# CSV CSV is a common data exchange format that stores tabular data in a plain text file. A CSV file stores the data in a delimited text file that uses commas to separate the values. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Quiz: Introduction to pandas DevFeed: [Quiz: Introduction to pandas]() Original publisher: [Read original article]() Author: Real Python Published: 2026-09-18T12:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [pandas](), [CSV](), [data](), [Data Science](), [Statistics](), [Web](), [save]() Tags: [csv](), [data](), [explore](), [file](), [how-to](), [interactive](), [introduction](), [pandas](), [save](), [statistics](), [table](), [tables](), [test](), [web]() ### AI overview An interactive seven-question quiz tests core pandas skills, including reading tables from web pages, saving data as CSV, understanding DataFrames, chaining methods, and calculating statistics. ### Source excerpt Test your pandas basics: read tables from the web, save data to CSV, explore a DataFrame, and calculate averages in this interactive quiz. ## Python Practice Problems: Parsing CSV Files DevFeed: [Python Practice Problems: Parsing CSV Files]() Original publisher: [Read original article]() Author: Jim Anderson Published: 2026-09-13T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [CSV](), [Python](), [Test-driven development](), [Command-line interface](), [pass]() Tags: [code](), [coding](), [command-line](), [data](), [python](), [testing](), [tutorial](), [tutorials](), [writing-code]() ### AI overview An intermediate-level tutorial that helps Python developers practice parsing CSV files through interview-style problems. It covers writing solutions, test-driven development with pytest, solution improvements, and the trade-offs between Python's built-in csv module and pandas. ### Source excerpt In this tutorial, you'll prepare for future interviews by working through a set of Python practice problems that involve CSV files. You'll work through the problems yourself and then compare your results with solutions developed by the Real Python team. ## Build an end-to-end RFI questionnaire workflow using Amazon Quick Automate DevFeed: [Build an end-to-end RFI questionnaire workflow using Amazon Quick Automate]() Original publisher: [Read original article]() Author: Chaytanya Kumar Published: 2026-09-10T16:08:57Z Content type: tutorial Language: en Sources: [Artificial Intelligence]() Topics: [Automation](), [Amazon S3](), [CSV](), [Amazon Web Services](), [datasets]() Tags: [advanced-300](), [amazon-quick-suite](), [amazon-s3](), [amazon-simple-storage-service-s3](), [automation](), [aws](), [data](), [how-to](), [technical-how-to](), [tutorial]() ### AI overview A tutorial for automating RFI questionnaire processing with Amazon Quick Automate. It reads multi-tab workbooks from Amazon S3, extracts and structures questions, and writes CSV output to Amazon S3. ### Source excerpt Learn how to build an end-to-end RFI questionnaire workflow with Amazon Quick Automate. Read a multi-tab RFI workbook from Amazon S3, use natural-language prompts to extract and structure the questionnaire data, refine the workflow through conversation, and write clean CSV output back to Amazon S3 -- cutting development from days to hours. ## New site for my John Snow Cholera Map data DevFeed: [New site for my John Snow Cholera Map data]() Original publisher: [Read original article]() Author: Robin Wilson Published: 2026-09-10T12:13:16Z Content type: release Language: en Sources: [Robin Wilson]() Topics: [data](), [GeoJSON](), [CSV](), [Website]() Tags: [academic](), [blog](), [csv](), [data](), [formats](), [gis](), [map](), [website]() ### AI overview The author announces a new website hosting digitized data from John Snow's cholera map. The site provides downloads in GeoPackage, GeoJSON, Shapefile, and CSV formats, and older blog posts now redirect to it. ### Source excerpt I've finally crossed something off my todo list, after putting it off for years. I've created a new site to host the data from John Snow's cholera map that I digitised back in 2012. Previously the data was only available from posts on this blog (specifically this original post and a follow-up), and it wasn't [...] ## Automate IAM Identity Center governance with continuous discovery and reporting DevFeed: [Automate IAM Identity Center governance with continuous discovery and reporting]() Original publisher: [Read original article]() Author: Jonathan Nguyen Published: 2026-08-31T17:18:31Z Content type: tutorial Language: en Sources: [AWS Security Blog]() Topics: [AWS IAM](), [CSV]() Tags: [advanced-300](), [authentication](), [aws](), [aws-iam-identity-center](), [aws-organizations](), [compliance](), [iam](), [iam-identity-center](), [identity](), [integration](), [security](), [security-blog](), [security-identity-compliance](), [technical-how-to]() ### AI overview