This table provides chatbot prompts for each of the SQL queries in the benchmark: https://github.com/ravibeta/ezbenchmark

ezbenchmark SQL Queries and AI Prompts side-by-side comparisons.

| SQL Query File | Natural Language Prompt |

|----------------------------|-------------------------------------------------------------------------------------------------------------------------------------------|

| q1_object_counts.sql | For each drone mission, tell me how many objects of each type were detected, and also give me the average number of detections per mission for comparison. |

| q2_mission_duration.sql | Identify the drone missions that lasted the longest and covered the largest geographic area, and show me their mission IDs and summary statistics. |

| q3_payload_accuracy.sql | Compare how different payload configurations (like thermal camera vs RGB camera) performed in terms of detection accuracy, and rank them by effectiveness. |

| q4_terrain_breakdown.sql | Break down object detections by terrain type — forest, urban, water — and highlight which terrain produced the highest detection counts during missions. |

| q5_weather_reliability.sql | Show me how mission success rates vary depending on weather conditions like clear skies, rain, or high wind. |

| q6_time_of_day.sql | Analyze detection counts across different time windows — morning, afternoon, evening — and tell me which time of day yields the most reliable detections. |

| q7_external_layers.sql | Correlate drone mission detections with external map layers, like vegetation density or building footprints, and show me where detections align most strongly. |

| q8_cost_efficiency.sql | Estimate the compute and storage costs for each drone mission, and compare them to the detection yield so I can see cost‑effectiveness. |

| q9_latency_comparison.sql | Compare the latency of object detection when run on edge devices versus cloud servers, and tell me which pipeline is faster and by how much. |

| q10_top_missions.sql | Rank all drone missions by detection quality and throughput, and list the top missions that achieved the highest benchmarks. |

| q11_anomaly_patterns.sql | Identify drone missions that experienced repeated anomalies, such as dropped frames or failed detections, and summarize the failure patterns. |

| q12_synthetic_vs_real.sql | Compare the synthetic workload generator outputs with real mission telemetry, and tell me how closely they match in terms of detection counts and mission duration. |

References:

Previous article: https://1drv.ms/w/c/d609fb70e39b65c8/IQDqrulu7lAcRrfqGYWxXmzpAQzvynSprwP0lbMQqGEaD1w?e=8RC7vg

 Sample queries to test Drone Video Sensing Pipeline:

1. Bounding box: (Metrics captured: # tokens used, AI quality - scale of 1 to 5)

Prompt: You are a vision-language assistant. Given an image and a question about locating or describing an object, give its bounding box. Return only the bounding box coordinates in the format: <bbox>[[x, y, w, h],[x, y, w, h]...]</bbox> with the point of reference as the bottom left corner of the image. Do not include extra text or reasoning or ask the user for more information.

Queries:

a. Give the bounding box for the green street crossing sign for bicycles at a street intersection.

b. Give the bounding box for the only red car in the image.

c. Give the bounding box for a building with circular roof structure.

d. Give the bounding box for a parking lot with available space.

e. Give the bounding box for a red car in this sequence of images.

f. Give the bounding box for a roof with solar panels in this image.

2. Color: (Metrics captured: # tokens used, AI quality - scale of 1 to 5)

Prompt: You are a vision-language assistant. Given a scene as an image and a multiple-choice question about an object, select the best answer. Do not include extra text or reasoning or ask the user for more information.

Queries:

a. What color is the largest paved motor road in the given image? A. dark brown, B. tan. C. dark gray, D. black

b. What color is the car in the center of the image? A. Red B. White C. Black D. Green

c. What color is the building dividing the street? A. Blue. B. Teal. C. Patina. D. Green.

d. What color is the most common among the cars in the top storey of this parking lot? A. Red B. White. C. Black D. Green

e. What color is the dedicated lane for bicycles in this image? A. Blue. B. Black. C. White. D. Green

f. What color are the windows of this multi-storeyed building in the bottom left of this image? A. Black B. Blue C. Brown D. Green

3. Counting: (Metrics captured: # tokens used, AI quality - scale of 1 to 5)

Prompt: You are a vision-language assistant. Given a scene as an image and an object, count the number of objects in the scenes. Return only the count in this format: {number}. Do not include extra text or reasoning or ask the user for more information.

Queries:

a. How many cars are there in this image?

b. How many buildings with circular roof structure?

c. How many available parking spaces are there in the parking lot on the right side of the image?

d. How many trees are there in this image?

e. How many cars are crossing the street intersections in this image?

f. How many pedestrians are in this image?

4. Distance: (Metrics captured: # tokens used, AI quality - scale of 1 to 5)

Prompt: You are a vision-language assistant. Given a scene as an image and an object, count the number of objects in the scenes. Return only the count in this format: {number}. Do not include extra text or reasoning or ask the user for more information.

Queries:

a. Which is farthest from me: tree, building, car, sedan, parking lot, street crossing?

b. Which is closest to me: tree, building, car, sedan, parking lot, street crossing?

c. which is closer to the building with a circular roof structure: parking lot, street splitting?

d. Which is closer to me: river, street intersection, parking lot, trees?

e. Which is closer to me: building with red roof or building with green roof?

f. Which is bigger: the park with trees or the building next to it?

5. Free space: (Metrics captured: # tokens used, AI quality - scale of 1 to 5)

Prompt: You are a vision-language assistant. Given a scene as an image and the location near an object in the scene, indicate a free space region as a set of (x,y) pixel co-ordinates with the bottom left of the scene as the point of reference. Return this list of co-ordinates. Do not include extra text or reasoning or ask the user for more information.

Queries:

a. Find the free space on the roofs of buildings that do not have any structures.

b. Find the free space for parking a car in a parking lot.

c. Find the free space for parking a sedate along the street curb.

d. Find the free space for parking a large semitrailer.

e. Find the free space in the lot occupied by a building with a hollow circular structure protruding from the roof.

f. Find the free space along the direction of traffic at the street split by a building with circular dome.

6. Function:(Metrics captured: # tokens used, AI quality - scale of 1 to 5)

Prompt: You are a vision-language assistant. Given a scene as an image and the location near an object in the scene or a set of (x,y) pixel co-ordinates with the bottom left of the scene as the point of reference, indicate its function or purpose. Do not include reasoning or ask the user for more information.

Queries:

a. What is the ground between buildings on the left side of the road useful for?

b. What is the purpose of the green lane between the road and curb?

c. What is the purpose of the large white objects parked by the side of the road?

d. What is the nearest shelter for a pedestrian crossing the street when it rains?

e. What is the object in the scene that indicates the proximity of a social and commercial place such as a market or mall?

f. What are some of the co-ordinates in the scene where traffic can arrive at a body of water?

7. Height:(Metrics captured: # tokens used, AI quality - scale of 1 to 5)

Prompt: You are a vision-language assistant. Given a scene as an image and the description of an objects in the scene or a set of (x,y) pixel co-ordinates with the bottom left of the scene as the point of reference, determine the object with the relative elevation that matches the query. Do not include reasoning or ask the user for more information.

