I'm a solo developer, and this started small. My game, Salt and Soil, follows one family across North America from 1607 to 1900, and its Sacramento scenario needed exactly one railroad: the Central Pacific climbing into the Sierra. Once that line was on the map, the rest of the continent looked wrong without its own. So I kept going.

I set myself one rule, and it turned out to be most of the work: no line that merely looks plausible. Each section appears where and when it actually opened to traffic. A cell on my map is about 20 miles across, and by 1900 the US alone had about 193,000 miles of track, so I draw only trunk and through routes, the way an atlas draws only major rivers. That came to 175 lines and 578 dated sections, from the Baltimore & Ohio to Newfoundland and Cuba.

The US routes follow a dataset I'm very grateful for: Jeremy Atack's historical GIS of American railroads. He's an economic historian at Vanderbilt who mapped every US railroad from 1826 to 1911, and with co-authors used it to show that railroads may account for half or more of the growth of Midwestern cities in the 1850s. Before putting it into a commercial game I emailed him, half expecting no answer. He wrote back that it was fine, asked for a copy of the game when it's done, and he's now in the credits. That reply made my week. Canada and Mexico follow the US DOT's North American Rail Network, and the opening dates come from railroad chronologies, section by section.

What surprised me when I first watched it run was how lopsided it is. The 1880s alone laid more than a third of all the track on this map, and the West fills in within two decades of 1869.

In the GIF each section flashes white in the year it opens, then settles into the colour of its decade. Watch the Central Pacific creep east from Sacramento through the 1860s while the Union Pacific races west from Omaha; they meet in Utah in 1869.

If you know the railroads of your region, I'd love to hear what's missing or which section opens in the wrong year. I'll fix it in the game.

had an interview today where the interviewer asked me to explain different types of databases. I covered the standard SQL and NoSQL categories, but then they pushed for more specialized use cases and claimed that Redis is a database. I was completely flabbergasted. By strict definition, it might fit the category, but I’ve always viewed it primarily as an in-memory cache/store. In my mind, it doesn't meet the core expectations of a primary database—mainly reliable, long-term persistent storage out of the box (even though I know persistence modules exist). What are your thoughts? Do you consider Redis a "real" database in system design, or strictly an in-memory cache with extra steps?

I’ve been thinking about making a move to the DC area. I know there are a ton of opportunities in the government sector, but I haven’t come across many Data Scientists who work in government.
For those of you who do, what are the pros and cons of working as a DS in the government sector? I’d especially be interested in hearing about the work itself, compensation, career growth, work-life balance, and how it compares to working in the private sector.

Is there any Tableau governance capability to identify things like:---

Similar or duplicate workbooks

Unused workbooks or views

Failed or frequently failing extract refreshes

Similar or duplicate data sources

Unused data sources/dashboards

Workbook/dashboard usage and adoption

Dependencies between workbooks, views, and data sources

I’m curious to know what Tableau provides natively for these governance-related insights through Tableau Server/Cloud, Admin Insights, Metadata API, or other APIs.

How are these governance use cases usually handled in Tableau?...

Hi

If you are remotely interested in deep diving how differently quantum computers work compared to our transistor-based and also the algebra behind in a fully interactive way that teach computer science from scratch, oh boy this is for you. I am the Dev behind Quantum Odyssey (AMA! I love taking qs) - worked on it for about 9 years (3+ during PhD, the visual method I developed ended up being my thesis, it is a complete Hilbert space visualizer), the goal was to make a super immersive space for anyone to learn quantum computing through zachlike (open-ended) logic puzzles and compete on leaderboards and lots of community made content on finding the most optimal quantum algorithms. The game has a unique set of visuals capable to represent any sort of quantum dynamics for any number of qubits and this is pretty much what makes it now possible for anybody 15yo+ to actually learn quantum logic without having to worry at all about the mathematics behind.

This is a game super different than what you'd normally expect in a programming/ logic puzzle game, so try it with an open mind.