The article explains how to plan identity governance for AWS IAM Identity Center and use an automated discovery and reporting sample solution to track application access assignments across an organization. ### Source excerpt AWS IAM Identity Center integrates with external identity provider (IdP) to provide customers with a centralized authentication and authorization solution for AWS resources across AWS Organizations. AWS continues to invest into IAM Identity Center with a growing number of AWS services that natively integrate with IAM Identity Center. As your AWS organization scales, maintaining visibility [...] ## Vercel Security Dashboard is now generally available DevFeed: [Vercel Security Dashboard is now generally available]() Original publisher: [Read original article]() Author: Jathin Singaraju Published: 2026-08-26T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [dashboards](), [Security](), [Vercel](), [Command-line interface](), [OpenID connect (OIDC)](), [Terminal](), [ci](), [CSV](), [JSON]() Tags: [ci](), [cli](), [coding-agents](), [documentation](), [git](), [json](), [oidc](), [report](), [scope](), [security](), [terminal](), [ui](), [vercel]() ### AI overview Vercel Security Dashboard is generally available on all plans. It centralizes security findings across accounts and projects, supports risk-ordered remediation and CSV export, and provides equivalent checks through the Vercel CLI. Agents can inspect findings, apply supported fixes, and re-check results, with JSON output available in CI and other non-interactive environments. ### Source excerpt The Vercel Security Dashboard is now generally available on all plans, giving you one place to see your security posture across every account and project. You can access the Security Dashboard in the UI or run vercel security check in the Vercel CLI. As teams grow and coding agents make it faster to spin up projects, small misconfigurations add up quietly. The Security Dashboard automatically flags issues like: Team members without 2FA Long-lived credentials that can be replaced with OIDC Public preview deployments Non-sensitive and stale environment variables The Security Dashboard UI Misconfigurations are ordered by risk with the most severe first, and each finding links to the settings that fix them. Findings that are just noise for your team can also be muted, and all results can be exported to a CSV file for triage or reporting. The Security Dashboard CLI You can also run the same checks in your terminal through the new Vercel CLI vercel security check command, allowing your agents to work through the findings for you. An agent using vercel security check --findings can read what failed, apply the fix and re-check to confirm. Fixes an agent can apply include: Turning on Git fork protection Marking an environment variable as sensitive Replacing a static credential with OIDC federation You can also scope the check to one project with --project when you want a narrow change set. In CI or any other non-interactive environment, the command writes the report to stdout as JSON automatically, giving agents structured output without extra flags. Get started by running your first scan from the Security Dashboard, and learn more in the Security Dashboard documentation. Read more ## Asynchronous I/O in DuckDB: Work, Thread, Work DevFeed: [Asynchronous I/O in DuckDB: Work, Thread, Work]() Original publisher: [Read original article]() Author: {"picture" =\> "/images/blog/authors/pedro\_holanda.jpg"} Published: 2026-07-31T00:00:00Z Content type: article Language: en Sources: [DuckDB]() Topics: [aio](), [CSV](), [Amazon S3](), [Amazon EC2]() Tags: [deep-dive](), [duckdb](), [latency](), [parquet](), [performance](), [s3]() ### AI overview DuckDB v2.0 will add asynchronous reads for Parquet and CSV files to improve query performance when remote I/O cannot saturate available bandwidth. The article contrasts local SSD workloads with remote data-lake setups using S3 and EC2, where concurrent reads can keep worker threads from waiting on network requests. ### Source excerpt Starting with v2.0, scheduled for fall 2026, DuckDB will support asynchronous reads of Parquet and CSV files. This can significantly speed up queries when synchronous I/O does not saturate the available bandwidth, as is typical in EC2/S3 compute-storage setups. ## AI Gateway logs now have a dedicated page DevFeed: [AI Gateway logs now have a dedicated page]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-07-31T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Artificial Intelligence](), [SIEM, Security, Observability](), [telemetry](), [Routing (disambiguation)](), [CSV](), [JSON]() Tags: [ai](), [ai-gateway](), [cache](), [cost](), [json](), [logs](), [model](), [routing](), [time]() ### AI overview Vercel AI Gateway now provides a dedicated Logs page for inspecting team and project