Queries:

a. which is higher between the river and the car on the road adjacent to the river?

b. which is higher between the building with the solar panels on the roof or the building to the leftmost of it?

c. which is higher between the building roof and the tree tops next to it?

d. which is lower between the object at the bottom right of the scene or the park next to it?

e. which is lower between the following object categories: river, street, vehicles, buildings and trees?

f. which is higher between the buildings on the left of the scene and the buildings on the right of the scene?

8. Landing: You are a UAV (drone) landing safety advisor analyzing a low-altitude aerial image. Provide a comprehensive landing safety assessment in the following JSON format with key landing_feasibility as one of SAFE or CAUTION or UNSAFE, a numerical confidence score between 1 and 100, a list of hazards with each hazard having level such as low, medium or high, location such as one of four quadrants of the scene and reason for the hazard. You may also include recommendations in the json. Do not ask the user for more information.

Queries:

a. Is it safe to land in the park between the buildings?

b. Is it safe to land on the roof top of the largest building in the image?

c. Is it safe to land on the street next to river?

d. Is it safe to land in the parking lot?

e. Is it safe to land in the park by the side of the river?

f. Is it safe to land on the street intersection?

9. Captions: You are an aerial drone image analyst. Describe the scene provided and elaborate on the objects detected and their spatial relationships. If there are multiple images of the same scene, describe the temporal changes to the scene.

Queries:

a. Image showing a building with a circular roof structure and a split in the road

b. Image showing an empty road beside a river

c. Image showing a large parking lot between an enclave of buildings

d. Images following the traffic around a bend of the city streets

e. Images from a flyover a park with trees between buildings

f. Images showing buildings of different elevations in a small block.

10. Pointing: You are an aerial drone image analyst. Given the scene and some objects, point to areas of interest with a list of bounding box co-ordinates pertaining to the query using the bottom-left of the image as reference and in the format (x,y,w,h). Do not ask the user for more information.

Queries:

a. Locate all the roofs of buildings that are occupied by stationary structures.

b. Locate all the multi-storey buildings that are greater than three storeys high.

c. Locate a parking garage and the available spaces on it.

d. Locate the empty spots for cars to park between buildings but not on streets.

e. Locate safe play areas for children where there is little or no traffic.

f. Locate the highest point in the scene that is safe to land.

11: Uncommon: You are an aerial drone image analyst. Given the scene and some objects, identify the object accurately at the location given in the query using the bottom-left of the image as reference and in the format (x,y) even if the object is uncommon. Do not ask the user for more information.

Queries:

a. Identify the object at location (132,235)

b. Identify the object at location (0,15)

c. Identify the object at location (450,80)

d. Identify the object at location (750,1025)

e. Identify the object at location (20,545)

f. Identify the object at location (225,235)

12. Spatial: (Metrics captured: # tokens used, AI quality - scale of 1 to 5)

Prompt: You are an aerial drone image analyst. Given a scene as an image and a multiple-choice question about spatial relationships, select the best answer. Do not include extra text or reasoning or ask the user for more information.

Queries:

a. What direction is the parking garage in from the buildings on the right side of the road assuming the top of the image is North? A. North, B. East. C. South, D. West

b. Where is the park from the street? A. Left B. Right C. Above D. Below

c. Where is the multi-storey building in the image? A. top-Left B. top-Right C. bottom-left D. bottom-right

d. Which direction would rainfall flow towards when falling on the dome of the building splitting the street? A. Left B. Right C. Above D. Below

e. Where is the sun given the shadows of the trees in the park? A. Left B. Right C. Above D. Below

f. Where is the object whose shadow is seen in the top-left of the scene? A. above or below the scene B. inside the scene C. Left or right of the scene.

#codingexercise: CodingExercise-12-14-2025.docx

 This is a summary of the book written by “The Scaling Value Playbook: A practical guide for creating innovation networks for impact and growth (De Gruyter Business Playbooks)” written by John Bessant and Ian Gray and published by De Gruyter in 2024. Innovators must build a strategic network to understand how value is created and scaled. The authors shows us how to map out and navigate relationships with consumers, competitors and others who can help or hinder our innovation’s adoption. Infographics, sidebars and exercises in this book help to drive home the message.

The authors begin by dispelling the myth of the “overnight success.” Icons like Steve Jobs, Richard Branson, and James Dyson have all remarked that what appears sudden is, in truth, the result of years of hard work. Consider the story of Otto Rohwedder, who invented the bread-slicing machine in the early 1900s. Rohwedder faced financial setbacks, a devastating factory fire, and skepticism from potential users. Only after years of persistence did his invention find commercial success, leading to a dramatic surge in bread sales and, eventually, transforming the way Americans consumed bread.

This anecdote illustrates a central theme of the book: an idea alone is rarely enough to scale. Instead, innovators must build systems and strategies that support their vision. The process of testing, refining, and launching new ideas is just the beginning. The real challenge lies in the “messy middle”—the uncertain, often perilous stages where many innovations falter. As Professor Rosabeth Moss Kanter notes, long-term success comes to those who approach scaling strategically, leveraging networks of mentors, partners, and diverse teams to navigate obstacles and seize opportunities.

Evidence is another cornerstone of successful scaling. Moving from a handful of early adopters to mainstream acceptance requires proof—both objective and emotional. Some stakeholders, such as those in the pharmaceutical industry, demand rigorous data before embracing a new product. Others are swayed by compelling stories and endorsements. The authors advise innovators to assess what kind of evidence their audience needs and to gather testimonials from experts, customers, and influencers. When people see that others trust and use an innovation, they are more likely to adopt it themselves.

The story of Earl Tupper and Brownie Wise exemplifies this principle. Tupper’s invention of airtight plastic containers initially struggled in the marketplace, despite positive media coverage. It was Wise’s innovative sales strategy—hosting home parties where women could see and use the product—that provided the social proof needed to drive adoption. Today, Tupperware is a global brand, a testament to the power of evidence and network-driven growth.

Scaling innovation also requires a clear vision and an understanding of the barriers ahead. The authors introduce the concept of the “scale panorama,” a three-part framework for planning growth. Innovators must articulate their long-term vision, define the value they aim to create, and specify tangible outcomes such as market share or the number of people served. They must also identify their target audience—whether regional, national, or global—and map out the route to scale, including critical success factors and potential obstacles. Setting short-term goals, achievable within one to three years, helps maintain momentum and focus.

No innovation scales in isolation. Building a network of partnerships is essential. The authors draw parallels to historic achievements, like the ascent of Mount Everest, which succeeded only through the coordinated efforts of a dedicated team. Scaling innovation similarly depends on cultivating relationships with a diverse ecosystem of contributors.

To help innovators understand their networks, the book introduces the “Nine Cs”—distinct roles that people and organizations play:

• Creators generate new value.

• Consumers use products or services.

• Captors (investors) capture value.

• Channels deliver products to consumers.

• Conveyors add value through components or expertise.

• Coordinators bring together various elements.

• Cartographers (regulators, policymakers) shape the environment.

• Competitors vie for resources and customers.

• Complementors provide supporting products or services.

