Repurposing Protein Folding Models for Generation with Latent Diffusion

Amy X. Lu   

PLAID is a multimodal generative model that simultaneously generates protein 1D sequence and 3D structure, by learning the latent space of protein folding models.

The awarding of the 2024 Nobel Prize to AlphaFold2 marks an important moment of recognition for the of AI role in biology. What comes next after protein folding?

In PLAID, we develop a method that learns to sample from the latent space of protein folding models to generate new proteins. It can accept compositional function and organism prompts, and can be trained on sequence databases, which are 2-4 orders of magnitude larger than structure databases. Unlike many previous protein structure generative models, PLAID addresses the multimodal co-generation problem setting: simultaneously generating both discrete sequence and continuous all-atom structural coordinates.

From structure prediction to real-world drug design

Though recent works demonstrate promise for the ability of diffusion models to generate proteins, there still exist limitations of previous models that make them impractical for real-world applications, such as:

• All-atom generation: Many existing generative models only produce the backbone atoms. To produce the all-atom structure and place the sidechain atoms, we need to know the sequence. This creates a multimodal generation problem that requires simultaneous generation of discrete and continuous modalities.
• Organism specificity: Proteins biologics intended for human use need to be humanized, to avoid being destroyed by the human immune system.
• Control specification: Drug discovery and putting it into the hands of patients is a complex process. How can we specify these complex constraints? For example, even after the biology is tackled, you might decide that tablets are easier to transport than vials, adding a new constraint on soluability.

Generating “useful” proteins

Simply generating proteins is not as useful as controlling the generation to get useful proteins. What might an interface for this look like?

For inspiration, let's consider how we'd control image generation via compositional textual prompts (example from Liu et al., 2022).

In PLAID, we mirror this interface for control specification. The ultimate goal is to control generation entirely via a textual interface, but here we consider compositional constraints for two axes as a proof-of-concept: function and organism:

Learning the function-structure-sequence connection. PLAID learns the tetrahedral cysteine-Fe2+/Fe3+ coordination pattern often found in metalloproteins, while maintaining high sequence-level diversity.

Training using sequence-only training data

Another important aspect of the PLAID model is that we only require sequences to train the generative model! Generative models learn the data distribution defined by its training data, and sequence databases are considerably larger than structural ones, since sequences are much cheaper to obtain than experimental structure.

Learning from a larger and broader database. The cost of obtaining protein sequences is much lower than experimentally characterizing structure, and sequence databases are 2-4 orders of magnitude larger than structural ones.

How does it work?

The reason that we’re able to train the generative model to generate structure by only using sequence data is by learning a diffusion model over the latent space of a protein folding model. Then, during inference, after sampling from this latent space of valid proteins, we can take frozen weights from the protein folding model to decode structure. Here, we use ESMFold, a successor to the AlphaFold2 model which replaces a retrieval step with a protein language model.

Our method. During training, only sequences are needed to obtain the embedding; during inference, we can decode sequence and structure from the sampled embedding. ❄️ denotes frozen weights.

In this way, we can use structural understanding information in the weights of pretrained protein folding models for the protein design task. This is analogous to how vision-language-action (VLA) models in robotics make use of priors contained in vision-language models (VLMs) trained on internet-scale data to supply perception and reasoning and understanding information.

Compressing the latent space of protein folding models

A small wrinkle with directly applying this method is that the latent space of ESMFold – indeed, the latent space of many transformer-based models – requires a lot of regularization. This space is also very large, so learning this embedding ends up mapping to high-resolution image synthesis.

To address this, we also propose CHEAP (Compressed Hourglass Embedding Adaptations of Proteins), where we learn a compression model for the joint embedding of protein sequence and structure.

Investigating the latent space. (A) When we visualize the mean value for each channel, some channels exhibit “massive activations”. (B) If we start examining the top-3 activations compared to the median value (gray), we find that this happens over many layers. (C) Massive activations have also been observed for other transformer-based models.

We find that this latent space is actually highly compressible. By doing a bit of mechanistic interpretability to better understand the base model that we are working with, we were able to create an all-atom protein generative model.

What’s next?

Though we examine the case of protein sequence and structure generation in this work, we can adapt this method to perform multi-modal generation for any modalities where there is a predictor from a more abundant modality to a less abundant one. As sequence-to-structure predictors for proteins are beginning to tackle increasingly complex systems (e.g. AlphaFold3 is also able to predict proteins in complex with nucleic acids and molecular ligands), it’s easy to imagine performing multimodal generation over more complex systems using the same method. If you are interested in collaborating to extend our method, or to test our method in the wet-lab, please reach out!

