NEW GEMMA 4 beats GPT-5.4: The A4B Model
NEW GEMMA 4 beats GPT-5.4: The A4B Model
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The Real Size of AI Frameworks: A Wake-Up Call
You Think You Know What You're Installing When someone says "just install PyTorch," you probably think "how bad can it be?" It's a deep learning library, right? A few hundred megabytes, maybe? Think again. I built pip-size to expose the hidden cost of Python packages. And what I found in the AI ecosystem is... shocking. The Numbers Don't Lie I ran pip-size on the most popular AI frameworks. Here are the results: Framework Package Size Total (with deps) torch 506.0 MB 2.5 GB 🤯 tensorflow 545.9 MB 611.9 MB paddlepaddle 185.8 MB 212.1 MB jax 3.0 MB 137.1 MB onnxruntime 16.4 MB 39.5 MB transformers 9.8 MB 38.4 MB keras 1.6 MB 29.5 MB The PyTorch Surprise Here's what happens when you pip install torch : torch==2.11.0 506.0 MB (total: 2.5 GB) ├── nvidia-cudnn-cu13==9.19.0.56 349.1 MB ├── nvidia
The AI Stack: A Practical Guide to Building Your Own Intelligent Applications
Beyond the Hype: What Does "Building with AI" Actually Mean? Another week, another wave of AI headlines. From speculative leaks to existential debates, the conversation often orbits the sensational. But for developers, the real story is happening in the trenches: the practical, stack-by-stack integration of intelligence into real applications. While the industry debates "how it happened," we're busy figuring out how to use it . Forget the monolithic "AI" label for a moment. Modern AI application development is less about creating a sentient being and more about strategically assembling a set of powerful, specialized tools. It's about choosing the right component for the job—be it generating text, analyzing images, or making predictions—and wiring it into your existing systems. This guide b
Why We Built 5 Products on FastAPI + Next.js (and Would Do It Again)
March 31, 2026 | 8 min read The Stack Decision That Shapes Everything Choosing a tech stack when you are a small team is one of those decisions that feels reversible but really is not. Sure, you can theoretically rewrite everything later. But in practice, whatever you pick on week one is what you are building on for the next two years. The frameworks you choose determine how fast you ship, how easy it is to hire (or onboard friends), and how much time you spend fighting your tools instead of building features. We are a group of friends from Tennessee building SaaS products under Obsidian Clad Labs. We have shipped five products on the same stack: FastAPI on the backend, Next.js on the frontend, PostgreSQL for the database. After a year of building, here is why we would make the same choice
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The Real Size of AI Frameworks: A Wake-Up Call
You Think You Know What You're Installing When someone says "just install PyTorch," you probably think "how bad can it be?" It's a deep learning library, right? A few hundred megabytes, maybe? Think again. I built pip-size to expose the hidden cost of Python packages. And what I found in the AI ecosystem is... shocking. The Numbers Don't Lie I ran pip-size on the most popular AI frameworks. Here are the results: Framework Package Size Total (with deps) torch 506.0 MB 2.5 GB 🤯 tensorflow 545.9 MB 611.9 MB paddlepaddle 185.8 MB 212.1 MB jax 3.0 MB 137.1 MB onnxruntime 16.4 MB 39.5 MB transformers 9.8 MB 38.4 MB keras 1.6 MB 29.5 MB The PyTorch Surprise Here's what happens when you pip install torch : torch==2.11.0 506.0 MB (total: 2.5 GB) ├── nvidia-cudnn-cu13==9.19.0.56 349.1 MB ├── nvidia
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