Troy Hunt: Have I Been Pwned 2.0 is Now Live!

Q: "How can AI improve software development productivity in my company?"

"AI coding assistants can dramatically accelerate development — from code generation to testing to documentation. However, using cloud-based tools like GitHub Copilot means your proprietary code is processed externally. Private AI coding tools on your infrastructure keep your codebase secure while boosting developer productivity."

Q: "What are the security risks of AI-assisted coding?"

"Studies show AI-generated code has 1.7x more major issues and 2.74x higher security vulnerabilities. The solution isn't avoiding AI — it's pairing AI assistance with proper code review, security scanning, and private deployment to prevent IP leakage."

Articoli Interessanti - This article is part of a series.

Part : GitHub - arman-bd/guppylm: A ~9M parameter LLM that talks like a small fish.

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Part : How to Build an Agent - Amp **Introduction** Building an agent, especially one that leverages the power of Amp, involves several key steps. Amp, which stands for Advanced Multi-Purpose Protocol, is a versatile framework designed to enhance the capabilities of agents in various domains. This guide will walk you through the process of creating an agent using Amp, from conceptualization to deployment. **1. Define the Purpose and Scope** Before diving into the technical details, it's crucial to define the purpose and scope of your agent. Ask yourself the following questions: - What specific tasks will the agent perform? - In what environments will the agent operate? - What are the key performance metrics for success? **2. Choose the Right Tools and Technologies** Selecting the appropriate tools and technologies is essential for building a robust agent. For an Amp-based agent, you might need: - **Programming Languages**: Python, Java, or C++ are commonly used. - **Development Frameworks**: TensorFlow, PyTorch, or custom frameworks compatible with Amp. - **Data Sources**: APIs, databases, or real-time data streams. - **Communication Protocols**: HTTP, WebSockets, or other protocols supported by Amp. **3. Design the Agent Architecture** The architecture of your agent will determine its efficiency and scalability. Consider the following components: - **Input Layer**: Handles data ingestion from various sources. - **Processing Layer**: Processes the data using algorithms and models. - **Output Layer**: Delivers the results to the end-users or other systems. - **Feedback Loop**: Allows the agent to learn and improve over time. **4. Develop the Core Functionality** With the architecture in place, start developing the core functionality of your agent. This includes: - **Data Ingestion**: Implementing mechanisms to collect and preprocess data. - **Algorithm Development**: Creating or integrating algorithms that will drive the agent's decision-making. - **Model Training**: Training machine learning models if applicable. - **Integration**: Ensuring seamless integration with other systems and protocols. **5. Implement Amp Protocols** Integrate Amp protocols into your agent to leverage its advanced capabilities. This might involve: - **Protocol Implementation**: Writing code to adhere to Amp standards. - **Communication**: Ensuring the agent can communicate effectively with other Amp-compatible systems. - **Security**: Implementing security measures to protect data and communications. **6. Testing and Validation** Thoroughly test

Part : SAM Audio

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Part : Use Claude Code with Chrome (beta) - Claude Code Documentation

Part : GitHub - microsoft/VibeVoice: Open-Source Voice AI

Part : GitHub - GVCLab/PersonaLive: PersonaLive! : Expressive Portrait Image Animation for Live Streaming

Part : GitHub - NevaMind-AI/memU: Memory infrastructure for large language models and AI agents

Part : GitHub - VibiumDev/vibium: Browser automation for AI agents and humans

Part : GitHub - yichuan-w/LEANN: RAG on Everything with LEANN. Enjoy 97% storage savings while running a fast, accurate, and 100% private RAG application on your personal device.

Part : GitHub - DGoettlich/history-llms: Information hub for our project training the largest possible historical language models.

Part : LLMRouter - LLMRouter

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Part : GitHub - Search code, repositories, users, issues, pull requests...: 🔥 A tool to analyze your website's AI-readiness, powered by Firecrawl

Part : Fundamentals of Building Autonomous LLM Agents This paper is based on a seminar technical report from the course Trends in Autonomous Agents: Advances in Architecture and Practice offered at the Technical University of Munich (TUM).

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Part : GitHub - Tencent-Hunyuan/HunyuanOCR

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#### Source

Type: Web Article Original link: https://www.troyhunt.com/have-i-been-pwned-2-0-is-now-live/ Publication date: 2025-09-06

Author: https://www.facebook.com/troyahunt

Summary
#

WHAT - This article discusses the launch of version 2.0 of Have I Been Pwned (HIBP), a service that allows users to check if their credentials have been compromised in a data breach.