requests, including cost, token usage, duration, model, provider, region, routing, filtering, search, chart-based time ranges, and CSV or JSON export. Request details include token categories, time to first token, retention and region settings, and provider fallback attempts with failure reasons. ### Source excerpt AI Gateway now has a dedicated Logs page listing every request your team sends through the gateway, newest first, with cost, token counts, duration, and the model, provider, and region that served it. Open any request to see how it was routed. It's available at the team scope and per project. Filter and search Filter by provider, model, modality, credentials, or status, or search by model, provider, or request ID to jump straight to one request. Every filter is stored in the URL, so you can share a filtered view by copying the address. A chart above the table shows request counts over the selected range, and you can drag across it to zoom into a narrower window. You can also export the current view as CSV or JSON, with your filters applied. Drill into a request Click any row to open its details next to the list: total cost, a token breakdown across input, output, reasoning, and cache reads and writes, duration, time to first token, and whether Zero Data Retention and region restrictions were applied. The fallback path lists every provider attempt for the request in order. Each attempt shows the model, provider, status code, credentials, and how long it took, and failed attempts include the reason, such as a provider timeout or an exhausted routing budget. To learn more about AI Gateway logs, read the documentation. Read more ## Harness Database DevOps: Reference Data Rollbacks DevFeed: [Harness Database DevOps: Reference Data Rollbacks]() Original publisher: [Read original article]() Author: Animesh Pathak Stephen Atwell Published: 2026-07-28T00:00:00Z Content type: tutorial Language: en Sources: [Harness Blog]() Topics: [data](), [Databases](), [Git](), [CI/CD](), [Deployment](), [CSV](), [GitOps](), [pull-requests]() Tags: [ci-cd](), [data](), [database](), [deployment](), [devops](), [files](), [gitops](), [how-to](), [pull-requests]() ### AI overview This tutorial explains how to manage database reference data with Liquibase OSS and Harness Database DevOps. It recommends storing versioned CSV files in Git, applying them with loadUpdateData, and deploying previous versions to support safer rollbacks across environments. ### Source excerpt Learn to version reference data with Liquibase OSS, automate deployments in Harness Database DevOps, and enable safe rollbacks. | Blog ## \[May-Jun 2026\] AI Community -- Activity Highlights and Achievements DevFeed: [\[May-Jun 2026\] AI Community -- Activity Highlights and Achievements]() Original publisher: [Read original article]() Author: Nari Yoon Published: 2026-07-22T01:22:04Z Content type: article Language: en Sources: [Google Developer Experts - Medium]() Topics: [google-antigravity](), [Google](), [Google AI](), [AI Agent](), [Agent Skills](), [Model Context Protocol (MCP)](), [Windows Subsystem for Linux](), [Python](), [HTML](), [Command-line interface](), [CSV](), [SDKs]() Tags: [2026](), [ai](), [ai-agent](), [ai-agents](), [antigravity](), [cli](), [community](), [google](), [google-ai](), [here](), [html](), [mcp](), [python](), [sdk](), [skills](), [wsl]() ### AI overview This article highlights May-June 2026 activities and achievements from Google AI communities. It summarizes community projects, codelabs, experiments, and guides involving Google Antigravity, Gemini, the Antigravity SDK, AI agents, MCP servers, agent skills, CLI and IDE workflows, HTML dashboards, CSV data, and Windows Subsystem for Linux. ### Source excerpt We love sharing the accomplishments of the Google AI communities over the month. We appreciate all the hard work and dedication of our community members. Without further ado, here are the key highlights by products! Agentic DevelopmentAntigravity [Codelab] Building Trustable AI at 100 MPH by GDEs in the US: Hemanth HM, Vikram Tiwari, Lynn Langit, Sebastian Gomez, Rabimba Karanjai; Googler: Ajeet Mirwani; and the partner: Ocupop guides you in building a trustable AI prototype inspired by the field test from the Unstoppable GDE Cohort. The project summary, Bridging the Domain Gap: AI Race Coach built with Antigravity and Gemini and the members of the July cohort were featured on Google for Developers Blog✨. Skills over System Prompts: Building an Anki Tutor with the Antigravity SDK by AI GDE Ertuğrul Demir (Türkiye) demonstrates the value of a modular approach to AI agent design by creating custom Python tools and reusable skill packages for an Anki flashcard tutor using Antigravity SDK. (Image source) Startup on a Shoestring -- Building with a 50c Budget using Google Antigravity Cost & Token Monitors💸 by AI GDE Jigyasa Grover (US) conducted