When doing research, I usually use a handful of AI tools to summarize papers, find information, and understand technical topics more easily.

Some of my usual tools are Google’s NotebookLM, ChatGPT, Claude, Grok, and AnswerThis.

You might already be familiar with the first four tools, but probably not the last one.

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Most AI chatbots can explain a topic or summarize a document. The problem starts when I need to verify the information, find the original paper, manage several sources, and use those sources in the final draft.

I often end up with too many tabs open. One contains the research paper, another has my conversation with an AI, and another contains the draft. Sometimes I copy useful information into my notes and later forget exactly where it came from.

AnswerThis tries to keep those parts of the research process together.

In this guide, I will go deeper into what AnswerThis is, how it works, and why I think it is one of the best AI tools for citation-backed research.

Let’s get started.

What is AnswerThis?

AnswerThis is an AI research assistant made for people who work with academic and scientific sources.

You can use it to find papers, generate literature reviews, upload and analyze your own research, ask questions about a paper, manage citations, and write a draft. Instead of opening a separate application for each task, you can keep the research inside one project.

The platform says its academic search covers more than 300 million research papers. You can search for a topic and narrow the results based on factors such as publication date, journal quality, source type, and minimum citation count. You can also limit the search to papers that you already saved in your library.

This is helpful because research is not only about finding more papers. It is about finding papers that are actually relevant to your question.

A highly cited paper may be useful when you want to understand the foundation of a topic. A newer paper with very few citations may be more helpful when researching something that was released only a few months ago. Being able to control the search helps reduce the amount of unrelated information that you have to screen manually.

The tool also connects its answers to the sources it used. When it gives you an explanation, you can inspect the cited paper and check the section behind the answer.

Here are some of its key features:

• Chat with papers: Upload a research paper and ask questions about its methods, findings, terminology, or limitations.

• Literature review and paper search: Search for related academic work and create citation-backed literature reviews.

• Citation Mapper and bibliometric analysis: See how papers are connected and analyze publication, citation, journal, and author trends.

• Research library: Save uploaded files and newly discovered papers inside the same project.

• AI Writer and export tools: Turn your research into a draft and export it as PDF, DOCX, Markdown, or LaTeX.

Most of these tools are built around the same library. If I save a paper during the research stage, I can still access it later when I start writing.

How To Use AnswerThis for Research

Head over to AnswerThis, sign in, and create a new project.

You can then add the reference materials you want to use. For academic work, the main option is uploading a PDF. You can also save papers found through AnswerThis or import existing references from tools such as Zotero and Mendeley.

For this example, I wanted to research Distilling the Knowledge in a Neural Network.

This is the 2015 paper by Geoffrey Hinton, Oriol Vinyals, and Jeff Dean that introduced one of the most widely known approaches to knowledge distillation.

I wanted to understand this topic because distillation helps explain how some smaller AI models learn useful behavior from much larger models. Meta used distillation while training Muse Glimmer, for example, which is a 30-billion-parameter model designed to run on a single consumer GPU.

I uploaded the paper, and AnswerThis read it and gave me an overview of what it was about.

From here, I could ask questions, inspect the cited information, create tables, and perform bibliometric analysis.

I started with a basic question:

Prompt: How does knowledge distillation work?

The AI gave me a concise answer based on the uploaded document.

Knowledge distillation uses a larger teacher model to train a smaller student model. The student is not only trained on the final answer selected by the teacher. It can also learn from the probabilities the teacher assigns to the other possible answers.

For example, imagine that a teacher model is classifying an image.

It might assign an 80% probability that the image contains a dog, 15% that it contains a wolf, and 5% that it contains a fox.

A normal hard label would only tell the student that the image is a dog. The other probabilities provide more information. They show that the teacher considers a wolf more similar to the correct answer than a fox.

The original paper calls these outputs “soft targets.” It also uses a temperature setting to make the probability distribution softer, allowing the student to learn more about the relationships between possible answers.

This does not make the student identical to the teacher. The smaller model still has less capacity. Distillation is more about transferring useful behavior into a model that is cheaper or easier to run.

I could have asked ChatGPT or Claude to explain the same concept. What I liked about AnswerThis was that the answer remained connected to the uploaded paper.

When I clicked on the reference, it brought me back to the source so I could check the relevant part of the document.

That saves time, especially when reading a long paper and trying to remember where a certain explanation came from. I do not have to search the PDF again or ask the AI to provide a source after it has already answered.

The quality of the answer still depends on how specific the question is. A broad prompt may give a broad summary, while a narrower question is more likely to point me toward the exact part of the paper I need.

After understanding the starting paper, I can look for related research.

One way to do that is through the Citation Mapper.

Select a paper, enter a topic or DOI, and generate a citation map. The map shows the papers referenced by the original study and the newer papers that cited it.

This is useful because a normal keyword search does not always show the complete development of an idea.

The title of a newer paper may not contain the words “knowledge distillation,” even if the method is important to its training process. Following the citation network can reveal later applications, improvements, criticisms, and comparisons that a simple search could miss.

Starting with the Hinton paper, for example, I could look for newer studies that applied distillation to language models, healthcare systems, multimodal models, or AI agents.

I can then save the useful papers into the same project.

AnswerThis says its literature review system searches a database of more than 300 million papers. I can also apply filters if the initial result contains too many unrelated sources.

I might limit the search to papers published after a certain year, require a minimum citation count, or focus on a particular academic source. These filters are useful, but they need to match the research question.

Using a high minimum citation count for a newly released AI technique could remove the newest papers. At the same time, removing the filter entirely might return many papers that have not received much attention or independent evaluation.

Citation count is helpful for finding influential work. It does not prove that the paper is correct.

When I’m done with the research, I can move on to writing the draft.

AnswerThis has a built-in AI Writer, so I do not need to switch to a separate platform immediately. The papers saved in the project are still available while I write.

I can create an outline, draft individual sections, add citations, improve the wording, or continue a paragraph. I prefer doing this one section at a time rather than asking the AI to write the entire article from one short prompt.

That gives me more control over which papers are used and what each section is trying to explain.

When the document is finished, AnswerThis supports exporting it as PDF, DOCX, Markdown, or LaTeX. Citations can also be formatted in thousands of academic styles or transferred to reference managers such as Zotero and Mendeley.

And that’s it.

I started with one paper, asked questions about it, checked the cited information, found related studies, saved them into a project, and used the same research while writing the draft.

Why should you care?

You might be wondering why I don’t simply use ChatGPT, Claude, Grok, or NotebookLM for all of this. I still use those tools.

ChatGPT and Claude are useful when I need help understanding a technical idea or improving an explanation. NotebookLM is great when I already have several documents and want to ask questions about them.

The problem is that research involves more than getting an answer from a chatbot.

Researchers may need to screen hundreds or even thousands of papers before writing anything. They have to check the title, abstract, research method, sample, dataset, findings, and limitations before deciding whether the paper is useful.

Doing that manually can take weeks.

AI can speed up some of this work, but inconsistent citations create another problem. A model may provide a source that exists but does not support the exact claim. It may also summarize a qualified result as if it were universally true. A citation is not a guarantee that an answer is correct.

AnswerThis itself recommends checking important citations because large language models are still involved in the final output. The benefit is that the source is already connected to the answer, making the verification easier.

I can click the citation, inspect the paper, and check whether the wording is accurate. I am still responsible for deciding whether the evidence is strong enough to use. The other problem is managing too many applications.

If the search happens in one tool, the PDF analysis in another, the references in Zotero, the notes in a document, and the writing in a chatbot, it becomes easy to lose context.

I may remember a useful finding but forget which paper it came from. I may save the same paper more than once. I may cite an abstract without realizing that the full paper gives a more limited conclusion.

AnswerThis gives me one place for the search, paper library, cited answers, research tables, and draft. I can still use other AI tools when they are better for a particular task, but the main evidence stays inside the research project.

The platform can also be used by teams.

You can invite co-authors or other researchers into a shared workspace, share a library, and work from the same collection of papers. This is better than everyone maintaining a separate folder and sending different versions of the reference list to each other.

Privacy is also a big factor.

AnswerThis says uploaded documents are not used to train its models and are not shared with other users. Its data-handling guide says uploaded documents are stored temporarily and removed periodically.

The company also says research data is encrypted at rest and in transit. Enterprise accounts include additional controls such as role-based access, project permissions, SSO, and audit logs.

I would still review the exact privacy terms before uploading regulated medical data, personal information, unpublished research, or confidential company documents. Different organizations have their own rules about where research data can be stored.

For ordinary academic and technical research, I like knowing that the platform has at least considered this problem. Many people upload documents to AI tools without checking whether those files will be stored or used for training.

Final Thoughts

AnswerThis helped me understand the knowledge-distillation paper without separating the questions, citations, related research, notes, and final draft into several different tools.

I could upload the paper, ask what a technical term means, check the section behind the answer, find newer research through the citation map, and save the useful papers into the same project.

I also appreciate that its creators put a lot of focus on information accuracy and user privacy. The tool can still make mistakes, and I would never use an AI-generated citation without checking it. But having the source connected to the answer makes that checking much easier.

Have you tried any other research-focused AI tools before? Let me know in the comments.

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Picking an open-source model is the enjoyable part. The harder moment arrives when one agent needs an embedding model, a reranker, an OCR model, a safety model, and a small language model. Each one brings a container, a health check, an autoscaler, a GPU budget, and a dashboard.

That is where a model choice turns into an infrastructure project.

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In this article, I compare three routes for deploying open-source models to production in 2026. You can build the stack yourself with vLLM, TEI, or SGLang, use managed inference APIs, or run a multi-model server such as SIE. By the end, you’ll know which route fits a single model, a low-volume prototype, or a fleet of task-specific models.

What “Production-Ready” Actually Means

A production-ready deployment can handle traffic changes, failures, model updates, and routine debugging without constant manual intervention.

It needs autoscaling that follows demand, multi-model support for agent workflows, and sensible defaults that remove the need to tune every model by hand.

It also needs observability for latency, throughput, errors, and quality drift, plus a reproducible setup that behaves consistently on a laptop and in a production cluster.

A demo can survive with one model and one GPU. Production starts when 100 concurrent agents arrive, a worker crashes, or a model update changes latency. The infrastructure beneath the model determines whether the service recovers cleanly.

Free model weights still come with inference costs. GPU time, networking, storage, monitoring, and engineering work belong in the budget.

Why Self-Host Open-Source Models?

Teams self-host open-source models for control over data, cost, and model choice.

• Model quality on focused tasks: Research covered by MIT Sloan found that open models reached about 90% of closed-model performance at release and often caught up within months. For embeddings, reranking, classification, and extraction, a task-specific evaluation matters more than the logo on the model card.

• A different cost profile: Managed APIs are inexpensive to start, but agents can make several model calls for every user request. Self-hosting replaces that repeated usage bill with compute and operating costs. The savings depend on traffic, GPU utilization, and how much maintenance the stack needs.

• Data control: A self-hosted deployment can keep patient records, financial data, or proprietary code inside your cloud or on-premises environment. That can simplify data-residency decisions, although your team still owns the security work.

Self-hosting changes how you pay. It pays off when the workload is steady enough to use the hardware well, and the operating burden stays under control.

Three Routes to Production

The right route depends on how many models you serve, how much control you need, and who will maintain the system.

1. DIY With vLLM, TEI, or SGLang

The DIY route gives you direct control over the serving layer. vLLM and SGLang are high-throughput frameworks for large language and multimodal models. Hugging Face’s Text Embeddings Inference, or TEI, serves embedding, reranking, and sequence-classification models.

The software pricing is straightforward:

• vLLM: The Apache 2.0 software is free. You pay for GPUs, networking, storage, monitoring, and engineering time.

• TEI: The Apache 2.0 server is free. Your bill comes from the hardware and the surrounding production stack.

• SGLang: The Apache 2.0 framework is free. Infrastructure and operations are additional costs.

Complexity grows when an agent needs several model types. Each server may need its own deployment, capacity rules, health checks, and dashboards. You get maximum tuning control, but you also own every connection between the pieces.

This route fits one heavily used model or a team with strong machine-learning operations experience.

2. Managed Inference APIs

Managed inference APIs are the fastest route to a live endpoint. The provider provisions hardware, monitors it, and handles most scaling work. Your application sends a request and receives a result.

Pricing varies by provider, model, and billing unit. Hugging Face’s dedicated Inference Endpoints start at $0.033/hour for a CPU instance and $0.50/hour for a GPU instance, with usage calculated by the minute. Other providers may bill by token, request, or compute time.

The table below shows currently available GPU instances on Hugging Face and their hourly pricing.

The trade-offs are lower infrastructure control, provider-specific model catalogs, and data that leaves your environment unless the service offers a suitable private deployment. Costs can also climb quickly when one agent request triggers several model calls.

Managed APIs fit prototypes, uneven traffic, and teams that want to avoid running inference infrastructure.

3. Superlinked Inference Engine

Superlinked Inference Engine, or SIE, takes a multi-model approach. It serves more than 100 preconfigured models through one API and loads them on demand. The catalog covers search, reranking, document conversion, structured extraction, content safety, vision, and generation.

The self-hosted version is Apache 2.0 software, so you pay for your own cloud resources. SIE Cloud uses task-based usage pricing. Current examples include $6 per 1 billion tokens for e5 embeddings, $10 per 1 billion tokens for a bge reranker, $0.20 per 1,000 pages for Docling, and $90 per 1 billion input and output tokens for Qwen3–4B generation.

