healthy” or “unhealthy,” the “shape” and “consistency,” whether there was blood or mucus in the poop, and the quantity (“large,” “normal,” or “small”), and whether it was “floating” or not. Each of these data points also had a “confidence” score for how confident the AI was in its analysis. Each image also had user-reported information, which included the answers to a series of questions including “when did you have your last meal,” “any discomfort while pooping? (“Hard to pass;” “burning”; “sharp pain” etc); “How long did it take?” “Did it smell stronger than usual?” “Coffee or alcohol in the last 12 hours?” The data also included demographic information, which includes age ranges, sex, height, weight, and sensitivities such as “lactose intolerance” or “irritable bowel syndrome.” Each image is tied to a specific user through a field called “externalIndividualID.”
Soft All Things is not exactly quiet about the database that it has created. On the Poop Check website, it has a page called “For Business,” which advertises its database. It sells access to both the “Stool Analysis API,” which “turns a stool photo into a structured health report,” as well as the “Annotated Dataset,” of 140,000+ images to “train your own models.” It advertises this as the “largest consumer stool image dataset we know of.”
It maybe should not be terribly surprising that a free app in which you upload images of your poop to a random company would have a business model focused on packaging and selling that data. But this type of data collection—of our literal poop—highlights how almost anything we do on our phones can ultimately end up for sale. The fact that it is advertising this for sale at all indicates that there is an AI goldrush for any and all types of data, even our literal waste.
Research has shown, over and over again, that de-identified “anonymous” data doesn’t necessarily remain anonymous when combined with other datasets. Toward the end of last year, the appliance giant Kohler endured a security shitshow when a researcher showed that its stool-analyzing smart toilet camera was not actually properly encrypting the images that it sent to Kohler. The concern there was that your poop data would be somehow accessed by bad actors. In the case of PoopCheck, anyone can simply buy access.
After I told Marco I was writing an article about PoopCheck and its database, he stopped responding to me and did not answer any of my questions.
Since the beginning of 2026, at least four landslides are reported to have killed hundreds of people at the Rubaya mines in the Democratic Republic of Congo (DRC), a major global source of coltan. Coltan is widely used in smartphones, laptops and e-vehicles.
In the absence of reliable on-the-ground coverage, Bellingcat used open source methods to examine statements from the authorities and media reports. Bellingcat confirmed several incidents in which villages were engulfed in the landslide and residents living near the mine were among those killed.
Estimated area affected by M23 activity in 2026, based on ACLED incident data.
Landslide No.1 – January 28
Reports of a deadly landslide killing more than 200 people began appearing in international media in late January and early February.
Three days after the incident, the DRC government made a statement on Facebook outlining that at least 200 people had been killed. They said the landslide was “a consequence of the rampant and illegal mining by Rwanda and the M23/AFC”.
In response, the M23-appointed local governor, Lumumba Muyisa, told Reuters that at least 200 people had been killed, but attributed the landslide to heavy rains.
Landslides are common in small-scale mines, especially during the rainy season, which in Rubaya spans from September to May and peaks between March and April.
According to local journalists, it took several days for the injured to reach Goma due to poor road conditions and cellular network problems. Image: Screenshot from Le Journal Afrique TV package.
Bellingcat cross-checked local media reports against one of the few social media posts about the incident, geolocating the phone footage to a mining pit south-east of Rubaya. In the video, the narrator speaking in Kinyarwanda (the national language of Rwanda, also spoken in eastern DRC) pans from the top to the bottom of the slope. Filmed at a distance, no bodies are visible in the footage.
Left: Layered frames from phone footage. White box highlights the tree line. Yellow box highlights a cluster of buildings. Right: Pre-landslide image from Google Earth Pro (March 14, 2025) with aligned white and yellow boxes.
Satellite imagery captured before and after the first landslide shows how the mud advanced down the slope.
Satellite imagery before (left) and after (right) the first landslide. Affected area highlighted by white box. Source: Planet Labs PBC
Landslide No.2 – March 3
Just over a month later, a second landslide was reported. On Facebook, the DR Congo Ministry of Mines released a statement including a provisional death toll of more than 200 people:
However, senior M23 official Fanny Kaj, speaking to AP, rejected the DRC government’s claims, stating:
“I can confirm what people are publishing is not true. There was no landslide; there were bombings, and the death toll isn’t what people are saying. It’s simply about five people who died,” Kaj said.
