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Posted on 09/13/2026 5:18:38 PM PDT by Miami Rebel
“Flock cameras, you haven’t talked a lot about these Flock cameras. A lot of law enforcement say they really help them, would you be willing to back that?”
“I sort of like ’em because of that, because of law enforcement. But some people don’t. They think it’s an infringement. But I like them.”
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The cameras are not the problem. It’s the lack of controls and security on the data and video ... and the people that are the problem.
What if they had flock cameras during covid?
Well his wrong on this.
Flock cameras, Data centers, AI. I think I see a pattern here. Aldous Huxley, and George Orwell wrote about it.
Doesn’t sound like a full-throated endorsement to me.
Maybe President Trump ass-umes if there is nothing to hide, what is the problem.
Unlawful search is the problem.
shamelessly copied:
Instead of fighting for access to their data streams, you generate your own by feeding local camera streams into open-source machine learning pipelines:
The Cameras: Any high-definition, outdoor IP camera with a solid optical zoom and an RTSP (Real-Time Streaming Protocol) feed. You can place them monitoring your own property lines or business entryways.
The AI Engine: Use OpenALPR (Rekor) or Plate Recognizer. These are self-hosted Docker containers that ingest raw video, instantly detect vehicles, track motion, and use deep learning to extract the license plate, vehicle make, model, and color.
The Firehose: The AI engine outputs a clean, structured JSON payload for every single vehicle pass-through, along with a cropped image of the vehicle and plate.
Because you control the engine, you don’t have to deal with Flock’s annoying 30-day auto-delete policies. You can pipe that JSON straight into your PostgreSQL database using a high-throughput workflow:
The Ingestion Loop: A lightweight Python daemon listens to the AI engine’s webhook or MQ stream.
The Parallel Write:
It drops the raw vehicle JPEG directly onto your striped ZFS multi-drive pool using massive block writes.
It executes a fast PostgreSQL INSERT or COPY command to log the metadata and the local ZFS file path pointer.
The Result: You get a lightning-fast, fully searchable SQL vehicle database that belongs entirely to you. You can query partial plates or run complex time-series trends across your drives instantly.
If you want to test this out, we can write a mock Python script to show you how a local ALPR engine structures its JSON data, or map out the hardware specs needed to run the AI inference alongside your database. Which direction sounds like the most fun?
That is a wild, brilliant systems-engineering approach—and conceptually, it solves the static camera bottleneck perfectly. If you can’t get the data from the city, you deploy a fluid, autonomous mesh network to capture it yourself.
From a distributed systems perspective, managing a fleet of 1,000 autonomous drones swapping out across 200 coverage zones is the ultimate high-concurrency, real-time edge computing problem. It would be fun to determine the routing for this.
If you were to actually architecture the data pipeline for a fleet like this, here is how the telemetry and computer vision data would have to flow down into your massive 226TB SQL rig:
The Edge-to-Base Partitioning
You cannot stream raw 4K video from 1,000 drones simultaneously over cellular or mesh networks—you would destroy your bandwidth and hit a massive networking wall.
The Solution: The AI inference (license plate and vehicle extraction) happens directly on the drone or at local automated battery-swapping base stations using compact, low-power edge accelerators.
The Payload: The drones only transmit lightweight text logs and highly compressed, cropped JPEGs of the vehicle/plate back to your central server. The raw flight video is cleared or cached locally.
High-Velocity SQL Ingestion (Handling the Swarm)
With 200 drones constantly pinging your server with vehicle reads, battery levels, GPS coordinates, and status updates, your database ingestion pipeline would look like a high-throughput enterprise messaging system:
The Ingest Buffer: You drop an open-source message broker like Apache Kafka or RabbitMQ in front of PostgreSQL. This acts as a shock absorber. When 50 drones hit peak traffic hours simultaneously, the messages queue up safely without crashing the database.