SIE also ships the surrounding production components, including a load-balancing gateway, KEDA autoscaling, Grafana dashboards, and Terraform modules for AWS, Google Cloud, and Azure. Its cluster-wide queue is designed to pack mixed requests efficiently across shared GPUs.

This route fits agents that call several task-specific models and teams that want to keep the data and infrastructure in their own cloud.

Quick Comparison

Use this table to match each route to the operating model you already have.

DIY deployment requires the most setup and charges for GPU and runtime costs, but keeps data in your cloud and gives you full control.

Managed APIs are easier to start with, billing by tokens, requests, or compute, while the provider handles operations.

SIE sits between the two, offering free self-hosting or managed usage pricing, support for more than 100 models, and built-in gateway, autoscaling, dashboards, and Terraform modules.

In short, DIY fits one carefully tuned model, managed APIs suit prototypes and uneven traffic, and SIE is designed for teams running a fleet of task-specific models while retaining cloud and data control.

Deploying With SIE

SIE exposes four primitives that cover the common small-model workflow:

• Encode: Turn text, images, or documents into dense, sparse, or multi-vector representations that software can compare.

• Score: Rerank a shortlist so the most relevant results rise to the top.

• Extract: Pull structured data or named entities from messy text and documents.

• Generate: Run an open language model for the agent loop or another text-generation step.

The practical sequence is short:

1. Run SIE locally on Apple Silicon or an NVIDIA GPU and connect one task through the SDK.

2. Test the chosen model against your own data, latency target, and expected request volume.

3. Move the same model IDs and application code to a production cluster.

4. Add autoscaling limits, dashboards, alerts, and a rollback plan before opening the endpoint to traffic.

The SIE quickstart covers local setup, and the deployment guide provides Terraform and Helm options for the major cloud platforms. The same stack can also stay fully self-hosted, which keeps the production path close to the local one.

Final Thoughts

The easiest production path depends on the shape of the workload. Managed APIs win when speed and low operational effort matter most. vLLM or SGLang makes sense when one large model needs close tuning. TEI is a focused choice for embeddings and reranking.

SIE is the strongest fit in this comparison for an agent that depends on a mixed fleet of task-specific models. It reduces the number of production systems you need to assemble while preserving self-hosting and cloud control.

In 2026, the practical architecture is the one that lets the model count grow without making the operations stack grow at the same rate.

Frequently Asked Questions

What is the easiest way to deploy open-source models to production in 2026?

The easiest route is a managed endpoint for one or two models. For an agent that uses several task-specific models, a multi-model server with routing, autoscaling, and monitoring removes more of the production work.

Is SIE a replacement for vLLM or SGLang?

No. vLLM and SGLang are strong serving engines for large generative models. SIE can use SGLang as a backend and focuses on running the broader collection of models around an agent through one production stack.

Are open-source models good enough for production?

Yes, open-source models can be strong production choices for embeddings, reranking, classification, extraction, vision, and many generation tasks. Evaluate each candidate on your own data because benchmark leaders don’t always match the latency, accuracy, or hardware limits of your application.

Does self-hosting save money?

Yes, self-hosting can save money when traffic is high, and the hardware stays well used. It can cost more at low volume because you still pay for idle capacity, monitoring, maintenance, and engineering time.

Can I try SIE without an NVIDIA GPU?

Yes. SIE runs locally on Apple Silicon and NVIDIA GPUs. The managed cloud option also lets you test the API without operating your own hardware.

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Seedance 2.0 has dominated the AI video space over the past couple of weeks. Its realism quickly went viral, along with its ability to recreate famous people and copyrighted characters. That also made the model controversial, drawing criticism from Hollywood groups and actors’ unions over copyright and consent concerns.

Now, Seedance 2.5 has arrived on video-generation platforms like Pollo AI, and I was curious to see how it compares with Seedance 2.0.

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For this post, I tested both models using the same four scenarios: an anime action sequence, a realistic battle, a water and gravity test, and a dramatic human performance.

I compared their motion, camera control, consistency, physics, facial expressions, and prompt adherence.

Both Seedance 2.5 and Seedance 2.0 are available from the model selector in Pollo AI, making it easy to run the same prompt through each one.

Before we get into the actual video comparison, let’s see how the features compare.

Seedance 2.5 vs Seedance 2.0 Feature Comparison

The biggest changes in Seedance 2.5 are longer videos, more reference inputs, and better editing controls. Here is how the two models compare.

The jump from 15 to 30 seconds is the biggest upgrade for me. Seedance 2.5 can fit more action, dialogue, and narrative progression into a single generation without stitching several clips together.

Its reference limit is also much higher, which should help with projects involving multiple characters, products, locations, or motion references.

The quality difference is less straightforward. ByteDance says Seedance 2.5 improves image, audio, and motion quality, while Pollo AI advertises native 4K output.

However, my tests showed that Seedance 2.0 can still produce better results in certain scenes, particularly anime action and emotional performances. Seedance 2.5 has more features, but it is not automatically better for every prompt.

Alright, now let’s do a side-by-side comparison now.

Prompt #1: Anime action sequence

A cinematic anime swordswoman sprints across a neon-lit rooftop in the rain and clashes with a masked opponent. The camera tracks beside her, circles both fighters during three rapid strikes, then follows as she slides under an attack and returns to her feet. Maintain consistent characters, readable choreography, smooth camera movement, and realistic rain and reflections.

I chose an anime action sequence to test how well each model handles stylized movement without losing visual consistency. The scene combines running, sword fighting, rain, reflections, energy effects, and a moving camera, giving the models several details to manage at once.

Fast anime choreography can easily become blurry or disconnected, especially when characters cross paths or the camera changes position.

I expect this test to reveal differences in character stability, body movement, shot composition, and whether the action remains easy to follow.

Here are the results:

Seedance 2.5

This result looks fantastic. The neon lights reflecting on the wet rooftop add depth to the scene, while the camera shake makes the character’s sprint feel fast and intense. I also liked the brief slow-motion moment as she slid across the ground.

The movement feels smooth and cinematic, but I wish the scene included more actual fighting. Most of the clip builds toward the confrontation rather than showing it.

Seedance 2.0

I was more surprised by this result. It looks closer to a finished anime sequence, with more action and a stronger sense of choreography. The camera movement is also excellent and manages to keep up with the fight without making it difficult to follow.

Winner: Seedance 2.0

Prompt #2: Realistic battle scene

A soldier runs through a war-damaged city as an armored vehicle speeds behind him. The handheld camera follows closely while an explosion sends dust, concrete, and glass across the street, forcing him to duck behind an overturned car. Show realistic movement, debris physics, vehicle weight, motion blur, and spatial continuity with no cuts or graphic injuries.

The realistic battle scene raises the difficulty by combining human movement, a fast vehicle, an explosion, and flying debris within one continuous shot.

I want to see whether the models can maintain a believable sense of space while several objects move at different speeds. Details such as the soldier taking cover, the vehicle turning, and debris reacting to the explosion should expose weaknesses in motion and physical interaction.

This test should make improvements in camera control, environmental consistency, motion blur, and high-speed rendering more noticeable.

Here are the results:

Seedance 2.5

The action looks very convincing here. The explosion, debris, and collapsing building have a real sense of scale and weight. I especially liked how the destruction continued behind the soldier instead of appearing as a brief effect.

It did miss one clear instruction: the car he ducks behind is not overturned. That is a noticeable prompt-adherence issue, even though the scene itself looks realistic.

Seedance 2.0

The camera work is the strongest part of this result. It follows the soldier closely, and the handheld shake adds intensity without making the action unreadable. The explosion, smoke, and dust also look convincing.

The main problem happens when the car enters the scene and hits the armored vehicle. The transition looks unnatural, and the vehicle seems to disappear during the collision. That breaks the spatial continuity the prompt was designed to test.

Verdict: Both models missed some details, which suggests that long action prompts are still difficult to follow precisely. Seedance 2.5 produced the more convincing scene overall.

Prompt #3: Water and gravity test

A glass pitcher falls from a kitchen counter and releases water toward a rubber ball, metal spoon, and folded cloth. The spoon falls and rotates, the ball bounces onto the floor, the cloth absorbs water, and the remaining liquid pours over the edge into a spreading puddle. Show realistic gravity, collisions, splashes, reflections, refraction, and object consistency.

For the physics test, I included water and several objects with different weights and materials. The spoon should fall and rotate, the ball should bounce and roll, the cloth should absorb water, and the liquid should flow naturally over the counter.

Video models often produce attractive water effects while getting the underlying physics wrong, so this scene gives me several specific interactions to examine. Any difference in gravity, collisions, fluid motion, reflections, or object permanence should be visible without relying too much on personal taste.

Here are the results:

Seedance 2.5

The pitcher tipping over on its own looks unnatural, and water appears on the cloth before the main stream reaches it. Those details make the beginning of the clip feel artificial.

Once the water starts moving, though, the result becomes much more convincing. The splashes have weight, and the way the water pushes the spoon looks surprisingly realistic. The interaction between the liquid and the objects is the best part of the clip.

Seedance 2.0

The water reacts too late after the pitcher falls, so the physics already look wrong within the first few seconds. The pitcher and liquid seem disconnected rather than part of the same event.

The rest of the video is much better. The objects respond to the water, and the cloth appears to absorb it instead of behaving like a solid surface. Even so, the delayed water movement is too noticeable to ignore.

Winner: Seedance 2.5.

Prompt #4: Dramatic human scene

A woman reads a handwritten letter alone at a kitchen table late at night. Her guarded expression gradually shifts to disbelief, her eyes fill with tears, and she looks toward an empty chair with a small smile before lowering her gaze. Show restrained facial acting, natural blinking and breathing, accurate eye focus, stable identity, and a gentle camera push-in.

The final prompt slows down the action and focuses on a subtle human performance. A convincing emotional shift requires more than a sad facial expression.

The model needs to manage eye focus, blinking, breathing, facial muscles, tears, and small changes in posture while keeping the person’s identity stable. I included a letter and an empty chair to give the character clear emotional cues without using dialogue.

This should reveal whether the newer model can create a more natural performance and maintain emotional continuity throughout the shot.

For this test, I wanted to see how both models handle a starting frame. So I fed both of them with this image:

Here are the results:

Seedance 2.5

The performance is restrained but still communicates a lot. I particularly liked the moment when she looks up and gives a small smile. It makes you wonder what she read without spelling out the emotion.

Her expression remains natural, and the model keeps her face stable throughout the scene. It feels like a quiet acting performance rather than a sequence of exaggerated expressions.

Seedance 2.0

Seedance 2.0 also handles the performance well. The slow camera push draws attention to her face, while the tears and subtle expression create a stronger sense of heartbreak.

Even without dialogue, the scene gives you a clear impression of what the letter might contain. Seedance 2.5 is more restrained, but I found the Seedance 2.0 result more emotionally affecting.

Winner: Seedance 2.0

Which Model Should You Choose?

My results ended in a tie, with each model winning two tests. The better choice depends on the type of video you want to create.

Seedance 2.5 performed better in the realistic battle and physics tests. I would choose it for scenes involving explosions, debris, water, environmental destruction, or several objects interacting at once. Its output often had a stronger sense of weight, although it still missed some prompt details.

Seedance 2.0 won the anime and dramatic performance tests. It produced more complete action in the anime scene and created the more emotionally affecting human performance. For stylized animation, close-ups, or scenes driven by facial expressions, Seedance 2.0 remains a strong option.

Neither model followed every instruction perfectly. If your prompt contains several actions, objects, and camera directions, expect some details to be ignored or changed. Shorter prompts and additional generations may still be necessary.

Final Thoughts

I expected Seedance 2.5 to win most of these tests, but the results were much closer than that. It produced more convincing physics and realistic destruction, yet Seedance 2.0 still did better with anime action and human emotion.

Seedance 2.5 feels like an improvement in specific areas rather than a complete replacement for Seedance 2.0. The newer model can handle complex physical scenes well, but that advantage does not carry across every style or type of movement.

For now, I would keep both models available and choose between them based on the scene. Seedance 2.5 is my pick for realism and environmental action, while Seedance 2.0 remains surprisingly competitive when style and emotion matter more.

What do you think? Let me know your thoughts in the comments.

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If you’ve been using LoRAs to give your AI images a custom character or style, you know how annoying it is when the output barely changes. When your LoRA isn’t working, you add it, hit Generate, and stare at the result wondering whether it did anything at all.

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A LoRA, short for Low-Rank Adaptation, is a small add-on that teaches an image model a character, style, outfit, pose, or other visual concept. It should guide the result toward those learned details.

I’ve had the same problem in PixAI. My first instinct was to raise the weight and pack more tags into the prompt. Sometimes the image got stranger while the style I wanted stayed faint.

So I tested the same LoRA with and without its trigger phrase, at three weights, and across different model families. The results pointed to five settings you can check before blaming the file.

Missing or Wrong Trigger Word

A LoRA trigger word activates a character, style, outfit, pose, or visual feature learned during training. If it is missing or formatted incorrectly, the expected effect may not appear.

The first place I check is the PixAI Model Market. Its LoRA pages show the architecture, category, creator, and available trigger words.

For this test, I selected PixAI’s official Haruka v2 style LoRA. Its page labels it DiT.2 and lists harukav2 style as the trigger phrase. To use the model, just click on the “Use this LoRA” button.

I loaded the LoRA at the same weight for both images and began with this prompt:

Create a cinematic anime illustration of a young woman with a short silver bob and vivid green eyes standing beneath a transparent umbrella in a rain-soaked city at night. She wears a fitted black jacket while neon storefront signs reflect across the wet pavement behind her.

