# dataset Published articles for dataset. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Scaling Federated Learning Across Docker, Kubernetes, and Slurm with NVIDIA FLARE DevFeed: [Scaling Federated Learning Across Docker, Kubernetes, and Slurm with NVIDIA FLARE]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-15T15:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Federated Learning](), [Docker](), [Kubernetes](), [Nvidia](), [Server](), [dataset](), [Deployment]() Tags: [architecture](), [compute](), [configuration](), [container](), [data-analytics-processing](), [data-science](), [dataset](), [deployment](), [docker](), [docker-container](), [federated-learning](), [gpu](), [job](), [kubernetes](), [nvidia](), [nvidia-flare](), [server]() ### AI overview This article explains how NVIDIA FLARE scales federated learning across sites with different infrastructure, including Docker, Kubernetes, and Slurm. Its two-layer architecture separates persistent federation services from on-demand job execution, while allowing each site to retain local control over compute, data, secrets, and scheduling. ### Source excerpt Federated learning (FL) projects often begin with a straightforward setup: one server, a few clients, and one dataset at each site. As those projects grow, the... ## Your AI coding spend bought 25% more output. Duplication rose 81%. DevFeed: [Your AI coding spend bought 25% more output. Duplication rose 81%.]() Original publisher: [Read original article]() Author: Steve Fenton Published: 2026-09-14T14:39:14Z Content type: article Language: en Sources: [The New Stack]() Topics: [ai-coding](), [Claude Code](), [cursor](), [datasets]() Tags: [agentic](), [ai](), [ai-coding](), [ai-impact](), [ai-operations](), [ai-strategy](), [ai-tools](), [claude-code](), [coding](), [contributed](), [contributed-octopus-deploy](), [cursor](), [dataset](), [software-development]() ### AI overview The article examines the return on investment from AI coding tools. It reports that heavy users gained 25% over their previous velocity, while code duplication rose 81%, and argues that output measures such as lines of code, pull requests, and feature counts do not reliably represent business value. ### Source excerpt Since they arrived on the scene, a great swathe of the software industry has pinned its hopes on AI tools, The post Your AI coding spend bought 25% more output. Duplication rose 81%. appeared first on The New Stack. ## How BitRobot Crowdsources Real-World Data for Embodied AI, with Jonathan Victor DevFeed: [How BitRobot Crowdsources Real-World Data for Embodied AI, with Jonathan Victor]() Original publisher: [Read original article]() Author: Amira Valliani Published: 2026-09-07T07:00:00Z Content type: article Language: en Sources: [Solana News Feed]() Topics: [datasets](), [Robotics](), [Robot Navigation](), [Solana](), [Artificial Intelligence]() Tags: [ai](), [blockchain](), [blockchain-technology](), [crypto-news](), [cryptocurrency](), [dataset](), [defi](), [depin](), [nfts](), [payments](), [podcasts](), [robot-navigation](), [robotics](), [solana](), [solana-ecosystem](), [training](), [web3]() ### AI overview The article discusses how BitRobot crowdsources real-world interaction data for embodied AI. It describes FrodoBots, which generated roughly 2,000 hours of urban robot navigation data, and explains how BitRobot uses a network model to produce and reward contributors of robotics data. ### Source excerpt BitRobot open-sourced 2,000 hours of robot navigation data and uses Solana to track and reward embodied AI data contributors. ## GHarchive data has become unreliable for measuring GitHub activity DevFeed: [GHarchive data has become unreliable for measuring GitHub activity]() Original publisher: [Read original article]() Author: KD (noreply@blogger.com) Published: 2026-09-03T16:00:00Z Content type: opinion Language: en Sources: [Google Open Source Blog]() Topics: [data](), [datasets](), [GitHub](), [Crawler](), [GitHub API](), [Open Source](), [GraphQL]() Tags: [collect](), [data](), [dataset](), [datasets](), [github](), [google](), [google-open-source](), [graphql](), [open-data-sets](), [open-source](), [oss](), [pipeline](), [pull-requests](), [retention](), [stream](), [volume]() ### AI overview This article examines the reliability of open source data, focusing on GHarchive's coverage of GitHub events. It argues that GHarchive should not be used for