# benchmarking Published articles for benchmarking. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## First VMmark 4.1 Power-Performance and VMware Cloud Foundation 9.1 Results DevFeed: [First VMmark 4.1 Power-Performance and VMware Cloud Foundation 9.1 Results]() Original publisher: [Read original article]() Author: vmwareblogs Published: 2026-09-16T18:20:25Z Content type: release Language: en Sources: [VMware Blogs]() Topics: [vcf 9.1](), [benchmarking](), [Benchmark](), [virtualization](), [Hardware]() Tags: [benchmarking](), [benchmarks](), [cloud-infrastructure](), [dell](), [efficiency](), [home-page](), [performance](), [vcf-9-1](), [vmmark](), [vmware](), [vmware-cloud-foundation](), [vsphere](), [vsphere-9-1]() ### AI overview VMware reports Dell Technologies benchmark results using VMware Cloud Foundation 9.1 and VMmark 4.1. The article describes VMmark 4.1's power-performance measurement and reports higher performance and tile count for VCF 9.1 than VCF 5.2 in the tested environment. ### Source excerpt We're excited to announce two new VMmark results today from Dell Technologies: First VCF 9.1 Benchmarks: These are the first results using VMware Cloud Foundation (VCF) 9.1. VCF 9.1 maximizes hardware efficiency using a Next-Gen Topology-Aware CPU Scheduler that optimizes memory and cache locality for intensive enterprise workloads. A separate VCF 9.1 evaluation demonstrated a ... Continued The post First VMmark 4.1 Power-Performance and VMware Cloud Foundation 9.1 Results appeared first on VMware Blogs. ## Translating CUDA Tile Operations from Python to Rust Using Agentic AI DevFeed: [Translating CUDA Tile Operations from Python to Rust Using Agentic AI]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-16T16:28:59Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [CUDA Tile](), [Rust](), [Agent Skill](), [Artificial Intelligence](), [AI Agent](), [GPU](), [benchmarking](), [Compiler]() Tags: [agent-skill](), [agentic-ai](), [agentic-ai-generative-ai](), [ai-agent](), [benchmarking](), [cuda](), [cuda-tile](), [cutile](), [developer-tools-techniques](), [gpu](), [mixture-of-experts-moe](), [python](), [rust]() ### AI overview This NVIDIA developer article explains a multi-agent workflow for translating cuTile Python and Triton-TileIR GPU kernels into cuTile Rust. The team ported 24 public TileGym operators, covering about 40 kernels, and achieved 99.5% of cuTile Python performance on average, with correctness and performance checks at each stage. ### Source excerpt cuTile Rust (cutile-rs) is a tile-based system for safe, idiomatic GPU kernel authoring in the Rust programming language. Extending the Rust ownership model to... ## Linux Randomly Picking Idle CPU Cores Shows Modest Win On High Core Count Systems DevFeed: [Linux Randomly Picking Idle CPU Cores Shows Modest Win On High Core Count Systems]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-16T13:30:04Z Content type: news Language: en Sources: [Phoronix]() Topics: [Linux](), [Linux Kernel](), [cpu](), [benchmarking](), [systems]() Tags: [benchmarking](), [cpu](), [desktop-linux](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-kernel](), [linux-performance](), [linux-server-benchmarks](), [open-source-graphics](), [patches](), [phoronix](), [phoronix-test-suite](), [scheduler](), [ubuntu-benchmarks](), [ubuntu-hardware]() ### AI overview A Linux kernel patch series proposes randomizing the selection among equally shallow idle CPU cores to reduce scan-order bias and prevent concurrent tasks from converging on the same core. Testing on a 160-core Ampere Altra system showed throughput improvements of up to a few percent in Stress-NG benchmarking, while the benefits on smaller systems remain uncertain. ### Source excerpt Arm engineer Christian Loehle sent out a patch series today proposing that the Linux scheduler code randomize its picking of equally shallow idle CPU cores. This randomization is showing to deliver a win at least for high CPU core counts for better distributing workloads... ## Linux 7.3 Delivering Some Performance Gains On Intel Panther Lake / Framework Laptop 13 Pro DevFeed: [Linux 7.3 Delivering Some Performance Gains On Intel Panther Lake / Framework Laptop 13 Pro]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-14T14:42:00Z Content type: article Language: en Sources: [Phoronix]() Topics: [Linux](), [benchmarking](), [intel](), [Hardware](), [Ubuntu]() Tags: [benchmarking](), [desktop-linux](), [hardware](), [intel-core](), [kernel](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [open-source-graphics](), [performance](), [phoronix](), [phoronix-test-suite](), [ubuntu](), [ubuntu-benchmarks](), [ubuntu-hardware]() ### AI overview The article benchmarks the in-development Linux 7.3 kernel against stable Linux 7.2 on an Intel Core Ultra X9 388H in a Framework Laptop 13 Pro running Ubuntu 26.04 LTS. It reports performance gains in some areas without concerning regressions. ### Source excerpt Linux 7.1 brought some performance improvements for Intel Core Ultra Series 3 "Panther Lake", Linux 7.2 enhanced the performance for the integrated Arc B390 Xe3 graphics, and now for the in-development Linux 7.3 kernel are various performance improvements at large. Today's article is looking at the performance of the in-development Linux 7.3 kernel compared to Linux 7.2 stable using the Core Ultra X9 388H within the Framework Laptop 13 Pro. ## On-Device AI Series (Part 5): LiteRT-LM DevFeed: [On-Device AI Series (Part 5): LiteRT-LM]() Original publisher: [Read original article]() Author: Oğuzhan Aslan Published: 2026-09-14T05:59:12Z Content type: tutorial Language: en Sources: [ProAndroidDev - Medium]() Topics: [LiteRT](), [On-device AI](), [Large Language Model](), [Inference](), [Streaming](), [GPU](), [LLM evaluation / benchmarking](), [PyTorch]() Tags: [ai](), [android](), [android-development](), [benchmarking](), [gpu](), [inference](), [litert](), [llama](), [llm](), [multimodal](), [on-device](), [on-device-ai](), [programming](), [pytorch](), [qwen](), [software-development](), [streaming]() ### AI overview This tutorial explains LiteRT-LM for running large language models on-device. It covers the Engine/Session API, streaming output, system prompts, tool calling, multimodal inputs, thinking mode, and CPU-versus-GPU benchmarking. The article also discusses tradeoffs involving privacy, network independence, latency, memory, sampling configuration, and model capability compared with cloud APIs. ### Source excerpt Put your phone in airplane mode. Open the app, type a question, and watch the answer arrive one token at a time -- no spinner waiting on a network round-trip, no API key, no per-token bill, and nothing you typed ever leaving the device. LiteRT-LM removes the genuinely hard parts of running an LLM on-device -- KV-cache management, token streaming, backend selection -- but it doesn't remove your job so much as relocate it. What's left on your plate is a short, specific list: sizing a combined input+output token budget, owning your own sampling defaults, hand-building system prompts and tool calling out of raw text, and one native-library collision that presents as a SIGSEGV rather than a build error. Know those going in and the API itself is a clean three-step pattern. We'll get there in that order: Why you'd choose this runtime and what it costs you versus the cloud. The Engine/Session model you need to read the code at all. Real implementation samples -- streaming, system prompts and tool calling, multimodal inputs, thinking mode, and CPU-vs-GPU benchmarking. The anti-patterns to avoid. A developer-friendliness rating on the same rubric as Parts 1-4. Why Use LiteRT-LM? You reach for LiteRT-LM instead of hand-rolling generation on top of raw LiteRT when: You need multi-turn conversation, not single-shot inference -- session state and KV-cache bookkeeping are handled for you, and resetting a conversation is a session swap, not a model reload. You need streaming output -- token-by-token delivery for a responsive chat UI, instead of a blocking call that returns everything at once. You're choosing between CPU and GPU per device -- the explicit backend parameter turns that into a runtime decision instead of a build-time guess. You want a pre-converted model without doing your own PyTorch-to-LiteRT conversion work -- the Model Zoo covers Gemma, Qwen, Llama, and more out of the