u/DirtLight134710

This mofo said many times 'I'm a paraplegic!' and they pulled him out the car, cuffed him on his face, then dragged him to the police car to slide him in. Did you notice his pants were at his ankles? That's how much they dragged him. Do better guys. This is f*cked up. If he was pretending, I would have been fooled.

Did the cop tell him to call the supervisor on the phone he's recording with because he knows the video will stop recording 😬

Why do millipedes live on top of their food? 🐛

Our Florida Ivory Millipedes’ habitat is made of bioactive substrate, which also doubles as their typical meal! While they also receive fruits and veggies a couple times a week, their main source of nutrition is primarily made up of the substrate, which consists of leaf, litter, rotting wood, and decaying plants. They also like to make the most of their meal, using it as a tunnel system and a place to lay their eggs.

Source: [https://www.youtube.com/watch?v=ep2Vgo2MoV8\](https://www.youtube.com/watch?v=ep2Vgo2MoV8)

JACKSONVILLE BEACH, Fla. — A federal civil rights lawsuit filed June 10, 2026, exposes a critical dependency on automated facial recognition software within Florida law enforcement networks, raising urgent questions about algorithmic policing.The complaint, brought by 52-year-old Robert Dillon with backing from the ACLU of Florida, details how a computer-generated statistical probability translated directly into a real-world felony arrest warrant without independent police corroboration.

The underlying incident began in August 2024 when Jacksonville Beach detectives investigated an attempted child luring at a local McDonald’s restaurant. Lacking immediate leads, investigators submitted low-resolution, off-axis surveillance footage to the Face Analysis Comparison and Examination System (FACES), a statewide biometric network maintained by the Pinellas County Sheriff’s Office.

The proprietary algorithm generated a 93 percent match score identifying Dillon, a Fort Myers resident living more than 300 miles away from the crime scene.According to court filings, the high-percentage automated match created an immediate confirmation bias that overrode standard investigative protocols. When a restaurant employee selected Dillon from a physical photo lineup, the witness explicitly noted that the actual suspect was a well-known "regular" at the store. Investigators bypassed the contradiction, ignoring the fact that Dillon had never been to the municipality.

Detectives also possessed Automated License Plate Reader logs showing Dillon's vehicles were never in the county, yet they left this exculpatory evidence out of the arrest warrant affidavit. Dillon subsequently spent days in maximum-security detention, pledged his truck title to secure a bond, and spent more than two months facing prosecution. The State Attorney's Office ultimately dropped all charges after verifying Dillon's workplace electronic logs and text messages placed him across the state during the offense.

The defense strategy presented by the Pinellas County Sheriff’s Office exposes a severe legal loophole in automated justice. The agency publicly maintains that the FACES network serves strictly as an investigative lead generator rather than a declaration of probable cause. By using this legal framework, software providers and database administrators shift the entire burden of liability onto local arresting officers, who routinely lack the technical literacy required to challenge a high-percentage statistical match.The systemic risks of this operating model extend far beyond a single faulty arrest. Because proprietary algorithms are guarded by corporate trade secret laws, defense attorneys are legally barred from auditing the underlying source code. Defendants are left entirely unable to challenge how a 93 percent match was mathematically calculated.

While these systems are benchmarked using high-resolution, perfectly lit booking photographs, real-world inputs rely on heavily compressed, low-frame-rate convenience store footage, which causes the software margin of error to spike exponentially. Independent evaluations by the National Institute of Standards and Technology confirm these systems suffer from severe demographic disparities, yielding much higher false-positive rates for minorities, women, and older individuals.

Dillon’s detention represents the 15th publicly documented wrongful arrest in the United States tied directly to facial recognition failure. Because police forces face no uniform requirement to disclose when an algorithm initiates a criminal inquiry, the true baseline of algorithmic detentions remains completely unquantified.

At least 7 different pairs found now :))

Edit fucked up, raspberry not English native lol