Building an A2A Agent for telex.im using Mastra

Phishing continues to pose a significant threat in cybersecurity, but there's hope with the development of PhishNet, an AI agent designed to detect phishing attempts in real time. This innovative tool not only identifies threats but also educates users on how to recognize them, making online interactions safer.

I recently developed an AI Goal Planner Agent that works with Telex, a platform like Slack, allowing users to chat with intelligent agents for task management. Using Mastra, a framework that streamlines AI workflows, I aimed to create an agent that automatically plans your week, making life easier.

WebForms Core is an innovative server-driven web technology that enables dynamic client-side actions through structured server responses. It features a configurable security layer called Security section in WebFormsOptions, ensuring safe execution of commands that can modify the DOM or load modules.

StudySync AI is an innovative conversational study partner that enhances learning by engaging users in natural dialogue. Powered by Mastra and Google’s Gemini 2.0, it remembers past sessions and adapts to individual learning styles, making study sessions feel more human and supportive.

During the HNG Internship, a modular text transformation chain was developed using FastAPI microservices, seamlessly integrated with Telex.im’s AI coworker platform. This innovative workflow includes various agents that perform specific tasks, such as capitalizing text and reversing word order, showcasing the potential of microservices in enhancing productivity and efficiency in text processing. This project not only demonstrates technical skills but also highlights the importance of collaboration in the tech industry.

In a recent article, a developer shares their journey of learning React, highlighting the excitement of creating reusable components. Coming from a background in multi-page applications like PHP and vanilla JavaScript, they found the transition to React's component-based architecture both challenging and rewarding. This insight is valuable for anyone looking to enhance their web development skills, as understanding component composition is crucial for building efficient and scalable user interfaces.

As we move into 2025, there's a growing emphasis on mastering vanilla JavaScript for DOM manipulation, moving away from jQuery. This shift is significant because it encourages developers to understand the core of JavaScript, leading to better performance and more efficient coding practices. By focusing on native JavaScript, developers can create faster, more responsive web applications that are easier to maintain and scale.

Exciting news for JavaScript developers! You no longer need to learn Python to create AI agents. A new article by Timmydee introduces a straightforward way to build an AI Summarizer Agent using TypeScript. This is significant because it opens up AI development to a broader audience, allowing more developers to harness the power of AI without needing to switch languages. The article provides practical insights and a step-by-step guide, making it easier than ever to dive into AI projects.

Recent advancements in LLM Multi-Agent Systems are making it easier to manage numerous tools and sub-agents effectively. The introduction of Tool-to-Agent Retrieval aims to enhance agent selection by providing a clearer understanding of tool functionalities, leading to better orchestration and improved performance.

Tool Zero introduces an innovative approach to training language models using pure reinforcement learning from scratch. This method aims to enhance the capabilities of language models for complex tasks, overcoming the limitations of traditional supervised fine-tuning that often struggles with unfamiliar scenarios.

This article explores the advantages of deep models over shallow ones in a framework that doesn't depend on specific network implementations. It discusses how deep models can be understood as abstract state-transition semigroups and presents a bias-variance decomposition that highlights the role of depth in determining variance.

This article presents a groundbreaking model called the Structurally Adaptive Predictive Inference Network (SAPIN), which draws inspiration from biological neural cultures. Unlike traditional neural networks that use global backpropagation, SAPIN employs active inference principles to enhance learning and adaptability, showcasing a promising direction for future computational models.

A new approach to deep reinforcement learning tackles the challenges posed by non-stationary environments. By focusing on maintaining the flexibility of the critic network and enhancing exploration strategies, this method aims to improve stability and performance in dynamic settings.

VidEmo introduces a new approach to understanding emotions in videos, leveraging advancements in video large language models. This innovative method aims to tackle the complexities of emotional analysis, addressing the dynamic nature of emotions and their dependence on various cues.