Entities may play multiple roles, and these roles can shift over time. For example, Lebanon’s Ministry of Education acted as a regulator, consumer, and conveyor in its partnership with the education innovator QTL.

The book encourages innovators to regularly assess their relationships within this network, rating their confidence in each area and identifying gaps. Companies like Nike have evolved from creators to coordinators, relying on partners to expand their reach. Liverpool Football Club’s fans serve as both complementors and unofficial cartographers, influencing the club’s decisions and promoting its brand.

Scaling requires organizational agility. Innovators must choose models—franchising, licensing, open source—that suit their goals and be prepared to adapt. Impact Hub’s shift from a centralized to a distributed support model enabled rapid expansion. Limiting bureaucracy and empowering staff, as Netflix does, fosters flexibility and responsiveness. Netflix’s evolution from distributor to creator, conveyor, complementor, and cartographer demonstrates the dynamic nature of scaling in practice.

 While on-board vision LLMs gain incredible capability by training on relevant datasets, the cost-effectiveness of the agentic retrieval system must be comprehensive to cover all commercial analytics query such as the following:

1. Agriculture

Drone data in agriculture is primarily used for precision farming and crop management.

• "Generate multispectral imagery of a specific field section to detect early signs of crop stress and disease." 1

• "Process RGB optical images to create a high-resolution orthomosaic map for identifying weed locations and optimizing pesticide application."2

• "Analyze a series of time-lapsed drone flights to track crop growth and development over the season for yield prediction." 3 4

2. Construction and Infrastructure

Drones in this sector are used for surveying, monitoring progress, and conducting safety inspections of hard-to-reach areas.

• "Capture high-resolution aerial imagery of a construction site to create a 3D model for progress monitoring and comparison with the original blueprint."

• "Perform a thermal imaging scan of a building's roof to identify heat anomalies or insulation defects."

• "Conduct an automated visual inspection of a bridge using an optimized flight path to detect structural defects like cracks or corrosion."

3. Mining

Mining operations use drones for surveying, volume calculation, and site safety.

• "Process drone data to calculate the exact volume of stockpiles (e.g., coal, ore) for inventory management."

• "Generate a high-precision topographic map of the mine pit using LiDAR data to assess terrain changes and plan future excavation."

• "Capture real-time video footage of blasting zones to ensure the area is clear of personnel before detonation and to assess post-blast results."

4. Energy

The energy sector benefits from drones for the inspection and maintenance of critical infrastructure like solar farms, wind turbines, and power lines.

• "Conduct a thermal inspection of a solar panel array to identify non-functioning or overheating panels that require maintenance."

• "Capture high-resolution images and video of wind turbine blades to check for micro-cracks or erosion without requiring manual climbing inspections."

• "Perform an environmental survey of potential renewable energy sites (e.g., for a new solar project) to map sensitive habitats and plan appropriate setbacks."

5. Environmental Monitoring

Drones are used to monitor environmental health, track changes over time, and help with conservation efforts.

• "Generate multispectral or hyperspectral data to assess the health of a forest area affected by drought or deforestation."

• "Capture aerial imagery to map the extent of an oil spill or a flood, aiding in disaster response and damage assessment."

• "Perform a survey of a coastal area to monitor shoreline erosion or coral reef health over time using detailed imagery and bathymetry maps."

6. Urban Planning

Drones provide city planners with high-resolution imagery and 3D models for informed decision-making.

• "Generate a 3D city model from drone photogrammetry for urban development visualization and impact assessment."

• "Map all rooftops within a designated area using high-resolution imagery to track construction density and identify unauthorized modifications."

• "Conduct a rapid aerial survey to create a high-precision orthomosaic map that can be integrated into existing GIS software for infrastructure planning and management."

 Drone Analytics Benchmarks

Drone video pipelines are evaluated not only by model-specific metrics (like mAP) but also by system-level performance indicators that span video quality, throughput, latency, scalability, and mission effectiveness. Research studies and commercial whitepapers highlight metrics such as frame alignment accuracy, bitrate efficiency, latency-to-decision, operator situational awareness scores, and mission coverage ratios. These provide a holistic view of drone video sensing pipelines across diverse applications (traffic monitoring, agriculture, surveillance, emergency response).

System-Level Performance Metrics for Drone Video Pipelines

Metric Category Specific Metrics Context / Source

Video Quality & Compression PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity Index), BD-Rate (bitrate savings), RGB-PSNR UAVBench video compression benchmarks show ~20% BD-Rate savings with learned codecs 1

Detection & Tracking mAP (mean Average Precision), MOTA (Multiple Object Tracking Accuracy), ID switches, IoU (Intersection over Union) UAVDT & VisDrone benchmarks; UAVBench tracking protocols

Latency & Throughput End-to-end pipeline latency (ms/frame), FPS throughput, decision latency SkyQuery platform emphasizes frame alignment speed and query execution latency 2

Situational Awareness & Coverage Mission coverage ratio (area/time), operator video quality scores, trajectory optimization efficiency ALPHONSE project metrics for drone operator training 3

Scalability & Robustness Multi-agent coordination efficiency, resilience to occlusion/motion blur, adaptability to weather/altitude UAVBench spatial intelligence benchmarks; commercial Drone Industry Insights reports

Operational Effectiveness Task completion rate, hazard detection accuracy, resource utilization (battery, bandwidth) SkyQuery case studies in traffic hazard detection and pedestrian mapping

The trend graphs that are popular in this domain include:

• Rate–Distortion Curves (BD-Rate vs. PSNR): UAVBench shows learned codecs outperform HEVC in outdoor UAV footage but underperform in indoor fish-eye scenes.

• Latency vs. Accuracy Trade-offs: SkyQuery demonstrates that faster frame alignment reduces latency but slightly lowers detection accuracy.

• Coverage vs. Time Graphs: ALPHONSE project visualizes drone trajectory optimization, showing improved coverage ratios with optimized flight paths.

• Scalability Trends: Drone Industry Insights reports highlight increasing demand for real-time video analytics at scale, with commercial pipelines trending toward edge-cloud hybrid architectures.

But with the usual caveats that:

• Domain Shift: Metrics like BD-Rate savings collapse under fish-eye distortion or night UAV footage.

• Latency Bottlenecks: Even high-mAP models fail operationally if pipeline latency exceeds ~200 ms/frame in real-time UAV monitoring.

• Operator-Centric Metrics: Whitepapers stress that situational awareness and mission coverage are as critical as technical accuracy.

• Commercial Bias: Industry reports (DroneII) emphasize market growth and scalability but may underreport technical limitations.

Existing versus Proposed Benchmark

UAVBench1 (1) comes close to fulfilling most of the above criteria but it mandates that the analytics be packaged as vision-LLMs (VLM) where as Drone-Analytics-Benchmark as proposed (2) is more flexible.