box. You're willing to own sampling -- the engine won't pick sane decoding defaults for you; that's on the ## Benchmaxxing: When the Benchmark Becomes the Target DevFeed: [Benchmaxxing: When the Benchmark Becomes the Target]() Original publisher: [Read original article]() Author: Nathan Danneman Published: 2026-09-12T11:17:51.295154Z Content type: article Language: en Sources: [Blog]() Topics: [Benchmark](), [Artificial Intelligence](), [Cybersecurity](), [Ground truth / benchmark quality](), [Detection engineering]() Tags: [ai](), [ai-and-cybersecurity](), [benchmark](), [benchmarking](), [benchmarks](), [blog](), [cybersecurity](), [leaderboards](), [securing-ai](), [security](), [validation]() ### AI overview The article explains how public AI and cybersecurity benchmarks can become targets for optimization, a practice it calls "benchmaxxing." It argues that gaming, ceiling effects, data leakage, binary scoring, omitted costs, and aggregate scores can make benchmark results poor proxies for real-world defensive capability. The article proposes task-coupled internal benchmarks intended to evaluate end-to-end cyber agents and support rigorous science rather than visibility-driven score optimization. ### Source excerpt The more attention a benchmark receives, the stronger the incentive to optimize for it. In AI and cybersecurity, this can have significant consequences. ## Coding Challenge #136 - Hyperfine DevFeed: [Coding Challenge #136 - Hyperfine]() Original publisher: [Read original article]() Author: John Crickett Published: 2026-09-12T08:03:11Z Content type: tutorial Language: en Sources: [Coding Challenges]() Topics: [benchmarking](), [coding](), [Command-line interface](), [Processes]() Tags: [benchmarking](), [coding](), [command-line](), [performance](), [process](), [programming]() ### AI overview A coding challenge asks readers to build a command-line benchmarking tool modeled on hyperfine. The tool should run commands repeatedly, measure execution time and variation, compare commands, export results, and warn about unreliable measurements. ### Source excerpt This challenge is to build your own benchmarking tool. ## Beyond the price per token: Choosing the right OpenAI model on Amazon Bedrock for your workload DevFeed: [Beyond the price per token: Choosing the right OpenAI model on Amazon Bedrock for your workload]() Original publisher: [Read original article]() Author: Nick McCarthy Published: 2026-09-11T18:24:38Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [AI Chat](), [AI Bots](), [ai observability]() Tags: [advanced-300](), [agent](), [agentic](), [amazon-bedrock](), [api](), [benchmarking](), [cost](), [evaluation](), [generative-ai](), [open-source](), [openai](), [technical-how-to]() ### AI overview The article presents an open-source benchmark for comparing OpenAI models on Amazon Bedrock with OpenAI API baselines by cost per correct answer, multi-turn agent trajectory cost, and deliverable quality. ### Source excerpt Comparing models on dollars per million tokens misses what production workloads actually pay for: outcomes. This post shares an open-source benchmarking harness that measures cost per correct answer, agent trajectory cost, and rubric-graded deliverable quality across OpenAI models on Amazon Bedrock. ## CachyOS vs. Windows 11 vs. Ubuntu 26.04 LTS On Intel Wildcat Lake + 8GB RAM DevFeed: [CachyOS vs. Windows 11 vs. Ubuntu 26.04 LTS On Intel Wildcat Lake + 8GB RAM]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-11T15:08:55Z Content type: comparison Language: en Sources: [Phoronix]() Topics: [benchmarking](), [Linux](), [Ubuntu](), [Windows](), [cpu](), [Hardware](), [intel](), [Linux performance]() Tags: [benchmarking](), [desktop-linux](), [hardware](), [intel](), [intel-core](), [laptop](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [lts](), [memory](), [microsoft](), [nvme](), [open-source-graphics](), [performance](), [phoronix](), [phoronix-test-suite](), [release](), [review](), [ssd](), [testing](), [ubuntu](), [ubuntu-benchmarks](), [ubuntu-hardware](), [windows](), [windows-11]() ### AI overview This comparison measures Ubuntu 26.04 LTS, CachyOS, and Windows 11 performance on a $449 CHUWI