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Monitor Amazon products at scale with real-time data you can trust. From product listings to pricing and Buy Box shifts, Rainforest API gives you structured data to power dynamic pricing, content optimization, and competitive strategy.

$18/mo

Charts, apps, and api for amazon product data

Detailed Price History charts for over 5 billion Amazon products.

$49/mo

Returns details about a listings item for a selling partner.

contains pricing

Search for a list of Amazon catalog items and item-related information. You can search by identifier or by keywords.

The Grafana Kubernetes Monitoring Helm chart deploys a complete monitoring solution for your Cluster and applications running within it. The chart installs systems, such as Node Exporter and Grafana Alloy Operator, along with their configuration to make these systems run. These elements are kept up to date in the Kubernetes Monitoring Helm chart with a dependency updating system to ensure that the latest versions are used.

OpenCost is a vendor-neutral open source project for measuring and allocating cloud infrastructure and container costs in real time. Built by Kubernetes experts and supported by Kubernetes practitioners, OpenCost shines a light into the black box of Kubernetes spend.

NVTOP stands for Neat Videocard TOP, a (h)top like task monitor for GPUs and accelerators. It can handle multiple GPUs and print information about them in a htop-familiar way.

Currently supported vendors are AMD (Linux amdgpu driver), Apple (limited M1 & M2 support), Huawei (Ascend), Intel (Linux i915/Xe drivers), NVIDIA (Linux proprietary divers), Qualcomm Adreno (Linux MSM driver), Broadcom VideoCore (Linux v3d driver).

Apple’s release notes detail that RDMA integrates with the Thunderbolt framework to enable zero-copy data transfers, meaning data moves directly from one device’s memory to another’s without intermediate buffering. This eliminates bottlenecks associated with TCP/IP protocols, which Thunderbolt previously emulated. Insiders note that while Thunderbolt 5 offers peak speeds, real-world performance depends on factors like cable quality and device compatibility—only M4 and later chips fully support this enhanced mode.

Diving deeper into the technical specifics, Apple’s developer documentation explains that RDMA over Thunderbolt is exposed through new APIs in the macOS networking stack. Developers can initialize clusters using Swift or Objective-C calls that negotiate memory mappings directly over the Thunderbolt bus. This is a departure from traditional Ethernet-based RDMA, which relies on Infiniband or RoCE (RDMA over Converged Ethernet), adapting instead to Thunderbolt’s point-to-point topology.

For those building apps, the update introduces protocols for fault-tolerant clustering. If a device drops out—say, due to a disconnected cable—the system can redistribute workloads dynamically, minimizing disruptions. Testing scenarios outlined in the notes suggest latency as low as microseconds for small transfers, rivaling dedicated high-performance computing setups.

Security is paramount in such a powerful feature. Apple’s notes emphasize built-in encryption for RDMA transfers, preventing unauthorized memory access. A separate 9to5Mac report on the update’s patches reveals fixes for kernel vulnerabilities that could have been exploited in clustered environments, ensuring that the feature doesn’t become a vector for attacks.

Looking at adoption, early sentiment on X suggests enthusiasm among AI researchers. One thread discussed collaborative model training, where multiple users contribute compute power via clustered Macs, democratizing access to high-end AI tools. This could disrupt markets dominated by cloud providers, offering cost savings for startups avoiding subscription fees.

RDMA lets the Macs all act like they have one giant pool of RAM, which speeds up things like massive AI models.

exo connects all your devices into an AI cluster. Not only does exo enable running models larger than would fit on a single device, but with day-0 support for RDMA over Thunderbolt, makes models run faster as you add more devices.

Run, manage, and scale AI workloads on any AI infrastructure. Use one system to access & manage all AI compute (Kubernetes, 20+ clouds, or on-prem).

The Ultimate Express. Fastest http server with full Express compatibility, based on µWebSockets.

This library is a very fast re-implementation of Express.js 4. It is designed to be a drop-in replacement for Express.js, with the same API and functionality, while being much faster. It is not a fork of Express.js. To make sure µExpress matches behavior of Express in all cases, we run all tests with Express first, and then with µExpress and compare results to make sure they match.

npm install ultimate-express -> replace express with ultimate-express -> done

µWebSockets.js is a standards compliant web server written in 10,000 lines of C++. It is exposed to Node.js as a simple-to-use, native V8 addon and performs at least 10x that of Socket.IO, 8.5x that of Fastify. It makes up the core components of Bun and is the fastest standards compliant web server in the TechEmpower (not endorsed) benchmarks.

Easily fine-tune 100+ large language models with zero-code CLI and Web UI

we in- troduce SWE-bench, an evaluation framework consisting of 2,294 software engineering problems drawn from real GitHub issues and corresponding pull requests across 12 popular Python repositories. Given a codebase along with a description of an issue to be resolved, a language model is tasked with editing the codebase to address the issue. Resolving issues in SWE-bench frequently requires under standing and coordinating changes across multiple functions, classes, and even files simultaneously, calling for models to interact with execution environments, process extremely long contexts and perform complex reasoning that goes far beyond traditional code generation tasks.

Easy to use, well documented fine-tuning. NVIDIA optimized with AMD support and Apple M support in the works.