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Medium

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Fueling the Future:

MangoNetwork is launching a Token Generation Event (TGE) to introduce the $MGO utility token for its modular blockchain ecosystem.
$MGO is a utility token that will unlock features, fuel dApps, and enable seamless interaction across MangoNetwork's Layer 1 and Layer 2.
The TGE signifies the live, real, and scalable nature of MangoNetwork, offering developers a secure blockchain foundation with a native utility token.
With $MGO, MangoNetwork can activate its ecosystem, incentivize builders and users, and connect various applications on its layers through the token.

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Dev

469

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Announcing : Global Agentic AI and LLM Bootcamp May 16-18 Bay Area

The tech industry is experiencing a crisis, with skilled professionals being replaced due to lack of AI skills.
Over 7,000 tech layoffs occurred in the Bay Area last quarter, signaling more cuts to come.
Companies are prioritizing AI expertise over years of experience, making it essential for professionals to acquire AI skills.
The Global Agentic AI & LLM Bootcamp in the Bay Area from May 16th-18th, 2025 offers hands-on training to stay relevant and employable.

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Medium

116

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The NuGet Earthquake: Reinventing the Wheel in the Face of Paid Licenses?

Several cornerstone libraries in the development community are considering paid licensing models for their features, marking a shift from the era of free and unlimited Open Source tools.
Maintainers of open-source projects are seeking economic models to sustain their work and offer professional support, given the immense investment of time and resources required.
Understanding the underlying patterns and tools behind these libraries becomes crucial in justifying the need for potentially paid dependencies and finding alternatives.
The paradigm shift prompts a conscious and pragmatic approach to architectural decision-making, while recognizing the value provided by open-source contributors.

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VentureBeat

194

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What’s inside the LLM? Ai2 OLMoTrace will ‘trace’ the source

The Allen Institute for AI (Ai2) has launched OLMoTrace, an open-source tool to trace LLM output to training data, increasing transparency in AI decision-making.
OLMoTrace, part of Ai2’s Open Language Model project, directly links model outputs to training datasets, enhancing understanding of AI responses.
Differentiating from existing methods like Perplexity and ChatGPT Search, OLMoTrace focuses on tracing model outputs without external sources.
By providing direct evidence of model learning sources, OLMoTrace helps users make informed assessments instead of relying on potentially flawed confidence scores.
The tool highlights text sequences in model outputs and reveals corresponding training documents, aiding in understanding how the model generates outputs.
OLMoTrace facilitates enterprises in improving training data quality, enhancing transparency, and building trust with customers regarding model behaviors.
The technology is open-source under Apache 2.0 license, allowing organizations access to implement similar tracing capabilities for their AI models.
As AI governance frameworks advance, tools like OLMoTrace will become vital for verification and auditability in regulated industries using AI systems.
For enterprises striving for AI adoption, OLMoTrace offers a practical way to enhance transparency and accountability in AI systems, even for proprietary models.
The adoption of OLMoTrace can assist organizations in ensuring AI models' accuracy and improving AI decision-making through better understanding of training data.
OLMoTrace's unique approach of tracing model outputs back to training data sets makes it a valuable tool for enterprises seeking to enhance trust and transparency in their AI implementations.

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Pymnts

302

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Report: BaaS Startup Solid Files for Bankruptcy, Aims to Sell

The company had raised $63 million in its funding round and offers a platform to launch and scale FinTech products.