UniBook laptop with an Intel Core 3 Wildcat Lake processor, 8GB of memory, and a 256GB NVMe SSD across various workloads. ### Source excerpt Often times when testing different Linux distributions or comparing Windows vs. Linux it's on leading flagship desktop or server hardware, but today we are looking at the Ubuntu vs. CachyOS vs. Windows performance at the opposite end of the spectrum. With the new CHUWI UniBook $449 laptop powered by Intel Core 3 Wildcat Lake and with 8GB of system memory, here is a look at how those three operating systems compare across a variety of workloads. ## How Uber and Other Companies Are Reducing AI Costs with Open Models and Model Routing DevFeed: [How Uber and Other Companies Are Reducing AI Costs with Open Models and Model Routing]() Original publisher: [Read original article]() Author: Ivan Klaric Published: 2026-09-10T17:22:31Z Content type: article Language: en Sources: [The Pragmatic Engineer]() Topics: [Model Routing](), [Artificial Intelligence](), [Inference](), [LLM evaluation / benchmarking](), [Benchmark]() Tags: [ai](), [benchmark](), [benchmarking](), [cost](), [inference](), [model-routing](), [models](), [optimize]() ### AI overview The article reports that companies including Uber, Pinterest, Stripe, Coinbase, Ramp, and AT&T are reducing AI costs by using open models, model routing, benchmarking, and other optimization techniques. It details Uber's use of cheaper inference services, optimized model selection, smaller subagent models, prompt caching, and automatic compaction. ### Source excerpt Uber, Pinterest, Stripe, Coinbase, Ramp, and AT&T are making large savings on their AI bills by dropping proprietary models and using smart model routing. ## IBM releases SOTA Granite Time Series PatchTST-FM-r2 model with commercial-friendly license DevFeed: [IBM releases SOTA Granite Time Series PatchTST-FM-r2 model with commercial-friendly license]() Original publisher: [Read original article]() Author: Roman Vaculin; Wesley M Gifford; Jiri Navratil; Chandra Reddy; Ayhan Sebin Published: 2026-09-09T15:36:24Z Content type: release Language: en Sources: [Hugging Face - Blog]() Topics: [foundation-models](), [Temporal data](), [releases](), [benchmarking](), [Open Source Models & Datasets](), [Inference]() Tags: [apache](), [benchmarking](), [foundation-models](), [ibm](), [inference](), [model-architecture](), [open-source](), [releases](), [time-series](), [zero-shot]() ### AI overview IBM released Granite Time Series PatchTST-FM-r2, a roughly 385M-parameter time-series foundation model for zero-shot forecasting. The article covers its architecture, probabilistic forecasting, missing-value imputation, benchmark results, licensing, and available reproducibility resources. ### Source excerpt Time-series foundation models are changing the way forecasting systems are built. Instead of training and maintaining a separate model for every dataset, users can use a pretrained model and generate forecasts zero-shot. IBM has released Granite Time Series PatchTST-FM-r2, the latest model in the Granite TSFM family (github, blog). ## Python Timer Functions DevFeed: [Python Timer Functions]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-08T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Python](), [benchmarking](), [Code]() Tags: [benchmarking](), [python](), [tutorial](), [video]() ### AI overview This video course teaches how to measure Python code execution time with the time module. It builds a reusable Timer class, extends it for multiple timers, converts it into a data class, and uses it as a context manager. ### Source excerpt Learn how to time your Python code with the time module, then build a reusable Timer class that works as a context manager. ## Cybersecurity Benchmarking: Why, Why Not, When and How DevFeed: [Cybersecurity Benchmarking: Why, Why Not, When and How]() Original publisher: [Read original article]() Author: Phil Venables Published: 2026-09-05T15:27:39Z Content type: opinion Language: en Sources: [Risk and Cyber]() Topics: [benchmarking](), [Cybersecurity]() Tags: [benchmarking](), [cybersecurity](), [performance](), [risk]() ### AI overview The article argues that cybersecurity