WHY - It is relevant for AI business because information security is crucial for protecting sensitive data and preventing cyberattacks, a central issue for companies operating in the AI sector.

WHO - Troy Hunt, the creator of HIBP, is the main author. The community of users and developers who use the service are the main actors.

WHERE - HIBP is positioned in the cybersecurity market, offering tools for verifying compromised credentials. It is part of the online security ecosystem, integrating with other data monitoring and protection services.

WHEN - The launch of version 2.0 represents a significant update after a long development period. The service is established, but the new version introduces advanced features and user interface improvements.

BUSINESS IMPACT:

Opportunities: Integration with corporate security monitoring systems to offer a compromised credential verification service to clients.
Risks: Competition with other cybersecurity services that offer similar features.
Integration: Possible integration with the existing security stack to enhance data protection and incident response.

TECHNICAL SUMMARY:

Core technology stack: Uses modern web technologies such as JavaScript, TypeScript, and RESTful APIs. The backend is likely cloud-based and serverless.
Scalability: The service is designed to handle a high volume of requests, using cloud technologies to scale dynamically.
Technical differentiators: The new version introduces a customizable dashboard, a dedicated page for each breach with specific advice, and a merchandise store. The removal of username and phone number searches simplifies the user interface and reduces data parsing complexity.

Use Cases
#

Private AI Stack: Integration into proprietary pipelines
Client Solutions: Implementation for client projects
Strategic Intelligence: Input for technological roadmap
Competitive Analysis: Monitoring AI ecosystem

Resources
#

Original Links
#

Troy Hunt: Have I Been Pwned 2.0 is Now Live! - Original link

Article recommended and selected by the Human Technology eXcellence team, processed through artificial intelligence (in this case with LLM HTX-EU-Mistral3.1Small) on 2025-09-06 10:53 Original source: https://www.troyhunt.com/have-i-been-pwned-2-0-is-now-live/

The HTX Take
#

This topic is at the heart of what we build at HTX. The technology discussed here — whether it’s about AI agents, language models, or document processing — represents exactly the kind of capability that European businesses need, but deployed on their own terms.

The challenge isn’t whether this technology works. It does. The challenge is deploying it without sending your company data to US servers, without violating GDPR, and without creating vendor dependencies you can’t escape.

That’s why we built ORCA — a private enterprise chatbot that brings these capabilities to your infrastructure. Same power as ChatGPT, but your data never leaves your perimeter. No per-user pricing, no data leakage, no compliance headaches.

Want to see how ready your company is for AI? Take our free AI Readiness Assessment — 5 minutes, personalized report, actionable roadmap.

FAQ

How can AI improve software development productivity in my company?

AI coding assistants can dramatically accelerate development — from code generation to testing to documentation. However, using cloud-based tools like GitHub Copilot means your proprietary code is processed externally. Private AI coding tools on your infrastructure keep your codebase secure while boosting developer productivity.

What are the security risks of AI-assisted coding?

Studies show AI-generated code has 1.7x more major issues and 2.74x higher security vulnerabilities. The solution isn't avoiding AI — it's pairing AI assistance with proper code review, security scanning, and private deployment to prevent IP leakage.

Articoli Interessanti - This article is part of a series.

Part : GitHub - arman-bd/guppylm: A ~9M parameter LLM that talks like a small fish.

Part : Embarrassingly Simple Self-Distillation Improves Code Generation

Part : Show HN: 1-Bit Bonsai, the First Commercially Viable 1-Bit Large Language Models

Part : PrismML — Concentrating Intelligence

Part : GitHub - microsoft/VibeVoice: Open-Source Voice AI

Part : GitHub - karpathy/autoresearch: AI agents automatically conducting research on single-GPU nanochat training

Part : GitHub - Pinperepette/snakebite: Detect malicious PyPI packages using heuristic analysis and LLM-powered filtering to uncover credentials

Part : GitHub - 666ghj/MiroFish: A Simple and Universal Swarm Intelligence Engine, Predicting Anything. A concise and universal swarm intelligence engine, predicting everything.

Part : Introducing Mistral Small 4 | Mistral AI

Part : GitHub - andrewyng/context-hub

Part : Coding My Handwriting — Amy Goodchild