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Plus: A ransomware group is now stealing data in person, BusPatrol wants to hand its license plate surveillance data to the cops, and more.

This is a submission for the Hermes Agent Challenge: Write About Hermes Agent Hermes Agent...

A $0 multi-model decision agent: three LLMs debate, Hermes judges, and it learns who to trust.

Hiring an AI Development Company? Ask These 7 Questions First Most AI projects fail long before deployment. Not because the model is bad. Because teams skip the hard engineering questions. If you're evaluating an AI development company, ask these 7 questions first: 1. How is data secured? AI systems process sensitive business information. Ask: Where is data stored? Is encryption enabled at rest and in transit? Who has access to prompts, logs, and embeddings? Are enterprise security standards followed? Security should be designed in from day one. 2. What observability exists? You can't improve what you can't monitor. A production AI system should include: Request tracing Prompt/version tracking Latency monitoring Cost visibility Error reporting If nobody can explain what happened after a bad output — that's a problem. 3. How do you handle model drift? AI performance changes over time. Questions to ask: How are outputs evaluated? Is feedback collected? How are prompts/versioning managed? What happens when accuracy drops? Production systems need iteration loops. 4. What happens during failure? No system is perfect. Ask: Is there fallback logic? Human review? Retry handling? Graceful degradation? Failure handling matters more than demos. 5. How is access controlled? Enterprise AI systems require permissions. Examples: Role-based access API authentication Audit logs Team-level controls Not everyone should access everything. 6. What compliance assumptions exist? Especially important for regulated industries. Ask whether the system considers: GDPR SOC2 HIPAA Financial or internal compliance rules Compliance cannot be an afterthought. 7. Who owns the infrastructure? Clarify ownership before signing anything. Ask: Who owns the source code? Cloud infrastructure? Models and prompts? Data pipelines? You should avoid vendor lock-in. AI success is rarely about flashy demos. It's about secure infrastructure, reliability, observability, and long-term maintainability. What question

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built I revisited my personal website and decided to turn it into a more complete and polished portfolio. The project originally started as a simple personal website hosted on GitHub Pages. While it was functional, many planned features and improvements were never completed. For this challenge, I am working on improving the mobile experience, adding dark mode support, enhancing the Projects section, improving SEO, fixing existing issues, and making the website more interactive and professional. My goal is to transform an unfinished personal website into a modern portfolio that better represents my work, skills, and projects. Demo Live Website https://ehsankahrizi.github.io/ GitHub Repository https://github.com/Ehsankahrizi/Ehsankahrizi.github.io Current Status This project is currently being improved as part of the GitHub Finish-Up-A-Thon Challenge. Planned improvements include: Better mobile responsiveness Dark mode support Enhanced Projects section Improved SEO More interactive user experience Better website performance Additional pages and content Fixing existing usability issues The Comeback Story When I returned to this project, I realized that many ideas I originally had for the website were still unfinished. Although the website was online, it still had several limitations: The mobile experience needed improvement. Dark mode was not available. The Projects section was incomplete. The contact functionality needed attention. SEO optimization was missing. The website relied on a mostly single-page structure. User interactions were limited. Performance could be improved. Instead of starting a new project, I decided to revisit this existing one and finally complete the improvements that had been postponed. This challenge provided the perfect opportunity to continue development, clean up the codebase, improve the user experience, and turn the website into something I can confidently share with ot

The Journey I set out to build an efficient automation backend utility designed to classify incoming customer inquiries and automatically suggest optimized replies. My goal for this challenge was to document the transition of a basic, fragile text-parsing utility into a highly resilient, enterprise-ready application script. Production Evolution The Before (Fragile Prototype Base): The initial design relied entirely on rigid, case-sensitive string matching. It contained no persistent database engine, meaning analytics data was lost instantly upon script termination, and it lacked structural exception guarding, making it highly susceptible to standard runtime crashes if empty or unexpected values were inputted. The After (Fault-Tolerant Enterprise Core): I fully refactored the utility's entire architectural layout. The system now initializes a local SQLite relational table structure ( analytics.db ), appends automated date/time timestamps for granular metrics tracking, leverages strict array-based substring evaluation, and traps fatal exceptions seamlessly via localized error handling catch loops. Public Code Repository The complete operational source code, database persistence schemas, and functional verification blocks can be inspected directly via my public repository: https://github.com/playsmai/biz-responder-automation-