benchmarking is unhelpful when it focuses only on inputs such as budgets instead of outcomes such as control effectiveness. It recommends comparing leading indicators and examining how they influence lagging performance indicators, while noting that budget comparisons may not be meaningfully comparable. ### Source excerpt tl;dr Benchmarking is a waste of time when focused solely on inputs (e.g. budgets) rather than outcomes (e.g. effectiveness of controls). The budget comparisons are never "apples for apples" and may often end up setting risk tolerance only marginally ahead of others who may be in a bad state to begin with. Instead, we need to decouple this and compare leading not lagging indicators of performance to show (i) how those leading indicators drive the lagging indicators in the right direction and... ## Project HydraFusion: Frontier quality via multi-model orchestration DevFeed: [Project HydraFusion: Frontier quality via multi-model orchestration]() Original publisher: [Read original article]() Author: GitHub Staff Published: 2026-09-04T16:04:14Z Content type: release Language: en Sources: [GitHub Engineering]() Topics: [Orchestration](), [Model Routing](), [GitHub Copilot](), [AI-assisted coding](), [Optimization](), [Claude]() Tags: [agentic-coding](), [ai-ml](), [benchmarking](), [benchmarks](), [claude](), [cost](), [github-copilot](), [llms](), [optimization](), [orchestration](), [routing]() ### AI overview GitHub introduces Project HydraFusion, a GitHub Copilot research preview that selects multi-model execution workflows to balance quality, cost, and latency for coding tasks. ### Source excerpt In controlled offline evaluations, HydraFusion's selective coding workflows matched or exceeded the evaluated Opus 5 baseline while reducing estimated workflow cost. Now available as a research preview in GitHub Copilot. The post Project HydraFusion: Frontier quality via multi-model orchestration appeared first on The GitHub Blog. ## How we built a benchmarking framework to horizontally accelerate transaction model research DevFeed: [How we built a benchmarking framework to horizontally accelerate transaction model research]() Original publisher: [Read original article]() Author: Nubank Editorial Published: 2026-09-03T13:53:30Z Content type: article Language: en Sources: [Nubank]() Topics: [benchmarking](), [Framework](), [Transformer](), [data]() Tags: [architectures](), [automated](), [benchmarking](), [data](), [data-science-machine-learning](), [experimentation](), [framework](), [model](), [performance](), [pipelines](), [training]() ### AI overview Nubank built an automated benchmarking framework for horizontally evaluating transformer-based transaction representation models across multiple downstream tasks and trials. The framework made experimentation reproducible and statistically rigorous, helping the team identify improvements that generalize across applications. It increased the team's capacity to run experiments by roughly five times per month while reducing operational overhead. ### Source excerpt The framework that transformed weeks of manual experimentation into automated pipelines for horizontal transaction model research The post How we built a benchmarking framework to horizontally accelerate transaction model research appeared first on Building Nubank. ## Expert Evaluation: Identify Problems Before You Recruit Users DevFeed: [Expert Evaluation: Identify Problems Before You Recruit Users]() Original publisher: [Read original article]() Author: William Hudson Published: 2026-09-03T00:00:00Z Content type: article Language: en Sources: [UX Daily - User Experience Daily]() Topics: [Usability](), [User experience (UX)](), [User interface design](), [Interaction Design](), [benchmarking](), [Artificial Intelligence]() Tags: [ai](), [benchmarking](), [cost](), [evaluation](), [interaction-design](), [testing](), [usability](), [ux]() ### AI overview This article explains expert, or heuristic, evaluation as a cost-effective way to identify usability problems before recruiting users. It describes using structured guidelines such as Nielsen's heuristics, extending the method to benchmarking, and treating AI-generated critiques