标签：#ha

The biggest reason NVIDIA began providing GPL-licensed kernel modules is that its driver architecture evolved to the point where Linux integration, distribution, and maintenance could be greatly simplified while keeping the GPU's critical intellectual property in firmware and user-space components . To be precise, NVIDIA did not open-source its entire driver stack. The components that became open are primarily the following Linux kernel modules: nvidia.ko nvidia-drm.ko nvidia-uvm.ko nvidia-modeset.ko User-space components such as CUDA, OpenGL, Vulkan, and the GSP firmware remain proprietary. ( NVIDIA Developer ) 1. To make integration with Linux distributions easier Previously, NVIDIA's proprietary kernel modules had to be built, signed, and distributed separately from the Linux kernel. A DKMS-based workflow, which rebuilds modules after every kernel update, commonly led to problems such as: Kernel modules failing to build after kernel updates Unsigned modules being blocked by Secure Boot Linux distributions having difficulty maintaining the driver as an official package Increased complexity when integrating with custom kernels or cloud environments By publishing the source code, distributions such as Ubuntu, Red Hat, and SUSE can integrate NVIDIA's kernel modules into their own packaging, signing, and update infrastructure much more easily. NVIDIA itself cites tighter OS integration and simpler signing and distribution as key motivations. ( NVIDIA Developer ) 2. To improve debugging and security review Kernel modules interact with deep parts of the operating system, including memory management, interrupts, inter-process synchronization, PCI Express, and display subsystems. With the source code available, Linux distribution developers and enterprise users can: Trace where execution stops inside the kernel Analyze interactions between GPU events and workloads Fix incompatibilities with custom kernels Review security-related issues Submit patches to NVIDIA NVIDIA stat

In a review triggered by OpenAI’s Hugging Face incident, Anthropic discovered three of its AI models had breached real organizations during third-party evaluations.

Github: https://github.com/betocmn/widen I was paying for DataGrip for several years as my desktop database GUI but if I wanted text-to-SQL with LLMs I'd have to pay even more on top. So I decided to build my own and I've been using it for a few weeks now. At any time I'm usually working with 3 to 6 databases, and having them set up in something like Conductor (for parallel agentic coding), but for Postgres databases, lets me quickly start a session, ask questions to my database in plain English

I've worked on several projects writing and implementing specifications (particularly CLN): I've found the specs I write are much better when I quote them in the implementation, so I can see what implementers need to know. Also, when specs change in development, it's almost trivial to find where to update the code. This project is a formalization of my various hacky scripts which ensure the requirements are accurately quoted, and give coverage if any are missing. Not a major Opus, but I hope som

This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry . A payment webhook sounds simple until a successful payment doesn't actually result in the service the customer paid for. That was one of the more interesting bugs I encountered while building The Listening Ear, an appointment and online consultation platform. The requirement was straightforward: A customer pays for a session → the application confirms the payment → the customer's appointment is booked → a Zoom meeting is created. The reality was much more complicated. Project Overview The Listening Ear connects online payments with appointment scheduling and Zoom-based consultations. The application was built with technologies including Next.js 14, TypeScript, Supabase, Prisma, PostgreSQL, Zoom, and payment-provider APIs. The payment workflow was particularly important because payment confirmation was effectively the gatekeeper for the rest of the booking experience. The intended flow looked like this: Customer │ ▼ Payment Provider │ │ webhook ▼ Next.js Webhook │ ├── Verify / interpret payment │ ├── Create Zoom meeting │ └── Create appointment record │ ▼ Customer receives access to their scheduled session The problem was that the webhook sat directly in the middle of all of these operations. Bug Fix or Performance Improvement The bug appeared when I was implementing the payment webhook that would unlock the Zoom scheduling workflow. My initial implementation listened for the payment event and checked whether the event was: if (event === 'charge.success') { Once that condition was met, the webhook immediately continued into the booking workflow. That workflow included: Reading appointment metadata from the payment event. Handling special emergency appointments. Building the Zoom meeting payload. Calling the Zoom meeting API. Creating the appointment record in the database. Returning a successful response to the payment provider. The problem was that all of these operations were effecti