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Mastering Express.js with MongoDB and PostgreSQL

Parker Lennox
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0
Mastering Express.js with MongoDB and PostgreSQL

1. Introduction to Databases
Databases are essential for any web application to store, retrieve, and manage data efficiently.

2. MongoDB vs PostgreSQL: Key Differences
Choose MongoDB for flexibility and PostgreSQL for structured and relational data.

3. Setting Up MongoDB
3.1 Install MongoDB Locally
3.2 Set Up MongoDB Connection URL

4. Connecting MongoDB to Express.js
4.1 Install MongoDB Dependencies
4.2 Create Database Configuration File

5. Setting Up PostgreSQL
5.1 Install PostgreSQL Locally
5.2 Set Up PostgreSQL Connection URL

6. Connecting PostgreSQL to Express.js
6.1 Install PostgreSQL Dependencies
6.2 Create Database Configuration File

7. Best Practices
Use environment variables for sensitive data, validate data before saving, and properly handle database connection errors.

8. Environment Variables for Database Configuration

9. Conclusion
Choose the database based on your project requirements.

Man in Frame

Phil
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0
Man in Frame

Man Looking in Camera – Stablediffusionaigenerator

Introduction

The image of a man looking into a camera is a common and relatable scene. However, when it comes to AI-generated images, this scene takes on a new level of complexity. In this article, we will explore the concept of Stable Diffusion AI Generator and its ability to create realistic images of a man looking into a camera.

What is Stable Diffusion AI Generator?

Stable Diffusion AI Generator is a type of AI model that uses a process called diffusion-based image synthesis to generate high-quality images. This model is trained on a large dataset of images and uses a combination of machine learning algorithms and computer vision techniques to generate new images that are similar in style and content to the training data.

How Does it Work?

The Stable Diffusion AI Generator works by using a process called diffusion-based image synthesis. This process involves creating a series of images that are gradually transformed from a random noise pattern to the final desired image. The model uses a combination of machine learning algorithms and computer vision techniques to control the transformation process and ensure that the final image is high-quality and realistic.

Man Looking in Camera – Stablediffusionaigenerator

The image of a man looking into a camera is a great example of the capabilities of the Stable Diffusion AI Generator. The model is able to generate a realistic image of a man looking into a camera, complete with details such as facial features, clothing, and background. The image is also highly detailed, with subtle textures and shading that give it a realistic appearance.

Reactions

The image of a man looking into a camera has elicited a range of reactions from viewers. Some have praised the image for its realism and attention to detail, while others have expressed concern about the potential consequences of AI-generated images.

Reactions List


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Conclusion

The image of a man looking into a camera generated by the Stable Diffusion AI Generator is a remarkable example of the capabilities of AI-generated images. The model’s ability to create realistic and detailed images has significant implications for a wide range of fields, from art and design to entertainment and education.

FAQs

Q: What is the Stable Diffusion AI Generator?

A: The Stable Diffusion AI Generator is a type of AI model that uses a process called diffusion-based image synthesis to generate high-quality images.

Q: How does the Stable Diffusion AI Generator work?

A: The model works by using a process called diffusion-based image synthesis, which involves creating a series of images that are gradually transformed from a random noise pattern to the final desired image.

Q: What are the implications of AI-generated images?

A: The implications of AI-generated images are significant and far-reaching, with potential applications in a wide range of fields, from art and design to entertainment and education.

Q: Are AI-generated images realistic?

A: Yes, AI-generated images can be highly realistic, with subtle textures and shading that give them a realistic appearance.

Affordable 2-in-1 Laptop Covers Creative Basics

Phil
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0
Affordable 2-in-1 Laptop Covers Creative Basics

HP Envy x360 15 Review: A Budget-Friendly 2-in-1 Laptop

Design and Screen
The HP Envy x360 15 is one of the biggest 2-in-1 laptops you’ll find, with a 15.6-inch OLED touchscreen display. It’s great for a convertible laptop, but it’s heavy and bulky to carry around, weighing in at 1.77kg.