as preliminary input that requires expert review. ### Source excerpt Before you have a single real user in a testing session, you can already find a significant proportion of the usability problems in a design. Expert evaluation, also known as heuristic evaluation, is one of the most cost-effective methods available to UX practitioners. It requires no recruitment, facilities, or scheduling. What it does require is expertise, judgment, and a structured set of guidelines to work from.Here we show how expert evaluation works, walk through Nielsen's widely used set of 10 heuristics, and explain how the same principles can be extended to benchmarking: a more formal process of scoring designs against detailed criteria and comparing performance across sites or over... ## How we saved 100 terabytes of memory by optimizing 1.1.1.1's DNS cache DevFeed: [How we saved 100 terabytes of memory by optimizing 1.1.1.1's DNS cache]() Original publisher: [Read original article]() Author: Sebastiaan Neuteboom Published: 2026-08-27T17:02:35Z Content type: article Language: en Sources: [Cloudflare Blog]() Topics: [Cache](), [Caching](), [Cloudflare](), [Rust](), [benchmarking](), [Latency](), [datacenter]() Tags: [1-1-1-1](), [benchmarking](), [cache](), [cloudflare](), [data-center](), [deep-dive](), [dns](), [engineering](), [latency](), [memory](), [net-maui](), [optimization](), [performance](), [rust]() ### AI overview Cloudflare describes five Rust-level changes to the memory layout of Big Pineapple, the platform behind 1.1.1.1 and other DNS services. The changes reduced DNS cache entry size by over 50%, freed roughly 100 terabytes of memory across the fleet, increased insert throughput by 43%, and reduced lookup latency by 19%. ### Source excerpt Five Rust-level memory optimizations to the DNS cache layout of Big Pineapple cut per-entry memory by 56%, freeing approximately 100 TB of memory across Cloudflare's fleet. ## Benchmarking vector indexes DevFeed: [Benchmarking vector indexes]() Original publisher: [Read original article]() Author: Evgeniy Patlan Published: 2026-08-27T13:35:32Z Content type: article Language: en Sources: [Blog - Percona]() Topics: [benchmarking](), [Ground truth / benchmark quality](), [Databases](), [ann](), [datasets](), [Sorting]() Tags: [ai-vector](), [ann](), [benchmark](), [benchmarking](), [benchmarks](), [blog](), [blog-post](), [data](), [database-performance](), [database-trends](), [databases](), [datasets](), [embeddings](), [index](), [measurements](), [model](), [open-source](), [percona](), [points](), [report](), [run](), [search](), [sorting](), [vector-search](), [vectorsearch]() ### AI overview This article explains how Percona's vector-bench measures vector indexes by building database engines from pinned versions, running them in identical containers with the same data and hardware, and comparing consistent measurements. It describes embeddings, exact search, approximate nearest neighbour search, and ground truth as the brute-force reference needed to evaluate index accuracy. ### Source excerpt Nearly every database has vector search now, and every one of them has a blog post with a big number in it. Almost none of those numbers can be checked, because the thing that makes them meaningful is usually missing. We built a vector-bench to stop guessing. You name the engines you want, build them ... Continued The post Benchmarking vector indexes appeared first on Percona. ## Building a New Rust Driver for ScyllaDB's DynamoDB API - with 58% More Throughput DevFeed: [Building a New Rust Driver for ScyllaDB's DynamoDB API - with 58% More Throughput]() Original publisher: [Read original article]() Author: Cynthia Dunlop Published: 2026-08-27T13:10:38Z Content type: article Language: en Sources: [ScyllaDB]() Topics: [SDKs](), [networking]() Tags: [alternator-for-dynamodb](), [api](), [benchmarking](), [drivers](), [engineering](), [optimization](), [performance](), [routing](), [rust]() ### AI overview The article describes a dedicated Rust driver for ScyllaDB Alternator that load-balances DynamoDB-style requests across cluster nodes. It also covers extending the Latte benchmarking tool for the DynamoDB API and reports