an SDK-powered cost engineering experiment to observe an AI agent prioritize, panic, and ship an MVP landing page under intense budget pressure. Google Antigravity CLI: Orchestrating Parallel AI Agents by AI GDE Aashi Dutt (India) explores how to create an interactive HTML dashboard from a raw CSV file with a single command and explains the benefits of the subagent architecture. (Image source) Configuring MCP Servers and Skills for Antigravity CLI and IDE by Cloud GDE Darren Lester (UK) provides a deep-dive guide on configuring MCP servers and agent skills for the new Antigravity CLI and IDE. He also shared Resolving WSL Friction with Google Antigravity: the Agy 2.0 and Agy IDE Edition discussing how to resolve friction between the Antigravity suite and the Windows Subsystem for Linux environment. Google Antigravity 2 & Gemini 3.5 ## Sending bulk email in Python? Prioritize practices that get your mail delivered DevFeed: [Sending bulk email in Python? Prioritize practices that get your mail delivered]() Original publisher: [Read original article]() Author: Greg Svoboda (gsvoboda@activecampaign.com) Published: 2026-07-14T18:13:00Z Content type: tutorial Language: en Sources: [Postmark (en-US)]() Topics: [Python](), [Code](), [CSV]() Tags: [code](), [deliverability](), [email](), [email-delivery](), [orchestration](), [python]() ### AI overview This tutorial explains how to send bulk email with Python while protecting sender reputation and deliverability. It argues that a basic smtplib script, CSV mailing list, and sending loop can cause delivery problems without a solid reputation-management foundation. ### Source excerpt Bulk email has a reputation problem, but users often misplace the blame. Recipients associate bulk email with spam, inbox clutter, and irrelevant marketing campaigns. Senders associate it with poor deliverability, complicated orchestration, and the constant fear of landing in spam folders. If you handle bulk email, these problems may sound familiar. The DIY Python approach -- install smtplib, divvy up your mailing list in a CSV file, set up a loop function, hope for the best -- all but guarantees delivery problems. The mail itself isn't the problem, though. Done properly, bulk email remains one of the most effective ways to communicate with customers at scale. The challenge is that mailbox providers aggressively punish bad sending behavior, even when it's unintentional. Successful bulk email happens when you prioritize your reputation and choose professional-quality tools to maintain it. We'll start with a high-level look at why we focus on reputation and then offer guidance on bulk email sending. Reputation: The start and end goal of successful bulk email Bulk email helps build relationships, educate customers, promote products, and keep brand image fresh. It can be tempting to dive in and start chipping away at those goals, but it's strategy that wins the race every time. Before sending anything to your bulk mailing list, let alone automating your bulk mail with Python code, turn your attention to your sender reputation: Mailbox providers use sender reputation scores to keep bad actors out of inboxes. Bad reputations mean undelivered mail. Your score combines signals from your domain, your content, and how recipients engage with your emails. Recipients read emails from senders they trust, improving your reputation. If they ignore, delete, unsubscribe, or report messages as spam, your reputation declines. The most successful bulk senders understand that reputation, deliverability, and engagement are tightly connected. And they only start sending when reputation manag ## Access and share AI Gateway leaderboard data DevFeed: [Access and share AI Gateway leaderboard data]() Original publisher: [Read original article]() Author: Jerilyn Zheng Published: 2026-07-14T00:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [Artificial Intelligence](), [API](), [CSV](), [inference-providers](), [data]() Tags: [ai](), [ai-gateway](), [api](), [data](), [images](), [inference-providers](), [leaderboard](), [metrics](), [model](), [open](), [ranking](), [time](), [tokens](), [videos]() ### AI overview Vercel has opened the data behind the AI Gateway leaderboards under the CC BY 4.0 license. The data can be downloaded as CSV or queried through an export API, while charts can be shared as branded PNG images. The leaderboards track daily production usage across models, labs, apps, and inference providers. ### Source excerpt We are making the data behind the AI Gateway leaderboards open under the CC BY 4.0 license. You can now download or query the data through the leaderboard-export API endpoint and render any chart as a shareable image. The AI Gateway leaderboards show how AI is used in production, ranking traffic for models, labs, apps, and providers. Data is aggregated daily across trillions of tokens, so you can see