Features
The 2-in-1 functionality and the OLED touchscreen are the headline features on the HP Envy x360 15. In tent mode, the screen can become a miniature ‘cinema screen’, and with the brightness and colour gamut on show here, any TV show or film was presented in vivid colours with deep true blacks. It can also function as a makeshift easel for drawing or artworking on the touchscreen.

Performance
Sporting an Intel Core i5-1335U processor with Iris Xe graphics and only 8GB of RAM, this review unit has less power than the 13-inch variant we reviewed in late-2022. As it isn’t an ‘AI’ laptop, those specs and components will look rather old-fashioned in the rapidly moving tech space of next-gen laptops, but the upshot is that it can become a bit of a bargain prospect, thanks to the nice touchscreen and tablet functionalities.

Benchmark Scoring
| Geekbench 6: | CPU Multi-core: 7,428 | GPU OpenCL: 12,452 |
| Cinebench R23: | CPU Multi-core: 7,860 |
| Handbrake: | Transcoding 10m34s 4K video to FHD: 9m20s |

Price
The retail price for the HP Envy x360 15 is £999, but you can frequently find it discounted now, including a tempting offer for any student over Christmas, with it down to £699, a very competitive price for the spec.

Who is it for?
Students and hybrid workers looking for an affordable general-work laptop who like the touchscreen functionality and the tablet mode should be shortlisting this machine.

Buy it if…

  • You want an affordable 2-in-1
  • You like drawing on a tablet but also have a laptop
  • You don’t mind the limited screen resolution

Don’t buy it if…

  • You need to deal with any 3D, animation or heavy rendering work
  • You want a 3K/4K screen
  • You need an ‘AI-ready’ laptop

Conclusion
The HP Envy x360 15 is a budget-friendly 2-in-1 laptop that offers a great tablet experience and a good screen. While it may not be the most powerful laptop, it’s a great option for students and hybrid workers who need a reliable device for general work.

AI-Powered Zero Trust Cyber Defense

Jordan Waverly
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0
AI-Powered Zero Trust Cyber Defense

Cybersecurity in the Age of AI: A Zero-Trust Strategy

The Importance of Zero-Trust Security

Modern cyber threats have grown increasingly sophisticated, posing significant risks to federal agencies and critical infrastructure. According to Deloitte, cybersecurity is the top priority for governments and public sectors, highlighting the need to adapt to an increasingly digital world for efficiency and speed.

The Need for 100% Data Visibility

At its core, cybersecurity is a data problem. As the number of connected users and devices expands, organizations are generating more data than they can effectively collect, manage, and analyze. If we cannot observe 100% of the data across the entire enterprise for every user and machine, how can we build a robust model to detect all deviations?

The Limitations of Traditional Rule-Based Mechanisms

A zero-trust strategy assumes that no entity is trusted by default and verification is required from everyone to gain access. However, this approach requires increased visibility into every application and user on the network for continuous authentication and monitoring. Traditional rule-based mechanisms cannot keep pace with the escalating adversary landscape.

The Power of AI and Generative AI

Using AI and generative AI technologies for advanced data analytics and automation is crucial. NVIDIA Morpheus, a GPU-accelerated cybersecurity AI framework, can help. Morpheus enables organizations to build optimized AI pipelines for filtering, processing, and classifying large volumes of real-time data.

Digital Fingerprinting and Anomaly Detection

Morpheus uses deep learning and unsupervised learning to overcome the limitations of traditional user behavior analysis. By analyzing large volumes of unlabeled data, it can identify and learn the normal behavior and detect deviations from these learned patterns, flagging them as potential anomalies.

Automating CVE Analysis with Generative AI

Patching software security issues is increasingly challenging as the number of vulnerabilities reported in the CVE database continues to grow at an unprecedented pace. Legacy systems are vulnerable to evolving cyber threats, so keeping up with the latest security updates becomes essential for government IT teams to defend against breaches.

