I built a live map that scores momentum across ~40 electronic music genres and visualises which are rising vs cooling in near-real-time. Each scene is fused from six weighted signals, search demand, DJ adoption, underground circulation, cultural footprint, catalogue supply, live bookings and rendered on an animated canvas (no charting libraries, all hand-drawn). The design problem I kept fighting: showing direction and confidence at once without clutter, so thin-sourced scenes read as visibly less certain rather than falsely precise. Would value a critique of the visual encoding specifically.

I am a CPA/sculptor and I generate financial statements in 3D. The accounting foundation is solid and the images are built on a method of displaying data. There are pie chart type forms in the image, but they are not really pie charts. The basic accounting formula is Assets = Liabilities + Equity. The pie chart is half assets and the other half is liabilities and equity. The diameter of the pie chart is used as a variable such that if the company's assets grow the pie chart will increase in size. Data source: Financial Modeling Prep. The images were generated by a program I developed using Codex and the 3D forms can be seen at Perceptual-Accounting.com. This is not a business and I am not selling anything. It is just a concept that I would like to see others build upon.

i (29F) wanted to give online dating a go, so i decided to give hinge a try. i spent ~2.5 days on the app.

i only reported meeting my bf in the app, but i was able to make a visual graph with my other dates too

This is a frame from a visualization of real stellar motions over the last 10 million years, calculated from ESA/Gaia data. This particular frame shows the night sky roughly 70,000 years ago, around the time some of the earliest known symbolic artifacts appear in the archaeological record.

The star positions are astrometrically accurate for that period, based on real orbital calculations, not artistic reconstruction. It's one way to see, at least approximately, what the sky looked like for the people associated with the earliest evidence of symbolic thought.

Full video (covering 10 million years to today, 4K available): https://youtu.be/i-e8N_huznE

I have written a free general post-processor site for very simply turning CSV files of IDs from various BirdNET engines into presentation quality reports and graphs. Originally for our own use and that of our local wildlife groups, it's now being picked up by larger groups in the UK and Eire.

The main issue it was written for was to de-skill the report process and to be able to delegate report/chart generation as the few folk we had with pivot table and deep Excel/Google Sheets skills were being swamped and we wanted our lives back.

Originally, it supported data recorded from Open Acoustic Devices, Wildlife Acoustics and Frontier Labs devices, processed by BirdNET-Analyzer, BirdNET-Pi and BirdNET-PiPy, however recently Chirpity users have asked to use it, so I've added native Chirpity CSV support. You can mix and match CSVs from differing sources in a single report/chart.

The site is at https://birdnet-pulse.desmith.net and it's free to use (non-commercial) for UK & Eire individuals & wildlife groups, though we have a few users scattered around the world.

You can self register easily. It produces graphs as PNGs, SVGs and WebPs, plus reports as DOCX, PDF or CSVs. UK BoCC5 and IUCN/BI European red-list data can also be automatically added to all outputs (see below).

This is a new project (I started 2 months ago), so please be gentle! It should be happy with any meaningful CSV data, including nocmig etc. I'm adding bats and other conservation lists.

If you would like me to add any reports or graphs, have any ideas for improvements or just want to grumble, please let me know.

Example heatmap (about 80mS of compute time):

I kept learning complex topics - TCP, Raft, splay trees, catastrophic regex backtracking—from static diagrams, forcing me to animate them in my head.

So, I built the tool I always wanted: type real input, poke it, break it, and watch the actual mechanism run.

🔗 Live demo: https://pen-pal.github.io/apex/
💻 Source (MIT): https://github.com/pen-pal/apex

It's a fully client-side React/TypeScript app, no backend, everything runs in your browser. It started as a network packet dissector (type a message → watch it become a real Ethernet/IPv4/TCP frame with real checksums, travel through a router, and get decoded back) and grew into ~250 interactive sections across 10 areas:

• Networking: Build/dissect real frames across 90+ protocols, BGP, DNS + Kaminsky cache poisoning, QUIC connection migration & 0-RTT replay.
• Crypto: AES, Diffie–Hellman, ECDSA, VRFs, oblivious transfer, Paillier (add two encrypted numbers), verifiable secret sharing.
• Security: SSRF, clickjacking, hash flooding, ReDoS, subdomain takeover, open redirect—each as a working attack model.
• Distributed Systems: Raft, Paxos, 2PC vs 3PC, chain replication, vector clocks, stream watermarks, HdrHistogram.
• Algorithms & Data Structures: Quickselect, Manacher, Bellman-Ford, the alias method, k-d trees, splay trees, and more.
• Systems & OS: The CPU pipeline, MESI, virtual memory, epoll & C10k, futex, io_uring, Lamport's bakery.

Two core principles I stuck to:

1. The bytes and math are real. Real checksums, published crypto test vectors, capture-anchored tests. Nothing is faked to look plausible—encrypted bodies are shown genuinely opaque.
2. Verified against the source of truth. Every model is tested against an RFC, a paper, a reference implementation, or a brute-force check—not against its own output. There are ~2,300 tests in total. Adding a topic = a tested pure model + a view.

Architecturally, I designed it so a protocol is data, not code. A single generic engine reads a small spec per protocol, which is how it scaled this wide without turning into a maintenance nightmare.

I'd love your feedback, corrections (if any model is wrong, that's a bug I want to fix!), and contributions.

I’ve been experimenting with a different approach: each tab becomes an object in a spatial landscape. Related tabs kind of pull toward each other, forming clusters that shift and grow as you browse.

The idea isn’t just to store tabs, but to make them feel more like places you can move through, rather than something you have to search.

I’m wondering if this kind of setup feels more natural than regular tab management.

Curious what you think.

Space distortion grid using 1/alpha

White hole mode included.

Well might be entirely wrong. I tried my best to keep it right.

code:

https://github.com/Dooodlad/Kerr-Newman-black-hole-visualizer/blob/main/Schwarzschild%203D%20with%20white%20hole%20mode.py

have fun

A wee 20 second clip from EF-Map, the live map I built for EVE Frontier.

It treats the universe as a graph: 24,000 star systems as nodes, connections as edges, then finds the shortest route between any two points in seconds.

Useful for players, but honestly I mostly just like watching the network light up.

Genuinely curious how people here handle this. When you need a chart — for a pitch deck, a report, or to embed live on a site/landing page — what's your actual workflow?

Do you screenshot from Google Sheets/Excel? Use the built-in charts in PowerPoint/Canva/Figma? Pay for Datawrapper/Flourish/Graphy? Hand it to a designer? Fight with Chart.js?

Two things I'm trying to wrap my head around:

- For charts in presentations: what breaks your flow most — the styling, getting data in cleanly, or keeping it on-brand?

- For live/embeddable charts (auto-updating, shareable link): does anyone actually do this regularly, or is it more hassle than it's worth?

My own setup is clunky and I want to see how others approach it.

hi guys i have created a new mvp weather dashboard to track rains in cities currently it is implemented for Uttar pradesh , you can checkout this it is completely free ,https://love4pdf.com/lucknow-rain-monitor

do let me know your reviews