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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. TOOL · dev.to — LLM tag English(EN) ·

    I built an open-source LLM eval framework as a BCA student — hallucination detection, red-teaming, regression tracking

    A BCA student has developed an open-source framework to evaluate Large Language Models (LLMs), addressing the challenge of ensuring AI product performance. The framework includes a 27-test suite for accuracy, safety, and hallucination detection, utilizing a three-tier scoring system. It also features automated adversarial prompt generation for red-teaming and regression tracking across model versions, all presented through a live dashboard. AI

    I built an open-source LLM eval framework as a BCA student — hallucination detection, red-teaming, regression tracking

    IMPACT Provides a free, open-source tool for developers to monitor and improve LLM performance, potentially accelerating AI product development.

  2. COMMENTARY · MarkTechPost English(EN) ·

    Upstash for Redis vs Supabase vs Neon: Which One Fits Vibe Coding Workflows in 2026?

    This article compares Upstash for Redis, Supabase, and Neon, clarifying their distinct roles in modern application development, particularly for "vibe coding" workflows that leverage AI assistants. Upstash offers serverless Redis for caching and rate limiting, functioning as a complementary layer rather than a direct competitor to databases. Neon is presented as a standalone serverless PostgreSQL database optimized for instant branching and scalability. Supabase, built on PostgreSQL, provides a comprehensive backend-as-a-service platform including authentication, storage, real-time capabilities, and edge functions, making it a full-stack solution. AI

    Upstash for Redis vs Supabase vs Neon: Which One Fits Vibe Coding Workflows in 2026?

    IMPACT Clarifies the distinct use cases of backend tools for developers building AI-assisted applications.