real-time or volume-based metrics because event retention has declined and some activity is omitted by the GitHub Event stream and API limitations. ### Source excerpt by Sophia Vargas, Google Open Source & Andrew Nesbitt, Ecosyste.ms Every second, open source contribution quietly shapes the software we rely on, and yet our view of this open ecosystem is surprisingly opaque. Open source development is performed in public spaces -- we can see the commits, issues and comments, the APIs and endpoints are free to use -- the logs are just sitting there, so why can't we just collect all of the data? ...Said every researcher, everywhere. However in most cases of open source related data, we are only looking at part of the whole. Why am I writing this post? Because many of us (including many business decision-makers) are too comfortable with unsubstantiated data. We've gotten used to it. Our models assume that it's smelly and we adjust the logic and weights to compromise. When it comes to open source, our confidence is even lower, even though our resulting decisions can directly impact individuals whom we collectively depend on. Let's consider one of my favorite datasets: GHarchive. Started as a hobby project in 2011, this crawler has amassed more than 15 years of event data from GitHub. While this source provides a historical record of open source development on GitHub, as a real-time or comprehensive source of metrics, it's unreliable and should not be a source for volume-based metrics. In 2025, GHarchive captured 14% fewer events than in 2024, despite steady growth in platform adoption. Since 2025, we estimate that data retention in GHarchive has fallen to ~50% and in 2026 it may be as low as 20% for some event types (see figure below). Prior to 2025, you could make the general assumption that the majority of events would be represented in this pipeline. Since 2025, we must now assume we may be missing at least half of events and possibly more -- not to mention all of the additional activity that's left out of the event API (see GitHub's GraphQL API.) The crawler logic behind this dataset is simple: give me all the events from the GitHub Ev ## Calibrating LLM-Based Population Estimates with Human Validation DevFeed: [Calibrating LLM-Based Population Estimates with Human Validation]() Original publisher: [Read original article]() Author: Hiroshi Urata Published: 2026-08-12T00:29:34Z Content type: article Language: en Sources: [Indeed]() Topics: [Large Language Model](), [human feedback](), [data](), [LLM evaluation / benchmarking]() Tags: [classification](), [data](), [data-science](), [dataset](), [evaluation](), [false-negative](), [false-positive](), [llm](), [machine-learning](), [measurement](), [validation]() ### AI overview The article explains how human validation can calibrate LLM-based population estimates. It distinguishes an LLM's apparent positive rate from true prevalence, uses human-validated samples to estimate sensitivity and specificity, and applies those error estimates to correct population-level measurements and quantify uncertainty. ### Source excerpt Key Idea Human validation is not only for evaluating an LLM. It can also calibrate how the LLM is used as a scalable measurement instrument for population estimation. An LLM can classify thousands of records at low cost, but the proportion it classifies as positive is not necessarily the true proportion in the population. By [...] ## 10X more data, same 4 seconds: single-query scaling in Redpanda SQL on 1TB DevFeed: [10X more data, same 4 seconds: single-query scaling in Redpanda SQL on 1TB]() Original publisher: [Read original article]() Author: Marcin Grzebieluch Published: 2026-07-29T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [SQL](), [Benchmark](), [dataset](), [Apache Iceberg](), [Streaming]() Tags: [apache](), [benchmark](), [dataset](), [kafka](), [sql](), [streaming]() ### AI overview This article benchmarks how a single analytical query in Redpanda SQL scales as the dataset grows from 100 GB to 1 TB and as cluster resources increase. Redpanda SQL combines live-streaming topics with historical Apache Iceberg tables through bridge queries, and the benchmark uses skewed NYC Taxi trip data to evaluate strong scaling and query feasibility. ### Source excerpt A benchmark on how a single analytical query behaves in Redpanda SQL as the dataset and the cluster grow. ## DigitalOcean Evaluations: Production