The same day the second landslide was reported, another M23 spokesperson, Lawrence Kanyuka, announced an attack involving “combat drones and heavy artillery”, at a location more than 250km from Rubaya.
Speaking to eyewitnesses at the mines, international media reported a landslide triggered by heavy rains, with no mention of bombings – only of workers buried under the earth.
Bellingcat verified several social media videos of the second incident, in which dozens of people are seen digging for those buried under the mud. The clip below is an edited excerpt that excludes graphic images of bodies.
Edited video clip (left) geolocated to the camera icon (right). The white line (right) shows the camera’s movement as it pans across the slope. Source: Planet Labs PBC, March 26, 2026.
Later in the video, as the camera zooms in on several bodies, the narrator speaking in Kinyarwanda says: “Those you can see here have just been pulled out. These people are dead, but others are continuing to the search operations.”
Due to the low quality of the footage, an accurate body count was not possible.
Bellingcat geolocated footage of landslide No. 2 to the same location as landslide No. 1, shown in the satellite imagery below.
Satellite imagery before (left) and after (right) the first landslide. Affected area highlighted by white box. Source: Planet Labs PBC, Copernicus Sentinel Data / Browser.
M23 did not respond to a request for comment on findings contradicting senior official Fanny Kaj’s claim that no landslide occurred on 3 March.
Landslide No.3 – March 7
Four days later, a third landslide was reported, with estimates of more than 300 people killed, according to civil society official Telesphore Nitendike. Speaking to EFE, Nitendike said the landslide had affected “more than 40 families” as houses were “swept away” by the mud.
Satellite imagery shows the landslide advancing from east to west as mud surged down the slope.
Before and after the third landslide on March, 3. Source: Planet Labs PBC.
Bellingcat verified more than a dozen social media videos from the third incident, the majority posted on X by local media accounts. Almost all contained highly distressing content, including the bodies of young children. In one video, the narrator walks through a crowd of more than a hundred people, then stops and pans across several bodies covered with blankets, saying:
“These bodies were found here in Gatabi [name of village], inside houses. You can see how the houses were swallowed. The search for residents is still ongoing. It is truly a tragedy.”
As he continues filming, at least seven unclothed bodies, all young children, are seen being carried down the slope.
“You see, there, that’s another child’s body. These are children who were sleeping in their homes. Some were still in bed when they were swallowed by the landslide.”
Left: Video clip shows a body covered with a blanket on a stretcher. Right: Video clip shows the community-led rescue effort. The background satellite image shows geolocated pins marking the videos. Source: Planet Labs PBC, 16 February 2026.
A fourth landslide was reported at the end of March by local outlets, describing the collapse of two mining shafts and the death of at least nine workers.
Satellite analysis, combined with the geolocation of one social media video, indicates the fourth incident took place at the same location as landslides No.1 and No.2.
Before and after the fourth landslide on March 27. Yellow box highlights houses engulfed in the mud. Source: Planet Labs PBC.
Despite repeated attempts by Bellingcat to contact the DRC government and M23 for updated casualty figures across all four incidents, neither party responded.
In February of this year, human rights group Global Witness called on companies and governments using or trading DRC’s coltan to ensure mine operators adhere to international human rights and environmental standards.
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Bellingcat also contacted the DRC government spokesman and minister for communication and media, Patrick Muyaya, regarding a post he made on X that Bellingcat found to be promoting misinformation about the rate of expansion of the mines while under M23 control.
In the post, Muyaya urges followers to watch a video that presents itself as an open source report but includes satellite imagery falsely attributed to Bellingcat and “Planet Labs Inc.” We can confirm that this is not our work. The imagery also appears not to be from Planet Labs PBC, but from Google Earth Pro (illustrated below).
The fabricated video was originally posted in 2025 by the Facebook account, Congo Kinshasa.
Left: Screenshot from Congo Kinshasa’s video, mislabelled ‘Avril 2024’ (April). Yellow box highlights false attribution to Bellingcat and “Planet Labs Inc.” Top Right: Satellite imagery from Google Earth Pro, 2019, matching fake video on left (minus a colour filter). Bottom right: Authentic Planet Labs image from 2024, April 19.