what gets adopted and how that changes over time. For deeper analysis, see the July AI Gateway Production Index. What's ranked There are four leaderboards, each with its own metrics: Leaderboard Ranks Metrics Models Individual models Requests, token volume, spend, images or videos generated Labs Model labs Requests, token volume, spend, images or videos generated Apps Opted-in apps built on the AI Gateway Token volume, spend Providers Inference providers Token volume, spend Models and labs can be filtered by modality (text, image, video) and show a daily percentage share over time; apps and providers are aggregated across all modalities and show a ranked top list. Open data The data behind the leaderboards is open, published under Creative Commons Attribution 4.0 (CC BY 4.0). You are free to use, share, and adapt it, including commercially, as long as you give credit, link to the license, and indicate if changes were made. Every chart and ranked list has a download button that exports the current view as a CSV. For programmatic access, use the export endpoint, which returns the same data and is cached for 24 hours: For models and labs, each row is one entity's daily share of a single metric. One response includes rows for requests, tokens, spend, imageCount, and videoCount, so filter on the metric field to pull out the series you want. Share a chart Every chart has a share button that turns the current view into an image. Pick an aspect ratio (landscape, square, or portrait), then download it as a PNG or copy it to your clipboard. The image includes the legend, title, a ## Open source Chrome extension recreates LinkedIn from your data exports DevFeed: [Open source Chrome extension recreates LinkedIn from your data exports]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2026-06-28T17:41:27Z Content type: article Language: en Sources: [Alex Ewerlof Notes]() Topics: [Extension](), [Chrome extension](), [Open Source](), [App](), [data](), [CSV](), [GitHub](), [Single-page application (SPA)]() Tags: [app](), [chrome](), [chrome-extension](), [code](), [csv](), [extension](), [gdpr](), [github](), [offline](), [open-source]() ### AI overview The article introduces LinkedOut, a free open-source Chrome extension and offline single-page application that lets users inspect and browse data exported from LinkedIn. It describes the author's findings about the breadth of the export and explains how the app processes the data locally without gathering or transmitting it. ### Source excerpt An open source extension to recreate LinkedIn from your data exports ## Good Data For Bad Golf DevFeed: [Good Data For Bad Golf]() Original publisher: [Read original article]() Author: jgamblin Published: 2026-05-13T14:05:06Z Content type: article Language: en Sources: [Jerry Gamblin]() Topics: [Python](), [CSV](), [GitHub Pages](), [GitHub Actions](), [Self-hosted]() Tags: [analytics-pipeline](), [csv](), [github-actions](), [github-pages](), [pipeline](), [python](), [self-hosted](), [uncategorized]() ### AI overview The article presents jgamblin/golf, a self-hosted Python analytics pipeline that converts Garmin Golf app CSV exports into a multi-page GitHub Pages dashboard. It describes features including session analysis, club-level consistency, session replay, gapping, coaching recommendations, and data-quality tracking, with deployment automated through GitHub Actions. ### Source excerpt I play golf. I am not good at golf. But I have a Garmin Approach R10 launch monitor, a Python interpreter, and too much free time, so naturally I spent way more time building a dashboard to analyze my swing data than I did actually swinging a club. The result is jgamblin/golf, a self-hosted analytics ... Read more ## Ask Your Survey Anything: Building AI Analysis Pipelines with Airflow 3 DevFeed: [Ask Your Survey Anything: Building AI Analysis Pipelines with Airflow 3]() Original publisher: [Read original article]() Author: Apache Airflow Published: 2026-04-15T00:00:00Z Content type: tutorial Language: en Sources: [Apache Airflow Blog]() Topics: [airflow](), [Artificial Intelligence](), [Large Language Model](), [SQL](), [CSV](), [data]() Tags: [ai](), [airflow](), [apache-airflow](), [automated](), [community](), [configuration](), [csv](), [executor](), [llm](), [pipelines](), [providers](), [scheduled](), [sql](), [tutorial]() ### AI overview A tutorial showing how to build interactive and scheduled AI analysis pipelines with Airflow 3. The pipelines use survey CSV data, generate and run SQL queries, validate schemas, and either request human approval or email results automatically. ### Source excerpt The 2025 Airflow Community Survey collected responses from nearly 6,000 practitioners across 168 questions. You can open a spreadsheet and filter, or write SQL by hand. But what if you could just ask a question and have Airflow figure out the query, run it, and