
Supabase has open sourced Supabase Evals, its benchmark and framework for testing how well AI agents build using Supabase. It runs coding agents including Claude Code, Codex, and OpenCode against real tasks, such as building a schema, debugging a failed Edge Function, or fixing a broken RLS policy, then scores the result. It powers the public leaderboard at supabase.com/evals and an internal regression suite monitored daily. Is it deployable? Yes, today. supabase/evals is public under Apa
Will the Supabase Evals GitHub repo reach 1,000 stars by August 9, 2026?
Resolves by Aug 9, 2026
Supabase has released an open source benchmark framework called Supabase Evals that tests how well AI coding agents perform on real database and backend tasks, such as building schemas, debugging functions, and fixing security policies. The benchmark runs multiple AI agents against actual Supabase environments in containers rather than simulated versions, then scores results using a combination of automated checks and AI-based judgment. The framework matters for developers using AI agents to write code in regulated industries like fintech and healthcare, where mistakes in security policies could create serious vulnerabilities. The benchmark is publicly available and powers both a public leaderboard and internal testing, revealing that top models perform well without additional guidance while smaller models benefit more from skills and that different agents vary significantly in how often they consult documentation.

Microsoft has open sourced code-testing-generator, a polyglot agent that writes unit tests and then proves they work. It ships in the dotnet-test plugin inside the MIT-licensed dotnet/skills repository. The agent targets a gap that coding assistants usually leave open. A prompt like ‘generate unit tests’ does not say which framework, file location or assertions to use. code-testing-generator settles those decisions by reading the repository before it writes anything. It then pla

A new SaferAI report finds Z.ai's open-weight GLM-5.2 approaches frontier AI capabilities while lacking key safety mitigations, renewing concerns that powerful open models could outpace governance and safeguards.

In this tutorial, we design an end-to-end evaluation workflow for PerceptionBench. This multimodal benchmark measures fine-grained visual perception capabilities across tasks such as OCR, counting, localization, contextual reasoning, comparison, depth understanding, and hallucination detection. We begin by configuring a Colab-compatible environment, installing the required libraries, and loading a balanced subset of the dataset through a robust multi-stage streaming and download strategy. We th
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