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The race to trillion-parameter model training in AI is on, and this company thinks it can manage it for less than $100,000

Phison has developed a solution to reduce the cost of training a 1 trillion parameter AI model from $3 million to $100,000.
They achieve this by shifting some of the processing load from GPUs to high-performance SSDs using their aiDAPTIV+ software.
Phison's strategy aims to incorporate NVIDIA's GH200 Superchip to enhance performance and keep costs manageable.
Their approach could bring down training costs to just 3% of current projections, or less than 1/25 of the usual operating expenses.

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Medium

299

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The Role of Decentralized Technology in Enhancing Aviation Safety and Data Accessibility with Derad…

Aviation safety heavily relies on real-time data such as aircraft positions, weather conditions, and air traffic patterns, traditionally managed through centralized systems like ADS-B.
Centralized systems like ADS-B possess single points of failure, coverage gaps in remote areas, and limited data accessibility to stakeholders.
Derad Network, a Web 3.0 initiative, decentralizes flight tracking using blockchain to create a tamper-proof record of aviation data by incentivizing global contributors.
Derad Network integrates blockchain, DePIN, and AI to enhance coverage, ensure data integrity, and provide real-time predictive analytics for aviation safety.
Benefits of Derad include mitigating risks of outages, expanding coverage through crowdsourcing, ensuring tamper-proof data integrity, and enabling real-time predictive analytics.
Derad fosters democratization of aviation data access, benefiting small operators, supporting innovation, and engaging the public in the aviation ecosystem.
Real-world applications of Derad Network extend to various sectors beyond traditional air travel, paving the way for a decentralized aviation future.
Challenges like scaling participation, regulatory acceptance, and energy efficiency need addressing for the widespread adoption of Derad Network.
Derad Network's vision aligns with the shift towards decentralization, gaining traction among Web 3.0 enthusiasts and aviation stakeholders for a promising future.
Derad Network represents a stride towards a safer and more accessible aviation ecosystem by addressing the limitations of centralized systems through resilience, transparency, and inclusivity.
Decentralized technology like Derad Network is essential for ensuring aviation safety and navigating the increasingly busy skies of the future, revolutionizing the aviation industry.

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The Robot Report

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Flexxbotics updates FlexxCORE to help robotic companies scale production

Flexxbotics has updated FlexxCORE to help robotic companies scale production.
FlexxCORE enables longer unattended production, increased capacity, and improved product quality.
FlexxCORE allows industrial and collaborative robots to run multiple machines and operations for multiple part SKUs.
The update provides interoperable communication, compatibility with over 1,000 makes and models of machinery, and faster connector creation.

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6 Likes

Medium

322

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API Gateway with Spring Boot: Deep Dive with Interview Questions

API Gateway is a crucial component in modern application architecture, especially in microservices, cloud computing, and distributed systems.
An API Gateway is a server that acts as an entry point for client requests and routes them to the appropriate backend services.
It provides functionalities like authentication, rate limiting, caching, request transformation, and security to ensure efficient and secure communication between clients and services.
Think of it as the front door to a system of microservices, handling all incoming API requests.

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Beyondkey

432

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Create a Powerful Moodle VLE with These 5 Top Tips

A Moodle Virtual Learning Environment (VLE) is a powerful tool for team training, providing online courses, resources, progress monitoring, and collaboration.
The key to a successful Moodle VLE is making it easy to use for diverse user groups, such as teachers, experts, and students with varying tech skills.
Keeping content fresh and engaging is crucial to prevent the Moodle VLE from becoming stale, leveraging various content types and interactive tools.
Empowering learners by giving them their own space in the Moodle VLE through customization options like Moodle Dashboards and file storage enhances engagement.
Tracking learner progress through reports and providing feedback and recognition, such as badges, motivates learners to stay engaged and complete courses.
Ensuring accessibility by making the Moodle VLE available anytime, anywhere through the Moodle app with features like offline access and notifications benefits learners.
Creating a well-designed and user-friendly Moodle VLE can greatly enhance the learning experience, catering to individual needs and promoting active participation.
Constantly updating content, encouraging collaboration, and personalizing the learning environment are key strategies to optimize the Moodle VLE for effective training programs.
By implementing these top tips, organizations can create a dynamic and supportive learning environment that motivates learners to actively engage and successfully complete their training.
Beyond Key offers custom Moodle services to help organizations develop and manage a Moodle VLE tailored to their specific training requirements.