Conclusion

The two workflows demonstrate how AI and generative AI can address today’s security challenges, particularly in threat detection and vulnerability management. Morpheus can also extend to many other detection use cases, such as spear-phishing, sensitive information, and ransomware. These can be implemented across government agencies to bolster their zero-trust security strategies as the cybersecurity landscape evolves.

FAQs

Q: What is the purpose of a zero-trust strategy?
A: A zero-trust strategy assumes that no entity is trusted by default and verification is required from everyone to gain access.

Q: What is the limitation of traditional rule-based mechanisms?
A: Traditional rule-based mechanisms cannot keep pace with the escalating adversary landscape.

Q: How can AI and generative AI help in cybersecurity?
A: AI and generative AI technologies can help in advanced data analytics and automation.

Q: What is NVIDIA Morpheus?
A: Morpheus is a GPU-accelerated cybersecurity AI framework.

Q: What are the benefits of using Morpheus?
A: Morpheus can help in threat detection, anomaly detection, and automated CVE analysis.

Investment in Online Education Groups Plummets

Parker Lennox
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0
Investment in Online Education Groups Plummets

Global Investment in Online Education Companies Plummets

Global investment in online education companies has fallen to its lowest level in a decade as the industry comes under pressure from the rapid rise of free generative artificial intelligence tools that are undercutting their products.

Edtech Industry Struggles to Maintain Subscriber Growth

Edtech businesses, which offer services such as online tutoring and exam practice, received just $3bn of investment in 2024, compared with $17.3bn at the peak of the pandemic in 2021, according to data from PitchBook. This is the smallest amount since 2014, when edtech companies attracted $2.3bn.

The Impact of Generative AI

The huge decline comes as edtech companies, which soared in popularity during the pandemic amid mass school closures, have struggled to maintain subscriber growth after the end of the Covid-19 emergency.

It has been exacerbated by the fast development of generative AI over the past two years, which has further upended demand for edtech companies’ paid-for online learning tools and caused their valuations to plummet.

Investment in Generative AI Booms

However, investment in generative AI is booming. Investors have poured $51.4bn this year into the developing technology, up from $16.5bn in 2021, according to PitchBook.

Edtech Companies Rush to Integrate AI

Edtech companies are now rushing to integrate AI into their products, arguing that the technology has the potential to enhance their services.

Platforms Khan Academy and Coursera have integrated generative AI assistants to interact with users. Speak, a language learning platform, uses OpenAI’s voice model Whisper to teach through conversation.

Conclusion

The rapid rise of generative AI has put significant pressure on the edtech industry, leading to a decline in investment and a struggle to maintain subscriber growth. While some edtech companies are looking to integrate AI into their products, others are facing challenges in adapting to the changing landscape.

FAQs

Q: What is the current state of investment in edtech companies?
A: Global investment in edtech companies has fallen to its lowest level in a decade, with only $3bn of investment in 2024.

Q: What is the impact of generative AI on edtech companies?
A: The rapid development of generative AI has further upended demand for edtech companies’ paid-for online learning tools and caused their valuations to plummet.

Q: Are edtech companies struggling to maintain subscriber growth?
A: Yes, edtech companies have struggled to maintain subscriber growth after the end of the Covid-19 emergency.

Q: What is the future of edtech companies?
A: The future of edtech companies is uncertain, with some companies looking to integrate AI into their products and others facing challenges in adapting to the changing landscape.

Test DEV Community

Parker Lennox
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0
Test DEV Community

Levantamos la app

Para empezar, creamos un proyecto Astro con el comando npm create astro@latest -- --template blog. Luego, ejecutamos npm run dev para levantar la aplicación.

Content Layer API de Astro para integrar publicaciones de dev.to en tu sitio

Puedes utilizar la Content Layer API de Astro para integrar publicaciones de dev.to en tu sitio. Aunque no existe un cargador (loader) específico para dev.to, puedes crear uno personalizado que consuma su API y almacene las publicaciones en una colección de contenido en Astro.