Model and Router Testing for the Inference Stack DevFeed: [DigitalOcean Evaluations: Production Model and Router Testing for the Inference Stack]() Original publisher: [Read original article]() Author: Grace Morgan Published: 2026-07-01T15:41:47Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [inference-endpoints](), [Large Language Model](), [model-deployment](), [Testing]() Tags: [configuration](), [cost](), [data](), [dataset](), [digitalocean](), [evaluation](), [inference](), [inference-endpoints](), [latency](), [llm](), [metric](), [model](), [models](), [performance](), [pii](), [product-updates](), [production](), [quality](), [router](), [testing](), [token]() ### AI overview DigitalOcean Evaluations adds production testing for models and inference router configurations in the DigitalOcean Inference Engine. Teams can run LLM-as-a-Judge evaluations on their own prompts and data, compare quality, latency, and cost, and use built-in or custom rubrics across models, imports, and router setups. ### Source excerpt Choosing the right model or inference router for production means more than reading a leaderboard. It means validating any model or routing configuration on your own data using your prompts and your evaluation criteria before it ever reaches production, and comparing quality, latency, and cost in one place. Evaluations, now available on the DigitalOcean Inference Engine, lets teams validate any model or inference router configuration on their own data before production. Run structured LLM-as-a-Judge evaluations across catalog models, fine-tuned models, BYOM imports, and router setups without stitching together a separate evaluation stack. DigitalOcean Evaluations Capabilities Evaluations provide everything teams need to validate model and router performance before production. LLM-as-a-Judge scoring runs across any candidate in your inference stack and returns per-item scores with judge rationale, plus latency, token, and cost tracking per run. Six pre-built metrics cover the most common evaluation needs out of the box. For teams that need full control: custom rubrics, reusable presets, MCP support, and full dataset management -- all in the same platform as the inference endpoints you use in production. View YouTube video Pre-Built and Custom Rubrics: Score Against Criteria That Match Your Domain The six pre-built metrics, correctness, completeness, faithfulness, PII, toxicity, and bias, cover common evaluation needs. For specialized domains, custom rubrics let teams define their own judge instructions and scoring criteria directly in the judge prompt. The judge evaluates responses against these criteria and returns per-item scores with rationale. Custom rubrics can also adapt the built-in correctness metric to different data formats instead of relying on a default interpretation. Evaluation Presets: Save Configurations and Re-Run Without Rebuilding Without saved configurations, every re-run becomes a rebuild with different judge models, parameters, or prompts, making ## How LLMs are Actually Trained DevFeed: [How LLMs are Actually Trained]() Original publisher: [Read original article]() Author: Ashish Pratap Singh Published: 2026-06-11T04:50:19Z Content type: tutorial Language: en Sources: [AlgoMaster Newsletter]() Topics: [Training AI Models](), [LLM Techniques](), [datasets](), [Language models]() Tags: [dataset](), [llm](), [llm-training](), [llms](), [models](), [training](), [training-data]() ### AI overview This tutorial explains how large language models are trained in stages: pretraining, mid-training, and post-training. It describes pretraining datasets, next-token prediction, cross-entropy loss, and the creation and filtering of datasets such as Dolma 3 Mix. ### Source excerpt In the last lesson, we learned how the Transformer architecture powers an LLM. ## 10 Confusing LLM Concepts, Explained Simply DevFeed: [10 Confusing LLM Concepts, Explained Simply]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-06-01T15:52:18Z Content type: tutorial Language: en Sources: [Dr. Ashish Bamania]() Topics: [Large Language Model](), [Artificial Intelligence](), [AI Agent](), [grpo](), [dataset](), [coding](), [math]() Tags: [agent](), [ai](), [artificial-intelligence](), [coding](), [cpu](), [data-science](), [dataset](), [gpu](), [grpo](), [llm](), [llms](), [machine-learning](), [math](), [programming](), [quantization](), [technology](), [tpu](), [train](), [training]() ### AI