Further links

If you’ve found our papers useful in your research, please consider using the following BibTeX for PLAID and CHEAP:

@article{lu2024generating,
  title={Generating All-Atom Protein Structure from Sequence-Only Training Data},
  author={Lu, Amy X and Yan, Wilson and Robinson, Sarah A and Yang, Kevin K and Gligorijevic, Vladimir and Cho, Kyunghyun and Bonneau, Richard and Abbeel, Pieter and Frey, Nathan},
  journal={bioRxiv},
  pages={2024--12},
  year={2024},
  publisher={Cold Spring Harbor Laboratory}
}

@article{lu2024tokenized,
  title={Tokenized and Continuous Embedding Compressions of Protein Sequence and Structure},
  author={Lu, Amy X and Yan, Wilson and Yang, Kevin K and Gligorijevic, Vladimir and Cho, Kyunghyun and Abbeel, Pieter and Bonneau, Richard and Frey, Nathan},
  journal={bioRxiv},
  pages={2024--08},
  year={2024},
  publisher={Cold Spring Harbor Laboratory}
}

You can also checkout our preprints (PLAID, CHEAP) and codebases (PLAID, CHEAP).

Some bonus protein generation fun!

Additional function-prompted generations with PLAID.

Unconditional generation with PLAID.

Transmembrane proteins have hydrophobic residues at the core, where it is embedded within the fatty acid layer. These are consistently observed when prompting PLAID with transmembrane protein keywords.

Additional examples of active site recapitulation based on function keyword prompting.

Comparing samples between PLAID and all-atom baselines. PLAID samples have better diversity and captures the beta-strand pattern that has been more difficult for protein generative models to learn.

Acknowledgements

Thanks to Nathan Frey for detailed feedback on this article, and to co-authors across BAIR, Genentech, Microsoft Research, and New York University: Wilson Yan, Sarah A. Robinson, Simon Kelow, Kevin K. Yang, Vladimir Gligorijevic, Kyunghyun Cho, Richard Bonneau, Pieter Abbeel, and Nathan C. Frey.

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AI Doctors in Trials: Revolutionizing Healthcare with 97% Accuracy

The healthcare industry is on the cusp of a revolution, thanks to the integration of Artificial Intelligence (AI) in medical diagnosis. AI doctors, also known as clinical decision support systems, are being tested in trials worldwide, showcasing their potential to transform patient care. With an impressive accuracy rate of 97%, these AI systems are poised to become valuable assets for healthcare professionals.

How AI Doctors Work

AI doctors use machine learning algorithms to analyze vast amounts of medical data, including patient histories, test results, and medical literature. This enables them to identify patterns and make predictions about patient conditions, treatments, and outcomes. By leveraging this technology, doctors can:

Enhance diagnostic accuracy: AI systems can reduce errors and provide more accurate diagnoses, especially in complex cases.
Streamline clinical workflows: AI can help prioritize patients, optimize treatment plans, and automate routine tasks.
Improve patient outcomes: By analyzing large datasets, AI doctors can identify trends and predict patient responses to different treatments.

Trials and Results

Several trials have demonstrated the effectiveness of AI doctors in various medical specialties. For instance:

Cancer diagnosis: AI systems have been shown to detect breast cancer from mammography images with a high degree of accuracy.

Disease prediction: AI-powered algorithms can predict patient risk for conditions like diabetes and cardiovascular disease.

Rare disease diagnosis: AI doctors can help identify rare conditions by analyzing large datasets and identifying patterns.

Benefits and Future Directions

The integration of AI doctors in healthcare has numerous benefits, including:

Improved patient care: AI systems can help doctors provide more accurate diagnoses and effective treatments.

Reduced healthcare costs: By streamlining clinical workflows and reducing errors, AI doctors can help minimize healthcare expenses.

Enhanced patient experience: AI-powered chatbots and virtual assistants can improve patient engagement and education.

As AI technology continues to evolve, we can expect to see even more innovative applications in healthcare. With their potential to revolutionize patient care, AI doctors are set to become an integral part of the healthcare landscape.

 

Sora by OpenAI: Revolutionizing Video Creation with Just Text Prompts

Imagine typing a simple sentence like “a child playing with a balloon in a park” and watching it come alive as a realistic video. Thanks to Sora by OpenAI, this is no longer science fiction—it's our new reality.

🎥 What is Sora?

Sora is a powerful new AI model developed by OpenAI that can generate high-quality, realistic videos from just a text prompt. It understands the elements of a scene, character behavior, camera movement, and even storytelling logic.

This makes Sora not just a video tool — but a visual storyteller.

🔍 How Does Sora Work?

Sora uses advanced diffusion models and transformers (the same tech that powers GPT models) to create videos. But instead of predicting the next word, it predicts the next video frame.

This allows it to:

Render smooth motion

Respect real-world physics

Keep characters and actions consistent across time

Understand complex prompts like:

“A lion drinking water at sunset with birds flying in the background”

Sora can generate up to 1-minute high-definition clips, filled with realism and detail — from just a sentence.

💡 Why Sora is a Game-Changer

Here’s what sets Sora apart from anything we've seen before:

✅ Generates long, coherent video clips from text
✅ Handles dynamic environments and multiple characters
✅ Captures depth, lighting, motion, and emotion
✅ Useful across industries — education, entertainment, marketing, and more.

This is not just about generating a few seconds of animation — it’s about empowering everyone to become a creator.

