🚀 RELEASES & LAUNCHES
Meta adds MCP server for AI-driven WhatsApp Business setup
Meta launched a WhatsApp Business MCP server for AI-assisted setup and management.- Developers and businesses using WhatsApp Business can connect AI coding agents including Claude, Cursor, Codex, and ChatGPT.
- The WhatsApp Business Tools MCP server handles account creation, phone verification, Cloud API registration, messaging templates, testing, webhooks, and troubleshooting.
- It consolidates setup work previously spread across Meta’s Developer Console, Business Manager, API reference, and code editor.
- The tool lets AI agents monitor failures involving terms of service, payment methods, and business verification.
Sources 📎 Coverage: techcrunch.com · 👁 via TechCrunch AIPrior Labs releases TabPFN-3.5 tabular foundation model
Prior Labs released TabPFN-3.5 for tabular machine learning.- For tabular machine-learning practitioners working with up to 1 million rows and 20,000 features.
- TabPFN-3.5 leads TabArena and BeyondArena, including text-rich, high-cardinality, and high-dimensional data.
- TabPFN-3.5-Fast is in alpha at 6× the base model’s speed.
- TabPFN-3.5-Thinking is available via API and improves BeyondArena by 20 Elo and TabArena by 44 Elo over the base model.
Sources 📎 Coverage: reddit.com · 👁 via r/MachineLearning
🔬 RESEARCH
- Google Research proposes Retrieve-for-Train for faster complex AI search
Google Research introduced Retrieve-for-Train, a framework for efficient query fan-out retrieval.- Designed for search and recommendation systems that return coherent, complementary result slates.
- Uses offline reinforcement learning to compile reward-aligned query fan-outs into training data for a lightweight diffusion retriever.
- Generates query expansions in a single inference pass instead of relying on extensive autoregressive reasoning tokens.
- The ICML 2026 method targets redundancy and latency bottlenecks in database-aware query decomposition.
Sources 📎 Coverage: research.google · 👁 via Google Research Blog
📊 BENCHMARKS & EVALS
- UPDATE: Qwen3.8-27B GSQ-RCO reaches 88.84 quality on a 16GB GPU
ISTA-DASLab released a Qwen3.8-27B GSQ-RCO quantization for local inference.- For local LLM users running AMD Radeon GPUs with 16GB of VRAM.
- ISTA-DASLab's 27B Qwen3.8 GSQ-RCO GGUF reached 31.7 tokens per second with a 96,256-token context window; 38,337 tokens were used.
- The llm-bench result scored 88.84/100, the highest among more than 1,100 community benchmarks.
- The quantization used 10.59GB of GPU memory, enabling the model to run locally on a single Radeon RX 9070-class card.
Sources 📎 Coverage: llm-bench.io · 👁 via r/LocalLLaMA