Contacted by Bellingcat, Congo Kinshasa confirmed that they were the creator of the video. Asked to explain why the satellite images were mislabeled and the analysis wrongly attributed to Bellingcat, they responded: “I don’t understand you. What exactly is your problem?”
Minister Patrick Muyaya did not respond to our request for comment on his post promoting false information.
Claire Press contributed to this report.
Bellingcat is a non-profit and the ability to carry out our work is dependent on the kind support of individual donors. If you would like to support our work, you can do so here. You can also subscribe to our Patreon channel here. Subscribe to our Newsletter and follow us on Bluesky here, Instagram here, Reddit here and YouTube here.
Ein Lastenrad von Peugeot, vermutlich aus dem Jahr 1914. So lange gibt es die Dinger schon. Damals natürlich ohne Motorunterstützung. Kosten dafür waren 435 alte Frances, was nach INSEE heute etwa 1650 Euro entsprechen dürfte. Gar nicht mal so billig.
Microsoft’s GitHub code repository is a central dependency for open source software. Corporate development teams love it too.
But GitHub’s been having serious reliability problems lately — 86% uptime over the past 90 days. [Missing GitHub Status]
We don’t have direct smoking gun evidence that GitHub is dying of vibe code poisoning. But it sure feels vibe coded. Sucks if you work for a company trying to use GitHub for real work, hey.
But GitHub has competitors. The largest has long been GitLab, founded in 2011.
So GitLab has seized the day! And they’ve announced they’re going to shoot themselves in the foot too. With AI!
Yesterday, CEO Bill Staples posted “GitLab Act 2”: [GitLab]
The agentic era affords GitLab the largest opportunity in our history as a company, and we’re making the structural and strategic decisions to meet it.
You can translate that, right? He means layoffs. Announcements like this say “AI” to mean layoffs. You always go into your “largest opportunity” with less people, right.
Staples’ AI excuse for the layoffs is full agentic orchestration with all the salad. His full pitch sounds like Gas Town for Enterprise and should be about as efficient and effective:
Software will be built by machines, directed by people. AI is the substrate on which future software gets built. Agents will plan, code, review, deploy, and repair. Humans still own the judgment that matters most: architecture, deep understanding of the customer problem, the tradeoffs that require taste.
Of course, they won’t. The human in the loop rapidly acclimatises to management demands for 10× productivity by turning into a “looks good to me” machine and accepting anything that passes CI. The management get precisely what they asked for. The human fantasises about getting a physical labouring job.
I don’t believe GitLab is going to do any of the agentic guff in this announcement — it’s just C-level fever dreams. They will do the layoffs, though.
GitLab’s stock price was $52 a year ago and closed at $25.64 yesterday — so it dropped by over half in a year. It closed at $23.08 today — a 9% drop in one day. So the market does not love this clear desperation AI layoff either. And we already know that layoffs don’t boost your share price, they send it down.
This was a very weird and dumb announcement from GitLab, especially when this was the perfect moment to advertise “we’re the one that stays up and works” and tune up their migration tools. But they have CEO brain, so let’s try saying “AI”!
So what do we move to instead? Codeberg is run by a nonprofit and a lot of open source projects are going there.
Business users are mostly holding on and hoping GitHub gets it together. There’s also self-hosting a code repository with Forgejo, the open source software that Codeberg maintains and runs on.
But sometimes, services turn to vibe trash, and you have to rebuild from scratch yourself. You’ll get a lot of practice when the AI bubble’s finished popping.
Fiber-optic cable has become a staple of drone war. From Ukraine to the Sahel, combatants are fielding quadcopters piloted via kilometer-long lengths of cable that allows operators to control them across vast distances while insulating the drone from being knocked from the sky. This technique was once a cheap way for militaries to beat their opponents' electronic warfare, but demand for cable from data centers and war is raising the cost of every flight.
War is a cat and mouse game. One side deploys a devastating tactic and the other side figures out a way to defeat it. When small and cheap quadcopter drones began to dominate the skies, first by Islamic State and then in Russia’s war on Ukraine, fighters quickly learned it was easier to knock them out of the sky with electronic warfare than it was to shoot them down.