References:

1. https://www.emergentmind.com/topics/uavbench

2. https://github.com/ravibeta/ezbenchmark or https://1drv.ms/w/c/d609fb70e39b65c8/IQB7nJEDVKaPTKMMUlkpHmgPAZtX_gA7k2j3_xqzjyygTD4?e=ecVoMr

3. https://dspace.mit.edu/bitstream/handle/1721.1/143775/3486607.3486750.pdf?sequence=2

4. https://www.thinkmind.org/articles/icas_2022_1_40_20018.pdf

#codingexercise:

Codingexercise-12-11-2025.docx

 This is a summary of a book titled “Binge Times: Inside Hollywood’s furious billion-dollar battle to take down Netflix” written by Dade Hayes and Dawn Chmielewski and published by Wiliam Morrow in 2022. As Netflix moves to acquire Warner Bros. this looks at how it grew. Netflix had already positioned itself as a leader in streaming when pandemic struck. As the streaming industry boomed and brought more players including traditional media companies into a fierce competition, Netflix’s business became so mainstream that only Disney posed a significant threat.

In the mid-1990s, the seeds of a revolution in entertainment were quietly being sown. Streaming, as we know it today, had its humble beginnings in 1996, when visionaries like Jonathan Taplin launched Intertainer, a service that delivered movies to computers via the internet. Back then, the idea of watching a film online seemed almost fantastical—computers were slow, the internet was new, and streaming a movie required both technical ingenuity and a leap of faith. Yet, as high-speed internet became more widespread, the groundwork was laid for a new era in media consumption.

Amid this technological ferment, Netflix emerged—not as the streaming giant we know today, but as a mail-order DVD rental service. Subscribers paid a monthly fee to receive DVDs by mail, a model that quickly gained a devoted following. Netflix’s innovation didn’t stop there: in 1999, it introduced a system allowing customers to keep DVDs as long as they wanted, and soon after, a recommendation engine that harnessed user ratings to personalize suggestions. This data-driven approach would become a hallmark of Netflix’s strategy.

By 2007, with over half of American households enjoying broadband internet, Netflix recognized the time was ripe to move online. The company launched its first streaming service, Watch Now, which, though technically imperfect, marked a pivotal shift. The success of YouTube had already shown that audiences craved on-demand content, and Netflix was determined to deliver. Hollywood, wary of piracy after witnessing the upheaval in the music industry, hesitated, but Netflix pressed forward.

The true turning point came in 2013, when Netflix debuted its first original series, House of Cards. The company made a bold $100 million bet on 26 episodes, signaling its commitment to original content. The entire first season was released at once, inviting viewers to binge-watch—a radical departure from traditional television’s weekly release schedule. This strategy not only captivated audiences but also set a new industry standard. Netflix’s global reach meant that viewers around the world could watch the same shows simultaneously, transforming it into the first truly global television network.

As Netflix soared, traditional media companies scrambled to adapt. HBO, once a pioneer in premium cable, struggled to transition to streaming. Despite the storied history of groundbreaking shows like The Sopranos and Game of Thrones, HBO’s streaming ventures, such as HBO Now and HBO Max, were hampered by legacy contracts and late launches. Meanwhile, Apple entered the fray, leveraging its vast ecosystem of devices. By 2019, Apple’s services business, including streaming, was generating more revenue than its iconic Mac computers and iPads. Yet, Apple TV+ stumbled out of the gate, revealing the challenges tech companies face when entering the world of entertainment.

Disney, too, underwent a profound transformation. Under CEO Bob Iger, Disney recognized that the era of the traditional media conglomerate was ending. The company invested heavily in high-quality content, acquiring Marvel and Lucasfilm, and eventually launched Disney+, which rapidly amassed over 120 million subscribers in more than 50 countries. Disney’s approach was to focus on strong brands and global reach, but it still faced limitations as it remained primarily an exporter of American content.

Amazon, with its acquisition of MGM in 2021, further intensified the competition. Amazon Prime Video, initially an add-on to its retail membership, began to carve out its own identity with original films and series. The MGM deal brought a vast library of movies and iconic franchises under Amazon’s umbrella, fulfilling CEO Jeff Bezos’s ambition for a global entertainment powerhouse.

The COVID-19 pandemic in 2020 accelerated these trends. As lockdowns forced people indoors, streaming became not just a diversion but a lifeline. Netflix, already entrenched as the default streaming service, saw its influence grow even further. The global success of shows like Squid Game, which topped charts in 90 countries, underscored Netflix’s dominance, and the shift toward international, multilingual content. Between 2019 and 2021, the number of Americans streaming Netflix in languages other than English soared by 71%.

By 2022, Disney stood as the only real challenger to Netflix’s supremacy. Other competitors, like Apple TV+ and HBO Max, faced cultural and contractual hurdles. The old Hollywood model of exporting domestic titles was fading, replaced by a new paradigm of global, on-demand entertainment. Despite predictions that Netflix would be overtaken, it remained at the forefront, continually adapting and thriving in an industry defined by relentless change.

“Binge Times” thus chronicles not just the rise of Netflix, but the seismic shifts that have reshaped Hollywood and the way the world watches television. It is a story of innovation, disruption, and the fierce battles waged in pursuit of viewers’ attention in the streaming age.

#continuation of yesterday's article: https://github.com/ravibeta/ezbenchmark/ 

 TPC-H for aerial drone image analytics

This is a proposal for domain-adapted benchmark by taking the TPC-H v3 decision support queries which stress-test OLAP systems with business oriented data warehouse workloads) and reframes them for aerial drone image analytics. This would create a standardized way to evaluate drone video/image pipelines with SQL-like queries, but grounded in geospatial and vision tasks.

Step 1. Schema adaptation:

TPC-H schema has tables like CUSTOMER, ORDERS, LINEITEM. For drone imagery, we’d define analogous tables:

IMAGE: metadata for aerial images (id, timestamp, location, altitude, sensor type).

OBJECT_DETECTION: detected objects (image_id, object_type, bounding_box, orientation, confidence).

TRACKING: temporal sequences (track_id, object_id, trajectory, speed, direction).

EVENTS: higher-level events (traffic jam, unauthorized entry, wildfire hotspot).

REGIONS: geospatial polygons (urban, rural, restricted zones).

Step 2. Query adaptation:

The following table lists the adaptations:

TPC-H Query Original Purpose Drone Analytics Adaptation

Q1: Pricing Summary Report Aggregate line items by date Detection Summary Report: Count objects per type per region per day (e.g., vehicles, aircraft).

Q3: Shipping Priority Orders with high priority Event Priority: Identify urgent drone-detected events (e.g., accidents, intrusions) sorted by severity.

Q5: Local Supplier Volume Join across regions Regional Object Volume: Join detections with regions to compute density of vehicles/people per zone.

Q7: Volume Shipping Compare nations Cross-Region Traffic Flow: Compare object counts across multiple geospatial regions over time.

Q8: Market Share Share of supplier Model Share: Compare detection accuracy share between different drone models or sensors.

Q9: Product Profit Profit by supplier Event Cost Impact: Estimate resource usage (battery, bandwidth) per event type.

Q10: Top Customers Identify top customers Top Hotspots: Identify top regions with highest frequency of detected anomalies.

Q12: Shipping Modes Distribution by mode Flight Modes: Distribution of detections by drone altitude or flight mode.