📈 Real-World Applications of Sora

Sora has the potential to transform:

🎬 Filmmaking: Instant scene generation for pre-visualization

📚 Education: Bringing abstract concepts to life

💼 Marketing: Creating ad content in seconds

🎮 Gaming: Prototyping story scenes or backgrounds

📱 Social Media: AI-powered reels and shorts for content creators.

Even independent creators with zero animation skills can produce high-end video content with a few words.

🛠️ Is Sora Publicly Available?

Currently, Sora is in the research preview stage. It’s being tested by AI experts, developers, and creative partners to ensure:

Safe usage

Quality control

Prevention of misuse (e.g., deepfakes or misinformation)

OpenAI has committed to releasing it responsibly, just like it did with ChatGPT and DALL·E.

🧠 Final Thoughts

We’re entering a new creative era. With Sora, the only limit is your imagination. You no longer need expensive equipment or editing software — just describe your idea, and AI does the rest.

The future of storytelling is not typed in code. It’s imagined in words and brought to life by AI.

Whether you’re a filmmaker, a teacher, a marketer, or just curious — keep your eye on Sora. Because the way we create video content is about to change forever.

🔔 Stay tuned for more updates as OpenAI prepares Sora for the world!

 Discover how artificial intelligence is reshaping daily routines in 2025—from smart healthcare to AI-driven education. These 7 surprising use cases will change how you view technology.

Introduction

Artificial Intelligence (AI) has moved beyond labs and tech firms—it's now a part of our everyday lives. In 2025, AI is quietly influencing how we work, learn, shop, and even manage our health. This article explores 7 unexpected real-world applications of AI that are changing society today.

1. AI in Personal Finance Management

AI-driven apps like Cleo and YNAB now use real-time machine learning to predict spending habits and help users save more effectively. Banks also employ AI chatbots for 24/7 customer support.

2. Smarter Healthcare Assistants

AI tools like Google's Med-PaLM 2 and ChatGPT-powered health bots are assisting doctors with diagnostics, patient follow-ups, and even mental health support, all while reducing system strain.

3. AI-Powered Education Platforms

Platforms such as Khan Academy’s AI tutor or Sora by OpenAI are customizing lesson plans for students, offering real-time assistance, and even grading essays with human-like accuracy.

4. AI in Job Hunting & Recruitment

AI tools are now being used by both recruiters and job seekers. Platforms like LinkedIn use AI to match candidates with jobs, while applicants use AI to craft resumes and practice interviews.

5. Home Automation Gets Smarter

Beyond smart lights and thermostats, 2025 sees AI-integrated homes that detect emotions through voice and adapt accordingly—adjusting music, lighting, or even suggesting rest if you're stressed.

6. AI and Content Creation

Writers, marketers, and designers now use AI tools to brainstorm, draft, and edit content. From generating blog ideas to designing logos, AI is streamlining the creative process.

7. Retail and Customer Experience

AI in retail is enabling virtual try-ons, voice-based shopping, and real-time inventory suggestions. Amazon and Shopify stores now use AI to recommend products based on mood and preferences.

Final Thoughts

AI isn't just a buzzword—it's a daily tool. As 2025 progresses, understanding its real-world applications can help individuals and businesses adapt and thrive in the AI-driven world.

 Google Gemini 2.5 Pro & Flash: Latest AI Breakthroughs Explained

Gemini 2.5 Introduced with Long-Context Reasoning

Google has launched Gemini 2.5 Pro, featuring a massive 1 million-token context window. This upgrade enables the AI to better understand and respond to long and complex prompts, making it ideal for research, coding, and document analysis.

A new feature called "Deep Think Mode" has also been introduced. It allows the model to consider multiple possibilities before generating a response, improving performance in math, logic, and structured problem-solving.

Gemini Flash: Speed-Focused AI

For those who need quick results, Gemini 2.5 Flash is designed for speed and efficiency. It offers fast, responsive outputs with lower resource usage, making it suitable for enterprise environments and mobile applications.

Gemini App Upgrades

Gemini is now more powerful on mobile. The Gemini app includes "Gemini Live," a real-time interaction feature that uses your phone’s camera and screen for smarter help in real-world tasks.

New creative tools are also included: Imagen 4 for generating high-quality images, and Veo 3 for producing video content with audio, characters, and realistic effects.

Gemini in Chrome and Volvo Vehicles

Gemini has been integrated into the Chrome browser. It can now summarize, explain, and answer questions about any page you're viewing—all without switching tabs.

Volvo has become the first car manufacturer to add Gemini to its vehicles, offering intelligent voice interaction for navigation, communication, and in-car entertainment.

Project Mariner: The Next Step Toward Universal AI

Google has announced a long-term vision through Project Mariner. This initiative focuses on building a universal AI capable of planning, understanding complex situations, and completing tasks across apps and devices.

The goal is to create a helpful assistant that works proactively, understands you deeply, and supports everything from creative projects to everyday routines.