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26 Likes

Medium

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Is “Vibe Coding” ‘Cheating’ or The Future of The Programming Workplace?

The article explores the concept of 'Vibe Coding' and its implications in the programming workplace.
It demonstrates the limitations of LLMs in vibe coding scenarios, showcasing the need for prior coding knowledge.
LLMs can assist in debugging and optimizing code, but may struggle with reasoning and logic in complex scenarios.
The article provides a detailed example of debugging a Python encryption script using LLM assistance.
It highlights the importance of understanding coding principles beyond relying solely on LLMs for coding tasks.
Vibe coding, while helpful for quick tasks, may not be sufficient for real-world coding challenges that require in-depth knowledge.
The article emphasizes the significance of hands-on problem-solving and testing code thoroughly for real-world applications.
LLMs offer time-saving benefits but may not replace the need for human reasoning and expertise in coding.
The article discusses the role of LLMs as assistants rather than replacements for programmers in complex coding tasks.
It advises on best practices when using LLMs for coding tasks, including providing clear instructions and being actively involved in the coding process.

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4 Likes

Medium

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Why TVL is Crypto’s Biggest Lie (And What Really Matters)

Relying solely on Total Value Locked (TVL) can lead to misleading insights in the crypto industry.
Double counting is a common issue in calculating TVL, where the same funds are counted multiple times across different protocols.
TVL can be inflated by temporary incentives and locking of tokens, which may not necessarily represent real value creation.
Rather than focusing on TVL alone, it is more important to assess active engagement, productivity, and sustained growth in the crypto ecosystem.

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Siliconangle

207

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Security moves upstream as Chainguard redefines the software supply chain

Software supply chain is evolving to address the gap between speed and security, with a focus on integrating security into the coding process.
Chainguard, led by Dan Lorenc, is reshaping software supply chain security by embedding trust and automation into development workflows.
The industry shift towards security at every level of the stack includes leveraging tools like Chainguard Libraries and minimal VM images.
Kit Merker of Plainsight Technologies highlights the scaling challenges faced by enterprises in implementing computer vision.
Shift5 Inc.'s partnership with Chainguard streamlines compliance work, allowing focus on building secure defense systems.
Chainguard's nano-update strategy introduces continuous, incremental updates to improve system resilience.
Jason van Zyl discusses Chainguard Libraries, emphasizing the need for open-source libraries to be secure by default.
Chainguard's focus on reducing the attack surface and providing actionable visibility contributes to a strategy of rapid updates and precision remediation.
The industry recognizes the importance of security from source to runtime, with a shift towards securing every step in the software development process.
The industry event Chainguard Assemble, discussed by experts like Lorenc, Lewandowski, Merker, and Kirkland, highlights the latest advancements in software supply chain security.

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Towards Data Science

Automate Supply Chain Analytics Workflows with AI Agents using n8n

The article discusses the use of n8n, a low-code platform, to automate supply chain analytics workflows and build AI agents.
The author initially explored frameworks like LangChain and LangGraph before discovering the power of n8n.
n8n is an open-source workflow automation tool that allows easy connection between apps, APIs, and AI model frameworks.
The example provided in the article demonstrates the ease of replicating an AI agent built with Python using n8n with minimal code.
The AI Supply Chain Control Tower workflow in n8n includes sub-workflows, chat interfaces, AI agents, and SQL querying.
The process is smooth and requires limited configuration, making it accessible even for individuals with limited Python skills.
The article highlights that while n8n simplifies automation, Python is not obsolete and can still be valuable for supply chain analytics.
n8n offers easy connectivity to various services and the flexibility to enhance analytics products by adding additional connections.
The author, Samir Saci, is a Supply Chain Engineer who focuses on using data analytics to enhance logistics operations and reduce costs.
For consulting or advice on analytics and sustainable Supply Chain transformation, the author can be contacted via Logigreen Consulting.

