1. How Endava builds an agentic organization with Codex
Source: OpenAI Blog · Signal: 69 ·
Learn how Endava uses Codex to build an agentic organization, accelerating software delivery and reducing requirements analysis from weeks to hours.
2. OpenAI’s Frontier Governance Framework
Source: OpenAI Blog · Signal: 68 ·
Explore OpenAI’s Frontier Governance Framework and how our AI safety, security, and risk practices align with emerging EU and California regulations.
3. orval-labs/orval
Source: GitHub Trending — TypeScript · Signal: 67 ·
orval is able to generate client with appropriate type-signatures (TypeScript) from any valid OpenAPI v3 or Swagger v2 specification, either in yaml or json formats. 🍺 Generate Typescript clients from OpenAPI specification! Code Generation orval generates type-safe JS clients (...
4. MUFG aims to become AI-native with OpenAI
Source: OpenAI Blog · Signal: 66 ·
MUFG uses ChatGPT Enterprise to build an AI-native organization, improve workflows, and deliver new AI-powered financial services at scale.
5. Claude Opus 4.8: "a modest but tangible improvement"
Source: Simon Willison Blog · Signal: 65 ·
6. llm-anthropic 0.25.1
Source: Simon Willison Blog · Signal: 65 ·
7. Agyn: An Open-Source Platform for AI Agents with Scalable On-Demand Execution, Agent Definition as a Code, and Zero-Trust Access
Source: ArXiv cs.AI · Signal: 64 ·
arXiv:2605.27575v1 Announce Type: new Abstract: As organizations move toward production deployments of AI agents, which execute non-deterministic workflows, maintain stateful sessions, and often operate with privileged access to internal services, the engineering challenge shifts from building i...
8. Why LLMs Fail at Causal Discovery and How Interventional Agents Escape
Source: ArXiv cs.AI · Signal: 64 ·
arXiv:2605.27567v1 Announce Type: new Abstract: Causal discovery is a cornerstone of scientific reasoning, yet whether large language models can perform it reliably remains an open question. Recent benchmarks show that even fine-tuned models plateau on simple causal graphs and degrade as complex...
9. DynaSchedBench: Calibrated Dynamic Scheduling Benchmarks and Observability Paradox in LLM-based Scheduling Agents
Source: ArXiv cs.AI · Signal: 64 ·
arXiv:2605.27566v1 Announce Type: new Abstract: Progress in neural combinatorial optimization for Dynamic Flexible Job Shop Scheduling Problem (DFJSP) is currently hindered by a methodological tension: static benchmarks encourage benchmark overfitting, while uncalibrated generators obscure algor...
10. The Age of Async Agents — Cognition's Walden Yan & OpenInspect's Cole Murray
Source: Latent Space Podcast · Signal: 62 ·
80% Devin Commits, Spec-to-PR Workflows, Full VMs, Agent Memory, and PMs Shipping Code
11. [AINews] Cognition raises $1B in $26B Series D
Source: Latent Space Podcast · Signal: 62 ·
coding is an uncapped TAM market
12. Unlocking Fine-Grained and Within-Utterance Speaking Style Control in Prompt-Based Text-to-Speech Models
Source: ArXiv cs.CL (NLP/LLMs) · Signal: 61 ·
arXiv:2605.27376v1 Announce Type: new Abstract: While prompt-based text-to-speech (TTS) models enable natural language-driven speaking style control, they often provide limited fine-grained control and apply a single global style across an utterance. This restricts practical use cases that requi...