Q13: Customer Distribution Count customers by orders Object Distribution: Count detections by object type (cars, pedestrians, aircraft).

Q15: Top Supplier Best supplier Top Detector: Identify best-performing detection algorithm (highest precision/recall).

Q18: Large Volume Customer Customers with large orders Large Volume Region: Regions with unusually high detection counts (e.g., traffic congestion).

Step 3. Metrics and Evaluations:

Just like TPC-H measures query response time, throughput, and power, the drone benchmark would measure:

Query Latency: Time to answer detection/tracking queries.

Throughput: Number of queries processed per minute across drone streams.

Accuracy Metrics: Precision, recall, mAP for detection queries.

Spatial-Temporal Efficiency: Ability to handle joins across time and geospatial regions.

Resource Utilization: CPU/GPU load, bandwidth usage, battery impact.

Step 4. Sample query:

This query evaluates object detection density per region per week, analogous to TPC-H’s line item aggregation:

SELECT

    region_id,

    object_type,

    COUNT(*) AS object_count,

    AVG(confidence) AS avg_confidence

FROM OBJECT_DETECTION od

JOIN REGIONS r ON od.location WITHIN r.polygon

WHERE od.timestamp BETWEEN '2025-12-01' AND '2025-12-07'

GROUP BY region_id, object_type

ORDER BY object_count DESC;

Future:

This benchmark is reproducible for drone analytics pipelines and provides standardization. Vendors can compare drone video systems and pipelines. It performs stress-testing using geo-spatial joins, temporal queries, and detection accuracy at scale. We could call it the Drone-Analytics Benchmark proposal.

References:

• Full Specification: https://1drv.ms/w/c/d609fb70e39b65c8/EXuckQNUpo9MowxSWSkeaA8Bm1f-ADuTaPf_GrOPLKBMPg?e=uoA10o

 This is a summary of a book titled “How to handle a crowd” written by Anika Gupta and published by Simon Element in 2020. In the world of online groups and communities, the decorum is established not by traditional authorities but by moderators who facilitate virtual cooperation and interaction. She studies a wide range of digital communities in her book. She says that these communities tend to foster partisan echo chambers that increase polarization and deliberate effort is required to nurture healthy debate. The role of moderator is time-consuming and might even be thankless but with preparation, they shape the way people build relationships today.

The author’s research spans a wide array of digital communities, revealing a recurring challenge: the tendency for these spaces to become partisan echo chambers, amplifying polarization rather than fostering understanding. She argues that nurturing healthy debate in such environments requires deliberate effort and thoughtful moderation. The work of a moderator, she notes, is often time-consuming and thankless, yet with preparation and vision, these individuals profoundly influence how relationships are built in the digital age.

Moderators—sometimes affectionately called “mods”—are the architects of community identity and the guardians of online discourse. Their responsibilities are multifaceted, ranging from short-term regulatory actions, like banning users who break the rules, to long-term strategies that shape the very nature of the conversations within a group. They act as digital hosts and storytellers, drawing on traditions as old as the bard, weaving narratives that help communities thrive. Many moderators are volunteers, driven by a shared sense of purpose and an ethos of care and support.

People join online communities to engage in conversations that resonate with their interests and identities. Sociologists Jenny Preece and Diane Maloney-Krichmar define an online community as a group united by common interests or purposes, interacting according to agreed-upon policies in a virtual setting. Yet, maintaining these communities is no small feat. Without traditional elders or authorities, the question arises: who enforces the rules and shapes the boundaries? The answer, Gupta suggests, lies with the moderators.

The book highlights the dangers of echo chambers, where polarization grows unchecked. Drawing on a Pew Research Center study, <Author>Gupta</Author> notes that Americans often find political discussions with those holding opposing views to be exasperating, with such exchanges frequently deepening divides rather than bridging them. This phenomenon, known as “affective polarization,” has been on the rise in the United States since 1988.

Yet, the narrative is not entirely bleak. Gupta shares examples of communities that have managed to foster healthy dialogue across divides. One such group is Make America Dinner Again (MADA), founded by Justine Lee and Tria Chang. MADA began as a face-to-face dinner party where liberals and conservatives could “break bread” and engage in meaningful conversation. Its success led to branches across the country and, eventually, an online presence on Facebook. MADA’s moderators focus on relationship-building, reaching out to members individually when rules are broken, and guiding them toward more respectful interactions. They limit how often members can comment on threads, encouraging listening and preventing a few voices from dominating. Over time, these efforts have cultivated a more civil and respectful tone, offering a blueprint for other groups seeking understanding.

The book also examines the complexities of moderating conversations about racial justice. Groups like Pantsuit Nation, formed to support Hillary Clinton’s presidential candidacy, faced criticism for centering white members’ experiences and mishandling race-related topics. In response, the group hired Grace Caldara as Director of Engagement, reduced the moderator team, and implemented training to manage tension and prioritize moderator self-care. Meanwhile, a spinoff group, Real Talk: WOC & Allies for Racial Justice and Anti-Oppression, requires members to commit to active antiracism and undergo allyship training, ensuring that participants have confronted their own biases before joining.

Moderating neighborhood groups presents their own challenges. Moderators help members navigate crises and form connections, but they must also contend with platform instability and the threat of fake accounts. For example, Peggy Robin, who manages a neighborhood LISTSERV in Washington, D.C., had to quickly migrate years of content when Yahoo Groups shut down. She also enforces a zero-tolerance policy against fake advertisers and impersonators.

Despite the difficulties, many moderators find the work rewarding. Christi Ketchum, founder of the Sacramento Sister Circle, sees herself as a “bridge builder,” mentoring young women and fostering financial independence within her community.

The book touches on the world of online gaming, where guild leaders and moderators play crucial roles in shaping community culture and responding to issues like discrimination and polarization. These leaders often create inclusive spaces and support members through mentorship, though the emotional labor can be exhausting.

The author concludes that while moderators cannot fix all the problems of online discourse, their influence is significant. They rely on foresight, technical skill, and conflict management to create spaces for civil conversation. Though most are unpaid, their work endures, shaping how people meet, work, play, and connect in the digital age.

#continuation of yesterday's article: VideoSensingBenchmarks.docx

 This is a summary of a book titled “Elusive Cures: Why neuroscience hasn’t solved brain disorders – and how we can change that” written by accomplished neuroscientist Nicole Rust and published by Princeton University Press in 2025. She invites readers to reconsider the foundations of brain research and treatment. Drawing on decades of experience, Rust explores why the promise that a deeper understanding of the brain would lead to effective treatments for disorders like Alzheimer’s, depression, and schizophrenia has not been fulfilled. Her book is both a critique of prevailing scientific dogma and a call for a new way of thinking about the brain.

Rust begins by examining the “bench to bedside” approach that has dominated neuroscience for generations. This model assumes that discoveries at the molecular level—such as identifying genes or proteins involved in brain function—will naturally translate into clinical therapies. The narrative is so deeply ingrained in research culture that it is rarely questioned. Yet, Rust points out, despite enormous investments and scientific advances, reliable treatments for major brain disorders remain elusive.