For the second image, I added the trigger phrase near the beginning:

harukav2 style. Create a cinematic anime illustration of a young woman with a short silver bob and vivid green eyes standing beneath a transparent umbrella in a rain-soaked city at night. She wears a fitted black jacket while neon storefront signs reflect across the wet pavement behind her.

The first image still looked good, which made the problem easy to miss. The base model was doing most of the work. Once I added the exact trigger phrase, the intended style became much clearer.

Spelling, spacing, and placement all count. I copy the trigger from the LoRA page before testing my own variations, and I place identity or permanent-feature triggers early in the prompt.

PixAI’s LoRA trigger-word guide recommends placing identity and permanent-feature triggers early in the prompt.

LoRA Weight Is Too Weak or Too Strong

Once I know the trigger word is working, I test the LoRA weight next. This setting controls how much influence the LoRA has over the final image.

A lower weight gives the base model more control. Raising the weight makes the LoRA’s style easier to see, though pushing it too far can make prompt details less clear or natural.

I kept the Haruka v2 style trigger, prompt, base model, seed, and aspect ratio unchanged. Then I generated the same image at three weights: 0.4, 0.8, and 1.2.

I used this prompt:

Prompt: Handsome male celestial mage with flowing midnight-blue hair, golden eyes, ornate white-and-navy robes, floating spellbook, surrounded by glowing constellations and rings of magical symbols, standing on a cloud above an ancient fantasy city, soft moonlight, elegant composition, detailed anime illustration, luminous colors, ethereal atmosphere

The three weights produced noticeably different results:

0.4: The celestial mage and fantasy setting came through, though the Haruka v2 style was easy to miss. The image still looked close to what the base model might create on its own.

0.8: The style became much easier to recognize. The midnight-blue hair, floating spellbook, glowing constellations, and white-and-navy robes also stayed clear, giving me the best balance of style and prompt accuracy.

1.2: The LoRA had a much stronger hold over the image. Some of the smaller celestial details became less distinct, and the overall scene followed my prompt less closely.

For this image, 0.8 worked best. It made the style visible without overpowering the celestial-mage concept.

Other LoRAs may respond better to lower or higher values. Character, style, pose, and outfit LoRAs all behave differently, so I use 0.8 as a starting point and adjust in small steps.

PixAI’s official LoRA weight guide compares a wider range of settings, but the image in front of me is always the final judge.

Base Model Mismatch

That architecture label on the PixAI market page may look like fine print, but it decides which models can use the LoRA. A DiT.2 LoRA is designed for Tsubaki.2. DiT.1, SDXL, and other LoRAs belong to their matching families.

This is easy to overlook. A LoRA can have the right trigger and reasonable weight but still fail because it was trained for another model family.

The architecture label on the PixAI market page is not a minor technical detail. A DiT.2 LoRA is designed for Tsubaki.2, while older DiT.1 or SDXL LoRAs belong to their respective model families. PixAI specifically notes that Tsubaki.2 is not backward-compatible with LoRAs trained on Tsubaki, Tsubaki Flash, Serin, or other bases.

For my compatibility test, I first used a LoRA with its intended model. Its effect appeared as expected. I then checked the same concept with another model family, and the LoRA’s influence disappeared.

That result tells me more than another round of prompt edits. When a LoRA works with one model and goes quiet with another, I check the architecture before changing anything else.

Some creators publish versions for different bases. If a Tsubaki.2 version exists, I use it rather than forcing an older one into the workflow. PixAI’s LoRA training guide explains why separate versions are needed.

When the Prompt Fights Against the LoRA

Even a compatible LoRA can lose some of its influence when your prompt pushes the image toward a completely different subject or style.

The Shiitake-Mix sample I used has soft pastel colors, a cute young character, large blue eyes, cat accessories, and clean anime line work. For this test, I pushed those traits in the opposite direction by asking for an older male knight, dark colors, realistic textures, and a stormy setting.

I kept the model, LoRA, weight, seed, and aspect ratio unchanged. The image prompt was the only thing I replaced:

Prompt: Weathered elderly male knight with a scarred face, deep wrinkles, short gray hair, and a stern expression. He wears scratched blackened-steel armor and stands inside a ruined stone fortress during a violent thunderstorm. Rain runs down his face as smoke and torchlight fill the background. Dark charcoal and rust-red color palette, harsh side lighting, deep shadows, gritty realistic textures, dramatic low-angle composition, photorealistic fantasy portrait.

You see what I mean?

This test shows how much of the LoRA’s original look survives a conflicting prompt. I check whether the knight still has a youthful anime face, oversized eyes, soft pastel colors, or cute details. I also look at whether the dark armor, wrinkles, harsh lighting, and realistic textures came through clearly.

If the result lands somewhere between the two styles, the prompt and LoRA are competing. The character may become older and wear armor while keeping Shiitake-Mix’s softer face, brighter colors, or clean anime finish. Raising the LoRA weight may restore more of its usual style, though the darker knight concept could become less convincing.

I usually start with a short prompt and add the age, armor, setting, lighting, and visual style one group at a time. This makes it easier to see which instruction started pulling the image away from the LoRA.

The same issue can happen when you stack several LoRAs. A character LoRA, outfit LoRA, and style LoRA may work well separately and compete when combined. I remove them one at a time so I can find the source of the conflict without changing every weight together.

Character LoRA and Style LoRA Need Different Tests

I would not troubleshoot every LoRA with the same expectations. A character LoRA should preserve identity and recognizable traits. A style LoRA is supposed to influence line work, shading, colors, or the general visual treatment.

An outfit LoRA may only reproduce clothing, while a pose LoRA changes body position. If I expect a style LoRA to recreate a character, it will look broken even when it works correctly.

In my comparison, the character LoRA changed the face, hair, and identity. The style LoRA kept the subject but changed how it was drawn. It became overpowering sooner, so I preferred a lower weight.

Before adjusting anything, I check the LoRA category and example images in PixAI. That tells me whether I should be testing activation, character accuracy, overall style, clothing, or composition.

How I Test LoRA Settings in PixAI

The fastest way I’ve found to troubleshoot a LoRA is to change one variable per image. When I change the prompt, weight, model, seed, and LoRA stack together, I have no idea which edit helped.

I use this order:

1. Match the model: I confirm that the LoRA and base model use the same architecture.

2. Load one LoRA: I remove every extra LoRA until the first one works by itself.

3. Copy the trigger: I paste the exact trigger from the Model Market page near the start of a short prompt.

4. Create a baseline: I keep the seed, model, image size, and prompt fixed.

5. Adjust the weight: I test small changes and compare the images side by side.

6. Add details slowly: I bring back outfit, pose, lighting, background, and extra LoRAs one at a time.

This may sound slower than moving every slider and hoping for a lucky image. In practice, it saves credits because each result answers one question. I can tell whether the trigger worked, whether the weight is strong enough, or which new instruction caused the problem.

PixAI keeps the Model Market, LoRA selector, weight controls, and results in the browser. I can compare each version without downloading model files or setting up Stable Diffusion on my computer.

LoRA Troubleshooting Checklist

Before assuming a LoRA is broken, I return to its PixAI page and copy the exact trigger word. I also confirm that its architecture matches the selected base model and check whether it is a character, style, outfit, pose, or concept LoRA.

I then generate a simple baseline with one LoRA while keeping the seed and other settings stable. If the effect is weak, I adjust the weight gradually. If it works alone but fails with my full prompt, I look for contradicting instructions, an overly restrictive negative prompt, or another LoRA competing with it.

Final Thoughts

Alright, I just showed you the main reasons a LoRA may not work and how I troubleshoot them in PixAI. In many cases, the file is fine. The real problem is a missing trigger, unsuitable weight, an incompatible model, or a prompt that pulls the image in another direction.

My most reliable approach is to start with one LoRA and a minimal prompt, then test one variable at a time. PixAI makes that process easier because I can check the model page, switch compatible models, adjust weight, and compare outputs without dealing with local installation.

It still takes a few generations, but at least I know what each test is trying to prove.

What usually causes LoRA problems in your workflow? If you have another troubleshooting method that works well, let me know in the comments.

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Despite the advancements and power of today’s image models, there are still days when I get contextually incorrect results. For example, I asked for an anime girl with short silver hair, green eyes, and a red scarf. The model gave her long brown hair and removed the scarf instead. I added more details, generated again, and somehow ended up further from the character I had in mind.

Yeah, I know. It happens a lot, and it is annoying.

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A prompt can be clear to us while leaving plenty of room for the model to interpret. The selected model may favor certain faces or hairstyles, a LoRA may overpower the description, or text alone may not be enough to preserve a specific character.

Instead of endlessly rewriting the same prompt, I tested each possible cause in PixAI. I simplified the description, compared models, adjusted the LoRA weight, added a reference image, and edited individual details.

What I found is that a failed generation does not always require a better prompt. Sometimes the prompt is already fine. The problem is somewhere else in the workflow.

Common Prompt Problems in AI Anime Art

Prompt failures are not always dramatic.

Sometimes the image is technically good, but it does not match the prompt. Hair colors change, accessories disappear, outfits get redesigned, and faces look different with every generation.

For my first test, I used this prompt in PixAI:

Prompt: Anime girl standing on a train platform at sunset, short silver hair, green eyes, white jacket, red scarf, black headphones, carrying a transparent umbrella, she’s in a train platform, cinematic lighting, detailed anime illustration

The result looked good. The warm lighting, character design, and overall anime style were close to what I had in mind.

But PixAI also added a train in the background, even though I never asked for one.

I only mentioned that the character was standing on a train platform. The model apparently connected the location with the object most commonly associated with it and decided that a train should be part of the scene.

It was not a terrible addition. In fact, the train made the background feel more active and helped sell the location. But it still showed that the model was interpreting the prompt rather than following it literally.

This is one reason an image can look contextually correct while still including details that were never requested. AI models rely on visual associations, so words such as “train platform” can bring in trains, railway tracks, signs, crowds, or other elements that usually appear in similar scenes.

That may be helpful when I want the model to fill in the environment creatively. It becomes a problem when I need tighter control over the composition.

Why Adding More Words Does Not Always Fix the Prompt

My first instinct was to make the prompt more specific.

Since PixAI added a train that I never requested, I added “no train in the background” to the description:

Prompt: 1girl, short silver bob haircut, green eyes, red scarf, black headphones, white cropped jacket, holding a transparent umbrella, empty train platform, no train in the background, sunset, anime illustration

This time, the output was closer to the composition I had in mind. The character was still standing on a train platform, but the model no longer treated a train as a required part of the scene.

The change looks simple, but it shows how AI image models fill in details based on visual associations. Mentioning a train platform can be enough for the model to add a train, even when the prompt never asks for one. Adding “empty train platform” and “no train in the background” gave it a clearer boundary.

Still, this does not mean adding more words is always the answer.

A longer prompt can introduce even more objects, styles, and instructions for the model to balance. If I keep adding descriptions every time something goes wrong, the prompt eventually becomes harder to control.

The more useful approach is to be specific about the actual problem. In this case, I did not need to rewrite the character, lighting, or setting. I only needed to tell the model which unwanted object to leave out.

PixAI also supports negative prompts, so I could place terms such as train, passing train, locomotive, and railway vehicle there instead of relying only on “no train” in the main prompt. Negative prompts are especially useful when the same unwanted object keeps appearing across several generations. PixAI explains these features in its complete beginner guide.

This worked better than filling the prompt with extra scene details. I kept the parts I already liked and corrected the one assumption the model had made on its own.

How Model Choice Changes Prompt Accuracy

When a revised prompt still does not work, the model may be the problem. One anime model may favor soft portraits, while another leans toward saturated colors or dynamic poses. These tendencies affect how each one interprets the same words.

To test this, I used the shorter silver-haired character prompt with two PixAI models. I kept the prompt, aspect ratio, seed, and other settings as consistent as possible.

Both models understood the idea, but the results were different. Tsubaki.2 followed more small character details in my test, while Hinata V2 gave the scene stronger color and energy. I preferred Tsubaki.2 here because accuracy was my priority, although the other image was more eye-catching. I can easily imagine choosing Hinata V2 for a poster or action scene instead.

If the same feature keeps failing, another PixAI model can reveal whether the wording or model is responsible. The PixAI model and LoRA guide explain how these parts work together.

How LoRA Weight and Trigger Words Affect the Result

A base model controls the overall image, while a LoRA pushes it toward a character, outfit, pose, or style. That influence can improve accuracy, but it can also create conflict.

Some LoRAs require trigger words. If one is missing, the intended effect may appear weakly or not at all. Too little weight can make a LoRA barely visible, while too much can overpower the prompt or distort the image.

I tested one character LoRA with the same prompt at three weights: 0.4, 0.8, and 1.2.

Common prompt: Medium shot of a serene anime girl with long light blue hair and purple eyes, standing gracefully in a dreamlike ethereal environment, gently holding a glowing blue butterfly in her cupped hands at chest level while gazing at it with a soft, tender expression. She wears a pristine white collared sleeveless outfit with subtle fabric sheen and intricate stitching details. Surrounding her, numerous translucent blue butterflies ascend upwards in swirling patterns, interspersed with delicate floating bubbles that catch prismatic light refractions. The background fades into infinite soft gradients of blue and white

All three results were already good enough to use. At 0.4, the image looked great, although the colors were slightly washed out. The 0.8 version went much harder on the color and looked overly saturated to me, but it was still a very good result. At 1.2, the colors landed somewhere between the first two, and that balance worked best for me.