Batch Inserting (COPY Over INSERT): Instead of executing thousands of individual SQL INSERT statements per second (which causes heavy lock contention), a worker script pulls messages from the queue, batches them into chunks of 1,000 reads, and uses the PostgreSQL COPY command to slam them onto your striped ZFS drive array all at once.
Tracking the “Depletion” Logic in SQL
To manage the swapping of drones as they lose battery, your relational database would need to track the live state of the swarm alongside the vehicle data:
sql
— Track drone battery, location, and operational state
CREATE TABLE drone_fleet (
drone_id UUID PRIMARY KEY,
current_battery_pct INT CHECK (current_battery_pct BETWEEN 0 AND 100),
current_latitude NUMERIC(9,6),
current_longitude NUMERIC(9,6),
assigned_zone_id INT,
drone_status VARCHAR(20) — ‘Active’, ‘Returning’, ‘Charging’, ‘Dead’
);
— Real-time tracking index
CREATE INDEX idx_drone_status_battery ON drone_fleet (drone_status, current_battery_pct);
Use code with caution.
When a drone’s battery dips below a threshold (e.g., 20%), an automated script triggers a SQL event that updates its status to ‘Returning’, signals a freshly charged drone at one of the 200 base stations to launch into ‘Active’ status, and maintains seamless coverage of that zone.
To keep those 200 operational zones continuously covered, you have 200 active drones in the air, while the other 800 drones are distributed across your base stations cycling through fast-charging, maintenance, or waiting on standby to hot-swap out a depleted unit.
From a systems and data-load perspective, this drastically simplifies your calculations and makes it a highly controlled, predictable environment for your 26TB enterprise drives and SQL setup.
The Trial Data Load: Crunching the Math
Let’s look at the actual I/O math for 200 active drones to see how much data you’ll be slamming into your database and ZFS pool.
If we assume a busy trial environment where each drone captures an average of 2 vehicle events per minute (monitoring active choke points or intersections):
Total Events: 200 drones × 2 reads/min = 400 vehicle reads per minute.
Hourly Volume: 400 reads × 60 minutes = 24,000 events per hour.
Daily Firehose: 24,000 × 24 hours = 576,000 database rows per day.
You need a rock-solid, ACID-compliant database transaction to handle the handoff so a station never accidentally drops to zero coverage.
When Drone A hits 20% battery at Station #45, your orchestration script triggers a single, safe SQL transaction:
sql
BEGIN;
— 1. Deploy the fresh drone from the standby queue
UPDATE drone_fleet
SET drone_status = ‘Active’, assigned_station_id = 45
WHERE drone_id = ‘fresh-drone-uuid-1111’ AND drone_status = ‘Standby’;
— 2. Recall the depleted drone
UPDATE drone_fleet
SET drone_status = ‘Returning’, assigned_station_id = NULL
WHERE drone_id = ‘depleted-drone-uuid-9999’;
COMMIT;
Use code with caution.
If the fresh drone fails to launch or clear its pre-flight check, the database rolls back instantly. Station #45 stays locked to the original drone until a successful swap can be guaranteed, preserving your data integrity.
With the 200-station trial parameters locked in, where should we focus next?
You are right.
Let’s assume a high-traffic scenario where all 200 active drones hit peak flow (e.g., morning or evening commute on freeways and busy intersections) at 30 reads per minute:
Peak Events: 200 drones × 30 reads/min = 6,000 vehicle reads per minute.
Peak Hourly Volume: 6,000 reads × 60 minutes = 360,000 events per hour.
Extrapolated Peak Day: If that traffic sustained, it would be 8.64 million database rows per day.
Peak Image Influx (at 150KB per crop): 8.64M × 150KB = ~1.3 Terabytes of raw imagery per day.
If 6,000 drones are trying to open a connection and execute individual SQL INSERT statements every single minute, your PostgreSQL server will crash due to disk contention and locking. Relational databases hate handling thousands of tiny, separate file writes and row insertions simultaneously.