Configura el acceso a la API de dev.to

Primero, debes configurar el acceso a la API de dev.to. Para hacer esto, crea un archivo getArticles.js en la carpeta src/lib/ con el siguiente contenido:

import { DEV_API_KEY, DEV_TO_API_URL } from '../meta/env';

export async function fetchArticles() {
  const res = await fetch(`${DEV_TO_API_URL}articles/me/published`, {
    headers: {
      'api-key': DEV_API_KEY,
    },
  });
  const data = await res.json();
  return data;
}

Define una colección en Astro

En el archivo src/content.config.ts, define una colección para las publicaciones de dev.to utilizando la Content Layer API:

import { defineCollection } from 'astro/content';
import { fetchArticles } from '../lib/getArticles';

const devTo = defineCollection({
  loader: async () => {
    const articles = await fetchArticles();
    return articles.map((article) => ({
      id: article.id.toString(),
      slug: article.slug,
      body: article.body_markdown,
      data: {
        title: article.title,
        date: new Date(article.published_at),
        tags: article.tag_list,
        summary: article.description,
        image: article.social_image,
      },
    }));
  },
});

export const collections = { blog, devTo };

Conclusión

En este artículo, hemos visto cómo utilizar la Content Layer API de Astro para integrar publicaciones de dev.to en tu sitio. Primero, configuramos el acceso a la API de dev.to y luego definimos una colección para las publicaciones utilizando la Content Layer API. Finalmente, hemos exportado la colección como un objeto para utilizarla en nuestro sitio.

Preguntas frecuentes

  • ¿Cómo puedo personalizar la carga de publicaciones de dev.to?
    Puedes personalizar la carga de publicaciones de dev.to modificando el archivo getArticles.js para que se adapte a tus necesidades específicas.
  • ¿Cómo puedo agregar más campos a la colección de publicaciones?
    Puedes agregar más campos a la colección de publicaciones modificando el objeto data en el archivo src/content.config.ts.
  • ¿Cómo puedo utilizar la colección de publicaciones en mi sitio?
    Puedes utilizar la colección de publicaciones en tu sitio mediante el uso de la Content Layer API de Astro. Por ejemplo, puedes utilizar la colección para renderizar una lista de publicaciones en una página de inicio.

Low Latency Inference: NVIDIA GH200 NVL32 Boosts Time to First Token Performance

Jordan Waverly
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0
Low Latency Inference: NVIDIA GH200 NVL32 Boosts Time to First Token Performance

Time-to-First-Token Matters for Real-Time Use Cases

Many of the most exciting applications of large language models (LLMs), such as interactive speech bots, coding co-pilots, and search, need to begin responding to user queries quickly to deliver positive user experiences. The time it takes for an LLM to ingest a user prompt (and context, which can be sizable) and begin outputting a response is called time to first token (TTFT).

NVIDIA GH200 NVL32 Supercharges TTFT for Long Context Inference

To generate the first new token in response to an inference request, the input tokens must be processed by the LLM. This phase of inference, known as prefill, often has a large number of tokens and thus benefits from increased aggregate compute performance. It can be accelerated by splitting the calculations across multiple GPUs using parallelism techniques, such as tensor parallelism.

Llama 3.1 70B

A single GH200 NVL32 system achieves a TTFT of just 472 milliseconds when running Llama 3.1 70B, using an input sequence length of 32,768. In practical terms, this means that Llama 3.1 70B can begin outputting a summary of a 90-page document or coding suggestions on thousands of lines of code, in less than half a second.

Llama 3.1 405B

Llama 3.1 405B requires substantially more compute to generate the first token of a response, as the model incorporates nearly 6X the parameter count of Llama 3.1 70B. A GH200 NVL32 system can achieve a TTFT of about 1.6 seconds using a 32,768 token input. And, using a small codebase-sized 122,880 token input, GH200 NVL32 can begin responding in just 7.5 seconds.

Inference Continues to be a Hotbed of Invention

The pace of inference innovation across serving techniques, runtime optimizations, kernels, and more has been extraordinary. Advancements like in-flight batching, speculative decoding, FlashAttention, key-value caching, and more have been developed by both industry and academia. Collectively, these innovations are enabling more capable models and systems to be deployed efficiently and more cost-effectively in production, making powerful AI capabilities more accessible to the entire NVIDIA ecosystem.