Conclusion:-

 AMD Unveils AI-Powered Ryzen, Radeon, and Pro Series Hardware at Computex 2025

🧠 Ryzen Threadripper 9000 Series: Powering Next-Gen Workstations

AMD introduced the Ryzen Threadripper 9000 Series, designed for professionals handling demanding workloads like visual effects, simulations, and AI model development. The flagship Threadripper Pro 9995WX boasts:

96 cores and 192 threads

384MB of L3 cache

128 lanes of PCIe Gen 5

350W TDP, compatible with the sTR5 socket via BIOS update

AMD claims this processor is 2.2 times faster than Intel's 60-core Xeon W9-3595X in Cinebench 2024 multi-threaded rendering. The Ryzen Threadripper 9980X, targeting creators and enthusiasts, offers 64 cores, 128 threads, and 320MB of L3 cache. All models are expected to be available in July, with pricing details forthcoming. 

🎮 Radeon RX 9060 XT: Elevating 1440p Gaming

For gamers, AMD unveiled the Radeon RX 9060 XT, built on the RDNA 4 architecture. Key features include:

Up to 16GB of GDDR6 memory

32 RDNA 4 compute units

3.13GHz boost clock

Support for DisplayPort 2.1a and HDMI 2.1b

AMD asserts that the 16GB variant outperforms Nvidia's RTX 5060 Ti by approximately 6% in 1440p gaming across 40 tested titles. The RX 9060 XT will be available in two models: an 8GB version priced at $299 and a 16GB version at $349, launching on June 5.

🤖 Radeon AI PRO R9700: Accelerating AI Workloads

Addressing the needs of AI professionals, AMD introduced the Radeon AI PRO R9700 graphics card, featuring:

Tom's Hardware

128 AI accelerators

32GB of 20 Gbps GDDR6 memory

640 GB/s memory bandwidth

PCIe Gen 5 support

🖥️ Ryzen AI Pro 300 Series: Empowering AI-Driven PCs

In collaboration with ASUS, AMD announced the Ryzen AI Pro 300 Series processors, powering the new Expert P Series Copilot+ PCs. These processors deliver:

Over 50 TOPS (trillions of operations per second) of NPU performanceCompatibility with Microsoft Copilot+ AI features

These advancements aim to enhance enterprise AI applications, providing robust performance for AI-driven tasks.

AMD's announcements at Computex 2025 underscore its commitment to advancing AI capabilities across gaming, professional, and enterprise platforms. With these innovations, AMD positions itself as a formidable competitor in the evolving landscape of AI-powered computing.

#AMD #Computex2025 #Ryzen9000 #RadeonRX9060XT #Threadripper9000 #RyzenAI #AIPoweredPC #RDNA4 #AIHardware #TechNews #GamingGPU #WorkstationCPU #EdgeAI #AMDInnovation

Q1: What are the main highlights of AMD’s Computex 2025 announcement?

A: AMD unveiled new Ryzen Threadripper 9000 series CPUs, Radeon RX 9060 XT GPUs, AI PRO R9700 accelerators, and Ryzen AI Pro 300 Series processors

.Q2: Who is the Ryzen Threadripper 9000 series for?

A: It’s designed for professionals and creators handling heavy tasks like 3D rendering, simulations, and AI development

.Q3: What makes the Radeon RX 9060 XT special for gamers?

A: It offers great 1440p gaming performance, high boost clock speeds, and advanced ray tracing with RDNA 4 architecture

Q4: How is AMD supporting AI development?

A: Through GPUs like the Radeon AI PRO R9700 and CPUs with built-in NPUs for edge computing and on-device AI processing.

.Q5: When will these products be available?

A: Most products are expected to launch between June and July 2025, depending on the model.

 

AlphaEvolve – Google’s AI That Invents Algorithms Beyond Human Expertise

In a groundbreaking development, Google DeepMind has unveiled AlphaEvolve, an AI system that autonomously designs algorithms surpassing human-devised methods. This innovation marks a significant leap in artificial intelligence, showcasing the potential for machines to contribute novel solutions in complex domains.

What is AlphaEvolve?

Alpha evolve is an evolutionary coding agent powered by Google's Gemini large language models. It combines the creative problem-solving capabilities of these models with automated evaluators and an evolutionary framework to discover and optimize algorithms across various domains. This approach allows AlphaEvolve to iteratively improve upon algorithmic solutions, leading to innovations that were previously unattainable.

Key Features and Capabilities

• Autonomous Algorithm Design: AlphaEvolve independently generates and refines algorithms beyond traditional AI limits.
• Evolutionary Framework: It uses a genetic algorithm loop—testing, selecting, and evolving code candidates iteratively.
• Multidomain Application: Success across matrix math, data center scheduling, and hardware design demonstrates its versatility.

Notable Achievements

• Matrix Multiplication: AlphaEvolve improved on the Strassen algorithm—unchallenged for over 50 years.
• Data Center Optimization: Created energy-efficient scheduling models for Google’s infrastructure.
• Mathematical Discoveries: Solved previously unsolved tiling and geometry challenges involving hexagon packing.

