标签：#fusion

The grid has been straining under the weight of new AI data centers, and that has utilities courting fusion startups.

I've been building a free, structured ColdFusion course: Learn ColdFusion — no signup, no paywall. It covers the basics (variables, loops, conditionals), collections (arrays, structs, queries, JSON), a full OOP module (CFCs, inheritance, polymorphism, interfaces), Application.cfc and scopes, and reusable code (custom tags, cfmodule, cfinclude). What makes it different from other CFML tutorials: every single lesson ends with interview questions on that topic — real questions, phrased the way they actually come up in interviews, not generic recap quizzes. There's even a dedicated Scopes Interview Questions page. I haven't found this depth of interview prep anywhere else in ColdFusion learning content online. If you've worked with ColdFusion, I'd love to know what topics you got asked about in interviews — happy to add them to the course.

If you approach generative models as ""networks that create images,"" the field quickly turns into a collection of disconnected architectures. A more useful developer mental model starts one level lower: What probability structure could have produced the data, and how can we represent, learn, and infer that structure without making the computation impossible? That question connects autoregressive models, VAEs, flow-based models, GANs, and diffusion models. Their architectures look very different, but they all respond to the same underlying tension: high-dimensional data distributions are difficult to represent, learn, normalize, sample from, and reason about. Generative modeling can therefore be organized around three interacting problems: Representation: How do we represent a complex high-dimensional joint distribution? Learning: How do we make the model distribution approach the data distribution? Inference: Given an observation, how do we reason backward about hidden variables or the process that generated it? Once these three pieces are connected, the major families of deep generative models become much easier to understand. From prediction to distribution learning A discriminative model usually begins with a prediction problem. Given an input x , predict the most likely output y : f ( x ) = y ar g max ​ p ( y ∣ x ) The model focuses directly on the conditional relationship required for prediction. A generative model asks a broader question. Instead of learning only the path from x to y , it models the probability structure from which the data arises. For class-conditional modeling, for example, we can model p ( x ∣ y ) together with the prior p ( y ) and recover the posterior using Bayes' rule: p ( y ∣ x ) = p ( x ) p ( x ∣ y ) p ( y ) ​ In unsupervised generative modeling, the target becomes the data distribution itself. We assume the training samples come from some unknown distribution: x 1 ​ , x 2 ​ , … , x N ​ ∼ i.i.d. p data ​ ( x ) The model then construc

The company's new fundraising total now stands at $232 million.

Fusion startup Pacific Fusion broke ground on a demonstration facility in New Mexico that it says will generate enough energy to power itself.

Fusion power startup Inertia Enterprises reduced the fuel filling process from a week to just a few hours. It's one of ten hurdles the company must overcome to make a profitable power plant.

Fusion startups have raised $7.1 billion to date, with the majority of it going to a handful of companies.

Fusion power startups are turning to Kyoto Fusioneering to supply components for future power pants. The Japan-based startup just received a grant to build a part of the fuel system.

There are fresh signs that fusion power startup Commonwealth Fusion Systems will list in the next two to three years.

Commonwealth Fusion Systems raised $1 billion as the startup moves toward its first commercial fusion power plant.

Fusion power startup Thea Energy snagged a $20 million award from ARPA-E to scale production of its high-temperature superconducting magnets.

Turning entire novels into narrated, lip-synced, motion video — locally, on a single 16GB GPU, with FLUX, Wan2.2, PuLID, MuseTalk, and ComfyUI. There's an old line: a picture is worth a thousand words. I'd extend it — a video is worth a thousand pictures. So I spent the last few months testing that idea the hard way: I built a pipeline that turns an entire novel into a narrated, lip-synced, motion video. Feed it Pride and Prejudice , or a 400-year-old tale from Strange Tales from a Chinese Studio (聊斋志异), and out comes a finished film with consistent character faces across a hundred-plus scenes. One person. One RTX 4060Ti with 16GB of VRAM. Everything local, no cloud inference. I called it iTube. And honestly — the videos came out better than I expected. Watching a novel you know become a moving, voiced, paced thing is a genuinely different experience from reading a summary of it. That's the bet: that video is a better medium for conveying a story than text is, and the finished clips have mostly convinced me the bet was right. See for yourself before reading another word about how it works: 🎬 Pride and Prejudice — a Western public-domain classic 🎬 聊斋志异 / Strange Tales from a Chinese Studio — a playlist of classical Chinese tales But this post isn't a demo reel. It's about the part nobody warns you about — the part between the models — because that's where the real work turned out to be. The models are instruments, not magic boxes Here's the single most important thing I learned, and the thing I'd want any collaborator to understand before touching a pipeline like this: Each AI package can do exactly one thing well, and your entire job is knowing precisely where each one's ability ends — then writing your script logic to route around the gaps. The stack is not exotic. Most people in generative media know these pieces: FLUX generates the still image for a scene. That's all it does — a single frame. Beautiful, controllable with the right prompt, but frozen. Wan2.2 takes

An old Oscar Mayer factory in Wisconsin will become America's latest fusion power research and development hub.

General Fusion started trading on the Nasdaq following a reverse merger that saw high redemptions.

“We can take power from a plasma,” Kieran Furlong, co-founder and CEO of Realta Fusion, told TechCrunch. The milestone shows “what’s possible,” he added.

Un-0 is an image-generation system tool that shows for the first time how the company's technology can replicate conventional AI systems.

Fusion startups have raised $7.1 billion to date, with the majority of it going to a handful of companies.

Fusion power startup Avalanche Energy said its reactor prototype heated a plasma to over 10 million degrees C.

Five peer-reviewed papers update the design and model its expected output.

Fusion startup Xcimer fired up the world's largest privately owned laser.