Then, in 2023, Russia began to deploy FPV drones controlled via lengths of fiber-optic cable. The cable sits spooled in a tube below the drone that unwinds as it flies. The fiber-optic cable provides a fast and clear connection between a drone and its operator and no signal is flying through the air which makes it immune to jamming.
Ukraine took heavy vehicle losses when Moscow began using fiber-optic drones but Kyiv quickly adopted the tactic and now wheat fields in the country are covered in discarded cable. Three years ago, this was a cheap and effective means of slipping past enemy defenses. In 2026 it’s not nearly as cost effective.
“Fiber-optics is still happening at the battlefield, although not as much as it used to be. It's extremely pricey now. We used to buy 50km spool for $300, now it's easily $2500. Just so you know,” Dimko Zhluktenko, a Ukrainian soldier, said in a post on X on May 10.
The price of fiber-optic cable has been steadily rising since about 2023 and has almost doubled in just the past few months. In January, Shanghai based fiber-optic company Sun Telecom declared there would be a “fiber famine” in 2026. Last year, a kilometer of its G.652D fiber cable cost $2.20. By December of 2025 the same length of cable cost $3. A month later, Sun Telecom had increased the price again to $4.1.
One of the big market shifts driving up the cost of fiber is an increased demand for data centers as companies rush to build out the compute infrastructure they believe they’ll need for AI. “Almost every phone call I get from my customers is trying to see, how do we get them more? I think next year the hyperscalers will be our biggest customers,” Wendell Weeks, the CEO of fiber-optic cable manufacturer Corning, told CNBC after his company signed a deal with Meta for $6 billion in cable.
In a January LinkedIn post, North Carolina telecom company Brightspeed warned of “fiber-supply shortages.” Two other American ISPs told trade publication Broadband Breakfast said they’d seen orders for fiber unexpectedly cancelled. “We have heard concerns in recent weeks of timeframes slipping, and concerns about the ability to obtain supplies at all, as circumstances change,” Mike Romano, the CEO of NTCA, a rural broadband tradegroup, told Broadband Breakfast.
Data center driven demand is only part of the story. Wars in Ukraine, Iran, and the Sahel region of Africa are hungry for fiber-optic cable and manufacturers can barely keep up. Combined, Russia and Ukraine consume 50-60 million kilometers of fiber-optic cable every year, according to Kyiv Post. Most of this comes from China because both countries lack the domestic manufacturing base to produce that much cable. The demand has caused the price of a kilometer of Chinese fiber-optic to go from $2.33 in 2025 to $5.83 in 2026.
The core component of fiber-optic cables is a long piece of flexible and manufactured glass or plastic called an optical fiber. The delicate strands are about the width of a human hair. Ukraine doesn’t manufacture optical fibers. Russia had one factory in the city of Saransk but Ukraine destroyed it with drones in the spring of 2025. Now both countries rely on China to keep drones in the air. Exports on fiber-optic cable to Russia spiked after Ukraine destroyed the factory, hitting a height of 717.5 million meters in November of 2025.
“Ukraine has recently expanded its use of Starlink communications for attack drones, which are impractical for Russia to jam. The cost of a Starlink antenna—which is expended in an attack—is now lower than the cost of the longest-range FPV fiber-optic spools,” Roy Gardiner, an OSINT analyst at Defense Tech for Ukrainetold 404 Media. “The drive toward the development and deploying at least partial autonomous control for drones to defeat electronic warfare jamming will accelerate as fiber optic FPVs become less available.”
During war humans become great innovators. The game of cat and mouse continues and fighters are developing strategies to combat fiber-optic drones. In September of 2025, Russian and Ukrainian military bloggers began to report a new technique for countering the wire driven drones: a 150-meter-long fence made of spinning barbed wire. The theory is that the fiber-optic cable, dragged along the ground, will get caught in the fence and severed.
Despite rising costs and the dangers posed by barbed wire, the drones keep flying. In March, Iran used fiber-optic controlled drones to strike American targets in the gulf, including the destruction of a Black Hawk helicopter parked in Iraq. The known fiber-optic FPV drones top out at about 50 kilometers of cable, a distance that will clear the Strait of Hormuz at its narrowest point.
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Und das ganz zu Recht auch. 