Implications for the Future

AlphaEvolve's ability to autonomously generate novel algorithms signifies a paradigm shift in AI capabilities. It opens new avenues for research and application in fields requiring complex problem-solving and optimization. As AI systems like AlphaEvolve continue to evolve, they hold the promise of accelerating innovation and contributing to advancements across science and technology.

Conclusion

While still under development, AlphaEvolve shows that AI isn’t just a tool for following human instructions—it’s becoming a true inventor. This breakthrough could redefine how we approach problem-solving in computer science, engineering, and beyond.

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Tags: #AlphaEvolve #ArtificialIntelligence #AlgorithmDesign #DeepMind #GoogleAI #Innovation

 

Project Astra – Google’s Leap into the Future of AI Assistants

In a world increasingly shaped by artificial intelligence, Google’s DeepMind division has introduced one of its most ambitious creations yet: Project Astra. Unlike traditional digital assistants that react only when called upon, Astra is designed to be proactive—a smart, always-aware assistant that can understand, anticipate, and help without needing a prompt.

What is Project Astra?

Unveiled during Google I/O 2024, Project Astra is a research prototype aimed at becoming a universal AI assistant. Unlike its predecessors, it can interpret complex, real-time sensory input from the world around it. This includes audio cues, visual data, speech, environmental context, and more.

Think of Astra as an AI that not only listens to your commands but also observes your environment—helping you solve problems, remember tasks, and even answer questions about things it sees through your device’s camera or sensors.

A New Era of AI: From Passive to Proactive

Most current AI assistants wait for you to issue a command. Astra changes that. It uses a concept called contextual awareness. For example, if you're working on a math problem and seem stuck, Astra might offer a helpful suggestion or guide you to a solution. If you’re walking through a new neighborhood, it could point out interesting locations, safety information, or navigation tips—without being asked.

Key Features of Project Astra

• Multimodal Input Recognition
  Astra can analyze images, speech, gestures, and written language all at once. By combining these inputs, it forms a richer understanding of what’s happening and how it can help.
• Real-Time Assistance
  Instead of taking time to "think," Astra responds instantly. If you’re pointing your phone at a malfunctioning device, Astra might recognize it and offer a solution on the spot.
• Personalized Reasoning
  The AI learns from your habits, preferences, and history. If you often check the weather before running, Astra might remind you to dress appropriately when clouds are gathering.
• Seamless Integration Across Devices
  Whether you're on a smartphone, tablet, smart glasses, or other connected devices, Astra offers a unified assistant experience.
• Accessibility Innovation
  Features such as object recognition and spoken scene descriptions are particularly helpful for users with visual impairments.

Built on Google Gemini

Astra is powered by Gemini, Google’s most advanced AI model family. Gemini supports deep reasoning, multitasking, and multimodal understanding. Astra uses Gemini's capabilities to deliver intelligent, grounded assistance.

Potential Use Cases

• Education: Real-time tutoring and contextual help for students.
• Health: Lifestyle suggestions and wellness reminders based on behavior.
• Work: Document drafting, summarization, and meeting assistance.
• Travel: Real-time translations, navigation, and safety tips.

Limitations and Ethical Considerations

While promising, Astra is still in early development. It raises important questions about privacy, surveillance, and autonomy. Google claims that privacy and security are top priorities, with on-device processing and encryption baked into Astra’s core systems.

Conclusion

Project Astra represents the future of artificial intelligence assistants—tools that are intelligent, autonomous, and contextually aware. By evolving from reactive bots to proactive companions, AI is stepping closer to becoming a truly helpful presence in our daily lives.

Whether you're a tech enthusiast, a student, or simply someone fascinated by the future, Project Astra is a glimpse of what’s coming next. And with Google leading the charge, that future might be closer than we think.

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Tags: #GoogleAstra #AIassistant #GeminiAI #DeepMind #FutureTech #ArtificialIntelligence #Blogspot

 

How to Make Money Using AI Tools in 2025 (Beginner Friendly Guide)

Introduction:

Artificial Intelligence isn’t just for tech geeks anymore. In 2025, AI tools have become so powerful and accessible that anyone with a laptop and internet connection can start earning money online—no coding required. Whether you're a student, freelancer, or aspiring entrepreneur, this guide will show you how to turn AI into income.

Top AI Tools You Can Use to Make Money

1. ChatGPT / Claude AI / Gemini

Use for: Content writing, blog generation, email marketing, coding help.
How to earn: Offer content writing, tutoring, or customer support services on Fiverr or Upwork.

2. Midjourney / DALL·E / Leonardo AI

Use for: AI image generation.
How to earn: Sell AI-generated art on Etsy, Redbubble, or use them for social media marketing gigs.

3. Synthesia / Runway ML

Use for: AI video creation.
How to earn: Make YouTube videos, social media reels, or explainer videos for clients.

4. Jasper / Copy.ai

Use for: Ad copy, product descriptions, SEO content.
How to earn: Run a blog or affiliate site, or write landing pages and ads for businesses.