overview This tutorial introduces LLM concepts including on-policy and off-policy learning. It explains how models generate, score, and learn from responses, including the use of GRPO, teacher models, and datasets. The supplied excerpt also identifies CPU, GPU, TPU, pruning, and quantization as covered topics. ### Source excerpt The role of CPU/ GPU/ TPU in LLM workflows, Pruning, Quantization, and more. Continue reading on Level Up Coding " ## Re-autoresearching MSMARCO BM25, on Vespa DevFeed: [Re-autoresearching MSMARCO BM25, on Vespa]() Original publisher: [Read original article]() Author: andreer thomas Published: 2026-05-29T00:00:00Z Content type: article Language: en Sources: [Vespa Blog]() Topics: [Benchmark](), [dataset](), [Python](), [Large Language Model](), [generalization in machine learning](), [pandas](), [Google Search]() Tags: [benchmark](), [bm25](), [dataset](), [evaluation](), [experiment](), [google-search](), [information-retrieval](), [openai](), [pandas](), [python](), [ranking](), [retrieval](), [zero-shot]() ### AI overview This article reproduces an MSMARCO BM25 autoresearch experiment in Vespa. It compares LLM-driven Python reranking with an approach restricted to existing Vespa rank features and reports a comparable improvement on a 650,000-passage subset, with better generalization to the full dataset. ### Source excerpt BM25 is having a moment. We reproduce Doug Turnbull's MSMARCO autoresearch experiment in Vespa and get a comparable MRR@10 lift from existing rank features -- with twice the generalization to full MSMARCO. ## 5 security myths that Mythos ended (as told by a CISO) DevFeed: [5 security myths that Mythos ended (as told by a CISO)]() Original publisher: [Read original article]() Published: 2026-05-27T00:00:00Z Content type: opinion Language: en Sources: [Chainguard: Unchained]() Topics: [Security](), [Artificial Intelligence](), [Exploit](), [Vulnerabilities](), [anthropic](), [dataset]() Tags: [ai](), [ai-security](), [anthropic](), [anthropic-mythos-preview](), [chainguard-ai](), [chainguard-containers](), [cve](), [dataset](), [exploits](), [git](), [mythos](), [project-glasswing](), [security](), [vulnerabilities]() ### AI overview The article argues that Anthropic's Mythos and broader AI-driven vulnerability discovery are invalidating long-standing security assumptions. It focuses on shrinking exploitation timelines, the growing importance of proactive vulnerability elimination, and the need for security teams to retire reactive mental models. ### Source excerpt Mythos and AI-driven exploits are breaking old security assumptions. Learn the five myths security teams must retire to survive the new era. ## Using evals and user data to measure AI product improvements DevFeed: [Using evals and user data to measure AI product improvements]() Original publisher: [Read original article]() Author: hi@swizec.com (Swizec Teller) Published: 2026-05-01T00:00:00Z Content type: opinion Language: en Sources: [Swizec Teller]() Topics: [Artificial Intelligence](), [dataset](), [test]() Tags: [ai](), [dataset](), [evals](), [test]() ### AI overview The article argues that AI developers should use evaluations to measure whether changes improve a system. It recommends building test datasets from user behavior, testing models, prompts, and tools, avoiding overfitting, incorporating feedback from real-world use, and tracking human intervention in failure cases. ### Source excerpt When candidates put AI on their resume, the key thing I try to find out is whether they used evals. How did you measure making improvements? ## Scaling a Vespa Application: Feeding Fast and Furiously DevFeed: [Scaling a Vespa Application: Feeding Fast and Furiously]() Original publisher: [Read original article]() Author: Kai Borgen Published: 2026-04-28T00:00:00Z Content type: tutorial Language: en Sources: [Vespa Blog]() Topics: [AI search](), [Deployment](), [datasets](), [information retrieval](), [Cloud](), [Homebrew](), [Command-line interface](), [onnx](), [optimum](), [XML]() Tags: [ai](), [ai-search](), [cli](), [cloud](), [dataset](), [genai](), [how-to](), [information-retrieval](), [install](), [metrics](), [onnx](), [optimum](), [performance](), [rag](), [ranking](), [tensors](), [tutorial]() ### AI overview This tutorial demonstrates how to scale a Vespa application while feeding the full MS_marco passages dataset. It covers preparing the