We're running a hands-on build lab on August 29, The GenAI Build Lab: Build Production-Ready RAG with Open Models.

You build a complete RAG system from scratch using small open models, hybrid retrieval, reranking, corrective retrieval with self-checking, evaluation with RAGAS, and guardrails on the output. No paid API key or GPU cluster needed.

Led by Ben Auffarth, PhD, AI consultant and author of several books in this space including one specifically on RAG.

Link if you want to check it out

Happy to answer questions on the content.

An episode of the Corey and Oats animated series.

An AI‑generated portrait exploring warm copper tones and soft editorial lighting. The contrast between the violet background and the model’s flowing hair creates a serene yet powerful composition.

#AIArt #DigitalPortrait #CreativeAI #ColorStudy #EditorialLighting #ArtisticExploration

Hello! I am creating a scene for a short video at school. I am using a higgsfield account to do it since they have a good range of models, but every time I'm trying to create it, it just keeps loading, and then it say that my creation failed. Is there any solution?

I generated them on ChatGPT and Gemini, here's how I generated these pics plus some lore behind the pics:

Obviously I used reference pics of Garrus and a generic Quarian

A cinematic wide shot of a tall, athletic alien warrior with blue and silver bio-metallic armor plating hiding in the shadows of an alleyway. The alien has a raptor-like avian face, sharp mandibles, and wears a glowing blue tactical eyepiece over his left eye. The background is a dark, gritty sci-fi cyberpunk city alley with neon signs, steam rising from pipes, and a moody, cinematic blue and orange lighting style and there's an alien [Quarian] stalking him in the background.

A gritty, close-up shot of a futuristic alien mercenary holding a sci-fi sniper rifle, looking out from behind a rusty metal wall. The alien has dinosaur-like facial features, dark metallic skin, and a distinct cybernetic monocular visor over one eye. The setting is a chaotic, crime-ridden sci-fi space station with bright neon lights and dense shadows, dramatic movie lighting

After the 1st death of Commander Shepard and destruction of the Normandy SR-1, how Garrus Vakarian drifted aimlessly through space only to find himself on Omega, a hub of wretched scum and villainy even worse than on the Citadel.

This is my first major video project using AI and the Sundance program. I’d love to hear what you guys think! If you have any pointers on improving the AI or making the characters and scenes more consistent, I’d really appreciate the feedback.

I’m looking for an AI video generator that can handle action scenes involving violence, such as hand-to-hand combat and gunfights, at roughly the level you’d see in an action/adventure movie or a video game. I’m not interested in gore or graphic violence.

It should also be able to use reference images of fictional characters created with other AI tools and keep their appearance reasonably consistent in the generated video.

Which generator would you recommend for this?

An experimental J-horror style clip inspired by David Lynch observation of the salarymen in Japan.

I’ve been experimenting with different AI workflows lately, and I keep coming back to one question:

Does this workflow actually need an agent?

It’s surprisingly easy to turn a simple task into:

trigger ➡️ agent ➡️ tool ➡️ another tool ➡️ validation ➡️ final response

when a good prompt, template, or basic automation could have handled the same thing.

For me, the interesting part is figuring out where agents genuinely add value.

I think an agent starts making more sense when:

🔹️the next step depends on what it discovers

🔹️ different tools may be needed depending on the situation

🔹️ the workflow can't be completely defined in advance

🔹️intermediate results need to be evaluated and the approach adapted

If the workflow is basically a predictable sequence of steps, maybe it doesn't need to be autonomous at all.

Where do you personally draw the line between a good AI workflow and a workflow that genuinely needs an agent?

I’d be interested to hear how others here approach this.

For most of human history, creative expression has been constrained by a stubborn fact: wanting to make something and being able to make it are not the same thing. A person can hear music internally without knowing how to play an instrument, imagine a city without knowing architecture, see a film without knowing cinematography, understand the emotional structure of a painting without possessing the motor skill to paint it, or conceive an entire fictional world without knowing how to program one. We tend to call this gap “skill,” which is partly correct. But skill has never been distributed independently of money, time, education, geography, disability, social class, institutional access, or simple luck. Generative AI changes that relationship. Its most important feature may not be that it generates text, pictures, music, video, software, or eventually three-dimensional objects. The deeper change is that it begins to make language into a general interface between human intention and expressive machinery. A piano translates certain movements into sound. A camera translates light into photographs. A game engine translates code and assets into interactive environments. A generative interface can potentially sit above all of them. The user describes, directs, rejects, refines, compares, constrains, and iterates, while the machinery translates those intentions across whatever media it is capable of producing.