5. Tidio / Botpress

Use for: AI customer service bots.
How to earn: Build and manage bots for small businesses.

Real Ways to Make Money Using AI

• Freelancing with AI Assistance
• Selling Digital Products (eBooks, courses, printables)
• Affiliate Marketing using a blog or YouTube
• Social Media Content Creation using AI
• Build Micro SaaS or AI Tools

Tips to Get Started Today

• Use free trials: Start with ChatGPT, Canva AI, and DALL·E
• Set up a portfolio with AI-generated work
• Promote on platforms like Fiverr, Freelancer, LinkedIn
• Stay updated by following tech blogs like Synthex AI

Conclusion

AI is not a threat to your job — it’s your new business partner. The earlier you start exploring and experimenting, the quicker you can turn AI tools into real income. Whether you're creating content, images, or automating tasks, 2025 is the perfect time to ride the AI wave.

 

Best AI Tools I Actually Use: A Content Creator's Guide (2025)

 ChatGPT, the best AI application available today, boasts an incredible 200 million users as of October 2024. As a content creator, I've tested dozens of AI tools over the past year, discovering which ones actually deliver results and which fall short of their promises.

With this in mind, I've compiled a practical guide to the AI tools I genuinely use daily in my content creation workflow. From planning with ChatGPT's GPT-4o model to creating videos with Synthesia and Runway, these AI platforms have significantly improved my productivity. Particularly impressive is how tools like Fathom save hours each week by generating structured meeting summaries, while Rytr offers cost-effective writing assistance across 30+ languages starting at just $9/month.

This guide isn't about theoretical applications or the flashiest new AI apps—it's about the best AI tools that consistently perform in real-world content creation scenarios. I'll share exactly how I use each one, what they cost, and why they've earned a permanent place in my toolkit for 2025.

Planning and Research: Tools That Kickstart My Process

"Don't be that writer." — Kyle D. Stedman, Associate Professor of English at Rockford University

The foundation of great content starts long before I write a single word. My research phase determines whether a piece will be mediocre or outstanding. Over time, I've refined my process to include three **best ai tools** that consistently deliver results.

Perplexity: For fast, reliable research

Research that once took hours now takes minutes with Perplexity. Unlike traditional search engines, this ai platform synthesizes information from multiple sources and presents summarized results alongside citations, allowing me to verify sources instantly ^[1].

Recently, Perplexity launched its game-changing Deep Research feature that autonomously conducts comprehensive research by performing dozens of searches and analyzing hundreds of sources. When tested against industry benchmarks, Perplexity's Deep Research achieved an impressive 21.1% accuracy score on Humanity's Last Exam, outperforming many leading models ^[2]. Moreover, it scored 93.9% accuracy on the SimpleQA benchmark, demonstrating exceptional factual reliability ^[2].

What truly sets Perplexity apart is its efficiency—completing most research tasks in under 3 minutes ^[2]. This speed, combined with its citation capabilities, makes it my go-to for gathering reliable information quickly.

NotebookLM: For summarizing and organizing sources

After collecting research with Perplexity, I turn to NotebookLM to organize everything. This Google tool, powered by Gemini 2.0, allows me to upload various source materials—PDFs, websites, YouTube videos, audio files, and Google documents ^[3].

Once uploaded, NotebookLM transforms into a personalized AI expert on my research topic. It summarizes key points, makes connections between topics, and provides clear citations showing exact quotes from sources ^[3]. Furthermore, its Audio Overview feature converts my sources into engaging "Deep Dive" discussions with just one click ^[3].

For complex projects, I especially value how NotebookLM creates polished presentation outlines complete with key talking points and supporting evidence ^[3]. This organization saves hours of manual sorting through research materials.

ChatGPT: For brainstorming and outlining

Once my research is organized, ChatGPT becomes my brainstorming partner. As a content creator, I find it invaluable for generating fresh ideas when I'm stuck. In fact, it excels at:

• Suggesting podcast episode topics, social media captions, and YouTube video concepts ^[4]
• Creating structured outlines for articles and blog posts ^[2]
• Developing catchy titles and headlines that grab attention ^[2]

ChatGPT's ability to rapidly generate a wide range of ideas minimizes time spent on initial brainstorming ^[2]. Although it has limitations—such as relying on pre-existing data and occasionally suggesting overly generalized concepts ^[2]—I've found it consistently valuable for breaking through creative blocks.

By combining these three ai tools in sequence, I've created a research workflow that's both thorough and efficient. Each tool addresses a specific need in my planning process, ensuring I have solid foundations before creating content.

Writing and Scripting: Tools That Help Me Create Faster

Once research is complete, I turn to specialized writing tools to bring my content to life. After testing dozens of options, these three best ai tools consistently deliver the fastest results without sacrificing quality.

Claude: For structured writing and code help

When I need well-structured, thoughtful content, Claude is my go-to assistant. Unlike other AI platforms, Claude excels at complex cognitive tasks beyond simple text generation, making it perfect for analytical writing ^[5]. Its large context windows allow it to recall substantial information from conversations, which helps maintain consistency throughout longer articles ^[6].