dataset, configuring access, deploying a sample application, and using scaling and metrics to improve feed throughput and performance. ### Source excerpt A tutorial on how to scale the resources in a Vespa application to increase feed throughput. Using the metrics dashboard for informed and optimised scaling. ## EU AI Act Compliance: Requirements, Risks, and What to Document DevFeed: [EU AI Act Compliance: Requirements, Risks, and What to Document]() Original publisher: [Read original article]() Author: info@goteleport.com (Kayne McGladrey, CISSP) Published: 2026-04-15T00:00:00Z Content type: tutorial Language: en Sources: [Teleport]() Topics: [Artificial Intelligence](), [Requirements](), [Documentation](), [audit](), [data-governance](), [Logging](), [Monitoring](), [Deployment](), [data]() Tags: [ai](), [compliance](), [cybersecurity](), [data](), [data-governance](), [dataset](), [deployment](), [development](), [documentation](), [eu](), [iso-27001](), [lifecycle](), [logging](), [monitoring](), [nist](), [real-time](), [reproducibility](), [soc-2](), [versioning]() ### AI overview A practical guide to EU AI Act compliance covering requirements, risks, technical documentation, logging, data governance, lifecycle evidence, traceability, monitoring, and human oversight. It outlines key compliance milestones from August 2025 through August 2027. ### Source excerpt Cut through EU AI Act complexity with practical guidance on requirements, risks, and documentation. ## Fine-tuning a DistilBERT classifier with numerical and text inputs DevFeed: [Fine-tuning a DistilBERT classifier with numerical and text inputs]() Original publisher: [Read original article]() Author: Paloma Jol Published: 2026-04-10T10:04:33Z Content type: tutorial Language: en Sources: [FreeAgent]() Topics: [Fine-tuning](), [Transformer](), [foundation-models](), [Training AI Models](), [datasets](), [Kaggle](), [scikit-learn]() Tags: [ai](), [aws](), [data](), [data-ml](), [data-science](), [dataset](), [encoding](), [fine-tuning](), [hugging-face](), [kaggle](), [llms](), [machine-learning](), [model](), [models](), [nlp](), [performance](), [text-classification](), [training-data](), [validation]() ### AI overview This tutorial explains how to fine-tune a DistilBERT text classifier while incorporating a numerical feature into the same network. It uses the Kaggle wine reviews dataset, combining review descriptions with price and preprocessing the data for training, validation, and testing. ### Source excerpt Text classification is often done through fine-tuning of a pretrained foundation model with domain-specific data. In FreeAgent we use transformer based models to automatically classify incoming bank transactions. Specifically we use a DistilBERT model that is fine-tuned on hundreds of millions of bank transactions with customer-labelled accounting categories. The model inputs are currently text-based, built from a combination of bank transaction descriptions and amounts. In this post we describe an approach to fine-tuning the DistilBERT model and training the classifier including the numerical amount feature as a single network. ## Introducing Zed's Agent Metrics DevFeed: [Introducing Zed's Agent Metrics]() Original publisher: [Read original article]() Author: Katie Geer Published: 2026-04-09T00:00:00Z Content type: release Language: en Sources: [Zed Industries - Blog]() Topics: [AI Agent](), [agent observability](), [telemetry](), [data](), [dataset]() Tags: [agent](), [ai-agent](), [ai-agents](), [data](), [dataset](), [metrics](), [telemetry]() ### AI overview Zed launched Agent Metrics, a public weekly view of AI agent adoption and turn times within Zed. The tool compares sessions, turn volume, and response-time distributions, while the article describes trends from anonymized, aggregated data collected from opted-in users. ### Source excerpt A public, weekly view of AI agent adoption and turn times inside Zed, plus a few patterns worth watching. ## Our Config 2026 speakers on the biggest opportunities with AI DevFeed: [Our Config 2026 speakers on the biggest opportunities with AI]() Original publisher: [Read original article]() Author: Rose McLucas Published: 2026-03-03T23:13:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Artificial Intelligence](), [Machine learning](), [datasets](), [coding]() Tags: [2026](), [3d-printed](), [ai](), [algorithm](), [art](), [dataset](), [digital-art](), [machine-learning](), [speakers]() ### AI