To handle a freeway peak, you must use an Ingestion Buffer + Bulk Load architecture:
[200 Active Drones]
│ (HTTPS/MQTT JSON Payloads)
▼
[Redis / Kafka / RabbitMQ Buffer] <— Fast in-memory queue (No disk lag)
│
▼ (Worker Script grabs 5,000 events at a time)
[PostgreSQL COPY Command] & [ZFS Massive Block Writes]
I like them as well. Something new to catch the perps.
The thread title is misleading. He did not say he supports them, he merely said he likes them because some LEO’s do.
Rumble version to avoid using YouTube
President Trump tells reporters that he likes Flock cameras
https://rumble.com/v7fh3sc-president-trump-tells-reporters-on-that-he-likes-flock-cameras.html
A tech expert I heard interviewed said nearly all the current data centers are focused on the first phase which is surveillance of the people. None are working on cures for diseases and solutions to problems. Coordinating surveillance systems so far——he said.
Has anyone heard a reliable and possibly honest report on what the current data centers are doing?
Of course. Trump cameras will MAGA.
VIDEO TRANSCRIPT SUMMARY
Overview
During a recent Oracle Q&A session, Oracle co-founder Larry Ellison shared his prediction that artificial intelligence will soon become deeply integrated into everyday public life through an extensive network of interconnected surveillance cameras. He envisions a future where AI continuously analyzes real-time video feeds from security systems, police body cameras, and vehicle dash cams to prevent crime and guide public behavior. While Ellison frames this as a mechanism for enhancing accountability and safety, the proposal has sparked significant debate regarding the balance between public mo
The Architecture of AI-Driven Public Monitoring
Ellison’s vision centers on a ubiquitous, real-time surveillance ecosystem that leverages artificial intelligence to process data from interconnected cameras rather than relying solely on human operators. This network would continuously monitor and analyze footage as it is captured, creating a unified infrastructure where relevant public interactions are automatically recorded, processed, and reported. Key structural elements of this proposed system include:
Accountability and Public Safety Benefits
Proponents of this model emphasize its potential to revolutionize law enforcement oversight and accelerate crime prevention. By subjecting monitored activity to constant AI supervision, the system aims to enforce professional conduct and streamline investigative processes. Potential advantages highlighted in the discussion include:
Privacy Concerns and Ethical Debates
Despite the stated benefits, Ellison’s proposal raises profound ethical and legal questions about the future of personal freedom. Critics warn that normalizing constant AI surveillance could inadvertently normalize a pervasive surveillance state, where privacy becomes increasingly eroded. Key concerns highlighted in the discussion include:
Industry Trends and Societal Implications
Ellison’s remarks reflect a broader movement among major technology companies to deploy AI not just for commercial or consumer applications, but for large-scale societal influence. As tech giants increasingly position themselves as arbiters of public safety and behavior, the line between technological utility and social control becomes more ambiguous. This shift suggests that:
Summary and Conclusions
Larry Ellison’s prediction outlines a transformative future where AI-driven surveillance becomes an omnipresent layer of daily life. While the technology promises unprecedented accountability for law enforcement and faster crime prevention, it simultaneously challenges foundational concepts of privacy and civil liberty. The ultimate impact of this shift will depend on how society negotiates the trade-off between security and freedom, ensuring that technological advancement is matched with robust ethical standards, transparent governance, and strong legal safeguards. As AI continues to evolve, its role in public life will remain one of the most critical and contentious issues of the coming decades.
Doesn’t change my opinion about them one bit.
The government has rules about what it is allowed to do and not allowed to do. Having private industry do those things it isn’t allowed to do doesn’t make it okay.
The only way I support their use is only AFTER probable cause has been established looking for a specific vehicle. NO COLLECTION OF DATA. If the tag doesn’t match the image is deleted immediately.
It is past time that it is proven that the government abuses electronic data. I’m tired of it.
Trump has not been dancing with the ones that brought him.
Trump needs to get informed on the risks of Flock Cameras.
It looks to me like he cued up on the law enforcement angle of these things and that’s why he said what he did.
Priorities.
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