I would be happy with any of them depending on the look I wanted. This comparison also shows why there is no single best LoRA weight. Which of the three do you prefer?

A Practical PixAI Troubleshooting Workflow

Random regeneration can burn through credits without showing why the prompt failed. I got better results once I treated each generation as a small test.

I start with the character and three or four essential details. After the first output, I identify one specific failure.

I then change one variable. I simplify the prompt before testing another model with the same seed and settings. For a LoRA, I check its trigger words, compatibility, and weight. For character identity or composition, I add a reference. This sounds slower than changing everything at once, but it usually saves generations because I can see which adjustment actually helped.

In one test, the character, lighting, and background were already close to what I wanted, but the red cap was missing. I opened the image in PixAI’s editing workflow and used this instruction:

Prompt: Add a plain red cap on her head. Keep her face, hair, jacket, pose, lighting, and background unchanged.

This was faster than starting over. The hat was added without replacing the character or composition. I still had to inspect its edges around the hair, but that was easier than another unpredictable generation.

PixAI includes Flow Edit for natural-language changes, Edit Pro for more control, and inpainting for isolated areas. This turns troubleshooting into a workflow instead of a cycle of rewriting and regenerating.

Before and After Results

No single fix solved every problem. The overloaded prompt improved after I removed secondary details. Switching models changed how closely the output followed the character instructions, while adjusting the LoRA helped balance resemblance against image quality.

The reference had the clearest effect on consistency, although smaller features still changed. Editing worked better when only one part of a good image needed correction.

The biggest improvement came from isolating the problem. Some failures were caused by wording. Others only improved after I changed the model, adjusted the LoRA, or added a reference.

PixAI made the comparisons easier because its models, LoRAs, references, seeds, and editing tools sit in one platform. Testing still takes several generations and consumes credits, so some trial and error remains.

Prompt Troubleshooting Checklist

Before generating again, I would check the following:

• Are the essential character details clear and free from contradictions?

• Is the selected model suitable for the intended anime style?

• Does the LoRA require a specific trigger word?

• Is the LoRA compatible with the chosen base model?

• Am I testing only one change at a time?

• Would a reference image communicate the idea more clearly?

• Can I edit the failed area instead of replacing the entire image?

Final Thoughts

Alright, I just showed you some of the reasons AI anime prompts aren’t working and how to fix them with PixAI. A failed result does not always mean the prompt is badly written. Sometimes the selected model, LoRA settings, or lack of a visual reference is the actual problem.

Based on my tests, the best approach is to change one variable at a time. I got better results by simplifying the prompt, keeping the important character details clear, and using a reference image when text alone was no longer enough. PixAI’s model selection, LoRA controls, reference tools, and editor made this process much easier, although some trial and error was still unavoidable.

What do you think about this troubleshooting approach? If you know other reasons AI prompts might fail, or another method that has worked for you, let me know in the comments.

Hi there! Thanks for making it to the end of this post! If you enjoyed this content and would like to support my work, consider becoming a paid subscriber. Your support means a lot!

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There is something appealing about having your own presentation generator: one that knows your template, reads your data, and produces a fresh PowerPoint file without making you rebuild the same 12 slides every Monday.

AI tools such as Kimi and Genspark can already generate a deck from a prompt. They are useful when you need slides quickly.

But deploying your own workflow gives you a different kind of control. You decide where the source files come from, what instructions are always applied, who can trigger a run, where the finished deck goes, and when the whole thing happens.

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So, how would you build it?

For this walkthrough, I am using the Plus AI Presentation Agent API. We will make a small Python job that starts from an existing company template, adds the latest data, asks the agent to update the deck, and downloads an editable `.pptx`. At the end, we will put that job on a weekly cron schedule.

You do not need to build a full app to follow along. Think of this as the smallest useful version of an internal deck generator.

Plus AI Presentation Agent API

Plus AI currently exposes two presentation APIs. The regular Presentations API is the direct prompt-to-deck option. You submit a prompt, poll the job, and download the generated presentation.

The Presentation Agent API can open an existing PowerPoint, read attachments, edit the slides, and return PowerPoint and PDF versions.

That means the input can be more specific than “make a deck about Q3.” I can give it a branded weekly report and say:

Update this deck using the attached analytics export. Replace the KPI values, refresh the charts, keep the current slide order, and update the reporting date.

The .pptx acts as the canvas and the attachments provide source material. The result is a native PowerPoint file that remains editable. Someone will always ask to change a number five minutes before the meeting.

The API uses an asynchronous job model. A POST request starts a session and returns a sessionId and pollingUrl. A client can poll until the session reaches a terminal state, or provide a public HTTPS callback URL and wait for a webhook.

I will use polling in this walkthrough because it keeps the entire example inside one script.

How to Use the API

1. Create an API Key

You need a Plus AI plan that includes API access. Generate a key from the API settings page, then expose it to the script through an environment variable:

export PLUSAI_API_KEY=”your-key-here”

The key is sent with every request using bearer authentication:

Authorization: Bearer YOUR_KEY

Keep it out of the source code and Git history. On a deployed service, I would load it from the platform’s secret store. Plus AI currently permits one active API key per user, so replacing the key can affect every job running under that user.

2. Upload the PowerPoint Template

My starting file is a normal .pptx that already contains the layout I want the agent to follow. For a weekly performance report, it might have:

• A title slide with the reporting date

• A KPI summary

• Revenue and acquisition charts

• A slide for wins, risks, and next steps

• The correct logo, fonts, colors, and footer

Before uploading it, I would remove speaker notes, hidden slides, and old data. A template should provide structure without conflicting content.

The Files API accepts uploads as multipart/form-data:

curl -X PUT “https://api.plusdocs.com/r/v0/files/upload” \
  -H “Authorization: Bearer $PLUSAI_API_KEY” \
  -F “file=@weekly-performance-template.pptx”

The Files API reference currently lists a maximum upload size of 125 MB. It also says uploads are processed asynchronously, so an accepted upload is not necessarily ready to use in an agent session immediately.

Once processing finishes, the file appears in the file list:

curl “https://api.plusdocs.com/r/v0/files?limit=100” \
  -H “Authorization: Bearer $PLUSAI_API_KEY”

Find the template and save its id:

export PLUSAI_TEMPLATE_FILE_ID=”file_abc123”

I would reuse that ID across scheduled runs. When the design changes, I can upload a new version and update the configuration.

3. Upload the Latest Source Data

The template provides the design, but the agent still needs current information. In this example, that information comes from an Excel export generated by an analytics system.

Here is a helper that uploads the file and waits for it to appear in the file list:

import os
import time
from pathlib import Path
import requests
BASE_URL = “https://api.plusdocs.com/r/v0”
HEADERS = {
    “Authorization”: f”Bearer {os.environ[’PLUSAI_API_KEY’]}”
}

def upload_file(path: str) -> str:
    source = Path(path)
    with source.open(”rb”) as handle:
        response = requests.put(
            f”{BASE_URL}/files/upload”,
            headers=HEADERS,
            files={”file”: (source.name, handle)},
            timeout=120,
        )
    response.raise_for_status()
    accepted = response.json()
    if accepted.get(”fileId”):
        return accepted[”fileId”]
    for _ in range(30):
        listing = requests.get(
            f”{BASE_URL}/files?limit=100”,
            headers=HEADERS,
            timeout=30,
        )
        listing.raise_for_status()
        matches = [
            item
            for item in listing.json()[”files”]
            if item.get(”name”) == source.name
        ]
        if matches:
            return matches[0][”id”]
        time.sleep(2)
    raise TimeoutError(
        “The uploaded file did not finish processing”
    )

The helper checks for an immediate fileId, then falls back to the list endpoint. Each export needs a unique filename, such as analytics-2026-08-10.xlsx, so the lookup cannot select an older upload.

In production, I would retry network errors and selected 5xx responses. A 401 needs a credential fix, while a malformed request will keep failing regardless of backoff.

4. Start an Agent Session

The session request connects the template, the attachment, and the instructions:

def start_agent(data_file_id: str) -> str:
    payload = {
        “prompt”: (
            “Update the weekly performance review using the attached “
            “analytics export. Refresh every KPI and chart. Update the “
            “reporting date, preserve the existing branding and slide “
            “order, and add a short source note to every data slide. “
            “If a metric is missing, label it as unavailable instead “
            “of estimating a value.”
        ),
        “pptxFileId”: os.environ[
            “PLUSAI_TEMPLATE_FILE_ID”
        ],
        “attachments”: [
            {”fileId”: data_file_id}
        ],
        “language”: “en”,
    }
response = requests.post(
        f”{BASE_URL}/agent/sessions”,
        headers={
            **HEADERS,
            “Content-Type”: “application/json”,
        },
        json=payload,
        timeout=30,
    )
    response.raise_for_status()
    return response.json()[”pollingUrl”]

I treat the prompt as part of the application, not as disposable text. It contains business rules that should be reviewed and versioned with the code.

The rule about missing metrics is especially important. If a value is absent, I want the slide to say so. A plausible number in a polished chart can survive several reviews before anyone realizes it was inferred.

The request also accepts a model parameter. I would start with the default, then compare other supported models using the same input. I would measure execution time, credits, factual accuracy, and manual cleanup alongside slide quality.

5. Poll the Session and Download the PowerPoint

The session can move through states such as READY, RUNNING, and PENDING_TOOL_CALLS. The script should continue polling until it receives DONE, FAILED, or INTERRUPTED.

from datetime import date
def wait_for_deck(polling_url: str) -> dict:
    deadline = time.time() + (20 * 60)
    while time.time() < deadline:
        response = requests.get(
            polling_url,
            headers=HEADERS,
            timeout=30,
        )
        response.raise_for_status()
        session = response.json()
        if session[”status”] == “DONE”:
            return session
        if session[”status”] in {
            “FAILED”,
            “INTERRUPTED”,
        }:
            raise RuntimeError(
                f”Agent stopped: {session[’status’]}”
            )
        time.sleep(10)
    raise TimeoutError(
        “The presentation took longer than 20 minutes”
    )

def download_powerpoint(url: str) -> Path:
    output = (
        Path(”output”)
        / f”weekly-review-{date.today()}.pptx”
    )
    output.parent.mkdir(exist_ok=True)
    response = requests.get(url, timeout=120)
    response.raise_for_status()
    if not response.content.startswith(b”PK”):
        raise ValueError(
            “The downloaded file does not look like a PPTX”
        )
    output.write_bytes(response.content)
    return output

if __name__ == “__main__”:
    data_id = upload_file(
        “exports/analytics-latest.xlsx”
    )
    polling_url = start_agent(data_id)
    result = wait_for_deck(polling_url)
    saved_to = download_powerpoint(
        result[”pptxUrl”]
    )
    print(f”Saved {saved_to}”)

The timeout stops a stuck job from running forever. The signature check catches HTML or JSON error bodies saved with a .pptx filename. A PowerPoint file is a ZIP container, so it normally begins with PK.

A completed session can include pptxUrl, pdfUrl, and thumbnailUrl. I would save the PowerPoint for editing, use the PDF for review, and include the thumbnail in a notification.

I would generate several decks and compare every figure with the source before automating delivery. Any recurring error belongs in the prompt or validation logic.

6. Run It on a Cron Schedule

Cron does not usually inherit the environment from an interactive shell. A job that works in the terminal can fail on Monday morning because it cannot find the API key, working directory, or Python environment.

I would put those details in a wrapper script:

#!/usr/bin/env bash
set -euo pipefail

cd /opt/deckbot
set -a
source /etc/deckbot.env
set +a

exec /opt/deckbot/.venv/bin/python build_deck.py

The /etc/deckbot.env file should be readable only by the account running the job. The crontab can then stay simple:

0 7 * * 1 /opt/deckbot/run.sh >> /var/log/deckbot.log 2>&1

This runs every Monday at 7:00 a.m. Cron uses the server’s timezone, so verify that setting before relying on it for a customer-facing workflow.

For a serverless deployment, polling keeps the function alive unnecessarily. Use callbackUrl instead. Plus AI requires a public HTTPS URL and rejects localhost or private IP addresses. The webhook handler can download the output after the state changes.

Cost and Limitations

At the time of writing, the Plus AI pricing page lists Pro at $20 per user per month when billed annually, or $25 when billed monthly. It includes 3,000 AI credits.

• Team costs $30 per user per month annually, or $40 monthly, with 6,000 credits.

• Max costs $200 annually, or $240 monthly, and includes unlimited AI credits. These are monthly per-user prices.

• New customers can start with a seven-day trial containing 1,000 credits.

Plus says a typical agent task consumes around 40 to 170 credits. I would benchmark the real workflow several times, including failures, retries, and discarded outputs.

There are also a few operational limits to account for:

• API access requires an eligible paid plan.

• A file upload can be up to 125 MB.

• Upload processing and presentation generation are asynchronous.

• A user can currently have only one active API key.

• Agent jobs consume credits and can fail or be interrupted.

• Output is nondeterministic, even when the prompt and attachments stay the same.

• A template is guidance, not a pixel-level contract. Complex charts, custom fonts, animations, and unusual master layouts need to be tested with real files.

Privacy depends on the complete pipeline. I can use a service account, store the key outside the repository, remove unnecessary fields, delete old files, and control delivery.

The prompt, template, and attachments are still processed by Plus AI. Plus says it uses AES-256 encryption for data at rest, TLS for data in transit, and has completed a SOC 2 Type II audit.

