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FSCSS component architecture is built around a modular, composition-first model that compiles to plain CSS. It emphasizes reusable style units, design tokens, conditional logic, and selective imports—with almost no runtime JavaScript required for the final output. Components in FSCSS are treated as pure style definitions rather than framework-specific widgets, keeping stylesheets readable, highly reusable, and free of classic “mega-stylesheet” problems while still producing standard CSS that any browser understands. Core Building Blocks FSCSS provides a focused set of primitives for defining and composing styles: Primitive Purpose Best for Introduced / Key version str(name, "…") Named blocks of CSS declarations Simple reusable style snippets Core @fun(name){…} Key-value stores (design tokens) Spacing scales, color palettes, property groups Core @define name(params) Parameterized mixins Themed components, variants, full structures 1.1.15+ pattern(threshold: "desc", "…") Semantic / fuzzy matching Natural-language style injection 1.1.25+ @event name(param) Conditional value functions Themes, states, calculations Core @arr(name[…]) Arrays + iteration Generated classes, loops, scales Core @import Selective / wildcard module loading Modular architecture & ecosystem modules Core How Components Are Structured 1. Atomic / Token Layer ( @fun + variables) Design tokens sit at the foundation so every component draws from a single source of truth: @fun(tokens) { primary: #2563eb; radius-md: 8px; space-4: 1rem; shadow-sm: 0 1px 3px rgba(0,0,0,.1); } 2. Base Style Blocks ( str() or @fun full-block) Related declarations are grouped into reusable blocks that can be dropped into any selector: str(card-base, " padding: @fun.tokens.space-4.value; border-radius: @fun.tokens.radius-md.value; box-shadow: @fun.tokens.shadow-sm.value; background: white; ") 3. Parameterized Components ( @define ) True mixins accept arguments and can be composed freely: @define button(bg: #2563eb, fg: white,

Nvidia says Hugging Face will stay open even as the chipmaker takes control of a key AI hub.

TikTok’s comments section is getting an upgrade. The social network announced on Thursday that it’s rolling out new features for its comment sections, including voice comments, comment polls, photo carousel comments, and Live Photo comments. With the additions, the app is borrowing features from messaging apps as it looks to deepen engagement on its platform. […]

The long-rumored deal will give the chip giant access to—and help it promote—a huge repository of open-source AI models and data sets.

Nvidia said Hugging Face hosts over 3 million models and is used by over 18 million developers.

Full interview-prep notes, ##Episode 1 through 29. Episode 1 : Execution Context ============================== Everything in JS happens inside the execution context. Imagine a sealed-off container inside which JS runs. It is an abstract concept that hold info about the env. within the current code is being executed. In the container the first component is memory component and the 2nd one is code component Memory component has all the variables and functions in key value pairs. It is also called Variable environment. Code component is the place where code is executed one line at a time. It is also called the Thread of Execution. JS is a synchronous, single-threaded language Synchronous:- In a specific synchronous order. Single-threaded:- One command at a time. Episode 2 : How JS is executed & Call Stack ============================================= When a JS program is ran, a global execution context is created. The execution context is created in two phases. Memory creation phase - JS will allocate memory to variables and functions. Code execution phase Let's consider the below example and its code execution steps: var n = 2 ; function square ( num ) { var ans = num * num ; return ans ; } var square2 = square ( n ); var square4 = square ( 4 ); The very first thing which JS does is memory creation phase, so it goes to line one of above code snippet, and allocates a memory space for variable 'n' and then goes to line two, and allocates a memory space for function 'square'. When allocating memory for n it stores 'undefined', a special value for 'n'. For 'square', it stores the whole code of the function inside its memory space. Then, as square2 and square4 are variables as well, it allocates memory and stores 'undefined' for them, and this is the end of first phase i.e. memory creation phase. Now, in 2nd phase i.e. code execution phase, it starts going through the whole code line by line. As it encounters var n = 2 , it assigns 2 to 'n'. Until now, the value of 'n' wa

Nvidia has agreed to buy Hugging Face for $12.93 billion, bringing one of the most popular hosting platforms for open-source AI models, datasets, and tools under the ownership of the world's biggest AI chipmaker. Hugging Face is an online platform founded in 2016 that gives AI developers a space to share their projects and data […]

Restaurant menus with AI-generated food images are going viral for their off-putting aesthetic. But AI’s impact on modern menus runs even deeper.