approximately 58% higher throughput than the AWS SDK driver on a three-node cluster. ### Source excerpt How our new Rust driver load-balances DynamoDB-style requests across a ScyllaDB cluster, and how we extended Latte to measure its performance ## Performance Progression of Percona Server for MySQL 8.4 DevFeed: [Performance Progression of Percona Server for MySQL 8.4]() Original publisher: [Read original article]() Author: Bogdan Degtyariov Published: 2026-08-27T13:07:20Z Content type: article Language: en Sources: [Blog - Percona]() Topics: [MySQL](), [benchmarking](), [Benchmark](), [Scalability](), [configuration](), [cpu](), [Hardware]() Tags: [2026](), [benchmark](), [benchmarking](), [benchmarks](), [concurrency](), [configuration](), [cpu](), [data](), [database](), [hardware](), [intel](), [latency](), [load](), [measurement](), [mysql](), [percona](), [percona-server-for-mysql](), [performance](), [processors](), [run]() ### AI overview This article investigates read/write performance changes across Percona Server for MySQL 8.4 versions released in 2026. It describes benchmark scope, database and buffer-size combinations, configuration, and CPU-frequency controls, while excluding features, security patches, latency percentiles, and resource-utilization measurements. ### Source excerpt 1. Purpose and scope This performance investigation aims to look into the read/write performance of Percona Server for MySQL 8.4 and how it changed between versions released in 2026: 8.4.8-8 released on 12 March 2026 8.4.10-10 released on 30 June 2026 8.4.11-11 released on 20 August 2026 We want to see if there are improvements ... Continued The post Performance Progression of Percona Server for MySQL 8.4 appeared first on Percona. ## Rolling with the Punches: Why Cybersecurity is Backgammon, Not Chess DevFeed: [Rolling with the Punches: Why Cybersecurity is Backgammon, Not Chess]() Original publisher: [Read original article]() Author: phil7672 Published: 2026-08-22T16:49:47Z Content type: opinion Language: en Sources: [Risk and Cyber]() Topics: [Cybersecurity](), [benchmarking](), [Vulnerabilities](), [Threat Research]() Tags: [benchmarking](), [cybersecurity](), [leadership](), [risk](), [vulnerabilities](), [zero-day]() ### AI overview The article argues that cybersecurity is better understood as backgammon than chess because organizations must prepare for many possible outcomes amid complex dependencies, supply chains, changing technology platforms, and unpredictable attackers. It also argues that cybersecurity benchmarking should focus on control effectiveness and outcomes rather than inputs such as budgets. ### Source excerpt It is tempting to compare cybersecurity to a chess game. Two adversaries facing each other, plotting strategy and tactics. Move and counter move, anticipating actions and grinding out a win. In reality, in our complex world of dependencies, supply chains, constantly shifting technology platforms and unpredictable attackers, this is all way more haphazard. Indeed, a better analogy is backgammon, where you position yourself for many different possible outcomes to maximize your chance of... ## Gradle Is Going Agentic DevFeed: [Gradle Is Going Agentic]() Original publisher: [Read original article]() Author: Laura Kassovic Published: 2026-08-19T04:00:00Z Content type: opinion Language: en Sources: [The Gradle Blog]() Topics: [Gradle](), [Tooling](), [benchmarking](), [configuration](), [Cache]() Tags: [agentic](), [benchmarking](), [cache](), [configuration](), [context-window](), [gradle](), [tooling]() ### AI overview Gradle describes Agentic Gradle, a project focused on helping agents use Gradle effectively. It plans to provide official Gradle skills and add Gradle features for build workflows that agents make more difficult, supported by repeatable benchmarking scenarios. ### Source excerpt More and more of the people who run Gradle every day aren't typing ./gradlew build themselves. They're asking an agent to run a build, paste it the failure, then ask it to fix the thing that failed. The agent is now part of the build loop, whether the build tool invited it or not. This is not a Gradle-specific oddity. Every build tool is being