Next Up: Accelerating Agentic Workflows

Agentic workflows perform tree search, self-reflection, and iterative inferences to reason and produce answers to complex queries. This means that the number of inferences per prompt will grow by orders of magnitude. With each successive inference, we would need to process the aggregate response in the next agent as a new context — thus fast TTFT becomes even more important as workflows scale.

NVIDIA Blackwell GB200 NVL72 Powers a New Era of Computing

Looking ahead, as model sizes continue to grow rapidly, and as models support even longer context lengths, and agentic workflows become more popular, the amount of delivered compute performance required for fast inference continues to rise.

Conclusion

In this article, we have shown how NVIDIA GH200 NVL32 can achieve the fastest published TTFT for the Llama 3.1 models, even at very long contexts. We have also introduced the NVIDIA Blackwell GB200 NVL72, which delivers the next giant leap for generative AI and accelerated computing.

FAQs

Q: What is Time-to-First-Token (TTFT)?
A: TTFT is the time it takes for an LLM to ingest a user prompt (and context) and begin outputting a response.

Q: What is the significance of fast TTFT in real-time use cases?
A: Fast TTFT is crucial for real-time use cases, as it enables a seamless and interactive user experience.

Q: What is the NVIDIA GH200 NVL32 system?
A: The NVIDIA GH200 NVL32 system is a rack-scale solution that connects 32 NVIDIA GH200 Grace Hopper Superchips using the NVLink Switch system, providing outstanding TTFT for long-context inference.

Q: What is the NVIDIA Blackwell GB200 NVL72?
A: The NVIDIA Blackwell GB200 NVL72 is a next-generation compute tray that delivers up to 20 PFLOPS of FP4 AI compute and 1,800 GB/s of GPU-to-GPU bandwidth.

Object Storage Foundation for AI and Advanced Analytics Workloads

Parker Lennox
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0
Object Storage Foundation for AI and Advanced Analytics Workloads

The Object Storage Landscape in the Age of AI

The Data Storage Landscape is Evolving

The data storage landscape is undergoing a transformational change. AI is changing how systems need to handle both large amounts of data and the speed at which that data can be processed. Where enterprise organizations once depended on traditional SAN/NAS architectures, the explosive growth of unstructured data, now measured in petabytes, has made one thing clear – object storage has become the dominant technology for enterprise storage needs.

MinIO’s Object Storage and AI Report

To help better understand how IT leaders are leveraging object storage, MinIO, Inc., the company behind the popular open-source cloud storage software MinIO, has announced the findings of its Object Storage and AI Report. The report also reveals how AI is reshaping adoption and workload patterns.

Key Findings

  • More than 70% of enterprises’ cloud-native data is in object storage, and this percentage is expected to grow, with 75% of cloud-native data predicted to be in object storage within two years.
  • The top three factors for this explosive growth include the support offered by AI, performance requirements, and scalability.
  • Object storage is expected to play a crucial role in supporting AI workloads, with 96% of respondents reporting challenges due to AI, such as managing vast volumes of unstructured data and ensuring consistent performance at scale.
  • The survey reveals that the top three use cases for object storage include advanced analytics, AI model training, and data lakehouse storage.
  • 68% of respondents are concerned about the cost of running AI workloads and are considering a hybrid cloud approach to balance cost and performance.

Challenges Facing IT Leaders

  • Security and privacy (44%)
  • Data governance (27%)
  • Cloud-native storage (25%)

Hybrid Cloud Approach

While the public cloud remains popular, 68% of respondents shared that they are concerned about the cost of running AI workloads and are considering a hybrid cloud approach. This trend aligns directly with MinIO’s capabilities, as its object storage is designed to support both public and private cloud environments.

MinIO’s Object Storage

Earlier this year, MinIO shifted its focus from a general-purpose storage solution to an AI-centric platform with the launch of AIStor. The platform includes features such as promptObject API, enabling natural language queries of unstructured data, and high-speed RDMA support for seamless GPU integration. MinIO’s object storage is designed to support both public and private cloud environments, making it an ideal choice for organizations considering a hybrid cloud approach.

