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Robot Teachers & Hologram Tutors

Robot teachers use artificial intelligence to adapt lessons to students' pace, while holograms provide 3D experiences of abstract ideas.
AI in education is enhancing learning experiences rather than replacing human teachers, creating personalized and interactive learning environments.
Machine learning customizes lessons to fit individual needs and adjusts homework based on progress, making online learning more engaging.
AI tools in schools monitor student engagement, offer tailored hints, and combine virtual reality with hands-on activities for group projects.
Robot teachers analyze student reactions and adjust teaching methods accordingly to enhance learning effectiveness.
Studies show that students respond positively to robots in education, with robots being able to notice and address students' struggles more effectively than humans.
Holographic technology in education allows for interactive learning experiences, such as exploring anatomy in medical schools and testing designs in engineering.
Deep learning algorithms in adaptive curriculum analyze student data to tailor coursework, providing personalized learning experiences and predicting challenges.
Machine learning raises concerns about data privacy and fairness in education, prompting the need for solutions to ensure equal opportunities for all students.
Companies offer AI courses and certifications to equip individuals with skills for AI-related roles in education, such as AI Curriculum Designer or EdTech Specialist.
The future of education involves a collaboration between AI tools and human curiosity, where students develop skills in analyzing algorithms and problem-solving.

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Medium

426

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Scverse Hackathon: Iterating

The author participated in the Owkin X Scverse Hackathon focused on spatial transcriptomics.
They identified a bottleneck in the spatial statistics co_occurrence() and proposed to optimize it using C++/Rust.
They successfully implemented a new algorithm and reduced the memory footprint.
Ultimately, they dropped the Rust version and initiated a PR with a Python implementation for easier distribution.

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Towards Data Science

196

Master the 3D Reconstruction Process: A Step-by-Step Guide

Understanding the 3D Reconstruction process is essential for creating high-quality reconstructions, which involves structured steps building spatial information from flat images.
Successful 3D reconstructions rely on pipelines that work efficiently, spend less time processing, and allow for faster troubleshooting.
The article provides a step-by-step guide covering the complete 3D Reconstruction Workflow, starting with Natural Feature Extraction using feature extraction algorithms like SIFT, SURF, and ORB.
Feature Matching connects images by finding correspondences between matched points, visualizing spatial relationships and helping assess image quality and camera positioning.
Structure From Motion (SfM) reconstructs 3D scene structure and camera motion, estimating camera poses and triangulating 3D points to create a sparse point cloud.
Bundle Adjustment optimizes reconstruction accuracy by refining camera parameters and 3D point positions, ensuring global consistency of the model.
Dense Matching creates detailed reconstructions through Multi-View Stereo, Patch-based Multi-View Stereo, or Semi-Global Matching, followed by tools and practical automation examples for efficient processing.
Understanding the full pipeline empowers users to troubleshoot issues effectively, target problem areas, and ensure high-quality 3D reconstructions.
Practicing and automating the process with high-quality images and gradually tackling more complex scenes is recommended to enhance expertise in 3D reconstruction.
Provided references and resources include notable software tools like COLMAP, OpenMVG, Meshroom, and RealityCapture, along with algorithms such as SIFT, SURF, ORB, RANSAC, and more.
The author, Florent Poux, Ph.D., emphasizes educating engineers on AI and 3D Data Science, leading research teams and teaching 3D Computer Vision to tackle 3D challenges and drive innovations.

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Medium

121

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“The Only Prompting Formula You Need for ChatGPT and Google Bard (in just 2 minutes)!”

To obtain better AI responses from ChatGPT and Google Bard, it is essential to follow a prompting formula with six key components.
The formula involves specifying the task, providing relevant details, and adding constraints.
By using this formula, ChatGPT can generate clear and professional responses, even for fictional characters or summarizing reports.
Including tone suggestions and giving a detailed prompt leads to higher-quality outputs.

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PlanetPython

233

Robin Wilson: Learning resources for GIS in Python with cloud-native geospatial, PostGIS and more

A career talk led to the author sharing resources on GIS in Python and cloud-native geospatial with a student focusing on Data Analytics and AI.
Key tools recommended include GDAL for reading and writing geospatial data, command-line tools for basic processing, and Git for version control.
Python libraries such as GeoPandas, rasterio, and shapely were suggested for geospatial data manipulation.
Cloud Native Geospatial concepts and tutorials on STAC catalogues and COG files were recommended for understanding.
The author's blog and relevant posts on topics like aerial photo searching and PostGIS were shared for further learning.
Conference talks from PostGIS Day and FOSS4G UK highlighted specific talks on SQL queries, Mapbox Vector Tile creation, and QGIS plugins.
Various YouTube videos from FOSS4G conferences were mentioned for additional insights on geospatial topics.
Learning projects/tasks were provided, including tasks related to flood risk analysis and elevation data processing using tools like QGIS, Python, and GDAL.

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Dev

444

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Various .NET Benchmarks

BenchmarkDotNet is an open source library for benchmarking code in .NET/.NET Core
Creating benchmarks is simple with the use of the [Benchmark] attribute
The benchmarks provide timings and memory statistics for methods
The DotNetBenchmarks repository on GitHub offers performance benchmarks for various components

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Dev

152

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Tripping the circuit

The 'Circuit Breaker' pattern is an useful cloud pattern that improves application stability and resilience.
The pattern helps in dealing with failures in remote services, avoiding cascading failures.
Implementing a circuit breaker helps in managing and controlling failures in a controlled manner.
There are both C# and Go implementations available for the circuit breaker pattern.