Alzheimer’s disease serves as a cautionary tale. Researchers identified rare genetic mutations that increase risk and theorized that the accumulation of amyloid plaques in the brain was the root cause of neurodegeneration. Pharmaceutical companies poured billions into developing drugs to clear these plaques, only to find that, while the drugs worked as intended, they did not meaningfully slow the disease’s progression. By 2011, many companies had abandoned their efforts, having little to show for their investment.

Rust argues that the failure of these efforts stems from a narrow focus on molecular mechanisms. Brain dysfunction, she suggests, is influenced by a web of factors—genetic, environmental, socioeconomic, and behavioral. Non-pharmaceutical interventions can affect outcomes, and knowledge of molecular processes alone is insufficient for developing systematic treatments.

The book then delves into the rise of “molecular medicine,” which became central to neuroscience after the discovery of the genetic code in the 1950s. Researchers would identify a gene linked to a disorder, mutate it in animal models, and attempt to develop drugs to correct the resulting dysfunction. This “domino chain” approach, Rust explains, is tempting because it is simple and linear. But the brain is not a set of dominoes. It is a dynamic, adaptive organ, constantly responding to changing circumstances and regulating itself to optimize performance.

Rust highlights the limitations of reductionist thinking. Emergent properties like mood or consciousness arise from interactions among brain components, not from the components themselves. She suggests that it may be more fruitful to start with the behavior or disorder in question and work downward to the molecular level, rather than the other way around.

The history of psychiatric drugs further illustrates the unpredictability of progress. Many effective medications, such as Thorazine for schizophrenia and Ritalin for ADHD, were discovered by accident, not through targeted molecular research. Rust notes that the biggest obstacle to developing new treatments is our limited understanding of the causes of brain dysfunction. We may know what degenerates in diseases like Alzheimer’s, but not why degeneration occurs.

Recent advances in artificial intelligence have enabled scientists to build sophisticated models of brain activity, but linking mental disorders to specific genetic mutations remains a daunting challenge. Disorders like schizophrenia involve hundreds of genes, and technologies like fMRI have proven unreliable for diagnosis. Rust concludes that neither genes nor scans can credibly identify types of brain dysfunction, and a new model is needed.

One promising direction is to think of the brain as a computer—a system that processes information, makes decisions, and adapts to its environment. In this analogy, neurons are the hardware and the mind is the software. While this metaphor is useful, Rust cautions that it must be formalized into mathematical models to be truly explanatory. The gap between molecular effects and mental states remains vast.

Rust’s central thesis is that the brain is a complex adaptive system. Like the body, it seeks not just stability (homeostasis) but anticipates and adapts to future changes (allostasis). Feedback loops within the brain can lead to emergent properties and, sometimes, maladaptive patterns. For example, anxiety can spiral into a cycle of worry and demotivation, making it difficult for the brain to “relearn” healthier states.

Interventions in such a complex system are unpredictable. Treating disorders like depression or schizophrenia means regulating a dynamic network that can recalibrate itself in unexpected ways. Rust draws on models from recurrent neural networks, where feedback among neurons can push the system to the edge of chaos—a state that may be necessary for optimal function but is difficult to control.

Rust argues that effective treatment demands a precise understanding of what distinguishes healthy from unhealthy brains. Measuring consciousness and mental states is a major challenge, as these are not reducible to specific neural circuits. Research into brain activity patterns in patients with severe damage may help, but much remains unknown.

For future, Rust suggests that scientists may need to simplify their models, focusing on the most relevant variables for each disorder. Complex conditions may require clusters of treatments, and increasing brain plasticity to break maladaptive feedback patterns could be key. Her book is a call to embrace complexity, rethink old assumptions, and pursue new paths in the quest to cure brain disorders.

 Gist of Drone Video Sensing

Aerial drone imagery has emerged as a critical enabler of geospatial intelligence, with research steadily advancing from classical vision descriptors to transformer based deep learning architectures. The documents listed in the references, collectively illustrate a continuum of methods that span from lightweight statistical approaches to sophisticated end-to-end detection pipelines, each contributing uniquely to the operationalization of drone analytics.

Early work on aerial image count estimation emphasizes the importance of automated object occurrence counting, where drones capture wide area scenes and analytics pipelines tally entities such as vehicles, trees, or construction materials. This quantitative transformation of raw imagery underpins applications in traffic monitoring, forestry biomass estimation, and disaster response, where rapid counts of displaced populations or damaged assets are indispensable. Complementary to this, this research highlight the operational rigor required to scale such analytics, ensuring that pipelines remain reproducible, error resistant, and adaptable across deployments. These infrastructural insights, though not directly vision algorithms, reinforce the necessity of robust orchestration for drone-based workflows.

The evolution toward transformer-based detection models marks a significant leap in aerial vision processing. The DETR framework, detailed in end-to-end detection studies, eliminates anchor boxes and directly predicts object boundaries and classes. This approach proves particularly effective in aerial contexts where object scales and orientations vary widely, enabling reliable detection of vehicles, maritime vessels, and construction machinery. By integrating attention mechanisms, transformers overcome the limitations of convolutional networks, offering robustness and scalability in complex aerial environments.

Parallel to these algorithmic advances, market survey analyses situate technical methods within real-world demand. Urban planning benefits from infrastructure growth assessment through aerial imagery, agriculture leverages spectral and color-based analysis for crop health monitoring, and wildlife studies employ occurrence counts to track species movement. Security and surveillance applications further demonstrate the contextual relevance of drone analytics, where anomaly detection and activity recognition provide actionable intelligence. These surveys underscore the strategic positioning of drone vision processing as not merely experimental but operationally indispensable.

The broader ecosystem is enriched by techniques such as color histogram analysis, which provides lightweight descriptors for land cover classification, crop differentiation, and environmental anomaly detection. Similarly, scale resolution estimation bridges qualitative imagery with quantitative measurement, using reference objects to calibrate spatial dimensions for precision agriculture, construction monitoring, and geospatial mapping. Together, these methods form a layered toolkit: histograms and calibration for foundational descriptors, count estimation for quantitative insights, and transformers for robust automation.

These documents demonstrate that aerial drone analytics is no longer confined to isolated technical experiments. It represents a mature, integrated discipline where classical vision methods coexist with modern deep learning, and where infrastructural rigor ensures operational scalability. By aligning algorithmic innovation with industry adoption, drone image analysis has become a cornerstone of geospatial intelligence, reshaping sectors from agriculture to urban planning with precision, efficiency, and strategic impact.

#Codingexercise : Codingexercise-12-06-2025.docx

 This is a summary of a book titled “Elusive Cures: Why neuroscience hasn’t solved brain disorders – and how we can change that” written by accomplished neuroscientist Nicole Rust and published by Princeton University Press in 2025. She invites readers to reconsider the foundations of brain research and treatment. Drawing on decades of experience, Rust explores why the promise that a deeper understanding of the brain would lead to effective treatments for disorders like Alzheimer’s, depression, and schizophrenia has not been fulfilled. Her book is both a critique of prevailing scientific dogma and a call for a new way of thinking about the brain.