overview Figma introduces its Config 2026 speakers, including artists, designers, and creators exploring how AI is changing creative work, software projects, artistic practice, and collaboration. The article highlights projects involving AI tools, machine learning, 3D-printed textiles, and collective datasets. ### Source excerpt Here's a first look at our speakers and how they're interrogating craft, quality, and intention in an AI-powered world. ## Dual-Embedding Trust Scoring DevFeed: [Dual-Embedding Trust Scoring]() Original publisher: [Read original article]() Author: Eric Chang Published: 2026-02-25T00:00:00Z Content type: article Language: en Sources: [Scribd Tech]() Topics: [trust and safety](), [Embeddings](), [Artificial Intelligence](), [Machine learning](), [data](), [datasets](), [genai]() Tags: [ai](), [content-trust-series](), [data](), [dataset](), [embeddings](), [featured](), [generative-ai](), [machine-learning](), [machinelearning](), [pii](), [privacy](), [research](), [scribd](), [trust](), [trust-and-safety]() ### AI overview Scribd describes a Content Trust Score that combines Generative AI signals, proprietary multilingual embeddings, and classical machine learning to assess the severity of documents violating defined trust and safety pillars. The research covers illegal, explicit, privacy/PII, and low-quality content, using annotated data from roughly 100,000 documents. ### Source excerpt Scribd is a digital library serving academics and lifelong learners, offering hundreds of millions of documents. This very nature presents a significant concern: content trust and safety. Protecting our library from undesirable and unsafe content is a top priority, but the multilingual and multimodal (text and images) nature of our platform makes this mission very challenging. Also, while third-party tools exist, they often fall short, lacking the nuance to handle our specific trust and safety categories. ## BIY: Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks DevFeed: [BIY: Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks]() Original publisher: [Read original article]() Author: João Palmeiro Published: 2026-01-19T14:52:55Z Content type: article Language: en Sources: [Feedzai]() Topics: [Accessibility](), [LLM evaluation / benchmarking](), [multimodal-ai](), [Canvas](), [AI Models](), [dataset](), [SVG]() Tags: [accessibility](), [ai-models](), [artificial-intelligence](), [benchmarking](), [canvas](), [clustering](), [data-visualization](), [dataset](), [large-language-models](), [multimodal](), [multimodal-ai](), [outlier-detection](), [svg]() ### AI overview The article introduces Benchmark It Yourself (BIY), an effort to prepare a dataset and benchmark multimodal AI models for scatterplot-related tasks. It examines generating distribution-aware descriptions from scatterplot images to improve the accessibility of canvas charts, including direct description generation and structured-data extraction for predefined templates. Initial results were mixed. ### Source excerpt Benchmark It Yourself (BIY): Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks When we need to visualize and interact with millions, or even just thousands, of individual points while analyzing data, we typically resort to rendering them in the browser using a canvas. The other common approach for the web, SVG, doesn't scale when the number of individual elements increases to such quantities. However, while solving one problem, canvas charts introduce a new challenge: accessibility. Although SVG charts are not accessible by default, they can be by design. Each part of an SVG chart has a corresponding element on the web page, allowing for a programmable, accessible experience for screen reader users. We can simply think of SVG as HTML. On the other hand, a canvas chart is just like a PNG image. If a screen reader user tries to learn more about a canvas chart, unless the developer has prepared a detailed description of it, they will just hear the word "image". There's no way to get an idea of what one of these charts represents, let alone extract any insights. For static charts, the solution can be as simple as preparing a description and integrating it into the rendered chart. However, for platforms leveraging dynamic, large datasets, automatically generating these descriptions is not a simple task, especially for charts like scatterplots where