Additionally, Claude can analyze images—from handwritten notes to complex graphs—extracting valuable information I can incorporate into my content ^[5]. For technical posts, I appreciate how Claude helps me create websites in HTML and CSS or debug complex code bases with its excellent pattern recognition ^[5].

The recently released Claude 3.7 Sonnet model delivers impressive performance on coding tasks, achieving the highest score on SWE-bench Verified ^[7]. Its instruction-following accuracy has been particularly valuable when I need to explain technical concepts to non-technical audiences.

Sudowrite: For creative writing and expansion

For creative projects, Sudowrite stands out as an exceptional partner. This tool goes beyond fixing grammar—it suggests ways to improve writing style and helps make stories more engaging ^[8].

What makes Sudowrite unique is its Story Engine feature, which helps me generate detailed outlines including characters, synopsis, and chapter beats ^[8]. When I'm struggling with writer's block, I use the Write feature to generate 300 words that match my voice and style ^[8].

Furthermore, Sudowrite recently launched Muse, an AI model specifically designed for fiction that took over a year to develop ^[3]. This specialized focus results in more evocative, inventive prose than general-purpose AI tools.

Rytr: For short-form content like captions and ads

For quick social media posts and marketing copy, Rytr offers exceptional value. This affordable tool supports over 40 content types and more than 30 languages ^[9], making it versatile enough for various short-form projects.

Impressively, Rytr has saved users an estimated 25 million hours and USD 500 million in content writing costs ^[9]. I primarily use it for:

• Social media captions and ad copy
• Email responses
• Product descriptions

Starting at just USD 7.50 monthly for unlimited generations ^[9], Rytr provides the best balance of affordability and quality for routine short-form content needs.

Visual Creation: Tools That Bring My Ideas to Life

"Circle the verbs: analyze, argue, describe, contrast." — Kyle D. Stedman, Associate Professor of English at Rockford University

Visual content has become the cornerstone of my content strategy as engagement metrics consistently show higher retention rates with videos and images. After extensive testing, I've settled on three **best ai tools** that consistently deliver professional results without requiring advanced technical skills.

Synthesia: For avatar-based video content

Creating professional-looking talking-head videos used to require expensive equipment and studio time. Consequently, I now use Synthesia to generate avatar-based videos in minutes by simply typing my script. With over 230 AI avatars speaking 140+ languages, I can create globally accessible content without translation headaches ^[10].

What makes Synthesia particularly valuable is its custom avatar feature. By recording a short video with my webcam, I can create a digital version of myself that maintains my likeness across all content. This personal avatar automatically clones my voice in 29 languages, allowing me to reach international audiences authentically ^[11].

Runway: For cinematic and animated visuals

When I need more cinematic content, Runway stands as the industry leader. This powerful ai platform bridges the gap between imagination and execution, generating high-fidelity videos from text prompts ^[12].

Runway's Gen-4 technology offers unprecedented creative control. The Multi-Motion Brush feature lets me direct up to five subjects independently within a scene, while Camera Control allows precise movement direction ^[13]. For character-based content, the Act-One feature has transformed my workflow—I can create expressive performances using a simple video input without complex rigging ^[13].

Canva Magic Studio: For social media and presentations

For daily social media content and presentations, Canva Magic Studio has revolutionized my design process. This suite of ai tools handles everything from generating custom images to creating entire presentations from a simple text prompt ^[14].

I primarily use Magic Design to transform my ideas into professional-looking materials in seconds. When preparing presentations, I simply describe my concept, and Magic Design generates a complete deck with an outline, sample content, and cohesive visual style ^[2].

Another time-saving feature is Magic Media, which turns my words into striking images and videos. For short-form content, I can describe my vision and watch as Canva generates videos powered by Runway's technology—a powerful integration between two leading ai platforms ^[14].

Publishing and Promotion: Tools That Streamline My Workflow

After creating quality content, the final hurdle is effective distribution and promotion. I've found three best ai tools that save me countless hours in the publishing phase of my workflow.

OpusClip: For social-ready video clips

The challenge of repurposing long-form videos into social media snippets disappears with OpusClip. This ai platform analyzes my videos and automatically identifies the most engaging moments. Using big data analysis, it compares my content against trending patterns to ensure maximum impact ^[15].

What impresses me most is OpusClip's AI curation feature, which not only extracts highlights but rearranges them into cohesive viral-worthy shorts with dynamic captions that boast over 97% accuracy ^[15]. The tool's virality score feature predicts each clip's potential performance, helping me prioritize which clips to publish first.

Meanwhile, the AI reframing capability automatically adjusts my content for various aspect ratios (9:16, 1:1, or 16:9), intelligently tracking speakers and moving objects for optimal presentation ^[15].

Vista Social: For scheduling and engagement

To distribute my content, I rely on Vista Social—the first social media management tool powered by ChatGPT ^[16]. Initially, I was skeptical about another scheduling tool, but its AI assistant has transformed how I create social posts.