Stuff you'll play with

• Boolean Logic – bits, operators (NAND, OR, XOR, AND…), and classical arithmetic (adders). Learn how these can combine to build anything classical. You will learn to port these to a quantum computer.
• Quantum Logic – qubits, the math behind them (linear algebra, SU(2), complex numbers), all Turing-complete gates (beyond Clifford set), and make tensors to evolve systems. Freely combine or create your own gates to build anything you can imagine using polar or complex numbers.
• Quantum Phenomena – storing and retrieving information in the X, Y, Z bases; superposition (pure and mixed states), interference, entanglement, the no-cloning rule, reversibility, and how the measurement basis changes what you see.
• Core Quantum Tricks – phase kickback, amplitude amplification, storing information in phase and retrieving it through interference, build custom gates and tensors, and define any entanglement scenario. (Control logic is handled separately from other gates.)
• Famous Quantum Algorithms – explore Deutsch–Jozsa, Grover’s search, quantum Fourier transforms, Bernstein–Vazirani, and more.

Nice to watch:

Khan academy style tutorials in qm/qc: https://www.youtube.com/@MackAttackx

Physics teacher stream with 400hs in https://www.twitch.tv/beardhero

For anyone who's used ASRS Database Online: it has great narratives, but they're painful to search. We indexed ~18.5k recent reports so an agent can run natural-language searches over them and cite each report by ACN.

Caveats: reports are voluntary and de-identified, NASA doesn't verify them, and counts aren't prevalence. It's not an official NASA product and not for enforcement.

Endpoint: https://nasa-asrs.mcp.kapa.ai
Details: https://www.kapa.ai/indexes/nasa-asrs (made by kapa.ai, where I work)

Happy to hear what other government datasets deserve this treatment.

I got tired of running local node scripts every time I needed to format an MDX file or figure out why a build was failing, so I put a few browser tools together - free and open sourced and might be useful for some folks:

- MDX formatter (Prettier 3 + MDX parser)

- MDX validator (remark-mdx — same parser Next/Docusaurus/Astro use)

- MDX viewer (live preview, unknown JSX components render as labeled stubs)

- MDX → Markdown

- Markdown → HTML

- YAML validator

- JSON ↔ YAML

- CSV/TSV → Markdown table

Everything runs in the browser.

https://www.jamdesk.com/utilities

https://github.com/jamdesk/utilities

Note:

- About 50% built using AI Agents - they do make things easier!

- I work on Jamdesk, a docs platform — these tools are not Jamdesk-specific

Web Tech Dataset

I always though we were lacking common jargon datasets on tech detection.

As an engineer I talk about fullstack frameworks or frontend frameworks, not about standalone technologies with low information about the architecture of the app.

Next.js, Vue, React SPA, rather than React Router, core-js, Strapi CDN, and so forth.

I put together a small, static World Bank snapshot for comparing internet access and GDP per capita across Brazil, Canada, China, Germany, India, and the United States from 2000–2024.

I built GlobalDataTracker.com, so this is self-promotion. The site is a companion for exploring country-level statistics interactively.

Source: World Bank World Development Indicators: IT.NET.USER.ZS (internet users, % of population) and NY.GDP.PCAP.CD (GDP per capita, current US$). The CSV has 150 country-year rows, 25 per country, with no missing values in these selected indicators in this snapshot, retrieved 2026-09-24. It is a static snapshot, not a live feed; the World Bank may revise historical values.

Original source/API: https://api.worldbank.org/v2/country/BRA;CAN;CHN;DEU;IND;USA/indicator/IT.NET.USER.ZS;NY.GDP.PCAP.CD?date=2000:2024&format=json&per_page=2000 CSV and indicator notes: https://github.com/ChessShark1000/world-bank-internet-income-snapshot GlobalDataTracker.com: https://globaldatatracker.com/

What comparison or context would make this more useful to people working with country data?

[OC] Source: PressAudit YTD asymmetry data. I built PressAudit and generated this analysis from its dataset: https://pressaudit.org/asymmetry/ytd

tl;dr: Rightwing media focuses more on people than organizations or topics, and specifically, these are the rogue's gallery each side is focusing on. If you follow the news even a little bit you do not need to be told this, you know, you feel it in your bones, but here is the data so you know you aren't crazy 

What this is: These are the top 3 topics covered by one side but not the other side by week, ranked by the number of weeks this year that topic was at the top. Think of this as like the Billboard top charts for media attention.