For regulated or highly sensitive material, I would review the security documentation, privacy policy, retention terms, and model-provider arrangement before sending production data.

Final Thoughts

I hope you found this guide helpful. The API call is the easy part. A useful generator also needs a stable template, clean data, versioned instructions, retries, secure credentials, and a review process.

Plus AI handles the presentation-specific work inside that pipeline. It can edit an existing PowerPoint and return a file that remains editable, which is exactly what I would want for weekly reports, proposals, board updates, and other decks that still need a final human pass.

I would start with one presentation that already follows a predictable format. Run the script manually, compare the numbers with the source, and adjust the prompt until the failures are boring and understood. Then schedule it.

If you run into issues or have any questions, please let me know in the comments.

Hi there! Thanks for making it to the end of this post! If you enjoyed this content and would like to support my work, consider becoming a paid subscriber. Your support means a lot!

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2026 has been a big year for AI video models. I’ve seen people use them to create mini action films, UGC ads, explainer videos, physics simulations, and short videos for social media.

There are already several video models to choose from, and each one has its own strengths. The quality, generation speed, and price can also vary, so choosing a model isn’t as simple as picking the newest one.

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To see how three of the latest models compare, I tested Seedance 2.5, MiniMax H3, and Wan 3 using the same three prompts. The tests covered an action scene, a physics simulation, and a close-up emotional performance.

Creating the Videos With invideo agent

I used invideo agent as part of the video creation workflow. Instead of treating each generation as a separate task, invideo agent keeps the wider project context in one place, including creative references, characters, visual rules, and previous decisions.

This makes it useful when comparing or working across different AI video models while trying to keep the same creative direction. You can generate and refine individual shots, work manually inside Notebooks when you want more control, and then return to the agent without losing the context of the project.

For multi-scene videos, this is especially helpful because you do not have to re-explain the character, look, or creative intent every time you move on to the next shot.

Creating the Videos With Topview

I used Topview’s AI video generator to create all the videos in this comparison. Topview lets you switch between different video models from the same workspace, so I could reuse a prompt, select another model, and generate the next video without moving to a different platform.

It was straightforward to use, and it supports many of the most popular video models available right now. You can access Seedance 2.5, Wan 3, and MiniMax H3 directly through Topview.

Here’s a quick comparison table:

How I Tested the Three Models

I used three prompts that required different skills from the models.

1. The first involved two characters fighting in the rain.

2. The second tested how two balls with different weights behaved in water.

3. The final prompt focused on small changes in a person’s facial expression.

For each test, I compared how closely the videos followed the prompt. I also looked at movement, physical accuracy, camera work, object consistency, facial expressions, and sound.

AI video generation can produce a different result each time, so your experience may not be exactly the same as mine. These are my observations based on the videos I created for this comparison.

Test 1: An Anime Fight Scene

Prompt: Create an anime-style fight on a rain-soaked rooftop at night. In one continuous shot, show a white-haired swordsman dodging a red-haired warrior’s spear, counterattacking, and ending in a blocked strike that creates a shockwave through the rain. Keep the choreography readable, the characters consistent, and every attack physically connected without teleportation, duplicated weapons, or distorted limbs.

This prompt required the models to manage two characters, two weapons, rain effects, and a moving camera at the same time. I also wanted the attacks to connect properly instead of having the characters jump between unrelated poses.

Seedance 2.5

What can I say? Seedance 2.5 was my favorite in this round. Its biggest advantage was the amount of camera movement it added to the scene.

The camera moved around the two characters, changed between different zoom levels, and completed an almost 360-degree turn. The model also slowed down a few moments during the fight, which made the attacks look more dramatic.

My only complaint is that the video didn’t include enough fighting. A large part of the 15-second clip was spent showing the characters and building up the scene, so I wanted more attacks and counterattacks before the ending.

MiniMax H3

My initial impression to the video was… meh. The MiniMax H3 video took a few seconds before the action started. Once the characters began fighting, their movements were slower than I expected, especially for an anime scene.

The camera also moved less than it did in the Seedance and Wan videos. This made the scene look quite plain compared with the other two results.

The weapons were also inconsistent. They disappeared during parts of the fight and then appeared again, even though the prompt specifically asked the models to avoid duplicated or disappearing weapons.

Wan 3

Wan 3 gave the characters faster and more dynamic movements than MiniMax H3. I liked how the water reacted whenever their feet touched the rooftop because the splashes matched their movements quite well.

The last few frames were especially good. Wan 3 added slow motion and dramatic music to the final attack, and both choices worked well with the scene.

Wan 3 had an impressive ending, but I preferred the camera movement Seedance 2.5 used across the whole video. That gave Seedance a slight advantage in this round.

Winner: Seedance 2.5

Test 2: Gravity and Water Physics

Prompt: Create a slow-motion shot of a steel ball and an orange table-tennis ball falling into a clear tank of water at the same time. The steel ball should sink while the table-tennis ball briefly submerges, rises, and bobs on the surface. Show realistic splashes, bubbles, ripples, reflections, refraction, gravity, and buoyancy using a locked side-view camera and one continuous shot.

The goal of this test was to see whether the models understood how objects with different weights and densities should behave in water. I also checked the splashes, bubbles, and sound effects around the tank.

Seedance 2.5

The orange ball reached the bottom of the tank before the much heavier steel ball, which looked weird and wrong. I also don’t think the table-tennis ball should have travelled all the way to the bottom before returning to the surface.

The water and reflections looked fine, but the movement of the two balls didn’t match their weight and buoyancy. This was the weakest Seedance 2.5 result from the three tests.

MiniMax H3

MiniMax H3 handled the physics much better. The steel ball sank while the table-tennis ball returned to the surface and bobbed on the water.

The splashes also looked realistic. Some water even spilled over the side of the tank, which added a small amount of messiness that you would expect from a real experiment.

Both balls behaved much closer to the way I expected, and I didn’t notice a major problem with the water physics.

Wan 3

Wan 3 made the orange table-tennis ball much too large. It looked closer to the size of a small beach ball, so the model clearly misunderstood that part of the prompt.

The way the balls moved through the water was still quite good, and the splashes looked realistic. The sound effects could have been better because there was no clear metallic clink when the steel ball hit the tank or a noticeable “bloop” when the balls entered the water.

Wan 3 did a decent job with the movement, but MiniMax H3 produced the most accurate result.

Winner: MiniMax H3

Test 3: A Subtle Emotional Performance

Prompt: Create a photorealistic close-up of a woman receiving upsetting news. Her faint smile slowly fades, her eyes become wet, and one tear rolls naturally down her cheek as she tries to remain composed. Show the emotional change through subtle eye movement, blinking, breathing, and facial tension while keeping her appearance and lighting consistent, with no dialogue, exaggerated crying, or face morphing.

This test focused on small facial movements rather than a large physical action. I observed that a lot of users are using AI to create short drama videos, so I wanted the woman’s expression to change slowly, without exaggerated crying or any obvious changes to her face.

Seedance 2.5

Can I say that this performance could win a best actress award? I loved how she continued smiling while trying to hold back her tears.

You could slowly see her expression change through her eyes, breathing, and facial tension. She eventually sighed, and that was when the tear started flowing down her cheek.

The movement of the tear also looked realistic. It changed as it moved instead of staying in the same shape or looking like it had been placed on top of her face.

MiniMax H3

MiniMax H3 did a good job with the deep breathing. It looked like she was trying to remain calm while holding back her tears, which matched the prompt.

Her tears finally started flowing during the last few seconds. The problem was that they moved too slowly and barely changed in shape or size as they travelled down her cheeks.

The facial performance worked, but the tears didn’t look as realistic as the ones in the other two videos.

Wan 3

Wan 3 also did a great job with this prompt. The ominous sound in the background matched the mood of the scene without making it too dramatic.

The tears fell naturally, and their movement looked physically accurate. I also liked the way she sighed at the end because it showed how upset she was without using dialogue.

Seedance 2.5 gave me the better overall facial performance, while Wan 3 did slightly better with the tears and sound. Both videos handled the prompt well, so I’m calling this round a tie.

Winner: Tie. Wan 3 and Seedance 2.5

Which Model Is the Best?

Based on these three tests, Seedance 2.5 is my overall winner.

It won the anime fight test, tied with Wan 3 in the emotional test, and produced the clips I found the most interesting to watch.

Camera movement was its biggest advantage. The other models could create good-looking scenes, but Seedance was better at moving around the characters and choosing when to zoom in or slow down.

Its weak point was the physics test. The movement of the two balls didn’t make sense, so I wouldn’t automatically choose Seedance for a video that depends on accurate physical behavior.

MiniMax H3 is the model I would use for a physics simulation or a product demonstration where the movement of objects matters. Its anime fight was slow, however, and the disappearing weapons were difficult to ignore.

Wan 3 was more consistent across the three tests. Its character movement, water effects, tears, and sound were all good, although it didn’t clearly beat both competitors in any single category.

For cinematic videos and emotional scenes, I would choose Seedance 2.5. For realistic physics, I would use MiniMax H3. If I wanted a balanced model with good movement and sound, I would consider Wan 3.

Final Thoughts

Alright, I hope you enjoyed this comparison article.

Overall, Seedance 2.5 gave me the best overall results from these three tests. I enjoyed its camera movement, and its emotional video had the most convincing performance of the nine clips I generated.

MiniMax H3 was much better at physics, while Wan 3 stayed competitive in both the action and emotional tests. Their results show how much the choice of model depends on what you’re asking it to create.

I recommend running your actual prompt through more than one model when you can. The best model for an anime fight may produce the wrong result for a simple water simulation, even when both prompts are detailed.

For the kinds of videos I normally create, Seedance 2.5 would be my first choice. Wan 3 would be a close second, while I would keep MiniMax H3 in mind whenever physical accuracy matters more than dramatic camera work.

What do you think of these video models? Any personal preference that’s not the same with mine? Let me know your thoughts in the comments.

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The great decision to self-host has landed. The model weights are available, your data stays under your control, and there is no per-token bill waiting for the accounting department.

Then you open a list of inference servers and find more than a dozen options. Every project promises speed. Very few help you decide which kind of speed you actually need.

The most useful question is not simply, “Which server has the highest throughput?” It is this: do you need to serve one main model or a fleet of models?

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Single-model servers squeeze as much performance as possible from one primary model per worker. Multi-model servers are designed to run and route across a broader model fleet. That distinction matters because most AI agents do much more than generate text.

In this article, we picked and compared seven self-hosted inference servers for open models: Hugging Face TEI, LocalAI, NVIDIA Dynamo-Triton, Ollama, SGLang, SIE, and vLLM.

The goal is to match each server to the workload it handles best.

Why Self-Host Open Models?

Self-hosting is work, so it is worth checking why you want it before choosing the machinery.

The usual reasons are:

• Quality: Open models are good enough for a growing number of production tasks. Research discussed by MIT Sloan found that open models reached roughly 90 percent of closed-model performance at launch.

• Cost: Self-hosting changes the cost curve. Instead of paying for every token, you pay for hardware, idle capacity, engineering time, and operations.

• Data control: When prompts, documents, or outputs cannot leave your environment, self-hosting may be the simplest way to meet privacy and residency requirements.

These benefits come with a trade-off. Self-hosting moves work from an API bill to your infrastructure and engineering teams. A good inference server earns its place by shrinking that operational burden while using your hardware efficiently.

One pricing note applies to every option in this guide: free software does not mean free inference. You still pay for the machines, storage, networking, monitoring, and the people keeping everything alive.

Single-Model vs. Multi-Model Servers

A single-model server usually centers each worker or deployment on one primary model. It can then optimize batching, memory paging, attention kernels, and request scheduling around that model.

If you need three unrelated models, you will often run three workers or deployments.

A multi-model server is built to host or coordinate several models and route each request to the right one. Depending on the server, those models may remain loaded together, load on demand, or share a GPU pool.

Why does this distinction decide the article? Because an agent is rarely just one model.

A typical request might:

1. Embed a query.

2. Retrieve documents.

3. Rerank the candidates.

4. Extract structured fields.

5. Run a safety check.

6. Generate the final answer.

That workflow may involve five models, most of them small. Run each one as a separate single-model service, and you now have five deployments, each with its own container, scaling policy, and monitoring. Run the small-model fleet on a multi-model server, and you have far less infrastructure to operate.

Rule of thumb: One heavy generative model? Single-model server. A fleet of smaller task models? Multi-model server. Both at once (common)? You will run one of each, and that is fine.

Inference Servers at a Glance

The servers below are listed alphabetically, not ranked.

TEI focuses on embeddings and reranking, while LocalAI supports multiple model types on modest hardware. Dynamo-Triton targets heterogeneous enterprise workloads but has a large configuration surface.

Ollama is best for local development and offline use, while SGLang and vLLM focus on high-performance serving for a primary model. SIE is designed for an agent’s fleet of task-specific models, complementing rather than replacing large-LLM servers.

All options are free to self-host, except for infrastructure costs and optional managed services.

Hugging Face TEI (Text Embeddings Inference)

Text Embeddings Inference is Hugging Face’s Rust-based server for embedding, reranking, and sequence-classification models. It is a specialist, and that is its appeal.

TEI offers dynamic batching, token-based batching, optimized inference, OpenAI-compatible embeddings, and a native reranking endpoint. It runs on CPUs and several accelerator families. For a private embedding model or cross-encoder reranker in a retrieval pipeline, it is a clean production choice.

The catch is the deployment shape. A TEI instance serves one model, so an embedder and a reranker normally mean two instances. That is easy to understand and tune, but the operational count climbs as the model fleet grows.