bring the result back for your approval? This post builds two pipelines that do exactly that, using the apache-airflow-providers-common-ai provider for Airflow 3. The first pipeline is interactive: a human reviews the question before it reaches the LLM and approves the result before the DAG finishes. The second is scheduled: it downloads fresh survey data, validates the schema, runs the query unattended, and emails the result. If you haven't seen the common.ai provider overview yet, start there for a tour of all the operators. This post goes deep on a concrete end-to-end example. Two Pipelines, One Example File Both DAGs live in example_llm_survey_analysis.py and share the same schema context and datasource configuration. example_llm_survey_interactive: trigger manually, review at both ends: prompt_confirmation -> generate_sql -> run_query -> extract_data -> result_confirmation (HITLEntryOperator) (LLMSQLQuery) (Analytics) (@task) (ApprovalOperator) example_llm_survey_scheduled: runs @monthly, no human in the loop: download_survey -> prepare_csv -> check_schema -> generate_sql -> run_query -> extract_data -> send_result (HttpOperator) (@task) (LLMSchema (LLMSQLQuery) (Analytics) (@task) (@task / Email) Compare) The Data The Airflow Community Survey 2025 CSV has 5,856 rows and 168 columns covering everything from Airflow version and executor type to cloud provider, company size, and AI tool usage. A few highlights from the data: 3,320 respondents identify as Data Engineers 2,032 use AWS as their primary cloud provider for Airflow 1,445 are already running Airflow 3 1,351 say they often use AI tools to write Airflow code Those last two numbers together are part of why this example exists: the people most likely to use th ## Analyzing data with ChatGPT DevFeed: [Analyzing data with ChatGPT]() Original publisher: [Read original article]() Published: 2026-04-10T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Data analysis](), [datasets](), [CSV](), [dashboards]() Tags: [analysis](), [chatgpt](), [dashboards](), [data](), [datasets](), [executive](), [explore](), [generate](), [google](), [how-to](), [insights](), [learn](), [openai-academy](), [product](), [tools](), [work]() ### AI overview Learn how to use ChatGPT to explore datasets, analyze CSV or Excel files, create visualizations, identify anomalies, and turn findings into clear, actionable insights. ### Source excerpt Learn how to explore datasets, generate insights, create visualizations, and turn findings into actionable decisions with ChatGPT. ## Learn ChatGPT workflows for finance teams DevFeed: [Learn ChatGPT workflows for finance teams]() Original publisher: [Read original article]() Published: 2026-04-10T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Data analysis](), [DRIVE](), [CSV]() Tags: [analysis](), [business](), [chatgpt](), [communication](), [data](), [drive](), [finance](), [learn](), [openai-academy](), [tools](), [workflows]() ### AI overview This article explains how finance teams can use ChatGPT to structure messy inputs, analyze spreadsheets and CSV files, draft clear financial communication, and standardize recurring work such as variance commentary, forecasts, and close updates. It emphasizes that ChatGPT supports finance judgment rather than replacing it. ### Source excerpt Learn practical ChatGPT workflows for financial analysis, reporting, planning, and decision-ready communication. ## How to Sell Datasets and Data Products Online DevFeed: [How to Sell Datasets and Data Products Online]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-03-30T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [datasets](), [API Monetization](), [Data Infrastructure](), [API](), [CSV](), [JSON](), [Training AI Models]() Tags: [api](), [api-monetization](), [article](), [data-infrastructure](), [datasets](), [digital-products](), [guide](), [how-to](), [payments](), [technical]() ### AI overview This guide explains how to sell datasets and data products online, covering static CSV, JSON, and SQL datasets, real-time APIs, and machine-learning training data. It discusses data preparation, intellectual property, pricing models, billing, taxes, secure delivery, and using Dodo Payments as a merchant of record. ### Source excerpt A comprehensive guide on monetizing CSV/JSON datasets, API data feeds, and ML training data using Dodo Payments and modern data infrastructure. ## Streamdown 2.5 is now available DevFeed: [Streamdown 2.5 is now available]() Original publisher: [Read original article]() Author: Ben Sabic Published: 2026-03-16T13:00:00Z Content type: release Language: en Sources: [Vercel News]() Topics: [React](), [Streaming](), [Markdown](), [CSV](), [Tailwind CSS](), [Artificial Intelligence]() Tags: [bug-fixes](), [compatibility](), [css](), [markdown](), [react](), [streaming]() ### AI overview Streamdown 2.5 is a React component library release