Black Seed USA AI Hub
Bias-Aware Prompt Engineering to Improve Fairness in Large Language Models
Bias-aware prompt engineering helps reduce unfair outputs in large language models by changing how you ask questions-not by retraining the model. Learn proven techniques, real results, and how to start today.
Team Collaboration in Cursor and Replit: Shared Context and Reviews Compared
Cursor and Replit offer very different approaches to team collaboration: Replit excels at real-time, browser-based coding for learning and prototyping, while Cursor delivers deep codebase awareness and secure, Git-first reviews for enterprise teams.
Knowledge Boundaries in Large Language Models: How AI Knows When It Doesn't Know
Large language models often answer confidently even when they're wrong. Learn how AI systems are learning to recognize their own knowledge limits and communicate uncertainty to reduce hallucinations and build trust.
Data Retention Policies for Vibe-Coded SaaS: What to Keep and Purge
Vibe-coded SaaS apps often collect too much user data by default. Learn what to keep, what to purge, and how to build compliance into your AI prompts to avoid fines and build trust.
Agentic Systems vs Vibe Coding: How to Pick the Right AI Autonomy for Your Project
Agentic systems automate coding tasks with minimal human input, while vibe coding lets you build fast with conversational AI. Learn which approach fits your project-and how to use both safely in 2026.
Security Code Review for AI Output: Essential Checklists for Verification Engineers
AI-generated code is often functional but insecure. Verification engineers need specialized checklists to catch hidden vulnerabilities like missing input validation, hardcoded secrets, and insecure error handling. Learn the top patterns, tools, and steps to secure AI code today.
Style Transfer Prompts in Generative AI: Control Tone, Voice, and Format Like a Pro
Learn how to use style transfer prompts in generative AI to control tone, voice, and format-without losing meaning. Get practical steps, real-world examples, and pro tips for marketing and content teams.
Prompt Chaining for Multi-File Refactors in Version-Controlled Repositories
Prompt chaining lets you safely refactor code across multiple files using AI, reducing errors by 68% compared to single prompts. Learn how to use it with LangChain, Autogen, and version control.
Guarded Tool Access: How to Sandbox External Actions in LLM Agents for Real-World Security
Sandboxing LLM agents is no longer optional-untrusted tool access can leak data even with perfect prompt filters. Learn how Firecracker, gVisor, Nix, and WASM lock down agents to prevent breaches.
Secure Defaults in Vibe Coding: How CSP, HTTPS, and Security Headers Protect AI-Generated Apps
Secure defaults in vibe coding - CSP, HTTPS, and security headers - are critical to protect AI-generated apps from attacks. Learn why platforms like Replit lead in security and how to fix common vulnerabilities before they're exploited.
Security Telemetry and Alerting for AI-Generated Applications: What You Need to Know
AI-generated apps behave differently than traditional software. Learn how security telemetry tracks model behavior, detects prompt injections, and reduces false alerts-without relying on outdated tools.
Safety in Multimodal Generative AI: How Content Filters Block Harmful Images and Audio
Multimodal AI can generate images and audio from text-but harmful content slips through filters. Learn how companies are blocking dangerous outputs, the hidden threats in images and audio, and what you need to know before using these systems.