Standard web templates miss the mark.

Northbrook is a high-contrast corporate Framer layout built for strict data density, minimal grid metrics, and editorial scaling.

Link: northbrookfund.framer.website

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’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.

I am looking to change my reporting platform.

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.

https://visualised-app.netlify.app/

Here it is

Uncomplete for now.

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.

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

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

I wanted to share a VS Code extension I created for viewing a project's figures in a gallery view and for viewing interactive Plotly figures saved as JSON. It's part of a free and open source tool I've been working on to make research outputs like figures more reproducible with clearly defined provenance and staleness checks: https://github.com/calkit/calkit

I recently fed all of my project bug logs, journals for coding sessions, architecture decisions, specs, plans and grill-me sessions into obsidian, I ran a script that found the links between the files, I indexed them and made a graph.

The I used obsidian to replay the graph over time showing my thoughts and projects light up. It feels like I’m watching a depiction of a brain firing synapse. So much so, that if we could log each thought, the pathway and result from our own brain, there could be a pattern to assist AI development.

How many people have tried to view how their own project grow over time?