driven by a new kind of user: not a person with muscle memory for flags and log output, but a model with a terminal, a context window, and alarming confidence. New user, new failure modes. We have hit enough of them that pretending this is temporary started to feel silly. So we started a project. We're calling it Agentic Gradle. The practical version has two buckets: skills that teach agents how to use Gradle effectively, and new Gradle features for the places where agents make today's build tooling awkward. Not because Gradle is bad at this, but because agents push builds in ways we never had to support before. Picking it up meant we didn't do something else this cycle. We'll get to that, because it is the more interesting part of the story. But first, what we're actually building. What this looks like in practice There are three pieces, in the order we're putting real time behind them. Official Gradle skills. A skill is a small, focused set of instructions that teaches an agent how to do something specific with Gradle: read a build failure, run the right task, understand what a Build Scan is telling it. Skills also make vague prompts less dangerous. Give an agent "speed up my build" and, left to its own devices, it'll reach for whatever pattern it saw most often on the internet, which is often years out of date. A focused Gradle performance Skill can keep it closer to the boring advice that works: enabling the configuration cache, reaching for a version catalog, avoiding configuration-time anti-patterns it wouldn't otherwise think to check. We've been experimenting with these throughout the year, and a few are close to ready ## Benchmarking Secure-and-Functional Remediation and How Snyk Agent Fix Lifts Frontier-Model Fix Rates by over 14% DevFeed: [Benchmarking Secure-and-Functional Remediation and How Snyk Agent Fix Lifts Frontier-Model Fix Rates by over 14%]() Original publisher: [Read original article]() Author: Stephen Thoemmes Published: 2026-08-18T04:00:00Z Content type: article Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [benchmarking](), [LLM evaluation / benchmarking](), [vulnerability](), [AI-assisted coding](), [JavaScript](), [Python]() Tags: [agentic](), [ai](), [awareness](), [benchmark](), [benchmarking](), [benchmarks](), [blog](), [code-security](), [developer](), [devops](), [evaluation](), [interest](), [java](), [javascript](), [performance](), [python](), [security](), [security-labs](), [snyk-code](), [snyk-security-intel](), [vulnerability](), [vulnerability-insights]() ### AI overview A benchmark of about 150 vulnerable JavaScript, Java, and Python samples evaluates whether frontier models produce fixes that are both secure and functional. The article reports that models working alone reach roughly 72-75%, while Snyk Intelligence raises Opus 4.6 from 74.6% to 85.4% and improves Python results from 64% to 88%. ### Source excerpt A benchmark of secure, functional vulnerability fixes across JavaScript, Java, and Python shows Snyk Intelligence helps frontier models break past a 72-75% performance plateau. ## Introducing public preview runtimes on AWS Lambda, starting with Node.js 26 and Python 3.15 DevFeed: [Introducing public preview runtimes on AWS Lambda, starting with Node.js 26 and Python 3.15]() Original publisher: [Read original article]() Author: Jonathan Tuliani Published: 2026-08-15T14:09:35Z Content type: news Language: en Sources: [AWS Compute Blog]() Topics: [AWS Lambda](), [Node.js](), [Python](), [Amazon Web Services]() Tags: [announcements](), [availability](), [aws](), [aws-lambda](), [benchmarking](), [china](), [foundational-100](), [govcloud](), [infrastructure-as-code](), [node-js](), [python](), [release]() ### AI overview AWS Lambda introduces public preview runtimes for Node.js 26 and Python 3.15, allowing developers to test upcoming language versions before general availability and provide feedback while breaking changes remain possible. ### Source excerpt AWS Lambda introduces public preview runtimes, a new way to try upcoming language versions before GA. Start using Node.js 26 and Python 3.15 today, provide feedback, and help shape runtime quality before general availability. [Next page]()