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Medium

148

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Is Your Life TypeScript or JavaScript ?

Life can be compared to TypeScript or JavaScript. During exam week, life is fully typed, accepting only arguments from notes and oneself. On holidays, life is on 'type any', being unpredictable and open to different possibilities. Fasting can be seen as a strictly typed type, leaving no room for exceptions. Some people approach life like a strictly typed function, needing every detail pinned down, while others embrace spontaneity and live on 'type any'. Our approach to life reflects our approach to code, as we choose between the safety of defined boundaries or the freedom of spontaneity.
When referring to their approach to life, the author mentions that their preference is JavaScript, indicating a more spontaneous and flexible approach.

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Medium

242

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10 Productivity Hacks Every DevOps Engineer Should Know

DevOps engineers often feel overwhelmed with work and little time. Raj shares 10 productivity hacks to take control of time.
Automate repetitive tasks by writing scripts, saving hours in the long run.
Use the Pomodoro Technique to work in focused bursts, increasing productivity and reducing errors.
Setting boundaries and saying no to non-urgent requests helps protect focus time.

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Infoq

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JavaOne 2025 Day 3: The Future of Java is You

Day Three of JavaOne 2025 focused on the importance of Java community and the future of Java technology.
Key takeaway emphasized the significance of community collaboration, innovation, and involvement.
The Java Community Keynote highlighted the central role of developers in shaping the Java ecosystem.
An interactive session included a live podcast with industry experts discussing Java's impact and future trends.
Discussions centered on improving Java's quality and stability through testing open-source projects.
The audience engaged in a Java Quiz mobile app, exploring Java's history and cross-platform capabilities.
The session showcased Java in Education initiatives, promoting learning resources for beginners and educators.
The future of Java was discussed with a focus on compatibility and event-driven workflows.
A hands-on lab demonstrated building AI-powered applications using Helidon and LangChain4J.
JavaOne 2025 provided a platform for developers to explore new technologies and community initiatives.

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Medium

426

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Solana’s initial bull market indicates 65% SOL price increase by April

Solana's initial bull market indicates a 65% price increase for SOL by April.
The price surge is supported by SOL's breakout from a falling wedge pattern and improving momentum indicators.
The popularity of Solana-based memecoins, with over 8 million tokens launched on the platform, contributes to the upward trend.
Top Solana-based memecoins, such as TRUMP and Pump.fun's DEX, have also witnessed significant gains.

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Medium

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Beonbit Se Daily Earnings: Crypto Se Passive Income Kaise Banaye?

Beonbit is a crypto-based earning platform that offers users the opportunity to earn daily income through trading, staking, and referral programs.
Key Features of Beonbit include an auto trading system, daily payouts, referral income, and a secure platform based on blockchain technology.
To earn on Beonbit, users need to create an account, make a deposit, and participate in staking and trading activities.
While Beonbit emphasizes transparency and security, it's important to note that crypto investments always carry risk.

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Medium

166

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Three Weeks of ChatInsight in the Wild

Tech support is a full-time job, even for a hobby project.
Engineers often forget that end users are not engineers.
Encountered difficulties with WhatsApp chat format and resolved them after extensive testing.
Fixed server timeout issue to handle processing of large chat data.

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Dev

188

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Counting Words at Lightning Speed: Golang Channels & Worker Pools to Process Text Files

The article discusses using Golang channels and worker pools for speeding up word counting across text files.
Initially, a sequential approach is shown to take around 2 seconds for 20 files, each containing 11 words.
Introducing channels with a single worker does not provide speed gains, as it remains sequential with added coordination.
However, employing multiple workers, like 3 workers, leads to a significant speed improvement, reducing the time to around 704ms.
Utilizing a worker pool with buffered channels further enhances job distribution and result collection efficiency.
Optimizations like using buffered channels, pre-allocating results, and tuning worker numbers are suggested for improved performance.
Benchmark comparisons reveal that with 200 files, the worker pool approach with 50 workers achieves a 50x speedup compared to sequential processing.
Key takeaways include using worker pools, buffering channels, employing WaitGroups, and adjusting worker count based on workload for optimal performance.
The article emphasizes the power of Go's concurrency for parallel processing of tasks like log analysis, showcasing a production-ready code for efficient bulk I/O operations.
Overall, the journey from a slow sequential processor to a lightning-fast parallel machine demonstrates the effectiveness of Go's concurrency features when utilized correctly.
The article concludes by highlighting the importance of considering overhead, performance tuning, and proper resource management in concurrent processing tasks.

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11 Likes

Dev

247

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Why Python Is a Terrible First Language

Learning Python as a first language can make you lazy and lack discipline.
Python's simplicity and high-level nature abstract you from understanding how things work at the system level.
Python may give a warped view of what is considered 'hard' in programming.
Python is still amazing and can introduce beginners to programming, but it is important to dig deeper and learn other languages as well.
Pairing Python with a lower-level language can provide a more well-rounded understanding of programming.

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