Untrusted retrieval is now a more common production failure than a weak prompt, because agents ingest memory they never score. A seventy-minute workshop can add a cheap scoring gate, a replayable log, and a reject path before generation. Students leave with a runnable Python harness, a four-row decision table, and a timing plan they can repeat. The method stays useful if every product name is removed and the scoring host is only a free server. What you will build This workshop treats retrieved snippets as untrusted input, not as ground truth the model should quote. You will capture a retrieval batch, score each chunk against a written rubric, and allow only passing chunks into the prompt. A JSONL replay log records the fingerprint, score, and decision so later failures can be diffed. The generation model never sees dropped text, which keeps stale or planted memory out of the answer. Timing box 00:00–00:10 — install dependencies, copy the harness, and load the sample corpus 00:10–00:30 — Exercise 1: capture retrieval payloads and stable fingerprints 00:30–00:50 — Exercise 2: score chunks with a rubric and an optional free model 00:50–00:65 — Exercise 3: gate the prompt and replay one rejected case 00:65–00:70 — debrief against the decision table and list remaining holes The schedule is a teaching box, not a production SLA, and it assumes one laptop plus one HTTP scoring endpoint. If the endpoint is slow, freeze Exercise 2 after five scored chunks and continue with the logged samples. Do not expand the window to chase a perfect judge; the learning goal is a gate you can rerun. Why a scoring pass belongs in front of generation Cheap code generation has made it easy to wire a retriever into a chat loop in an afternoon. The failure mode that follows is quieter than a crash: the model answers fluently from a chunk that is expired, off-topic, or injected. Architecture diagrams rarely show that hop as a trust boundary, so teams skip scoring and jump to a larger generator. A

Two weeks after launching Buildside, 130 people had joined. I did not have an ad budget or a huge email list. There was no secret growth trick. I grew Buildside by sharing the journey on X and LinkedIn, speaking with users, and letting their feedback guide the app. The biggest lesson I learned was: You have to market your product long before you launch it. I started marketing before the app was ready Many founders wait until their product feels finished before they talk about it. Sharing early work can feel risky. You may worry that people will judge it, copy it, or ignore it. I chose to share anyway. Before Buildside launched, I posted about the problem I wanted to solve, why I cared about it, and what I was learning. I showed small bits of progress and asked people what they thought. That gave people a reason to follow the journey. On launch day, I was not asking strangers to care about a new app. I was inviting people who had already watched the idea grow. Y Combinator tells founders to release early. Its guide says waiting too long is often driven by fear and the wish to make everything perfect. An early version lets you ask, “What do you think?” That advice matched my experience . I shared the fails as well as the wins It is easy to post a new user count or a kind comment. It is harder to share what went wrong. Yet the honest posts often led to the best talks. When something failed, I said so. When I changed my mind, I explained why. I shared the good days too. The goal was not to make every post look impressive. It was to show the real work. Honesty builds trust. Buffer has shared parts of its business in public for years, through both strong and hard moments. The company says being open builds trust and keeps it accountable. Its open company page shows this in action . People do not need a founder to look perfect. They want to know there is a real person listening and trying to make something useful. I let users shape Buildside Building in public was not only

The company is reducing pressure on workers to use artificial intelligence tools while encouraging them to experiment with Hatch, its most advanced AI project yet.

On our new Real World AI stage, we’ll be focusing on the intersection between the digital and physical, and all the ways we’ll continue to see a blending of the two.

The US government wrote a letter in support of OpenAI’s argument that training AI on others' intellectual property is fair use.

A startup called Mostik has a wild new approach to combining the capabilities of AI models.

Google's Pro model updates are seemingly paused, but there's yet another Gemini Flash today.

X says U.S. creator payouts will now be handled through its X Money payments service, a change that appears to replace the previous Stripe-powered payout system.

Jio is betting it can turn an aging computer into an AI-ready PC for as little as about $11 for two months.

Christopher was sick of being ghosted by AI recruiters. So he unleashed ChatGPT on his robot interviewer.

He reported from space that only "fat" Japanese tree frogs enjoyed weightlessness.

This is Adobe's second acquisition out of India after Rephrase.ai in 2023