Conclusion

In conclusion, object storage has emerged as the dominant technology for enterprise storage needs in the age of AI. MinIO’s Object Storage and AI Report highlights the critical role that object storage plays in supporting AI workloads and the challenges faced by IT leaders in managing large volumes of unstructured data.

FAQs

Q: What is object storage?
A: Object storage is a type of data storage that allows organizations to store large amounts of data in a highly scalable and accessible manner.

Q: Why is object storage essential for AI workloads?
A: Object storage is essential for AI workloads because it can handle large amounts of unstructured data, support high-speed queries, and ensure consistent performance at scale.

Q: What are the top use cases for object storage?
A: The top use cases for object storage include advanced analytics, AI model training, and data lakehouse storage.

Q: What are the challenges facing IT leaders in supporting AI workloads?
A: The challenges facing IT leaders include managing vast volumes of unstructured data, ensuring consistent performance at scale, and balancing cost and performance in hybrid cloud environments.

Q: Why is MinIO’s object storage an ideal choice for organizations?
A: MinIO’s object storage is an ideal choice for organizations because it is designed to support both public and private cloud environments, allows for seamless integration with AI and machine learning workloads, and provides high-speed RDMA support for GPU acceleration.

Converse with Research

Emerson Blake
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0
Converse with Research

What is Illuminate?

Google has been developing new artificial intelligence-powered tools that transform writing and text materials into audio or podcasts to assist researchers in their learning processes. The company is now experimenting with a new AI-powered tool called Illuminate, which enables users to convert lengthy, dense research papers and books into concise AI-generated audio conversations.

How to use Illuminate

To test the technology, I signed in with my Google account and uploaded PDF links of technology-oriented papers I’ve been reading for work. On the Illuminate website, I uploaded a PDF link for a paper called "Mind the Gap: Foundation Models and the Covert Proliferation of Military Intelligence, Surveillance, and Targeting" by Heidy Khlaaf, Sarah Myers West, and Meredith Whittaker. After uploading the link, I chose to define my audio conversation as formal, but you can choose between casual, free-form, guided, or formal depending on the text you’re using.

How it Works

Illuminate uses AI to transform published papers and works into audio discussions with "two AI-generated voices in conversation" discussing the key points and takeaways of the paper. Google says, "Illuminate is currently optimized for published computer science academic papers."

Features

  • Users can upload PDF links of research papers and books
  • Choose between formal, casual, free-form, or guided audio conversation styles
  • Generate audio conversations with two AI-generated voices
  • Save audio conversations to personal library
  • Share audio conversations with others
  • Access public library of generated audio conversations

Conclusion

Illuminate is a powerful tool for researchers, students, and writers who engage with lengthy research papers daily. It can be used as a research assistant tool to help grasp key points in a paper and hone in on what might have been missed after reading. With its ability to generate concise audio conversations, Illuminate is an innovative solution for those looking to streamline their research process.

FAQs

Q: What is Illuminate?
A: Illuminate is a new AI-powered tool that transforms lengthy, dense research papers and books into concise AI-generated audio conversations.

Q: What types of content is Illuminate optimized for?
A: Illuminate is currently optimized for published computer science academic papers.

Q: How do I access Illuminate?
A: You can access Illuminate at illuminate.google.com, and you must have a Google account to sign in.

Q: What is the limit on audio generations per day?
A: Previously, users were limited to five audio generations per day, but now they have 19 audio generations per day.

Q: Can I share my audio conversations with others?
A: Yes, you can share your audio conversations with others by tapping the Share this content icon at the bottom.

The Ultimate VFX Showstoppers

Phil
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0
The Ultimate VFX Showstoppers

01. Alien: Romulus VFX
There’s been a lot of talk about the heavy use of practical effects in Alien: Romulus. But, although lots of physical models were used, VFX was used widely for everything from props to set-extensions, digi-doubles and creatures. Indeed, the fact that the practical effects are getting such acclaim could be a sign of how good the VFX was, with the CGI enhancing the film rather than overpowering it.

