The Local-First AI Inference pattern routes 70–80% of documents to deterministic local extraction at zero API cost, reserving Azure OpenAI calls for edge cases and flagging low-confidence results for human review. Deployed on 4,700 engineering drawing PDFs, it cut API costs by 75% and processing time by 55%, while bounding errors through a human review tier.
Azure OpenAI in Foundry Models | Microsoft Azure
Access and fine-tune the latest AI reasoning and multimodal models, integrate AI agents, and deploy secure, enterprise-ready generative AI solutions.
Cloudflare Workers AI | Open-source AI inference
Workers AI facilitates the scalable development & deployment of AI applications at the edge.
local.ai — Charting the transition from cloud AI to local AI.
Independent benchmark quality and measured serving performance for local hardware you can buy.
Together AI | The AI Native Cloud
Build what's next on the AI Native Cloud. Full-stack AI platform for inference, fine-tuning, and GPU clusters — powered by cutting-edge research.
OpenAccess.ai — Rigorous Open Access Publishing
$20 to submit, free to read. AI peer review. Open to human and machine authors. All articles CC-BY 4.0.
AI-Native Cloud | DigitalOcean
Run AI products in production with a unified stack for agents, inference, and cloud—built for control, performance, and economics at scale.
OpenAI can’t tell if something was written by AI after all
OpenAI’s tool struggled with accuracy.
High Performance AI Lab
High Performance AI Lab builds open inference systems and publishes the conditions behind every number — device, model, quant, and rep count.
OpenAI to acquire Ona
OpenAI plans to acquire Ona to expand Codex with secure, persistent cloud environments, enabling long-running AI agents across enterprise workflows.
OpenAI details more cases of AI agents taking unauthorized actions
OpenAI has presented new examples of what they call "AI model misalignment" from the past six months, including unauthorized file uploads, following self-generated instructions, hiding mistakes, and leveraging exposed API keys.
Research acceleration: The view inside OpenAI
Inside OpenAI, coding agents are reshaping AI research. Explore early data on agent usage, experiment velocity, task complexity, and research acceleration.
PaperOrchestra: A Multi-Agent Framework for Automated AI Research Paper Writing
Synthesizing unstructured research materials into manuscripts is an essential yet under-explored challenge in AI-driven scientific discovery. Existing autonomous writers are rigidly coupled to specific experimental pipelines, and produce superficial literature reviews. We introduce PaperOrchestra, a multi-agent framework for automated AI research paper writing. It flexibly transforms unconstrained pre-writing materials into submission-ready LaTeX manuscripts, including comprehensive literature synthesis and generated visuals, such as plots and conceptual diagrams. To evaluate performance, we present PaperWritingBench, the first standardized benchmark of reverse-engineered raw materials from 200 top-tier AI conference papers, alongside a comprehensive suite of automated evaluators. In side-by-side human evaluations, PaperOrchestra significantly outperforms autonomous baselines, achieving an absolute win rate margin of 50%-68% in literature review quality, and 14%-38% in overall manuscript quality.
OpenAI and Microsoft
We’re working with Microsoft to start running most of our large-scale experiments on Azure.
OpenAIReview — AI-Powered Academic Paper Reviewer
we recommend using uv, and there are additional guidelines for best results with PDF inputs
Alex Cheema on Twitter / X
This is why we need open benchmarks for local AI.Otherwise it turns into tribalism and name calling.We will be publishing the largest database of open benchmarks for local AI, tested on 1,000+ real hardware setups. Every device, every interconnect, different… https://t.co/ZsU3PCdSsZ— Alex Cheema (@alexocheema) March 9, 2026