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Nace AI has released Drex Max v1 as open weights. Drex Max is its most capable decision model, and it now runs on your own hardware. The release ships bf16 weights, a Q8_0 GGUF, an affine Q4 checkpoint and forks of llama.cpp and Ollama. For developers, Drex Max open weights mean routing, classification and policy
Nace AI has released Drex for Search, which brings its Drex decision models to document search. Drex for Search is a retrieval layer that helps people and agents find evidence. Coding agents such as Claude Code, Codex, Cursor and OpenCode call it through CLI, SDKs, MCP or REST. In Nace’s evaluation, it scores 92.05% on
An AI search agent can work in a demo but fail under production workloads when retrieval returns incomplete evidence, retries repeat unsuccessful actions, or execution exceeds time and cost limits. TinyFish, which provides web infrastructure for AI agents, has published two technical guides addressing these challenges. The first defines a production agentic search architecture, while
TinyFish Breaks Down Agentic Search Architecture for Production AI Agents Read More »
An AI agent that reads the web pays 2 kinds of cost. The first is the retrieval charge: what a search or fetch API bills per request. The second is model processing: content passed from retrieved pages into an AI model consumes tokens, while reasoning and generated responses add further costs. TinyFish, a web infrastructure
TinyFish Makes Search Free: What It Really Costs an AI Agent to Read the Web Read More »
Introduction Teams shipping AI agents at scale keep running into the same pattern. Even agents built on top-tier models like Fable and Astra struggle to carry out complex, end-to-end tasks inside a specific product consistently enough to deploy. So teams grind out evals for the workflows that matter most, often hundreds or thousands of hand-written
Open Source Summit Europe 2026 (OSS EU 2026) took place in Prague, Czechia, from October 7 to 9. On October 8, Min Liu, Executive Director of openKylin, delivered a main-stage keynote titled “From AI Runtime to System Agents: Reimagining Linux for the AI Era with openKylin,” introducing KART, a unified AI runtime, along with a system-level agent
Datalab has released OmniParseBench, an open OCR benchmark for document parsers. It scores parsers with 16,288 yes/no tests on 2,937 pages from 2,343 documents. The pages span 92 languages. The dataset and code are public. It helps engineers answer a sharper question than a leaderboard does: which parser handles documents like mine? Datalab sells a
Beta Lessons Learned After over 500 million downloads, years of requests from the open-source community, and being a top product on Hugging Face, Unsloth launched their beta desktop App, Unsloth Studio. Unsloth which makes it faster, easier, and more affordable to fine-tune and run AI models, including locally on your own hardware. The app centralizes
What Happens When a Trusted Model Repo Changes? Unsloth Studio Re-Checks Before It Runs Read More »
NVIDIA announced a new 64GB configuration of DGX Spark — from Acer, ASUS, Dell, Gigabyte, HP and MSI — its GB10-powered desktop AI system. It gives developers a way to start with one system for local models and agents, then cluster two 64GB units for 128GB of memory across the cluster and more compute when
Datalab has released OmniExtractBench, an open benchmark for structured document extraction. It tests how accurately a system fills a JSON schema from a PDF. The benchmark pools 620 documents from 4 existing benchmarks. One deterministic scorer grades all of them and explains each decision. The release lands while extraction vendors publish their own leaderboards. Datalab
Datalab Introduces OmniExtractBench to Fix Bias and Opacity in Extraction Benchmarks Read More »