VFX were led by Industrial Light and Magic (ILM) VFX supervisor Nelson Sepulveda. He’s noted that although there were animatronic xenomorphs, many of the xenograph shots were CG or CG enhanced/replaced. Director Fede Álvarez struck a great balance, dialing back CGI when it became too much, part of his aim to take sci-fi/horror franchise back to its roots. Also check out the Alien: Romulus concept art.

02. Dune: Part Two VFX
We can’t talk about VFX in 2024 and not mention Dune. For me, it was the most beautiful movie of the year. Many factors that contributed to that, from the photography and blocking to the use of vintage lenses, but the VFX played a huge role.

As we saw in our roundup of Dune design secrets, Denis Villeneuve used various techniques to create the unique aesthetic of Arrakis. Like with the first film, sand-coloured screens were used instead of (or as well as) traditional green or blue screens to make the desert light look more realistic and to avoid colour contamination. When inverted, the sand colour becomes blue, suitable for keying for VFX. And then there’s the sandworms, of course. DNEG’s incredible VFX continue to capture the sheer scale, speed and power of the fantastical creature.

03. Talking creatures in Wicked
Jon M. Chu’s big screen adaptation of the musical was in danger of becoming most famous for the Wicked poster controversy, but the film has been delighting audiences with its mix of a strong story, show-stopping choreography, and a lot of magic.

As the film is set in a magical world, ILM’s VFX work was crucial to immersing the audience in a setting where even gravity may not be as it seems. The story required artists to tackle one of the most difficult things you can do in CGI: taking animals! Characters like the goat Professor Dillamond used almost no motion capture. Instead, the creatures’ faces were entirely original CG animation. ILM visual effects supervisor Pablo Helman proposed the creation of a dedicated ‘animal unit’ of about 15 people who would stand in and perform for digital characters on set, allowing a more organic approach then what would be possible using green screen alone (or blue screen due to the challenges caused by Elphaba’s green skin tone).

04. The mix of subtle and dramatic VFX in Here
Many people think the best VFX movies tend to be in the sci-fi or fantasy genres, but Robert Zemeckis’s drama Here has some of the most interesting VFX of 2024. Most notably, AI was used to de-age Tom Hanks, but there were plenty of other examples of VFX, some of them very subtle.

The film’s unusual concept with its fixed camera position created a lot of challenges for DNEG, which was tasked with creating a sense of place across millennia. As Visual Effects Supervisor Alexander Seaman told us, "when you haven’t got a moving camera, everything in your scene has to move".

05. Mufasa: The Lion King

Released just in time for Christmas, Disney’s Mufasa: The Lion King may be a controversial choice on our list of the best VFX of 2024. A lot of people don’t care for Disney’s move towards a mix of live action and CGI (see the reaction to the Snow White trailer!), but this photorealistically animated feature is undeniably impressive.

But MPC’s photoreal computer-generated imagery achieves an incredible level of detail and fidelity. The style may leave some fans of the original Lion King cold, but this modern look is stunning audiences.

Conclusion

In conclusion, 2024 was a remarkable year for VFX, with a wide range of impressive and innovative uses across various genres. From the sci-fi and fantasy to drama and musicals, VFX played a crucial role in enhancing the storytelling and visual aesthetic of many films.

FAQs

Q: What is the best VFX movie of 2024?
A: Our top picks include Alien: Romulus, Dune: Part Two, Wicked, Here, and Mufasa: The Lion King.

Q: What are some of the most impressive uses of VFX in 2024?
A: Some notable mentions include the use of AI to de-age Tom Hanks in Here, the creation of photorealistic environments in Dune, and the innovative use of computer-generated imagery in Mufasa: The Lion King.

Q: What is the role of VFX in storytelling?
A: VFX can play a crucial role in enhancing the storytelling and visual aesthetic of a film, from creating fantastical worlds to animating characters and environments.

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