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Dev

376

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Managing React State with Context and useReducer: A Practical Guide

State management in React applications can be challenging as they grow in complexity, prompting the use of React's Context API and useReducer hook for elegant solutions without external dependencies.
React's Context API facilitates sharing global values across components, addressing prop drilling issues by avoiding the need to pass data through multiple levels.
The useReducer hook is valuable for situations where state updates depend on previous states, complex logic needs centralization, or multiple updates require coordination.
A practical example in this article illustrates implementing a state management system for customer data using Context API and useReducer in a React application.
Strongly-typed definitions for customer data and actions ensure type safety, minimizing runtime errors in the state management process.
The reducer function handles state updates immutably, generating new state objects based on the dispatched actions for predictable updates.
A Provider component encapsulates the application state and dispatch function, facilitating the use of the context throughout the components.
A custom hook, useCustomer, abstracts state access and dispatch logic, simplifying common operations in components while maintaining separation of concerns.
By utilizing the Context, reducer, and custom hook, components like CustomerForm demonstrate seamless data management and state updates in React applications.
The combined use of Context and useReducer offers centralized logic, type safety, predictable state transitions, abstraction through custom hooks, independence from external dependencies, and scalability for applications.
Best practices include separating concerns into logical domains, optimizing performance with useMemo and useCallback, handling errors and loading states within the context, and ensuring immutable updates for React state detection.

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Dev

233

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Day 1138 : Parts Unknown

Had a couple of meetings.
Integrated address verification and shipping price calculation into the demo application.
Used AI chat to get assistance in tackling a coding challenge.
Getting close to finishing up a project for launch on Saturday.

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14 Likes

Medium

394

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The Shocking Divide in DevOps: Can You Survive Without Programming?

The DevOps role has become divided between coding and non-coding professionals.
DevOps engineers range from those who struggle with coding to those who excel at infrastructure and system troubleshooting.
The definition of DevOps varies across the industry and within organizations.
Different DevOps engineers may have different toolsets and methods within the same organization.

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PlanetPython

129

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Test and Code: pytest-repeat - works fine on Python 3.14

pytest-repeat is a pytest plugin that makes it easy to repeat a single test, or multiple tests, a specific number of times.
It works fine on Python 3.14 and has been tested on Python 3.9-3.14. It probably works fine on 3.7 & 3.8 as well.
This episode also discusses the attempted April Fools episode.
pytest-repeat GitHub link: pytest-repeat. The April Fools episode: Python 3.14 won't repeat with pytest-repeat.

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Dev

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Build a Sleek Calculator with Dark Mode - Interactive Guide

This article guides you through building a modern calculator with a sleek design and functionality.
The calculator features a dark theme, full calculation history, keyboard support, responsive design, and smooth animations.
The HTML foundation includes a display area, number buttons, operator buttons, and special function buttons.
CSS styling gives the calculator a stylish dark theme, glowing operator buttons, and interactive button effects.
JavaScript is used for handling button clicks, managing calculations, supporting keyboard input, and displaying history.
The calculator ensures safe calculations using the Function constructor, input validation, keyboard support, and error handling.
Further enhancements can include adding a Light/Dark Mode toggle, scientific functions, and memory functions.
Features like avoiding eval(), visual feedback on button presses, responsiveness, accessibility, and easy extensibility make this calculator stand out.
With this calculator, you have a great-looking tool that offers a seamless user experience and can be easily customized with additional features.
Consider expanding its capabilities with features like voice control or calculation graphs for even more functionality.

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Dev

399

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Prompt. Generate. Ship. The Rise of Vibe Coding in 2025

Vibe coding is a new workflow where developers guide AI models to write code through iterative prompting.
It involves describing the desired features in natural language and the AI responds with working code.
Tools like Cursor, ChatGPT, Replit Ghostwriter, and Continue.dev enable vibe coding.
Vibe coding boosts productivity and creativity, but developers still need to read, test, and understand the code.