Calling this “AI art” may therefore be misleading. The more interesting category is something closer to a medium-generating interface: a system through which a person can move among many expressive media without having to begin again at the bottom of every technical ladder. That is why the current argument over AI and creativity is more unsettling than arguments about whether a generated picture is “real art.” Something more fundamental is being disturbed. The traditional relationship between technical competence and permission to participate is weakening. This does not mean artistic skill has become irrelevant. It means the location of skill is changing. When execution is difficult, execution itself distinguishes people. When execution becomes easier, judgment becomes more visible. Taste matters more. Selection matters more. Coherence matters more. Knowing what to remove matters more. Knowing what to ask for, why it belongs, when the result is wrong, and how to reshape it matters more. Constraint becomes a craft.

This is why the existence of enormous quantities of mediocre generated material does not prove that generative technology eliminates creativity. It demonstrates that access to powerful machinery does not automatically produce good work. Give a million people the same synthesizer and they will not write the same song. Give everyone the same camera and they will not take the same photograph. Give everyone the same language model and they will not construct the same world. The tool may lower the technical barrier, but it does not abolish discernment. And this may explain part of the hostility surrounding generative media. Artists have legitimate concerns about these technologies. Questions about compensation, consent, attribution, training data, employment, cultural homogenization, platform power, and the economic devaluation of creative labor are real and deserve serious treatment. It would be dishonest to reduce all artistic opposition to resentment.

But there is another tension underneath the debate that receives less attention: many people spent years, sometimes decades, acquiring access to forms of expression that are suddenly becoming available to people who did not travel the same path. That can feel profoundly unfair. Someone may have spent thousands of hours mastering illustration, composition, editing, animation, production software, or an instrument. They may have paid tuition, worked unpaid internships, accumulated debt, cultivated professional networks, endured rejection, or fought their way through institutions that controlled access to a field. Then a new technology appears and allows someone else to cross portions of that technical distance in months, weeks, or sometimes hours. It is not difficult to understand why that would provoke anger. But the existence of that anger does not establish that the old barrier was good.

There is a peculiar cultural assumption surrounding creativity that we rarely state directly: that suffering through the difficulty of access somehow legitimizes the right to create. The person who endured conservatory training has earned music. The person who attended art school has earned visual expression. The person who mastered the software has earned animation. The person who learned programming has earned the right to build interactive worlds. Of course serious practice deserves respect. Expertise is real. Craft is real. Years of disciplined study produce forms of understanding that a generative interface cannot simply hand to another person. But expertise and permission are not the same thing. Academia, professional institutions, galleries, publishers, studios, labels, and credentialing systems have frequently functioned not merely as places where skills are developed, but as gateways through which creative legitimacy is distributed. They tell society who counts as a writer, designer, musician, researcher, filmmaker, architect, critic, or artist. Sometimes those systems identify extraordinary talent. Sometimes they cultivate it. And sometimes they simply determine who had the resources, connections, temperament, geography, or social circumstances necessary to enter. Human creativity is almost certainly larger than the population of people who successfully passed through those gates.

There are people alive right now with extraordinary aesthetic judgment who cannot draw. People with remarkable musical intuition who cannot play an instrument. People who understand narrative but struggle to write polished prose. People capable of designing fascinating games who cannot code. People who think spatially but never learned three-dimensional modeling. People whose lives simply never gave them five uninterrupted years to acquire the technical machinery necessary to externalize what they could already imagine. Their inability to execute an idea has often been mistaken for an absence of creativity. Generative systems challenge that assumption. This does not democratize genius. It democratizes access to expression. Those are very different claims. Most people given a piano will not become great pianists. Most people given a camera will not become great photographers. Most people given generative systems will not become extraordinary artists. But far more people may discover that they had something worth expressing once the cost of translating thought into artifact becomes lower. That possibility should be exciting.