Pricing: TEI is free under Apache 2.0. You pay for your own compute. If you prefer a managed deployment, Hugging Face Inference Endpoints supports TEI and charges by the minute at an hourly instance rate; current entry prices start around $0.032 per CPU core-hour and $0.50 per GPU-hour.

Best for: Focused, self-hosted embedding and reranking workloads with one or two pinned models.

LocalAI

LocalAI is an OpenAI-compatible API that runs on your own hardware. Its selling point is breadth.

It supports many backends and model types, including language, vision, image generation, speech, and embeddings. It can run on commodity CPUs as well as GPUs, and the project ships container images for a relatively quick start.

That flexibility creates more configuration choices. Backend selection, model formats, memory settings, and hardware-specific tuning can all affect performance. LocalAI is a versatile generalist, but it does not remove the need to benchmark your exact models on your exact machine.

Pricing: LocalAI is free under the MIT license. There is no required platform subscription; your costs are the hardware, storage, and operations you bring to it.

Best for: Teams that want one OpenAI-compatible interface for several model types on local or modest hardware.

NVIDIA Dynamo-Triton

NVIDIA Dynamo-Triton, formerly Triton Inference Server, is an enterprise-grade server for running models from different frameworks behind HTTP or gRPC APIs.

One deployment can serve TensorRT, PyTorch, ONNX, OpenVINO, Python, and RAPIDS FIL models side by side. It supports dynamic batching, concurrent execution, streaming workloads, and model ensembles. Although it is most closely associated with NVIDIA GPUs, the current product also supports CPUs and some non-NVIDIA accelerators.

It is genuinely powerful and genuinely heavy. The model repository, backend choices, deployment settings, and surrounding Kubernetes or monitoring setup create a large configuration surface. This is usually a platform-team tool. In the right hands, it can consolidate a heterogeneous production estate.

Pricing: The open-source server is free to use, and you supply the infrastructure. Commercial support and production packaging are available through NVIDIA AI Enterprise, with pricing provided through NVIDIA or its partners.

Best for: Teams serving heterogeneous models across frameworks at scale, especially in an NVIDIA-centered environment.

Ollama

Ollama bundles model management, inference, and an HTTP API into a straightforward local experience. Pull a model, run it, and you have a private endpoint without first assembling a serving stack.

Ollama detects common acceleration options, including CUDA, ROCm, and Apple Metal, and can keep multiple models locally while loading and unloading them as needed. For getting a private model running on a laptop in a few minutes, it remains one of the easiest choices.

Its sweet spot is local and small-team use. You can tune parallelism and keep models warm, but Ollama is not primarily designed as a high-concurrency, multi-node production serving platform.

Pricing: Running Ollama on your own hardware is free. Ollama Cloud has a free plan, while Pro costs $20 per month or $200 per year. Cloud usage limits and available plans can change, so check the pricing page before committing.

Best for: Local development, prototyping, offline assistants, and single-user workflows.

SGLang

SGLang is a high-performance serving framework tuned for large language and multimodal models. Its RadixAttention system reuses shared prefixes, which can pay off in workloads with repeated system prompts, long conversations, RAG, or agent loops.

SGLang also puts serious attention into structured generation, speculative decoding, parallelism, and model routing. That makes it especially interesting when your application repeatedly calls the same large model with overlapping context.

The trade-off is focus. SGLang is built around high-performance generative serving, not around squeezing a mixed collection of embedders, rerankers, classifiers, and extractors into one small-model pool.

Pricing: SGLang is free under Apache 2.0, with no required paid tier. You pay for the infrastructure and production tooling around it. Managed services such as Hugging Face Inference Endpoints can also deploy SGLang at their own compute rates.

Best for: A large generative model in an agent, RAG, or structured-output pipeline, especially when prefix reuse matters.

SIE (Superlinked Inference Engine)

SIE approaches inference from the other direction. Where many engines spread one large model across several GPUs, SIE is designed to run a fleet of smaller task-specific models across shared resources.

Its catalog covers more than 100 models and tasks, including embeddings, reranking, extraction, content safety, OCR, image understanding, and smaller-scale generation. Models can load on demand and leave memory when capacity is needed elsewhere.

The open-source project includes more than the inference process. It also ships a load-balancing gateway, KEDA autoscaling, Grafana dashboards, Helm packaging, and Terraform modules for major cloud platforms. That makes it a production stack rather than only a local server.

SIE is not intended to serve one giant frontier-sized language model. Its value is consolidating the smaller models surrounding that model.

Best for: An agent’s fleet of task-specific models when you also want the deployment and autoscaling stack included.

vLLM

vLLM is the throughput workhorse of open-source LLM serving. PagedAttention, continuous batching, prefix caching, quantization support, and a broad model ecosystem make it a common starting point for production language-model inference.

It also has a large community and support across NVIDIA CUDA, AMD ROCm, CPUs, and other accelerator backends. If you need to serve one main generative model at high concurrency, vLLM is the safest default in this group.

Its center of gravity is still the large generative model. It supports features such as LoRA adapters and distributed serving, but a mixed fleet of embedders, rerankers, safety models, and extractors usually means additional workers or services.

Pricing: vLLM is free under Apache 2.0, with no required paid tier. Your bill comes from the GPUs or other compute running it, plus the orchestration and monitoring around those workers.

Best for: One large generative model at high throughput across a broad range of hardware.

How to Choose

The shortlist gets easier once you stop asking which server is “best” and ask what your workload actually looks like.

Then ask the harder question: what will it take to run this in production?

“It runs on my machine” is not the same as “it scales, exposes useful metrics, survives a traffic spike, and does not need a dedicated engineer to babysit it.” The raw server matters. So do model loading, routing, autoscaling, observability, upgrades, and failure recovery.

Heading to Production

The inference-server market roughly splits into two camps: servers that maximize the performance of one primary model and servers that coordinate a fleet.

If your workload is one large generative model, begin with vLLM or SGLang. If it is a focused retrieval service, TEI may be all you need. If you are building locally, Ollama or LocalAI will get you moving quickly. For a heterogeneous enterprise model estate, Dynamo-Triton has the broadest platform story.

If the shape of your problem is an agent’s fleet of small models, and the part you do not want to build is the production wiring around them, SIE deserves a close look. You can self-host it under Apache 2.0 or use the managed version.

See how to put together the LEGOs at Superlinked docs. Try it against your own eval, on your own hardware.

For many production agents, the answer will be a pair: vLLM or SGLang for the large generative model, plus SIE or dedicated TEI workers for the smaller task models around it. That architecture covers both halves of the workload without asking one server to be good at everything.

Benchmark the combination against your own evaluations, traffic shape, latency targets, and hardware. Inference servers are opinionated pieces of infrastructure. The right one is the one whose opinions match your system.

Frequently Asked Questions

What is the difference between a single-model and a multi-model inference server?

A single-model server usually dedicates each worker to one primary model and optimizes around it. A multi-model server is designed to host or coordinate several models and route requests among them. The boundary is not absolute, but it is a useful way to predict how much infrastructure a model fleet will require.

Which inference server is best for an AI agent?

It depends on the agent. If the agent relies mainly on one large language model, start with vLLM or SGLang. If it uses a fleet for embedding, reranking, extraction, safety, OCR, and other tasks, use a multi-model server such as SIE or combine specialized TEI workers with your own orchestration.

Is Ollama suitable for production?

Ollama is excellent for local development, prototypes, offline tools, and smaller internal deployments. You can tune it beyond the defaults, but high-concurrency or multi-node serving is not its main design center. Test it under your real load before making it the production layer for a public application.

Do I still need vLLM or SGLang if I use SIE?

Often, yes. They solve different parts of the stack. SIE is designed for the smaller task models an agent uses, while vLLM and SGLang specialize in serving large generative models at high throughput. Running both is often simpler than forcing either one to handle the entire workload.

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My research workflow is usually spread across too many places.

I find papers on platforms like arXiv, save the PDFs somewhere else, and use an AI chatbot when I need help understanding a dense section. Once I start writing in Word, nearly all that context stays behind. I have to move the useful findings manually, fix the formatting, check the citations, and repeat the process every time I revise the draft.

It works, but there is a lot of unnecessary back and forth.

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SciSpace is an AI research platform for finding papers, reviewing literature, analyzing PDFs, managing citations, and writing research documents.

The tool recently added two features that could make this workflow easier.

1. A Word editor that can open and revise a document directly inside the chat interface.

2. Its Agent can now work across an entire project inside the SciSpace Library.

I tested both features as part of the same research workflow. I started with a folder of papers, asked the Agent to compare the findings, and then used that research to revise an actual Word draft.

Setting up a research project in SciSpace

Research projects usually become difficult to manage once the number of papers starts stacking up.

For a literature review, technical report, thesis chapter, or grant proposal, I may need to compare methods, check whether findings agree, trace claims back to the original paper, and look for gaps that could lead to further work.

The sources end up scattered across local folders, browser tabs, reference managers, and previous AI conversations.

Uploading PDFs to a general chatbot like ChatGPT or Claude can help, but it often feels temporary. I may need to upload the same files again in another conversation, explain the project from the beginning, and remind the AI which sources it should use.

SciSpace’s Library Agent lets me organize the papers around the project instead.

For this test, I created a project called “Autonomous Agents topic.”

I added a computer science paper that formalizes memory engineering as the core state-management framework for autonomous AI agents.

SciSpace gives me several ways to build the collection. I can upload PDFs directly, add papers discovered through SciSpace, or bring sources in from Zotero.

Once the papers were inside the project, I could ask the Agent questions about the whole folder. I did not need to open each PDF or attach the same files every time I wanted to try another prompt.

The folder became the working context for the project rather than just another place to store papers.

Asking questions across the entire library

SciSpace returned a structured response titled “Transforming Stateless LLMs into Adaptive Agents.”

The answer explained that memory systems allow language models to persist, organize, and selectively recall information across interactions. Instead of treating every prompt as a fresh session, an agent can carry useful information forward and adjust its behavior based on previous work.

It then broke the topic into several mechanisms, including context compression, retrieval-augmented memory, reflective memory, and hierarchical memory management. The response described memory as a write-manage-read loop, which gave the explanation a clear structure.

I liked that the Agent did not stop at a short paragraph. It turned the answer into a small report with headings, bullet points, and numbered citations. SciSpace also created a file called memory_engineering_adaptive_agents.md, which opened beside the chat.

The chat stayed on the left, while the generated report opened in a larger document view on the right. It felt closer to reading a research note than scrolling through another chat response.

The numbered references were placed beside the relevant claims. I would still open them and compare the explanation with the original paper before using the material in an article or report, but the citations make that checking process easier.

This example only used one paper, although the same project could later include work on memory benchmarks, long-term agent behavior, retrieval systems, and self-improving agents. I could continue asking questions from the same folder without uploading the material again or explaining the topic from the beginning.

Editing a Word document inside the Agent

For the second example, I uploaded the paper DataSpace: Benchmarking Data Agents for Verifiable Analytics over Heterogeneous Workspaces and asked SciSpace to create a research document from it.

My prompt was:

Prompt: Create a Word document for a research about a benchmark where data agents produce verifiable tabular results from heterogeneous workspaces

SciSpace used the uploaded paper as the source and generated a structured Word document inside the Agent. The draft included a title, an introduction to the research problem, an explanation of the benchmark, and sections discussing how data agents work across different files, databases, and other data sources.

I liked that the result opened as an editable document beside the chat instead of appearing as another long response. I could read the draft, click into the document, and continue editing it without downloading the file first.

This is useful when I need a starting draft based on a research paper. I can ask the Agent to follow a specific structure, use a more technical tone, or focus on one part of the study. For example, I could ask it to explain how DataSpace verifies tabular outputs or add a section comparing the benchmark with earlier data-agent evaluations.

The next step is where the Word editor becomes more useful. I can ask SciSpace to revise the document it just created:

Tighten the introduction, remove repeated explanations, and make the description of the DataSpace benchmark easier to follow. Preserve the headings and citations, and show all changes as redlines.

SciSpace applies the edits directly inside the document. Additions and deletions remain visible, so I can review the revision before keeping it.

This gives me more control than asking a chatbot to rewrite the section and pasting the result into Word. I can see exactly which sentences changed, whether a technical term was replaced, and whether the Agent removed anything important.

I would still compare the draft with the original paper, especially when it discusses benchmark design, evaluation metrics, and results. The generated document gives me a useful starting point, but the final technical claims still need to be checked against the source.

How researchers and PhD candidates can use it

For researchers and PhD candidates, the Library Agent becomes more useful as a project collects more papers. A thesis chapter, systematic review, grant proposal, or conference paper can have its own folder. The Agent can then answer questions from that material instead of relying on a random mix of uploads.

A researcher could ask it to compare methods, extract definitions, list limitations, or prepare notes for a related-work section. The answers still need verification, but the first pass is easier to organize.

Once the work moves into a draft, a PhD candidate could ask the Word editor to tighten a section, move a paragraph, or revise an unclear explanation while keeping every change visible. The same setup also applies to analysts, technical writers, consultants, and research teams that work from source documents and deliver the final report in Word.

Someone working on a long research project could also create a separate folder for each chapter or workstream. That would keep the source material divided by topic instead of placing hundreds of loosely related PDFs into one collection.

I recommend checking out the various writing tools here.

For collaborative work, the redline editor may be particularly useful. One person can ask the Agent to revise a section, while the rest of the team can review exactly what changed before the document moves forward.

