Cotool threw frontier LLMs at real-world SecOps tasks using Splunk’s BOTSv3 dataset.GPT-5topped the chart in accuracy (62.7%) and gave the best results per dollar.Claude Haiku-4.5blazed through tasks fastest, just 240 seconds on average, maxing out tool integrations.Gemini-2.5-proflopped on both acc.. read more  

Google jammed a full no-code AI workshop into Gemini. The browser now bakes inOpal, a drag-and-drop app builder with a shiny newvisual editor. You can chain prompts, preview apps, and feed it text, voice, or images, without touching code. They also dropped theGemini 3 Flash model, built for dual rea.. read more  

AI coding agents are eating the lunch of low-complexity SaaS. Teams with a bit of dev muscle are skipping subscription logins and spinning up dashboards, pipelines, even decks, using Claude, Gemini, whoever’s fastest that day. Build vs. buy? Tilting back toward build. The kicker: build now takes min.. read more  

Google releases Magika 1.0, an AI file detection system rebuilt in Rust for improved performance and security.

Google supports the Model Context Protocol to enhance AI interactions across its services, introducing managed servers and enterprise capabilities through Apigee.

The author details how to effectively prompt Google’s Nano Banana Pro, a visual reasoning model, emphasizing that success relies on structured design documents rather than vague requests. The method prioritizes four key steps: defining the Work Surface (e.g., dashboard or comic), specifying the prec.. read more  

Skald spun up a full local RAG stack, withpgvector,Sentence Transformers,Docling, andllama.cpp, in under 10 minutes. The thing hums on English point queries. Benchmarks show open-source models and rerankers can go toe-to-toe with SaaS tools in most tasks. They stumble, though, on multilingual prompt.. read more  

Researchers trained small transformer models to predict the "long Collatz step," an arithmetic rule for the infamous unsolved Collatz conjecture, achieving surprisingly high accuracy up to 99.8%. The models did not learn the universal algorithm, but instead showed quantized learning, mastering speci.. read more  

Amp’s team isn’t chasing token limits. Even with ~200k available via Opus 4.5, they stick toshort, modular threads, around 80k tokens each. Why? Smaller threads are cheaper, more stable, and just work better. Instead of stuffing everything into a single mega-context, they slice big tasks into focuse.. read more