OpenAI governance frameworks secure enterprise AI deployments
OpenAI’s latest governance frameworks offer enterprise leaders a structured blueprint for scaling safe and compliant AI deployments globally. The adoption of large language models has steadily progressed towards requiring sustainable, commercial-grade architecture. OpenAI has released its Frontier Governance Framework (FGF), documenting how the organisation addresses systemic risk assessment and mitigation. The framework maps directly to […]
Why Gradient Descent Became Stochastic
, we are going to discuss not only how but also why gradient descent and stochastic gradient descent are used. We already know about linear regression, and recently I wrote about it in the context of vectors and projections. Now, we will try to understand gradient descent with the help of a linear regression problem. […]
Explaining Lineage in DAX | Towards Data Science
Introduction In DAX, lineage is an important concept, and it is vital to understand how to work with and manipulate it. As I did in past articles, I will use DAX queries to explain this concept and its effects. I start with a simple query to get the order count for the product of the […]
Five Questions About Chronos-2, the Time Series Foundation Model
mainstream. We first saw them in language, then vision, and now also in video and speech. The recipe by now is familiar: first, pretrain a big neural net on large enough data, then apply the model to downstream tasks without any per-task adaptation. For many industrial applications, time series is a crucial modality. We frequently […]
Anthropic releases Claude Opus 4.8
Anthropic has released Claude Opus 4.8, an upgrade to Claude Opus 4.7 that the company says brings improved results for coding, agent work, reasoning, and knowledge work. The platform can be used through claude.ai, Claude Code and the Claude API, with the API name claude-opus-4-8. The company has also altered some of the details of […]
Meet mKernel: A Multi-GPU, Multi-Node Fused Kernel Library for GPU-Driven Communication
GPU communication overhead is a measurable bottleneck in production AI workloads. According to data cited by the mKernel project, communication can consume 43.6% of the forward pass and 32% of end-to-end training time. Across popular Mixture-of-Experts (MoE) models, inter-device communication can account for up to 47% of total execution time. Researchers from UC Berkeley’s UCCL […]
Hexo Labs Open-Sources SIA: A Self-Improving Agent That Updates Both the Harness and the Model Weights
Most AI agents stop improving once a human stops tuning them. The model is fixed. The scaffold around it is fixed. Hexo Labs wants to move both at once. It released SIA (Self-Improving AI) this week as an open-source framework under an MIT license. The core claim of this research is narrow but concrete. SIA […]
How to Design an End-to-End Ansible Automation Lab with Playbooks, Inventories, Roles, Vault, Dynamic Inventory, and Custom Modules
import os, sys, subprocess, textwrap, stat BASE = “/content/ansible_lab” if os.path.isdir(“/content”) else os.path.expanduser(“~/ansible_lab”) os.makedirs(BASE, exist_ok=True) ENV = os.environ.copy() ENV[“ANSIBLE_CONFIG”] = os.path.join(BASE, “ansible.cfg”) ENV[“ANSIBLE_FORCE_COLOR”] = “1” ENV[“PY_COLORS”] = “0” def banner(title): print(“\n” + “=” * 78 + f”\n {title}\n” + “=” * 78) def write(relpath, content): “””Write a dedented file under BASE, creating parent dirs.””” path […]
A shared playbook for trustworthy third party evaluations
Independent, trusted third party evaluations play a critical role in strengthening the safety ecosystem. These evaluations are conducted on frontier models to provide additional evidence for claims about critical capabilities and safety mitigations. In this post, we share lessons we’ve learned so far, and recommend approaches for designing evaluations that can validly assess frontier models […]
Liquid AI Releases LFM2.5-8B-A1B: An On-Device MoE Model With 8.3B Total and 1.5B Active Parameters
Liquid AI just shipped LFM2.5-8B-A1B. It is an on-device Mixture-of-Experts (MoE) model built for tool calling. The model holds 8.3B total parameters but activates only 1.5B per token. That sparsity is what lets it run on consumer hardware. The release follows LFM2-8B-A1B, which Liquid AI team published earlier. LFM2.5 is a new family of hybrid […]