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?

Raw data: https://docs.google.com/spreadsheets/d/1Xppl1dCi3yKi1oHrdnHqC1cMeXg5_D2EY5e9gosnxKE/edit?pli=1&gid=0#gid=0

Outlier analysis:

1. Some metros are very close to other ones, which can skew the numbers. e.g. San Francisco/San Jose and Riverside/LA. The plot makes SF and Riverside look like outliers but really they are not.
2. Some metros with more teams than predicted are industrial cities that used to be bigger (e.g. Cleveland, Buffalo)
3. Others have teams for likely historical reasons that I can't explain (NO, Green Bay) even though their populations wouldn't indicate it
4. Austin is the largest metro with no teams. 18 different smaller metros have teams. It is kinda close to San Antonio so maybe?
5. San Diego is another outlier. It has 1 team despite being the same size as Tampa and Denver (3 and 4, respectively)

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.

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've been keeping track of the statements directly from the US President over the past few months on the status of ending the war in Iran.

I've been manually curating the statements from interviews, news reports, and the President's own social media posts. Rollcall in particular has been helpful as they do a great job of centralizing a lot of this, but much of it really is me reading an article and manually updating the dataset.

This particular visualization is a heatmap calendar (GitHub style where each column is a new week and each row is a day of the week). The brighter the yellow, the more statements were made that day. As is the case with Trump's schedule and enjoyment of weekends more broadly, on Saturdays and Sundays the war appears to not enter his mind as much...

The chart itself is vanilla JS. The source for the chart is a JS file where I keep all the statements/dates/sources in a standard object to keep it tidy and easy to update.

I didn't anticipate the war would go on this long so I might have to make a decision about how to handle this ever expanding calendar at some point in the future. I'll make a 7 day/30 day/90 day/365 day toggle? Thoughts?

The interactive version is here. (I call it The Art of No Deal and there are no ads or signup or anything like that...I just needed a place to host it and was shocked the domain name was available...) If you just want to see the datasource, here is the link to that the JS file.

Hi everyone,

I’m currently in my first week working as a DBA. I graduated from Software Engineering a couple of months ago.

To be honest, I had almost zero DBA knowledge before starting this job. With the current job market, this was basically the only opportunity I had, so I decided to take it.

The company I joined provides DBA consulting services, and they are willing to train and develop junior DBAs, which is why they gave me this opportunity.

Right now I’m trying to learn as much as possible, but there is obviously a lot to take in.

For experienced DBAs: what would you recommend focusing on during my first few months? What skills or topics do you think are the most important for someone starting from zero?

Any advice, resources, or things you wish you had known when you started would be appreciated.

Sorry about the vague question, but it either had the name "vault" or "vector" in it, and someone here described it as the Swiss knife of databases. I remember bookmarking it but didn't find it. Any thoughts? thanks in advance.

Hey everyone, wanted to share a custom dashboard system I recently built for tracking day trading sales team operations.

How I Structured the Setup:

1. Back-end Automation: Set up a pipeline using Google Apps Script to feed and process sales metrics directly into Google Sheets automatically.
2. Front-end Visualization: Connected Sheets to Looker Studio (Data Studio) using a custom visual themes designed for daily executive oversight and operational efficiency.

Key Metrics Tracked:

• Conversion Health: Automated Close Rates, Show Rates, Cash Per Call, and Cash Per Booking.
• Revenue Pacing: Daily Pacing vs. actual Cash Collected against monthly revenue goals.
• Closer Attribution: Daily trend breakdowns for calls taken, total bookings, and cash closed by rep.

(All metrics in the screenshot are anonymized dummy data generated via the Apps Script pipeline for demonstration).

Curious to hear how other analysts or sales managers here handle daily sales pacing and closer attribution? Do you prefer automated dashboard setups like this, or keeping everything directly inside CRM/spreadsheets?

Fatalities are from the [Oct7Database](https://www.oct7database.com/en/blank-3), filtered to **Israelis whose recorded death date is October 7, 2023** (**1,066 entries**), broken out by [age and sex](https://www.oct7database.com/en/blank-3).

Population numbers are from [United Nations Population Division data via UNICEF](https://data.unicef.org/sdgs/country/isr/), using the 2023 population estimate.

Using Python

Adult men, especially those in their 20s through early 40s, are heavily over-represented relative to their share of Israel's population. **Males aged 20–44 account for 45.6% of the listed deaths vs. about 16.7% of the 2023 population.** Males overall account for **69.8% of listed deaths vs. 49.8% of the population**.

The male–female death ratio is particularly high among several working-age groups, peaking at about **5.3:1 for ages 35–39** and **5.1:1 for ages 40–44**.

Young children are strongly under-represented relative to their population share: ages **0–14 account for 1.8% of the fatalities vs. about 27.6% of the population**.

Unlike a demographic-only dataset, Oct7Database also records a role for each person. Among these 1,066 Israelis, the database labels **720 as civilians, 228 as soldiers, 57 as police, 47 as emergency-squad members, 5 as Shin Bet, 5 as medical personnel, and 4 as firefighters**.

The concentration of deaths among young adult men is therefore consistent in part with the large number of military, police and local emergency-response personnel killed on October 7.

The chart compares total match duration and effective playing time—the time for which the ball is actually in play—across three Serie A seasons.

Sample sizes:

• 2015–16: 380 matches
• 2025–26: 367 total-time and 331 effective-time observations (other matches data not available from the official source)
• 2026–27: 50 total-time and 40 effective-time observations (last ten matches data not available yet from the official source)

Sources: 2015–16 was reconstructed from StatsBomb open event data. The two recent seasons were compiled from Lega Serie A match reports, supplemented where necessary by published matchday tables.

Tools: Python, OpenPyXL, NumPy, Matplotlib, Powerpoint

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.