data distributions can assume countless forms. At Feedzai, we started exploring ways to generate data distribution-aware descriptions for scatterplots from their respective images using recent multimodal AI models. When the raw data is not available, or the datasets are composed of several thousand or million instances, relying on chart images and these models becomes tempting. This combination has the potential to generate such descriptions and serve them alongside their respective charts, significantly improving the accessibility of canvas charts. That said, we focused on two main directions: using ## Murat and Aleksey Read Papers: "Cloudspecs: Cloud Hardware Evolution Through the Looking Glass" DevFeed: [Murat and Aleksey Read Papers: "Cloudspecs: Cloud Hardware Evolution Through the Looking Glass"]() Original publisher: [Read original article]() Author: Aleksey Charapko Published: 2026-01-14T15:41:13Z Content type: opinion Language: en Sources: [Aleksey Charapko]() Topics: [Cloud](), [Amazon Web Services](), [Hardware](), [Benchmark](), [dataset]() Tags: [analysis](), [architectures](), [aws](), [benchmark](), [cloud](), [compute](), [cost](), [cpu](), [dataset](), [efficiency](), [graviton](), [hardware](), [network](), [other-thoughts](), [paper](), [papers](), [reading](), [summary]() ### AI overview This article reviews the CIDR paper "Cloudspecs: Cloud Hardware Evolution Through the Looking Glass," which examines AWS virtual hardware capabilities over ten years from a cost-efficiency perspective. The paper finds that cloud CPU cost efficiency improved about twofold, while core counts improved tenfold for non-Graviton offerings; network bandwidth cost efficiency improved substantially more. The article also notes limitations in the paper's analysis of memory bandwidth and specialized hardware features. ### Source excerpt The "Cloudspecs: Cloud Hardware Evolution Through the Looking Glass" CIDR paper by Till Steinert, Maximilian Kuschewski, and Viktor Leis was the first paper I and Murat read this year. It was a short, but interesting read. Below is our reading video and my one-paragraph summary. The paper discusses the evolution of AWS cloud (virtual) hardware [...] ## Segmented models for personal income estimation at Mercado Libre DevFeed: [Segmented models for personal income estimation at Mercado Libre]() Original publisher: [Read original article]() Author: Samuel Vazquez Published: 2026-01-08T20:44:23Z Content type: article Language: en Sources: [Mercado Libre Tech]() Topics: [data](), [personalization](), [datasets](), [big-data]() Tags: [big-data](), [business](), [classification](), [data](), [dataset](), [estimations](), [personal-income](), [personalization](), [regression]() ### AI overview Mercado Libre describes a "silver shotgun" approach for heterogeneous real-world data: segmenting data across contexts, training specialized models for each segment, and combining their predictions. The article presents personal income estimation as an application supporting more personalized financial products and discusses how geographic scale changes the importance of features such as home GPS location. ### Source excerpt Before we dive in, let's give a huge shout-out to Julio Avila Torreblanca and Alejandro Hernandez Cuevas for their exceptional work in developing this system. Thanks also to Enrique Miranda for reviewing the text and providing valuable feedback. Introduction Real-world data is rarely homogeneous. The same feature can mean different things depending on the city, neighborhood, urban density, or user segment. When we force a single global model to explain all those regimes, we usually pay with lower performance. At Mercado Libre, we take a different approach: instead of looking for a "silver bullet," we use a "silver shotgun." This pattern segments the data in several ways, trains specialized models per segment, and combines their predictions into a single estimate. Motivation: Financial inclusion is a significant opportunity In Mexico, only 63% of adults have a formal bank account, only 37% have a credit product, and less than 16% have a bank credit card [1]. About 45% of employed people have a formal source of income, while the rest work in the informal sector [2]. This gap presents a great opportunity for Mercado Libre to enter a growing market. Hyper-personalization is key to our mission to democratize financial inclusion across Latin America (LatAM). To