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Dev

164

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Frida vs. Obscured WebView: Diagnosing the Path to an iOS CAPTCHA Automation

The article discusses the diagnostic phase using Frida to overcome an obscured WebView CAPTCHA during iOS automation research.
The diagnostic phase aimed at understanding obscured mobile components and anti-bot mechanisms through research and methodology demonstration.
Findings are based on research conducted to mitigate risks, shared for educational purposes and defensive awareness.
Frida was chosen for dynamic instrumentation to gain visibility inside the obscured WebView and understand its mechanics.
The setup involved a jailbroken iOS device, macOS VM machine running a Python script, and a custom Frida JavaScript agent.
Frida was used for SSL pinning bypass, WKWebView hooks, and networking hooks to gather essential data for analysis.
Key findings included how the CAPTCHA was loaded and the crucial communication channel using window.webkit.messageHandlers bridge.
The article highlighted that Frida analysis confirmed the WKWebView isolation from standard inspection tools like Appium.
The article concludes with implications, new strategy definition, and the next steps involving the development of an automated visual solving process.
Dynamic instrumentation with Frida provided crucial visibility to understand the CAPTCHA's integration mechanism during iOS automation research.

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9 Likes

Dev

380

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Building a RAG System for Video Content Search and Analysis

The article discusses building a system for video content search and analysis using Amazon Bedrock, Transcribe, and Aurora PostgreSQL.
It outlines the challenges in handling video content and presents a solution using Amazon services.
The solution involves creating searchable vector representations for visual and audio content.
Visual content processing includes extracting frames, generating embeddings, and selecting key frames for storage.
Audio content processing involves speech-to-text conversion, text segmentation, and generating text embeddings.
The system supports cross-modal search capabilities, enabling searches across visual and audio content.
Searches can be performed based on vector similarity using techniques like Cosine Similarity and L2 Distance.
The article also discusses implementing Retrieval-Augmented Generation (RAG) for context-based responses.
The solution allows for complex queries and responses based on context from images and text.
The article concludes with implementation notes and hints at a serverless solution for video analysis.

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Towards Data Science

201

The Invisible Revolution: How Vectors Are (Re)defining Business Success

Vectors play a crucial role in modern business success, offering a dynamic, multidimensional view of underlying connections and patterns.
Understanding vector thinking is essential for decision-making, customer engagement, and data protection in today's data-focused world.
Vectors represent a different concept of relationships, enabling accurate measurement in a multidimensional space for tasks like fraud detection and customer analysis.
Using vector-based computing, businesses can analyze patterns and anomalies, leading to more precise decision-making and personalized customer experiences.
Vector-based systems transform complex data into actionable insights, changing how businesses operate and make strategic decisions.
Cosine similarity and Euclidean distance are key metrics that influence the performance and outcomes of recommendation engines, fraud detection systems, and more.
Real-world applications, such as healthcare early detection systems using vector representations, showcase the practical impact of leveraging vectors for complex data analysis.
Leaders who grasp vector concepts can make informed decisions, drive innovation in personalization and fraud detection, and position their businesses for success in an AI-driven landscape.
Adopting vector-based thinking is crucial for businesses to stay competitive and thrive in an age where data and AI innovations define success.
Embracing vectors as a fundamental tool for data literacy and decision-making can propel businesses forward in the evolving landscape of AI technology.
Vectors are at the core of the invisible revolution reshaping business strategies, and mastering their principles is vital for leaders to navigate and lead in the data-driven future.

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Medium

298

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From Sensor Data to Smart Decisions: How We Built a Contactless Security System with AI

During Deep Blue Season 6, a team of students tackled the urgent need to automate residential society gate security.
The team designed an AI-powered contactless gate system using facial recognition, sensor data, and automation logic.
Initially designed for building entry, the system was moved to the main gate to accommodate the varied ways people enter societies.
The system had two parallel flows for walk-ins and vehicles, each with its own logic and triggers.
The core components used in the system included Python for logic, OpenCV for face detection, and IR temperature sensors for monitoring.
Challenges like face recognition with masks and gate triggering for vehicles were addressed through training and design adjustments.
Real-world behaviors, such as drivers lowering windows for interaction, were considered to ensure smooth vehicle entry.
Feedback mechanisms and defined fallback flows were implemented for every possible failure scenario.
Lessons learned included the importance of user experience design and interaction with real-world users.
The team presented a fully working prototype to mentors, showcasing real-time face recognition and gate control.

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