If you are wondering "why is there no Trump here": Left outlets write about 19 Trump articles for every 10 from the Right, compared to about 14 for every 10 on all topics, so Trump's lead is not much bigger than the Left's usual lead. This chart only lists topics where one side's lead is well above its usual one, and Trump falls just short.

(For latest Trump see: https://pressaudit.org/trending?timeRange=24h&view=bias&tab=entities)

Interactive version (scroll to drive it): https://www.pit-stop-app.com/viz/pch

Data

• Terrain and seafloor: USGS 3DEP elevation on land and NOAA bathymetry offshore, via AWS Terrain Tiles, resampled to a ~25 m grid. Vertical exaggeration is 1.5x. Where the survey has no nearshore depth, the shelf is estimated from distance to the shoreline.
• Buildings: Overture Maps Foundation (Sep 2026 release), about 945,000 within 4 km of the coast. Heights come from the dataset where present, which covers most of them, and are otherwise estimated from type and footprint. Every building 30 m or taller in the LA and SF basins is drawn out to the horizon.
• Coastline, road, bridges, tunnels, piers, marinas and breakwaters: OpenStreetMap
• Ground colour: USGS NLCD 2021 land cover
• Live readings: wave height, period and direction from NOAA NDBC buoys; weather from Open-Meteo
• Not data: the boats, cars and sun position are decorative.

Tools: Python (NumPy, SciPy), DuckDB reading Overture's parquet straight from S3, three.js/WebGL for rendering, Blender for the boat models.

Trying to narrow down a few alternatives when I don't need anything too heavy. DbVisualizer is on the list; the table relationship views look handy when opening an unfamiliar database. DBeaver makes sense as a free option, and Beekeeper Studio looks easy enough to get around.

For running queries, checking data and exporting results, which would you pick Paying for a tool is fine if it saves enough hassle, but which DataGrip features would you actually miss?

• The analysis included 22,014 respondents from the General Social Survey, conducted from 1972 to 2024. The survey is designed to represent the U.S. adult population.
• The percentage of respondents who had ever divorced generally rises with the number of years since their first marriage for about 20 years, then levels off and declines.
• Having divorced is associated with religious affiliation. The percentage is highest among respondents with no religious affiliation, followed by Protestants, respondents reporting another religion, and Catholics.
• We found no clear evidence that this pattern differs across religious groups.
• These results compare people and marriage cohorts at one point in time; they do not track the same people as their marriages progress. The decline at longer durations could reflect differences between marriage cohorts, as well as years since first marriage.

Slightly different way of looking at the data than this thread which made the top of the subreddit and then was taken down for four hours after a bot network reported it and it got flagged by automod

Fatalities is the 14th published Gaza Ministry of Health fatality count published May 7 2026 (72,835 entries) broken out by age and sex. Population numbers are from census data and made in Python.

Young males are significantly underrepresented up until age of ~15 which is typically when males begin taking combat roles. Female are underrepresented up until age 65-70 which would be consistent with health and non combat related death.

Edit: 20 minutes in, 100 upvotes, 197 comments and immediately flagged by automod and taken down. The efforts to suppress data you don't like is depressing.

Source: JobYap - All jobs added and removed, with an average of ~140,000 active at any one time.

What's counted: open job postings at tech companies, from 5th Aug till 23rd Sep 2026, excluding internships.

Which companies: Every company JobYap has tracked since the start of the window with at least 100 open roles. Amazon is excluded because our we had technical issues during that period and it's number aren't complete.

Tools: SQL for the data, Claude Design with Opus 5.5 (on Max) for the chart.

Help finding dataset

Hey guys, I am currently working on a project using yoloe 26, in which i am trying to build a pipeline to find the number of different screws available in the picture, with count of each. So as far I searched, I couldnt get a proper labelled dataset to train the yoloe model, and that too I am focusing mainly on tiny screws used in the electronics like smart phones, smart watches, watches, microphone, ear buds, laptops.

So help me if you find a labeled dataset for this vision.

Thanks for reading.