Amrita Chanda 🏳️‍🌈👩🏾‍💻🍸

"design tools continue to fall behind CSS" was a recurring theme at @CSSDayConf - typography features (so many!) - scroll animation - meaningful math tricks - layout capabilities - container queries / responsive capabilities - anchor positioning - :has() and presence awareness - quantity queries - scroll snap and so much more… it seems like design tools are competing against each other to be the best Photoshop variant. meanwhile, the web's features are pushing fast into a mega capable future and designers are blissfully unaware. (i'm aware this isn't all teams, but it's def the majority) web designers, sit with your web developers web developers, sit with your web designers also, someone (i wish i could work on this) make a tool that unlocks these features for designers. help them wield these capabilities, they're super fun and very meaningful. the delta is growing too fast between CSS and design tools, someone or something close this gap!!

When everyone is debating how X framework is better than Y language, remember what Why the Lucky Stiff taught us: > when you don't create things, you become defined by your tastes rather than ability. your tastes only narrow & exclude people. so create.

One of the best explanations for CSS specificity I have come across stuffandnonsense.co.uk/specisithity #css #Webdesign #webdevelopment

Data | #Rainwater harvesting could make a dent on the freshwater needs of Bangalore, but it pales in comparison to wastewater, @ShashankPalur and @RKulranjan write. trib.al/1TSd3jJ

Apple's latest announcement regarding Web Apps translated to plain English

sir, a second tech executive has left OpenAI

Our interview with @guy_laron looks into Israel's attempt to become an 'energy hub' transit state between the Middle East & Europe, and into Netanyahu's extremist settler friendly policy to prevent Palestinian unity by supporting Hamas with $$ from Qatar. phenomenalworld.org/analysis/octob…

There's a lot to say about the Humane AI pin, but top for me: Why does this industry constantly expend so much capital to solve non-issues (no more scrolling, now you just pinch your fingers!) at the cost of greater data surveillance and enormous environmental cost?

@kat_woman13 Hello there, would love an invite. Thank you!

Everyone remember big dumb boat and the little digger that could? Suez meme version of our essay phenomenalworld.org/analysis/globa…

How transformers evolved to become the dominant neural architecture All large language models and most state-of-the-art language, forecasting, and personalization AI models are based on Transformers. Before transformers, recurrent neural nets (RNNs) were used to process sequential data, like text. They had their issues, such as slow training times and the vanishing gradient problem, which made them hard to optimize for longer sequences. The vanishing gradient problem in RNNs occurs when gradients (the values used to update the network's weights) become extremely small as they are propagated back through time during training. This leads to earlier layers in the network receiving minimal updates, making it difficult for the model to learn and capture long-term dependencies in the data. The other popular architecture was convolutional neural networks (CNNs) which are excellent for image processing but aren't suitable for sequential tasks like natural language processing. In 2017, Vaswani and his team introduced transformer architecture, which was a game-changer. It solved many of the problems associated with RNNs by introducing the following key components: - Attention Mechanism: This allowed the model to focus on different parts of the input, making it more context-aware. - Parallel Processing: Unlike RNNs, which process sequences step-by-step, transformers could process all parts of a sequence simultaneously, making training much faster. Fundamentally, Transformers are optimized for GPUs - the chips that are used to train large neural networks. Unlike RNNs, where computations are dependent on the previous step, transformers can process all positions in the sequence simultaneously. This means that the computations for each position (or word) can be carried out at the same time, making full use of the parallel processing capabilities of GPUs. This leads to much faster training and inference times, making transformers more efficient and scalable when using GPU hardware. - Scalability: Transformers were designed to be scalable, making them suitable for large models and extensive datasets. - Continued Innovation: The success of models Like BERT and GPT: Following the introduction of the transformer architecture, models like BERT, GPT, and their variations demonstrated remarkable success in various NLP tasks, cementing the prominence of transformers. This led to a virtuous feedback cycle of investment and innovation. A lot of research community has been focused on Transformers and this has led to continual improvements and adaptations, making them even more effective and efficient. Versatility: The transformer architecture proved to be incredibly versatile, applicable to a wide range of tasks, not just in NLP but in other fields as well. In fact, we at Abacus.AI, use Transformers for SOTA forecasting and personalization (both sequential data problems) as well. In summary, the rise of transformers as a dominant neural network architecture can be attributed to their efficiency, scalability, and versatility, along with the well-timed solving of problems that plagued earlier models like RNNs. They provided an elegant solution to challenges faced in processing sequential data, leading to wide adoption across various domains. Paper introducing transformers: arxiv.org/abs/1706.03762 Pic credit and good reading - sanchman21.medium.com/evolution-of-t…

Pretty bleak future arxiv.org/abs/2307.02792…

Should designers code? yes yes yes yes
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@TotomInc Hey

✨ Hand-curated list of websites & subreddits where you can promote your SaaS! ◆ 26 websites where to promote your SaaS ◆ 8 different subreddits oriented for SaaS and startups Like, follow (so I can DM you) and reply with "Hey" and I'll send you the link #buildinpublic #saas

When you open your website on Safari for the first time twitter.com/grapefrukt/sta…

The best thing about switching your web search to ChatGPT is then the people who put all this info on the web don't get any credit and you have no idea how reliable the info is since all sources are jumbled into a giant opaque probability distribution. Oh wait, that's terrible.

Did you know this? 👀 When using @github, include “[skip ci]” in your commit message if you don't want to trigger CI. It can go _anywhere_ in the commit message. It can even go in the extended part. Useful, for example, when working in a draft PR. Thanks @_darrenburns

Pretty sure this is what Elon thinks coding is