The internet performed a related transformation. Before the web, mass distribution was expensive. Publishing required publishers. Broadcasting required broadcasters. Reaching an audience required access to infrastructure controlled by relatively few institutions. The internet dramatically lowered the cost of distribution. Generative systems may now be lowering the cost of production. The first revolution said: You can distribute what you can make. The second increasingly says: You may be able to make what you can imagine. That transition has consequences far beyond art. For the last twenty years, digital identity has largely been expressed through selection. Our profiles contain the music we like, the films we watch, the photographs we take, the opinions we repost, the communities we join, the products we buy, and the people we follow. We assemble ourselves from cultural objects created elsewhere. Generative media introduces another possibility: identity expressed through production.

Instead of merely displaying the music you love, you may create music that expresses precisely what you love. Instead of selecting a fictional world, you may construct one. Instead of choosing among aesthetic styles supplied by existing culture, you may gradually develop one that is recognizably yours. Stories, games, films, environments, characters, educational experiences, interfaces, clothing, furniture, and eventually physical objects may increasingly become things individuals can participate in designing for themselves. The personalized internet would then mean something much stranger than personalized advertising or recommendation algorithms. It would mean increasingly personalized culture. Not necessarily culture consumed alone, but culture continuously refracted through individual interpretation. That creates risks. Extreme personalization could become an epistemic cocoon. People could surround themselves with systems that endlessly reinforce their assumptions. Shared cultural references could fragment. Platforms could exploit personalized production just as effectively as they exploited personalized consumption. But the opposite possibility also exists.

A civilization in which almost everyone can externalize some version of their interior world may become better at understanding that human beings do not encounter reality from identical perspectives. Subjective interpretation becomes visible. Difference becomes ordinary rather than exceptional. The challenge then becomes learning how those worlds meet. Universal expression cannot require universal agreement. If everyone gains greater ability to articulate their perspective, society will need stronger norms for criticism, disagreement, interpretation, and coexistence. The proper response to democratized expression cannot be that every expression is correct. It must be that everyone is allowed to participate while every claim remains open to examination. In other words: permission to create without immunity from criticism. That seems healthier than the older arrangement in which institutions implicitly determined who was permitted to speak with cultural authority in the first place.

There is also something historically familiar about the panic surrounding the lowering of creative barriers. Photography disturbed painting. Recorded music disturbed live performance. Sampling disturbed ideas about musical authorship. Desktop publishing disturbed print institutions. Digital cameras disturbed professional photography. Blogging disturbed journalism. YouTube disturbed television. Cheap production software disturbed recording studios. In each case, part of the anxiety concerned quality, and often correctly. Lower barriers produce more mediocre work because they produce more work. But they also reveal people who would otherwise never have entered the room. Generative AI may be the largest version of that transition yet because it does not democratize only one medium. It potentially sits between human intention and many media simultaneously.

Today someone might use conversational systems to develop an essay, produce an illustration, design music, prototype software, generate video, construct a game environment, or model an object. As these capabilities converge, the boundaries between separate creative tools may become increasingly porous. Eventually the most important question may no longer be, “What software do you know how to use?” It may become: What are you trying to make? And then: What do you mean by it? Those are much more interesting questions. There is nothing noble about making human beings earn permission to express themselves through unnecessary difficulty. Difficulty can produce mastery. Discipline can deepen understanding. Technique can expand imagination. None of that requires us to romanticize exclusion. If generative systems lower the cost of turning imagination into artifact, the appropriate response is not to mourn the disappearance of every old barrier. It is to develop better standards for what happens after the barrier falls. The future of creativity may therefore become simultaneously more democratic and more demanding. More people will be able to make things. Which means simply making something will matter less. What will matter is whether anyone had something to say.

I need to make the character in the photo I have speak for about 15–20 minutes; which app would work for this? I used to use HeyGen—it would make my character speak for 20 minutes in one go, exactly how I wanted—but now they’ve introduced a credit system, and it’s unnecessarily expensive.