css tip 🌲
𝚌𝚕𝚒𝚙-𝚙𝚊𝚝𝚑: 𝚜𝚑𝚊𝚙𝚎() is 𝚙𝚊𝚝𝚑() in css units.
.𝚌𝚊��𝚍 {
𝚌𝚕𝚒𝚙-𝚙𝚊𝚝𝚑: 𝚜𝚑𝚊𝚙𝚎(
𝚏𝚛𝚘𝚖 𝟶 𝟶,
𝚑𝚕𝚒𝚗𝚎 𝚝𝚘 𝚌𝚊𝚕𝚌(𝟻𝟶% - 𝟼𝟸𝚙𝚡),
𝚊𝚛𝚌 𝚝𝚘 𝚌𝚊𝚕𝚌(𝟻𝟶% + 𝟼𝟸𝚙𝚡) 𝟶 𝚘𝚏 𝟼𝟸𝚙𝚡 𝚌𝚌𝚠,
𝚑𝚕𝚒𝚗𝚎 𝚝𝚘 𝟷𝟶𝟶%, 𝚟𝚕𝚒𝚗𝚎 𝚝𝚘 𝟷𝟶𝟶%, 𝚑𝚕𝚒𝚗𝚎 𝚝𝚘 𝟶
);
}
percentages resolve against the box, so the notch stays under the avatar at every width, whereas 𝚙𝚊𝚝𝚑() takes svg path data with no percentages, so the notch is stuck at the width you drew it at.
a little css trick i like ↓
adapt the radius gradually based on available space
shrink from --r to 0 within --g 🧑🍳
.card__surface {
--r: 42px;
--g: 24px;
border-radius: clamp(
0px,
(100vi - 100cqi) / 2 *
var(--r) / var(--g),
var(--r)
);
}
GOOGLE CREÓ LA MEJOR HERRAMIENTA DE ESTUDIO DEL MUNDO
Es gratis. Lleva meses disponible.
Y el 90% de la gente no la conoce.
Te doy 6 prompts de NotebookLM para aprender lo que sea en tiempo récord.
📌Guárdalo, te salvará en tu próximo examen
`image-set` should be the first choice when dealing with background images 🤌
One CSS line fixes:
→ 1x/2x and even 3x resolution switching
→ Modern format fallbacks - AVIF, JPEG, WebP
Zero JS. Pure CSS! 💪
✅Widely available since September 2023
Algorithm Visualizer devrait être dans la boîte à outils de tout·e développeur·se qui apprend ou enseigne des algos — la visualisation en direct à partir du code rend les mécanismes abstraits vraiment lisibles.
https://t.co/LMycabGIbQ
The CSS Custom Highlight API lets you highlight search results
— Without affecting the DOM structure.
::highlight(search-results) {
background-color: #ff0066;
color: white;
}
#CSS#WebAPIs
I thought I had found a bug that prevented counter() from working properly, but it was a feature!
https://t.co/M4lJgx5SqO
Another CSS gotcha that you'd better know about if you don't want to waste time trying to figure out why your code isn't working.
We’re open sourcing ArrowJS 1.0: the first UI framework for coding agents.
Imagine React/Vue, but with no compiler, build process, or JSX transformer. It’s just TS/JS so LLMs are already *great* at it.
AND run generated code securely w/ sandbox pkg.
➡️ https://t.co/y477CtT6qY
I built a .NET app that takes a receipt photo and turns it into structured data using Ollama + MEAI.
Here's what I covered:
- Local vision model setup
- Image analysis from .NET
- Prompt tuning for better extraction
- JSON + typed output
Full breakdown: https://t.co/LOVRCkf4lU