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2 Likes

Medium

436

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Full Onboarding Guide for Web3PvZ, Edition 7: WALLET: Account & Assets

The WALLET feature allows players to connect a cryptocurrency wallet to their game account for on-chain activities.
Steps to connect your OKX Wallet: For Telegram Players.
Game data cannot be transferred between wallets in Web3PvZ.
Game assets cannot be transferred between the LINE and web versions of Web3PvZ.

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26 Likes

Medium

273

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Learn JMS Queuing in Oracle Database

Learn JMS Queuing in Oracle Database
A database script is used to create a JMS queue in Oracle Database.
The QueueProducer and QueueConsumer classes are provided as reference implementations for producing and consuming messages from the JMS queue.
A JUnit test is included to concurrently run a producer and multiple consumers against a containerized instance of Oracle Database Free.

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16 Likes

Towards Data Science

342

AI Agents from Zero to Hero — Part 2

AI Agents are autonomous programs that perform tasks, make decisions, and communicate with others.
Agents organize tasks into a logical progression and adapt their strategies in dynamic environments.
Iterations involve repeating actions with slight changes or improvements in each cycle to refine output or reach optimal solutions.
Chains are sequences of linked actions where each step depends on the previous one, forming a continuous flow of operations.
The tutorial demonstrates using iterations and chains for AI Agents, with Python code examples for practical application.
Tools like YahooFinance APIs and matplotlib are utilized to download and visualize financial data.
The article introduces a function to process model responses and handle the usage of tools by the Agent.
By incorporating iterations, the Agent can retry actions multiple times to solve problems and adapt.
Chains allow for a linear sequence of actions, with outputs of one step used as inputs for subsequent steps, enhancing problem-solving capabilities.
The structured approach using iterations and chains enhances the reliability of AI Agents, enabling developers to create customized solutions for various scenarios.

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19 Likes

Arstechnica

270

Image Credit: Arstechnica

Google makes Android development private, will continue open source releases

Google is planning a major change to the way it develops new versions of the Android operating system.
All Android development work will now take place in Google's internal branch, with the public AOSP branch no longer receiving frequent updates.
The change is aimed at simplifying the development process and streamlining releases.
The internal branch will only be accessible to Google and companies with a Google Mobile Services (GMS) license.

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Sdtimes

163

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Android team unveils upcoming enhancements to make Google Play safer

The Android development team is announcing several upcoming updates that will make it easier for app developers to secure their applications.
Some of the upcoming enhancements include more pre-review checks, better navigation in the Policy Center, and updates to the Play Integrity API.
New ways to help developers understand Google Play policies and new badges for store listings will also be introduced.
The goal is to create a safe and secure ecosystem for app developers and users.

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9 Likes

Towards Data Science

287

Uncertainty Quantification in Machine Learning with an Easy Python Interface

Uncertainty quantification in machine learning (ML) models is crucial for understanding the precision of predictions, especially in real-world applications.
ML Uncertainty is a Python package that offers an intuitive interface to estimate uncertainties in ML predictions and model parameters using powerful mathematical methods.
The package supports linear regression, LASSO regression, ridge regression, elastic net, regression splines, and non-linear regression.
ML Uncertainty allows for uncertainty estimation in random forests by leveraging bootstrapping techniques to estimate prediction intervals.
The ErrorPropagation class in ML Uncertainty computes uncertainty in response variables due to uncertainties in input variables and model parameters.
ML Uncertainty distinguishes itself by enabling uncertainty propagation and catering to various models beyond linear regression.
The package optimizes computational efficiency by separating model training from uncertainty quantification to offer reliable estimates.
Future work involves expanding ML Uncertainty to other ML frameworks like PyTorch and TensorFlow, adding support for more models, and improving documentation.
ML Uncertainty aims to enhance the reliability and interpretability of machine learning models by providing efficient uncertainty quantification tools.
Contributions from the open-source community are encouraged to enhance the capabilities and applicability of the ML Uncertainty package.

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