How students can use it

Students could create one SciSpace project for each subject or major assignment. A folder might contain lecture readings, journal articles, course materials, and papers gathered for a final report.

They could compare theories, pull definitions from assigned readings, or prepare a study guide based only on the material in the folder.

After finishing a draft, a student could ask the Word editor for clearer transitions, a shorter introduction, or a more formal tone. The redlines show how the writing changed, so the student still has to review and understand each revision.

It could also help with assignments that require several sources. Instead of discussing each paper in isolation, a student could ask how the authors approach the same question differently or where their conclusions overlap.

The quality of the result will still depend on the papers added to the folder and the questions being asked. Uploading weak sources or using a vague prompt will not suddenly produce a good research paper. The student still needs to choose reliable material, read it, and decide what belongs in the final argument.

How teachers and research supervisors can use it

Teachers could organize papers around one course topic and use the Agent to prepare discussion questions, short comparisons, or an outline of the concepts covered across the readings.

A folder containing the assigned papers for one week could also become a reference workspace. Teachers could ask for connections between the readings, identify terms students may struggle with, or prepare questions that require students to compare the authors rather than repeat a definition.

Research supervisors could use the Word editor while reviewing student drafts. They might ask the Agent to flag repetitive sections, suggest clearer wording, or reorganize part of a literature review. Since the edits appear as redlines, the student can inspect each suggestion.

The tool should not replace academic feedback. A supervisor still needs to judge the argument, evidence, and research quality. It may reduce some repetitive editing and leave more time for comments that require subject knowledge.

There is also a useful teaching opportunity in the redlines themselves. Instead of handing a student a rewritten paragraph, a teacher can use the visible changes to explain why a sentence was shortened, why a claim needed qualification, or why two paragraphs worked better in a different order.

Final thoughts

After using both features together, I can see the workflow SciSpace is trying to build.

I can create a project for one research topic, add the relevant papers, ask questions across the whole collection, and turn the findings into an outline. When I move into writing, I can open the Word document in the same Agent and review the edits through redlines.

That removes several steps from the process and keeps more of the research context in one place.

For researchers who already spend most of their writing time in Word, the redline editor may be the feature that gets the most attention. I think the Library Agent is just as important, though. Without the project folder and its papers, the document editor would only be another rewriting tool.

If you are doing research with AI, I recommend using SciSpace. Let me know what you think in the comments!

SciSpace is also offering 35% off its Max plan for a limited time, including 40,000 credits for running research tasks. Use the code AOAI35 at checkout.

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Yesterday, I watched a nine-year-old kid on YouTube create his own animated short film. He did brainstorm a story, settle on a theme, and design his own custom characters. Forty minutes later, he had a 15-minute video for episode one of his series.

He used AI throughout the whole workflow, of course. It’s crazy to see how much creativity these tools unlock in these kids. For years, “making a series” meant a script, a crew, a location, a camera package, weeks of editing, and a budget that ruled out almost everyone with a story in their head.

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A single person can now sit down and produce an episodic drama with consistent characters and a real emotional arc, in an afternoon.

In this guide, I’ll walk through a tool called Drama Studio inside the Topview platform and show the detailed steps on how to use it in creating multi-video series with AI.

Let’s get started.

What Is Topview Drama Studio

Drama Studio is a workspace inside Topview built specifically for making drama scenes, either as a single episode or a full multi-episode series. You’ll see it on the left panel once you open the platform.

What I really like about this video generator tool is the level of creative control and flexibility it provides to creators.

Most AI video tools give you a prompt box and a render button, and everything else is your problem. Drama Studio gives you the writing, the casting, the storyboarding, and the rendering in one window, all aware of each other.

You can start with a one-line idea, a rough outline, a finished script, or a whole novel chapter. From there, the agent helps you build characters, write dialogue, plan shots, and turn all of it into video without ever opening a second app.

Check out this example, which is one of the cool scenes from a series titled “The Fight I Owe You.”

Pretty cool, right?

Drama Studio is powered by Seedance 2.5, so you can expect the quality to be really good.

Most tools do not fail because the video model is bad. They fail because everything is split across too many steps: writing in one place, images in another, voice somewhere else, and editing after that.

Drama Studio brings those steps into one flow. That is why a solo creator can actually finish a project here instead of giving up after three scenes.

How It Works

The flow runs in a straight line, and that linearity is why someone who has never touched a timeline can still reach the end.

You feed it a story. The agent expands that into a structured script split into scenes and beats. If you prefer to delegate the script writing to the AI, just describe the plot in the prompt field and select the type of writer from the dropdown below.

Here’s a sample plot:

Prompt: A vengeance-driven Human Paladin faces the Orc Warrior he has blamed for his brother’s death in a snow-covered arena at dusk, only to discover that the fight he waited ten years for ends not in blood but in an act of mercy that dismantles everything he believed.

Follow the on-screen instructions to guide the AI on how to write the outline and the script based on your prompt. In the end, you should have a very detailed storyline with multiple scenes and dialogues.

You also generate your characters, props, and locations as reusable references. There’s an option to upload your own characters and locations, or let the AI create them for you. You build each scene one shot at a time, attaching those references plus camera and audio direction.

Finally, you enter into the video production, stitch all the scenes, do all the necessary tweaks and adjustments, and export to a single video file.

This is what the final 2 minutes and 16 seconds-long episode looks like:

A handful of decisions separate this from a glorified prompt chain:

• Character consistency is built in. Generate a character once, and the same face, hair, and outfit carry through every scene.

• The script editor sits beside the video generator. Rewrite one line and regenerate that single shot, instead of rebuilding the whole episode.

• Multi-episode mode remembers things. Your characters and sets persist between episodes, so you never start from zero.

• Storyboards come before video. Preview each shot as a still, fix what looks wrong, then spend credits on motion.

Video eats far more credits than images do. Pinning your composition as a still before committing to motion is the difference between spending five credits and fifty on a shot that was never going to work.

The image generation runs on GPT-Image 2, which is why the character previews come out really good as they do. And again, the video render is powered by Seedance 2.5, ByteDance’s newest video model, currently at the top of Video Arena for both text-to-video and image-to-video.

Why I Prefer Drama Studio Over Other Tools

There are a lot of ways to make AI drama videos right now, and I do not think there is one perfect tool for everyone.

If you want maximum control, the best results still come from stitching together different tools. You can write the script in Claude or ChatGPT, generate videos in Topview, HiggsField, Veo, or Runway, then edit everything in FinalCut. That workflow can produce better-looking videos, especially if you know what you are doing.

The problem is that it also takes more work. You are managing characters, prompts, scenes, voices, subtitles, pacing, and edits across several apps. One small change can mean going back through the whole chain again.

That is where Drama Studio makes more sense for me.

It is not trying to be the most advanced tool in every single category. Instead, it gives you a cleaner workflow for making short drama videos from one place. You can move from script to characters, scenes, voice, and video without constantly jumping between tools.

I also like that it understands the episodic format better than a general video generator. A normal AI video tool is great for one impressive shot. Drama Studio is more useful when you are trying to build a small story with dialogue, character continuity, and multiple scenes.

That does not mean it replaces the pro stack. If I were making a high-budget trailer-style video, I would still consider using Kling, Veo, Runway, Seedance, ElevenLabs, and a proper editor. But for short-form drama content, especially the kind made for TikTok, Reels, and Shorts, I prefer having more of the workflow inside one tool.

It makes the process less annoying. This is incredibly helpful when you are not just making one clip, but trying to make episode after episode.

That does not mean it replaces the pro stack. If I were making a high-budget trailer-style video, I would still consider using Kling, Veo, Runway, Seedance, ElevenLabs, and a proper editor. But for short-form drama content, especially the kind made for TikTok, Reels, and Shorts, I prefer having more of the workflow inside one tool.

It makes the process less annoying. And honestly, that matters when you are not just making one clip, but trying to make episode after episode.

Let’s Talk About the Pricing

Topview is priced more like a video production tool than a simple AI editor, which makes sense once you start using Drama Studio.

The free plan is mainly for testing. You get limited video and image models, one task at a time, watermarked exports, and no commercial rights.

Pro is the real starting point at $29 per month. It gives you 80 credits a month, or roughly 20 Seedance 2.5 videos, plus access to all video models and four concurrent tasks. For solo creators making short drama videos, ads, or social clips, this is probably enough to start.

Business is $75 per month and gives you 250 credits, or around 62 Seedance 2.5 videos. This makes more sense if you are producing videos every week, testing more scripts, or working across multiple brands.

Ultra is $120 per month on the current offer, with 500 credits, or around 125 Seedance 2.5 videos. That is the plan for heavier production when Drama Studio becomes part of your regular content workflow.

The main thing to watch is credit usage. Video generation burns credits faster than images, especially when you rerun scenes, test scripts, or try different outputs. For most people, Pro is the safest starting point. Business is the better jump once you are making videos at volume.

Final Thoughts

After playing with Drama Studio, I can tell this tool rewards writers more than prompt engineers.

A good script with a simple prompt will beat a weak script with a clever prompt most of the time. Topview can help you build the scene, generate the characters, and turn the whole thing into a video. But it still needs something interesting to work with.

That is also why I liked the amount of control it gives you. You can rewrite a line, change an outfit, adjust a scene, or regenerate a shot without starting the whole thing from scratch. That makes it feel less like a random AI video toy and more like something you can actually use for a small production workflow.

It is not perfect yet. The lip syncing with the audio can still feel off sometimes, some high-speed movements do not look physically accurate, and some expressions do not always land the way you want. But for one person trying to make short drama videos or episodic content, this is one of the few tools I would keep coming back to.

A few years ago, making something like this meant hiring actors, editors, voice artists, and a small production team. Now the harder part is having a story people actually want to watch.

If you have tried Drama Studio or any other AI film tool, I’d like to hear how it went. What worked, what broke, and what surprised you?

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Getting a good AI-generated image is easy when I do not care much about the exact composition.

I can describe a character, setting, mood, and visual style, then let the model decide how everything should appear. The result might look impressive, but it is often not the image I originally had in mind.

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The problem becomes more obvious when I need a specific shot.

Maybe the character should stand on the left side of the frame while looking toward a product on the right. The camera might need to sit slightly below eye level. The subject should fill only half of the image, leaving enough negative space for text or another character.

Prompts can describe all of this, but they do not always translate into accurate composition.

I usually end up rewriting the prompt several times. Each failed attempt costs time and credits, and there is no guarantee that the next result will be any closer to the composition I had in mind.

A better approach is to give the model a visual reference that shows the layout of the scene. Instead of describing every position through text, I can show where the character, props, and camera should be placed.

Topview’s Canvas dashboard, powered by the Seedance 2.5 video model, now includes a 3D Shot Composer tool that lets creators stage scenes in 3D before generating the final image or video. It lets creators stage a scene in 3D, choose the camera angle, and use that setup as a visual guide before generating the final image or video.

How 3D References Improve Scene Composition

A 3D reference lets me compose the shot before generating it. Instead of asking the model to interpret character positions, prop placement, depth, and camera framing from a prompt, I can arrange those elements directly inside the scene.

This gives me more control over blocking and perspective.

For example, I can place a little boy sitting on top of a wall, set the camera slightly below him, and control how far he appears from the viewer. I can also adjust how much of the wall stays in frame, where his body sits in the composition, and how much background space surrounds him.

Bring in that 3D scene into the Canvas, use it as a reference, add some specific instructions on what the final output should look like, and generate the final image.

That gives me far more control than writing something like “a low-angle shot of a boy sitting on a wall.” The prompt describes the idea, but the 3D reference defines the actual shot.

What I like about this technique is how visual the process feels. I can move the camera, change the distance, reposition the character, and immediately see how each adjustment affects the composition before generating anything.

It is especially useful for cinematic videos, multi-scene stories, character interactions, short films, skits, and any shot where the exact blocking or camera angle matters. The prompt can focus on the final visual style, while the 3D reference handles how the scene is staged.

Topview’s New 3D Shot Composer in Canvas

Topview Canvas is a visual workspace for planning, generating, organizing, and reusing AI video workflows. Its new 3D Shot Composer adds more control over individual shots by letting me stage the scene before generating it.

You can find the new 3D Shot Composer in the Examples menu inside Canvas. Click it to open the media generation workspace and load the example workflow.

From the Canvas dashboard, select the 3D Shot Composer tab. This opens the 3D scene editor, where I can add characters and basic shapes to the scene.

I can then adjust the character’s position, camera angle, zoom level, and aspect ratio. The tool also includes several preset poses, which makes it easier to position the character without manually adjusting every part of the body.

For this example, I wanted to generate an image of a man running in a park. I selected a running pose, adjusted the camera distance and angle, and positioned the character where I wanted him to appear in the final frame.

Once the composition looked right, I took a screenshot of the 3D scene and saved it to my computer. This screenshot serves as the visual reference for the final generation.

Next, I returned to the Canvas workspace, uploaded the reference image, and described how I wanted the finished scene to look. This is also where I selected the image model, aspect ratio, resolution, and other generation settings.

Here is the prompt I used:

Prompt: Aman running through a park on a clear morning, captured mid-stride on a paved path lined with green trees and soft sunlight filtering through the leaves. He is wearing modern athletic clothing, including a fitted running shirt, shorts, and running shoes, with a focused but natural expression. The scene should feel active and fresh, with a sense of motion in his posture and slight background depth, while the park looks clean, open, and calm with grass, benches, and a few distant people or joggers in the background.