offer great credit products, such as credit cards, we need a reliable estimate of a person's income. Big data, big problems Imagine we want to build a personal income estimation model for people visiting a very small local park. Every day, our dataset would grow by a few hundred entries for children playing, people exercising, and dog walkers, among other activities. Because people in the park live just a few blocks away, home location would have a limited effect (same neighborhood means low GPS variance). The features with the biggest impact on estimated income would likely be age and occupation. If we scale the problem to an entire city, home GPS location will have a bigger effect on the estimate. Each neighborho ## What really matters for performance: lessons from a year of benchmarks DevFeed: [What really matters for performance: lessons from a year of benchmarks]() Original publisher: [Read original article]() Author: Tom Schreiber Published: 2025-12-22T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [Benchmark](), [clickhouse](), [Compression](), [data](), [Data Quality](), [dataset](), [MongoDB](), [dashboards](), [Bluesky](), [pull-requests]() Tags: [benchmark](), [benchmarks](), [clickhouse](), [compression](), [data-quality](), [dataset](), [performance](), [pull-requests](), [storage]() ### AI overview The article reviews benchmark-backed ClickHouse work and argues that practical performance depends on how efficiently a database engine moves data end to end. It highlights JSONBench, which compares ClickHouse with other systems using up to one billion Bluesky events across ingestion quality, storage size, and cold and hot query runtimes. The reported results show ClickHouse achieving substantially faster queries and lower storage usage than traditional JSON data stores such as MongoDB. ### Source excerpt A look back at some of my favorite benchmark-backed ClickHouse blog posts this year, and the lessons they revealed about performance in practice. ## The Transplant Story That Sparked a Hackathon Project DevFeed: [The Transplant Story That Sparked a Hackathon Project]() Original publisher: [Read original article]() Author: Carlota Soto Published: 2025-12-11T18:00:13Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [For the Love of Code](), [Data Science](), [dataset](), [Machine learning](), [Open Source]() Tags: [aws](), [case-studies](), [data-science](), [dataset](), [hackathon](), [ml](), [open-source](), [project]() ### AI overview NephroCompass is an open-source clinical decision support tool created by data engineer Deepti Bahel after her kidney transplant experience. Developed as a hackathon project, it uses a curated Chronic Kidney Disease dataset and machine learning models including XGBoost, random forest, and logistic regression to estimate CKD risk from laboratory values. SHAP provides explanations for the model predictions. ### Source excerpt "I've worked with Postgres, Snowflake, and AWS in my professional environment, but for building a side project, I wanted something easier to set up. It hardly took me 10 minutes to understand how to link Neon with my application and how to make my ML workflows connect to it" (Dee... ## How the 5 major cloud data warehouses compare on cost-performance DevFeed: [How the 5 major cloud data warehouses compare on cost-performance]() Original publisher: [Read original article]() Author: Tom Schreiber; Lionel Palacin Published: 2025-12-02T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [benchmarking](), [Cloud](), [clickhouse](), [Amazon Redshift](), [BigQuery](), [databricks](), [dataset]() Tags: [benchmark](), [benchmarking](), [bigquery](), [clickhouse](), [cloud](), [cloud-data](), [compare](), [comparison](), [cost](), [data](), [databricks](), [dataset](), [performance](), [redshift](), [storage](), [warehouse]() ### AI overview This article compares the cost-performance of Snowflake, Databricks, ClickHouse Cloud, BigQuery, and Redshift across analytical workloads containing 1 billion, 10 billion, and 100 billion rows. Using each system's real compute billing model, the benchmark concludes that ClickHouse Cloud provides substantially better value than the other systems at scale. ### Source excerpt We benchmarked the five major cloud data warehouses at 1B-100B rows using their real billing models to measure performance per dollar. Results show how cost-performance shifts as data grows. [Next page]()