for rendering streaming Markdown in AI-powered applications. It adds inline KaTeX support, staggered streaming animations, improved code-block rendering, CSV encoding fixes for Excel on Windows, and better Tailwind CSS v3 compatibility. ### Source excerpt Streamdown is a React component library that makes rendering streaming markdown content easy and beautiful. Built for AI-powered applications, it handles the unique challenges that arise when markdown is tokenized and streamed in real time. v2.5 adds inline KaTeX support, staggered streaming animations, and a round of fixes for code blocks, CSV exports, and better Tailwind v3 compatibility. Streaming parser improvements The new inlineKatex option auto-completes $formula to $formula$ during streaming, avoiding ambiguity with currency symbols, and the option defaults to false. Block KaTeX completion is also fixed when streaming produces a partial closing $. Separately, single ~ between word characters (e.g. 20~25°C) is now escaped to prevent false strikethrough rendering, controlled via a new singleTilde option that is enabled by default. Staggered streaming animations Streaming word and character animations now cascade sequentially rather than animating all at once. The timing is configurable via a new stagger option (default 40ms). Set stagger: 0 to restore the previous behavior. Code blocks Custom renderers now receive the raw metastring from the code fence via a new optional meta prop, and the lineNumbers prop lets you disable line numbers. Long lines now scroll horizontally instead of being clipped, completed blocks no longer re-render when new streaming content arrives, and unknown or truncated language identifiers fall back to plain text highlighting instead of throwing an error. Bug fixes save() now prepends a UTF-8 BOM for text/csv content, so Excel on Windows correctly detects encoding. Tailwind v4-only *:last: / *:first: syntax is replaced with arbitrary variant equivalents, fixing caret rendering in Tailwind CSS v3. Read the documentation to get started. Read more ## Airflow Survey 2025 DevFeed: [Airflow Survey 2025]() Original publisher: [Read original article]() Author: Apache Airflow Published: 2026-01-22T00:00:00Z Content type: article Language: en Sources: [Apache Airflow Blog]() Topics: [airflow](), [data](), [CSV]() Tags: [2025](), [airflow](), [analysis](), [apache-airflow](), [community](), [csv](), [download](), [form](), [graphics](), [interactive](), [report](), [responses](), [share](), [survey](), [users](), [view]() ### AI overview This page presents the Airflow Survey 2025 interactive report, hosted by Astronomer and sponsored and supported by the Apache Airflow community. The supplied evidence does not include the survey findings. ### Source excerpt The interactive report is hosted by Astronomer. The Apache Airflow community thanks Astronomer for running this survey, for sponsoring it and providing the report in this form, and for their effort in marketing, analysis, and preparing the graphics. View raw data Download survey responses (CSV) ## How to use Vertex AI Prompt Optimizer with ground truth data DevFeed: [How to use Vertex AI Prompt Optimizer with ground truth data]() Original publisher: [Read original article]() Author: Alexander Nohe; Elena Erbiceanu Tener Published: 2026-01-20T00:00:00Z Content type: tutorial Language: en Sources: [Firebase Blog]() Topics: [Prompt Engineering](), [Artificial Intelligence](), [Ground truth / benchmark quality](), [Google](), [CSV](), [data](), [Firebase]() Tags: [ai](), [cloud-storage](), [data](), [evaluation](), [firebase](), [format](), [gemini](), [google](), [google-cloud](), [how-to](), [learn](), [quality](), [vertex](), [vertex-ai]() ### AI overview This tutorial explains how to use Vertex AI Prompt Optimizer with ground truth data to iteratively tune prompts and evaluate output quality. It describes preparing scripts and target descriptions in a Google Sheet, exporting the data as CSV, uploading it to Google Cloud Storage, and configuring optimization in a Colab Enterprise notebook. ### Source excerpt Learn how to use the Vertex AI Prompt Optimizer to automatically tune your prompts to get better results by iterating on your prompts and then running an evaluation on the outputs assessing their quality to see if it has improved. ## Quick and dirty loading of CSV files DevFeed: [Quick and dirty loading of CSV files]() Original publisher: [Read original article]() Author: depesz Published: 2026-01-07T00:10:32Z Content type: article Language: en Sources: [select \* from depesz;]() Topics: [CSV](), [PostgreSQL](), [Database](), [JavaScript]() Tags: [ai](), [announcements](), [claude](), [csv](), [database](), [irc](), [javascript](), [node](), [pg-csv-loader](), [postgresql](), [uncategorized]() ### AI overview The author describes building a JavaScript/Node tool that automatically detects CSV