The platform allows me to schedule content across multiple networks including Instagram, Facebook, TikTok, LinkedIn, Reddit, and Snapchat from a single dashboard ^[17]. Furthermore, it maintains the highest photo quality, eliminating pixelated screenshots that plagued my earlier posts.

In contrast to other tools, Vista Social supports direct publishing of Instagram and Facebook Reels, TikTok videos, and complex carousel posts without reminders or extra apps ^[17].

Fathom: For summarizing meetings and follow-ups

To be sure, content creation involves numerous meetings. Fathom records, transcribes, and summarizes these sessions so I can focus entirely on the conversation. On average, this ai tool saves me 20 minutes per meeting—equivalent to 1.5 weeks annually ^[18].

What sets Fathom apart is its lightning-fast performance, delivering AI summaries in less than 30 seconds after meetings end ^[18]. The platform offers 17 different summary templates for various meeting types, including sales calls, project updates, and interviews ^[19].

As a result of using these tools together, my publishing workflow has become significantly more efficient while maintaining quality across all platforms.

Conclusion

Throughout this guide, I've shared the best ai tools that truly deliver results in my daily content creation workflow. After testing dozens of options over the past year, these twelve applications consistently outperform alternatives while saving me countless hours each week.

The AI landscape continues to evolve rapidly, yet these tools have earned their permanent place in my arsenal for 2025. Undoubtedly, what makes them stand out isn't just their cutting-edge technology but their practical applications across the entire content creation process—from initial research with Perplexity to final meeting summaries with Fathom.

Before incorporating any AI tool into your workflow, consider how it addresses specific challenges rather than chasing the latest hype. My productivity has increased significantly since adopting this strategic approach. These applications handle repetitive tasks that previously consumed much of my day, consequently freeing me to focus on creative decisions that genuinely require human insight.

While AI won't replace human creativity anytime soon, it certainly amplifies what we can accomplish. The tools outlined in this guide represent the sweet spot between automation and human guidance—powerful enough to transform your productivity yet accessible enough for creators at any technical level.

The future belongs to content creators who skillfully blend AI assistance with human creativity. Therefore, start with one or two tools that address your biggest workflow challenges, then gradually expand as you become comfortable with AI-assisted creation. This practical approach has transformed my content quality while reducing production time by nearly 60%—a winning combination for any serious creator in 2025.

FAQs

Q1. What are some of the best AI tools for content creation in 2025? Some of the top AI tools for content creation in 2025 include Perplexity for research, NotebookLM for organizing sources, ChatGPT for brainstorming, Claude for structured writing, Synthesia for video creation, and Canva Magic Studio for design. These tools help streamline various aspects of the content creation process.

Q2. How can AI tools improve a content creator's workflow? AI tools can significantly enhance a content creator's workflow by automating time-consuming tasks, providing creative inspiration, and streamlining processes. For example, tools like OpusClip can automatically create social media-ready video clips, while Vista Social assists with scheduling and engagement across multiple platforms.

Q3. Are AI-generated visuals becoming more sophisticated? Yes, AI-generated visuals are becoming increasingly sophisticated. Tools like Runway now offer features such as Multi-Motion Brush and Camera Control, allowing creators to direct multiple subjects independently within a scene and precisely control camera movements, resulting in high-quality, cinematic content.

Q4. How can AI assist with research and planning for content creation? AI can greatly assist with research and planning by using tools like Perplexity for fast, reliable information gathering, NotebookLM for summarizing and organizing sources, and ChatGPT for brainstorming and outlining. These tools help content creators build a solid foundation before diving into the creation process.

Q5. Will AI replace human creativity in content creation? While AI tools are becoming increasingly powerful, they are not expected to replace human creativity entirely. Instead, AI amplifies what content creators can accomplish by handling repetitive tasks and providing assistance, allowing humans to focus on creative decisions that require genuine insight and originality.

References

[1] - https://www.getblend.com/blog/10-best-ai-tools-to-use-for-content-creation/
[2] - https://www.canva.com/magic-design/
[3] - https://www.sudowrite.com/muse
[4] - https://www.forbes.com/sites/emmalynnellendt/2025/02/27/how-content-creators-are-using-ai-in-2025/
[5] - https://www.anthropic.com/claude
[6] - https://www.getresponse.com/blog/ai-writing-tools
[7] - https://www.anthropic.com/solutions/coding
[8] - https://www.elegantthemes.com/blog/business/sudowrite-review
[9] - https://rytr.me/
[10] - https://www.synthesia.io/features/avatars
[11] - https://www.synthesia.io/features/custom-avatar
[12] - https://runwayml.com/
[13] - https://runwayml.com/product
[14] - https://www.canva.com/magic/
[15] - https://www.opus.pro/
[16] - https://vistasocial.com/ai-assistant/
[17] - https://vistasocial.com/social-media-publishing/
[18] - https://fathom.video/
[19] - https://help.fathom.video/en/articles/640768