Rust begins by examining the “bench to bedside” approach that has dominated neuroscience for generations. This model assumes that discoveries at the molecular level—such as identifying genes or proteins involved in brain function—will naturally translate into clinical therapies. The narrative is so deeply ingrained in research culture that it is rarely questioned. Yet, Rust points out, despite enormous investments and scientific advances, reliable treatments for major brain disorders remain elusive.

Alzheimer’s disease serves as a cautionary tale. Researchers identified rare genetic mutations that increase risk and theorized that the accumulation of amyloid plaques in the brain was the root cause of neurodegeneration. Pharmaceutical companies poured billions into developing drugs to clear these plaques, only to find that, while the drugs worked as intended, they did not meaningfully slow the disease’s progression. By 2011, many companies had abandoned their efforts, having little to show for their investment.

Rust argues that the failure of these efforts stems from a narrow focus on molecular mechanisms. Brain dysfunction, she suggests, is influenced by a web of factors—genetic, environmental, socioeconomic, and behavioral. Non-pharmaceutical interventions can affect outcomes, and knowledge of molecular processes alone is insufficient for developing systematic treatments.

The book then delves into the rise of “molecular medicine,” which became central to neuroscience after the discovery of the genetic code in the 1950s. Researchers would identify a gene linked to a disorder, mutate it in animal models, and attempt to develop drugs to correct the resulting dysfunction. This “domino chain” approach, Rust explains, is tempting because it is simple and linear. But the brain is not a set of dominoes. It is a dynamic, adaptive organ, constantly responding to changing circumstances and regulating itself to optimize performance.

Rust highlights the limitations of reductionist thinking. Emergent properties like mood or consciousness arise from interactions among brain components, not from the components themselves. She suggests that it may be more fruitful to start with the behavior or disorder in question and work downward to the molecular level, rather than the other way around.

The history of psychiatric drugs further illustrates the unpredictability of progress. Many effective medications, such as Thorazine for schizophrenia and Ritalin for ADHD, were discovered by accident, not through targeted molecular research. Rust notes that the biggest obstacle to developing new treatments is our limited understanding of the causes of brain dysfunction. We may know what degenerates in diseases like Alzheimer’s, but not why degeneration occurs.

Recent advances in artificial intelligence have enabled scientists to build sophisticated models of brain activity, but linking mental disorders to specific genetic mutations remains a daunting challenge. Disorders like schizophrenia involve hundreds of genes, and technologies like fMRI have proven unreliable for diagnosis. Rust concludes that neither genes nor scans can credibly identify types of brain dysfunction, and a new model is needed.

One promising direction is to think of the brain as a computer—a system that processes information, makes decisions, and adapts to its environment. In this analogy, neurons are the hardware and the mind is the software. While this metaphor is useful, Rust cautions that it must be formalized into mathematical models to be truly explanatory. The gap between molecular effects and mental states remains vast.

Rust’s central thesis is that the brain is a complex adaptive system. Like the body, it seeks not just stability (homeostasis) but anticipates and adapts to future changes (allostasis). Feedback loops within the brain can lead to emergent properties and, sometimes, maladaptive patterns. For example, anxiety can spiral into a cycle of worry and demotivation, making it difficult for the brain to “relearn” healthier states.

Interventions in such a complex system are unpredictable. Treating disorders like depression or schizophrenia means regulating a dynamic network that can recalibrate itself in unexpected ways. Rust draws on models from recurrent neural networks, where feedback among neurons can push the system to the edge of chaos—a state that may be necessary for optimal function but is difficult to control.

Rust argues that effective treatment demands a precise understanding of what distinguishes healthy from unhealthy brains. Measuring consciousness and mental states is a major challenge, as these are not reducible to specific neural circuits. Research into brain activity patterns in patients with severe damage may help, but much remains unknown.

For future, Rust suggests that scientists may need to simplify their models, focusing on the most relevant variables for each disorder. Complex conditions may require clusters of treatments, and increasing brain plasticity to break maladaptive feedback patterns could be key. Her book is a call to embrace complexity, rethink old assumptions, and pursue new paths in the quest to cure brain disorders.

 Gist of Drone Video Sensing

Aerial drone imagery has emerged as a critical enabler of geospatial intelligence, with research steadily advancing from classical vision descriptors to transformer based deep learning architectures. The documents listed in the references, collectively illustrate a continuum of methods that span from lightweight statistical approaches to sophisticated end-to-end detection pipelines, each contributing uniquely to the operationalization of drone analytics.

Early work on aerial image count estimation emphasizes the importance of automated object occurrence counting, where drones capture wide area scenes and analytics pipelines tally entities such as vehicles, trees, or construction materials. This quantitative transformation of raw imagery underpins applications in traffic monitoring, forestry biomass estimation, and disaster response, where rapid counts of displaced populations or damaged assets are indispensable. Complementary to this, this research highlight the operational rigor required to scale such analytics, ensuring that pipelines remain reproducible, error resistant, and adaptable across deployments. These infrastructural insights, though not directly vision algorithms, reinforce the necessity of robust orchestration for drone-based workflows.

The evolution toward transformer-based detection models marks a significant leap in aerial vision processing. The DETR framework, detailed in end-to-end detection studies, eliminates anchor boxes and directly predicts object boundaries and classes. This approach proves particularly effective in aerial contexts where object scales and orientations vary widely, enabling reliable detection of vehicles, maritime vessels, and construction machinery. By integrating attention mechanisms, transformers overcome the limitations of convolutional networks, offering robustness and scalability in complex aerial environments.

Parallel to these algorithmic advances, market survey analyses situate technical methods within real-world demand. Urban planning benefits from infrastructure growth assessment through aerial imagery, agriculture leverages spectral and color-based analysis for crop health monitoring, and wildlife studies employ occurrence counts to track species movement. Security and surveillance applications further demonstrate the contextual relevance of drone analytics, where anomaly detection and activity recognition provide actionable intelligence. These surveys underscore the strategic positioning of drone vision processing as not merely experimental but operationally indispensable.

The broader ecosystem is enriched by techniques such as color histogram analysis, which provides lightweight descriptors for land cover classification, crop differentiation, and environmental anomaly detection. Similarly, scale resolution estimation bridges qualitative imagery with quantitative measurement, using reference objects to calibrate spatial dimensions for precision agriculture, construction monitoring, and geospatial mapping. Together, these methods form a layered toolkit: histograms and calibration for foundational descriptors, count estimation for quantitative insights, and transformers for robust automation.

These documents demonstrate that aerial drone analytics is no longer confined to isolated technical experiments. It represents a mature, integrated discipline where classical vision methods coexist with modern deep learning, and where infrastructural rigor ensures operational scalability. By aligning algorithmic innovation with industry adoption, drone image analysis has become a cornerstone of geospatial intelligence, reshaping sectors from agriculture to urban planning with precision, efficiency, and strategic impact.