delimiters, quote characters, column names, and data types before loading the data into a default PostgreSQL database. The tool was tested on several CSV files and handled PostgreSQL CSV logs, while the author added tests and improved datatype detection after using Claude to create the basics. ### Source excerpt Back in September 2025, David Fetter asked on IRC, about a tool to quickly load CSV to database. One that would require minimal configuration, will try to figure out as much as possible on its own. I thought that it would be great idea. Plus, I'm trying to learn more JavaScript / Node, so figured ... Continue reading "Quick and dirty loading of CSV files" ## Introducing the Spend by Date Range Billing View DevFeed: [Introducing the Spend by Date Range Billing View]() Original publisher: [Read original article]() Author: Rebecca Davis Published: 2025-12-16T21:27:44Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [Cloud](), [Finance](), [Optimization](), [API](), [CSV]() Tags: [api](), [billing](), [cloud](), [console](), [cost-optimization](), [digitalocean](), [download](), [reporting]() ### AI overview DigitalOcean introduces a custom date range billing view that lets customers analyze cloud spending over specific periods, with daily, weekly, and monthly breakdowns by product. The feature is available globally through the Billing Console, Cloud Console, and API, with CSV report downloads. ### Source excerpt If you've ever stared at an unexpected cloud bill spike and sifted through invoices trying to find the cause, you know how time-consuming cost investigations can be. Until now, identifying cost anomalies often meant waiting for your monthly invoice or manually calculating month-to-date usage. That changes today. All DigitalOcean customers now have access to the new custom date range billing view. This new feature is accessible in the Billing Console by going to Billing -> Insights. Additionally, you can download your report as a CSV so you can view your granular billing insights offline. What's included in this new feature This update gives you clearer, more granular visibility into your cloud costs (segmented into daily, weekly, and monthly spend by product) making budgeting and forecasting far easier. DevOps, Finance, and growing teams can now manage spend proactively instead of reacting after the fact. For all customers, this feature is available globally and fully self-service via the Cloud Console and API. Here is a close look into the features of this new billing view: Custom date range filtering: You are no longer limited to invoices for viewing your spending. Choose any start and end date to view total spend for that exact range. This makes it easy to align cloud costs with development sprints, project timelines, or product launches-and improves internal chargebacks and budgeting accuracy. Daily spend breakdown: Daily granularity is key for surfacing anomalies. Instead of a single aggregated number, you can see how your spend changes day by day. If a deployment three weeks ago caused a spike, you can pinpoint the exact day and correlate it with logs to find the root cause quickly. Cost by product breakdown: See exactly where your money is going with a breakdown of spend across services such as: Droplets Databases Spaces Bandwidth Financial adjustments at a glance: For organization-level reporting, the total costs now clearly include financial adjustments like ## Best places to commute from in Warsaw: public transit heatmap DevFeed: [Best places to commute from in Warsaw: public transit heatmap]() Original publisher: [Read original article]() Published: 2025-12-01T23:00:00Z Content type: article Language: en Sources: [Tomasz Nurkiewicz around Java and concurrency]() Topics: [data](), [API](), [CSV](), [Database](), [dataset](), [Google]() Tags: [api](), [csv](), [data](), [database](), [dataset](), [google]() ### AI overview The article describes a project to estimate commute times from Warsaw addresses to Metro Świętokrzyska using public transit and car travel. It uses Warsaw address data, Google Distance API, and a SQLite3 database, but the author collected data for only about 4% of the records after exhausting the API's free plan. ### Source excerpt Location, location, location! That's what the real estate agent will tell you when asked what's the most important factor when choosing the place to live. When living in a modern, busy city like Warsaw you have two choices: traveling by car or by public transit. I came up with the idea to automate finding the best place to live in Warsaw, transport-wise. My plan is to estimate the time it takes to commute from every address in Warsaw to the city center. I'm focusing on public transport and I'd like to find both hidden gems and transport black holes. That is, places with great commute time despite the distance and the opposite - respectively. [Next page]()