#codingexercise CodingExercise-12-04-2025.docx

 General Agents has quickly emerged as one of the most intriguing AI startups in the agentic computing space, capturing attention with its bold vision of creating autonomous digital operators. Their flagship system, known as Ace, is designed to move beyond the limitations of traditional chatbots and large language models by acting directly on a user’s computer. Instead of simply generating text or offering suggestions, Ace interprets the screen, understands instructions, and executes multi‑step workflows with human‑like proficiency. It can edit videos, copy data between applications, schedule meetings, book accommodations, or organize files, all by navigating the digital environment as if it were a skilled assistant sitting at the keyboard. This approach represents a significant leap toward agentic AI, where systems are not passive responders but active participants in achieving goals.

The technical foundation of Ace lies in proprietary models called ace‑control‑small and ace‑control‑medium. These are built on a video‑language‑action architecture, or VLA, which integrates visual input, natural language, and action sequences. VLAs are particularly well suited for robotics and embodied AI because they allow an agent to interpret video feeds and translate them into actionable steps. In the case of Ace, this means the system can “see” the computer screen, “read” the instructions provided by the user, and then “act” by clicking, typing, or navigating through applications. It is a fusion of perception, reasoning, and execution that positions General Agents at the forefront of digital labor automation. Their acquisition by Jeff Bezos’ Project Prometheus underscores the strategic importance of this technology, especially for industries like manufacturing and aerospace where agentic AI could transform workflows and reduce reliance on human operators for repetitive tasks.

The vision of General Agents is to eliminate digital drudgery by creating AI agents that perform tedious computer tasks, freeing humans to focus on higher‑value work. This aligns with the broader movement toward agentic AI, where systems autonomously achieve goals rather than simply assist with suggestions. Yet while their focus on digital environments and industrial robotics is compelling, it leaves a gap when it comes to interpreting and acting upon real‑world, geospatial data. This is precisely where our drone video sensing analytics software can elevate their efforts.

Our platform is built for the unique challenges of aerial imagery, where every frame carries not just pixels but geospatial meaning. At 100 meters above ground, a drone’s video feed contains terrain contours, object trajectories, and environmental anomalies that must be understood in real time. Our analytics pipeline fuses centimeter‑level geolocation with transformer‑based object detection, clustering, and multimodal vector search. This allows drones to detect convoys, identify vegetation encroachment, or flag infrastructure risks with semantic clarity. Integrating this capability into General Agents’ VLA models would extend Ace’s reach beyond the desktop, enabling it to interpret dynamic visual signals from the physical world and act upon them with the same agentic precision it currently applies to digital tasks.

The temporal intelligence embedded in our analytics also adds a dimension that General Agents’ current systems do not fully address. Drone video sensing requires tracking objects across frames, detecting behavioral patterns, and forecasting changes. Our software can identify unsafe proximity between personnel and heavy machinery, anticipate pedestrian flows near schools, or predict crop stress zones based on evolving spectral signatures. Feeding this temporal modeling into Ace’s agentic workflows would allow General Agents to move from reactive execution to anticipatory decision‑making, a capability that is critical in dynamic environments.

The synergy becomes even more powerful in multi‑agent scenarios. General Agents emphasizes orchestration across digital tasks, but our system can generate shared semantic maps for fleets of drones or autonomous vehicles. Coupled with Ace’s orchestration engine, this would enable collaborative autonomy in real‑world missions such as logistics, emergency response, or infrastructure monitoring. Each drone could act as both a courier and a sensor, contributing contextual intelligence to the swarm and enriching the collective decision‑making process.

General Agents is advancing agentic AI by teaching machines to act in digital environments with autonomy and precision. Our drone video sensing analytics complements this by adding contextual, geospatial, and temporal intelligence from the physical world. Together, they could create an ecosystem where agentic AI not only automates computer tasks but also perceives, interprets, and adapts to real‑world conditions. In this partnership lies the potential to transform autonomy from screen‑bound workflows into operational intelligence that bridges the digital and physical domains, redefining what agentic AI can achieve.

#Codingexercise: Codingexercise-12-03-2025.docx

 This is a summary of a book titled “The 7 commitments of a great team” written by Jon Gordon and published by Wiley in 2025. The author is an athlete and an advocate for team building skills. He brings his principles to life through the use of vivid role playing and fictional characters. He explores what it means to build a lasting, high-performing team. His principles include: commit to the vision and mission of the team, commit to staying positive together, commit to giving your best, commit to getting better, commit to connect, commit to each other and commit to valuing each other.  He emphasizes VALUE as validate, appreciate, listen, understand and empathize. “It’s the love that lasts”. 

The story centers on Tim, a former college football player, who visits his old coach, Richie, in the hospital. Thirty years have passed since Tim played for Coach Richie, yet the coach’s influence remains powerful. Richie’s military background shaped his coaching style, instilling discipline and unity in his players. His favorite message: “Teammates are forever”: lingers in Tim’s mind, prompting reflection on how such bonds endure even as life moves on. 

Tim, now a business owner, faces a crisis: his employees are disengaged, results are poor, and turnover is high. He realizes that to save his company, he must inspire his team just as Coach Richie once inspired him. At home, Tim recounts a pivotal moment from his college days. Coach Richie had asked each player to write down their goals, only to have them discard those notes. The lesson? Winning and dominating are common ambitions, but what sets a team apart are the commitments its members make to themselves and each other. 

Coach Richie encouraged his players to pledge personal commitments: extra skills work, more recovery time, deeper study of game films. He promised that if they upheld six core commitments, they would be unstoppable. Years later, Tim discovered a seventh commitment: always value your teammates. 

The seven commitments are the backbone of the book: 

1.  Commit to the vision and mission of the team: Unity of purpose is essential; teams succeed when everyone pulls together toward a shared goal. 

2.  Commit to staying positive together: Challenges and setbacks are inevitable, but maintaining a positive outlook as a group helps teams rise above adversity. 

3.  Commit to giving your best: Consistent effort, positive habits, and showing up every day with determination are key to success. 

4.  Commit to getting better: Continuous improvement requires honest conversations and a willingness to address weaknesses, both individually and collectively. 

5.  Commit to connect: Strong bonds and genuine connection make teams resilient and powerful. 

6.  Commit to each other: Honest feedback and mutual support help every member grow and strengthen the team as a whole. 

7.  Commit to valuing each other: Appreciation and respect are the foundation for all other commitments. Gordon emphasizes VALUE: Validate, Appreciate, Listen, Understand, and Empathize. “It’s the love that lasts.” 

After Coach Richie’s passing, Tim and his former teammates reunite, their bonds as strong as ever. Inspired by his coach’s legacy, Tim resolves to bring the seven commitments to his own company. He enlists Coach Amy, a respected leadership consultant, to help roll out the commitments, one each month, to his employees. Together, they stress the importance of VALUE, ensuring every team member feels seen and appreciated. 

Tim’s efforts: personalized appreciation videos, special commitment cards, and ongoing value training: gradually transform his workplace. He learns that real change requires active leadership and a deep commitment to the principles that make teams great. In the end, Tim’s journey echoes Coach Richie’s enduring lesson: “Commitment recognizes commitment. The more you commit, the more it will come back to you.” 

#codingexercise: CodingExercise-12-02-2025.docx