The uploaded 3D scene guides the model on the character’s pose, position, scale, and camera framing, while the prompt defines the setting, clothing, lighting, and overall appearance.

If the result still needs adjustments, I can return to the 3D Shot Composer, reposition the character, change the camera angle, or modify the distance before generating another version. I do not need to rebuild the entire scene from the beginning.

One advantage of working inside Canvas is that the generated image remains part of the wider workflow. I can select it and continue editing it with tools such as relighting, inpainting, and upscaling, or use it as the starting point for an image-to-video generation.

For the video generation, I kept the prompt focused on movement rather than repeating the visual details already present in the image. I asked Topview to animate the man running naturally along the path, add subtle camera tracking, and introduce small environmental movements such as swaying trees and shifting morning light.

Prompt: Animate the man running naturally along the park path, with realistic arm and leg movement, subtle camera tracking, gentle motion in the trees, and soft morning light shifting across the scene.

Here’s the final result:

Looks pretty cool to me. The running motion feels natural, and the subtle camera movement adds energy without messing up the framing I planned with the 3D reference. It still feels like the same shot, just brought to life.

For projects with several scenes, I can create separate compositions for wide shots, close-ups, product shots, and character interactions. Since everything remains inside Canvas, the assets and setups are easier to organize, refine, and reuse throughout the rest of the video workflow.

Why This Matters

The biggest advantage of the 3D Shot Composer is that I can solve composition problems before spending credits on generation. I can check the subject scale, spacing, depth, and camera angle instead of repeatedly rewriting the prompt.

I previously used rough 2D doodles to plan difficult scenes, but they could not represent perspective very well. A 3D setup gives me a much clearer view of how the final shot will look from the camera.

The feature can be useful across several creative workflows:

• Cinematic AI videos: Creators can plan wide shots, close-ups, and transitions before generating each scene, which is helpful for short films, skits, trailers, and multi-scene stories.

• Character-led scenes: The 3D setup makes it easier to control where characters stand, which direction they face, and how they interact inside the frame.

• Product marketing: Products can be positioned where they remain visible and large enough to become the focus, while leaving space for headlines, prices, logos, or calls to action.

• Animation and 3D work: Animators and 3D artists can use the composer for quick blocking and previsualization before spending time on detailed models, environments, or final renders.

• Social media content: Teams can reuse one scene and reposition the camera for landscape, square, and vertical formats without rebuilding the composition from scratch.

The 3D Shot Composer does not remove every inconsistency from AI generation, but it gives creators more control over the decisions that prompts often struggle to communicate.

Final Thoughts

I honestly had a great time generating scenes using 3D scenes as references. It lets creators position characters, props, and cameras in 3D, then use that scene as a visual guide for image or video generation.

In the past, I would usually use flat doodles for difficult scenes. It gives me a better sense of scale, depth, spacing, and perspective before I spend credits on the final output. Props to Topview for adding a tool that solves a real production problem and gives Canvas users more control over how each shot is built.

Creators working on product ads, character scenes, short films, animations, or multi-shot videos should find this feature helpful.

Alright, that’s about it for now. I highly encourage you to try this new image/video generation approach and let me know what you think in the comments.

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I spend most of my working day inside AI chat apps. I use them to research ideas, improve scripts, plan content, and work through drafts. The annoying part begins when the conversation turns into an actual media project.

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ChatGPT can generate images on its own, but its native video option is gone. Claude has the opposite problem. It can understand uploaded images and create diagrams or charts, but Anthropic still says it does not generate photos or illustrations like a dedicated image model.

So the usual workflow becomes more fragmented than it should be. I discuss an idea in ChatGPT or Claude, copy the prompt into another platform, upload the same references again, generate the asset, and return to the original chat when I need a revision.

MCP offers a much better way to connect those two parts.

The Model Context Protocol is an open standard that lets AI applications connect to external tools and services. Instead of asking Claude or ChatGPT to do something outside its native capabilities, an MCP connector gives the chatbot a tool that can handle the request on its behalf.

Topview MCP applies that idea to media production and marketing.

Once connected, you can ask ChatGPT, Claude, or any MCP agents to generate a video, edit an image, remove a background, create product visuals, or work on a larger campaign without moving the project into a completely separate workflow.

That is exactly what I tested and will share in this guide.

What Is Topview MCP?

Topview MCP is a remote Model Context Protocol server that connects supported AI assistants to Topview’s media generation and marketing tools.

The chatbot remains the conversational layer. It reads the request, understands the context, decides which Topview tool is needed, and sends the job to Topview. Topview’s server then handles the image or video processing and returns the result to the conversation.

When I edit an image through Claude, Claude does not suddenly become an image-generation model. It acts as the interface and reasoning layer, while Topview performs the media task using the models and production tools available on its platform.

Its current workflow covers market research, campaign strategy, content planning, and creative production inside one Campaign Workspace. It can pull signals from TikTok, YouTube, Amazon, and Shopee, then turn those findings into audience insights, message maps, campaign plans, content briefs, and creative assets.

The media tools cover video generation and editing, image generation and editing, product model images, background removal, storyboards, product visuals, UGC-style videos, ad edits, and creative variations.

Topview can also orchestrate several image and video models. The models currently shown on its MCP page include Seedance 2.5, Kling 3.0, Veo 3.1, Nano Banana 2.0, GPT Image 2, and Seedream 5.0 Pro.

The model routing can stay mostly invisible. I can describe the result in normal language instead of opening several dashboards or configuring separate APIs.

How to Set Up Topview MCP in ChatGPT

Before adding the custom plugin, enable developer mode in the Security and login tab in the Settings page. Depending on the account and workspace, OpenAI may place this option under Apps, Plugins, Advanced Settings, or the workspace’s connected-data permissions.

After enabling it, open the Plugins page and create a new connector. Name it Topview, then paste this URL into the connection field: https://mcp.topview.ai/chatgpt

ChatGPT will inspect the connector and ask you to sign in to your Topview account. Complete the authorization process and grant the requested permission.

You should then be redirected to ChatGPT, where Topview will appear in the Plugins list.

Take a moment to open the connector details before using it. The Permissions section controls whether ChatGPT can call Topview tools automatically or must ask before an action.

Here are the permission levels shown in my account:

• Always ask: ChatGPT will ask before reading or making changes.

• Allow read actions: ChatGPT can read without asking, but will ask before making changes.

• Allow low-risk actions: ChatGPT will automatically approve low-risk actions but may deny actions involving sensitive information.

• Allow all actions: Elevated risk. ChatGPT won’t ask before reading or taking action. This comes with elevated risk.

I kept the connector on Allow low-risk actions. It reduces repetitive confirmations during normal media work without giving every tool unlimited approval.

Generating AI Videos with Topview MCP

For my first test, I wanted to see whether I could create a realistic UGC advertisement from two reference images.

I uploaded a photo of the woman who would appear in the video and a separate image of the sunscreen product.

Then I used this prompt:

Prompt: Create a UGC video of a girl promoting a sunscreen product. Both images of the subject and the product are attached

That was the entire request. I did not write a full script, divide the video into scenes, describe every camera movement, or explain how the product should appear in the woman’s hand.

ChatGPT understood the request and passed the task to Topview. From there, Topview created the video and returned a link inside the same conversation.

Here’s the final video:

The final video looked better than I expected. The woman looked so realistic, her appearance stayed consistent across the shots, and the product did not feel pasted on top.

I also liked the scene transitions. They made it feel like a proper short-form ad rather than one AI avatar speaking in front of a fixed background.

The generated script was also on point. It sounded like something a creator might say in a quick sunscreen promotion, and the pacing fit the UGC format. I expected that part to require at least one rewrite, but the first version was already usable.

This is where the connection between ChatGPT and Topview worked really well. ChatGPT handled the request as part of the conversation, while Topview dealt with the production work. I did not need to re-upload the references elsewhere or rebuild the prompt in a video editor.

I can also stay in the same chat and ask for a shorter hook, another aspect ratio, a more casual delivery, or a version that focuses more heavily on the product. That is much easier than starting another project for every change.

How to Set Up Topview MCP in Claude

Claude users can connect Topview through the platform’s custom connectors feature.

Anthropic currently supports remote MCP connectors across Claude, Claude Desktop, Cowork, and mobile. Custom connectors are available on Free, Pro, Max, Team, and Enterprise plans, although Free accounts are limited to one custom connector.

Open Claude and go to Customize, then open the Connectors page. Click the option to add a custom connector.

Name the connector Topview and enter this address in the remote MCP server URL field: https://mcp.topview.ai/claude

Click Add and continue through the Topview sign-in and authorization process.

Once connected, Topview should appear in Claude’s connector list. Open its settings and choose the permission level that fits the workflow. My account used Needs approval by default, which means Claude asks before calling a tool.

That default is sensible while testing. After seeing which tools Claude calls, I can decide whether any should run without repeated approval.

The connector also needs to be enabled for the individual conversation. Before sending a media prompt, click the plus button beside the chat box, open Connectors, and switch on Topview.

Anthropic’s documentation confirms that remote connectors can be enabled or disabled per conversation from this menu.

Once that is done, Claude is ready to generate or edit media through Topview.

Editing an Image Inside Claude

For the Claude demo, I started with a simple image-editing request. I uploaded a photo of a red car and asked Claude to change it to white.

The Claude model does not matter very much for the actual pixel editing because the media processing happens on Topview’s side. Claude still needs to understand the request and choose the correct connector tool, but it is not the model repainting the car.

Here’s the side-by-side input and output:

The edit did what I asked. The red body of the car became white while the rest of the scene stayed largely intact.

Pretty cool, right?

For a long time, Claude has been able to inspect and discuss images, but Anthropic still doesn’t position it as a native photo or illustration generator. It can create charts, diagrams, and interactive visuals, but normal generative image work has required another service.

For small media jobs, that removes a surprising amount of friction. I no longer need another generator just to recolor an object, remove a background, or test a visual idea.

The Real Value of MCP Connectors in Our Favorite Chat Apps

Before MCP, I treated chatbots and media platforms as separate tools. Every handoff required copying prompts, uploading files again, and explaining the project from the beginning.

A connector reduces those handoffs. The chat already contains the product description, audience, references, and feedback, so the assistant is not working from an isolated prompt.

Topview’s wider marketing workflow makes this even more useful. Its MCP is not limited to one image-generation command. Topview says it can collect signals from TikTok, YouTube, Amazon, and Shopee, turn them into campaign documents, and then use the same Campaign Workspace for creative production.

That creates a more connected path from research to output.

I could begin with a product page, ask for recurring complaints in customer reviews, turn those complaints into possible ad hooks, draft a UGC concept, generate product images, and produce a video based on the selected direction. The work stays tied to the same campaign instead of becoming a pile of unrelated prompts spread across multiple tools.

It also separates the assistant from the production model. I do not have to choose one platform that is best at everything. Claude can handle the conversation while Topview handles the media task. ChatGPT can organize the campaign while Topview routes the generation job to an appropriate image or video model.

There are still limits. An MCP connector does not guarantee that every generation will be perfect, and the output still depends on the reference files, prompt, selected model, and task. Media generation may use Topview credits, and processing can take longer than a normal text response.

Final Thoughts

I originally connected Topview because I wanted to generate one UGC video inside ChatGPT. That alone worked better than I expected. The result looked realistic, the references stayed consistent, and the script needed less fixing than I had planned.

The Claude test was simpler, but it showed why MCP is useful beyond video generation. Claude still does not have a normal image generator of its own, yet I was able to edit an uploaded photo directly from the chat by giving it access to Topview.

I think this is how more AI tools will be used going forward. I do not want to keep opening a new dashboard for every model or feature. I would rather stay inside the assistant that already understands the project and call the right production tool when I need it.

Topview MCP already makes that workflow feel practical. It connects research, campaign planning, image work, and video production without forcing me to manually move every idea between platforms.

I remember someone said that MCPs could be as big as the birth of the internet. That’s a big claim, but the benefits of using it are really huge.

So what do you think about Topview MCP? Will you be using them in your favorite chat app? Let me know your thoughts in the comments.

Hi there! Thanks for making it to the end of this post! If you enjoyed this content and would like to support my work, consider becoming a paid subscriber. Your support means a lot!

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AI image and video generators are everywhere now. Give one of them a decent prompt, and it can produce something impressive in seconds. But 3D is a much harder problem. A complete 3D model has to work from every angle. The geometry needs to hold together, and the textures must wrap around the object correctly.

This may explain why many 3D artists are still cautious about using AI. Poliigon’s State of 3D 2025 survey collected responses from 3,779 artists around the world. Only 22% said they used AI in their work daily or a few times a week. For dedicated AI 3D model generators, regular usage was below 5%, while 68% said they had never used one.

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Most artists still use generative AI for concept art and look development. Only 18% had used it for final assets. People are curious, but placing an AI-generated model directly into a project is another story.

The investment is coming anyway. Autodesk’s 2025 State of Design & Make report covered 5,594 professionals across several industries. More than two-thirds of business leaders planned to increase AI spending, even though only 40% said their companies were close to achieving their AI goals.

The wider 3D mapping and modeling market is also projected to grow from $7.12 billion in 2024 to $16.78 billion by 2030. That forecast covers the overall 3D industry, not just AI-generated models, but it shows how much demand there is for faster ways to create 3D content.

Generating a model is only the first step, though.

I wanted to know if an AI tool could produce something with decent geometry, detailed textures, and enough export options to be useful outside its own preview window. That brought me to Meshy AI.

What is Meshy AI?

Meshy AI is a browser-based platform for generating and editing 3D models with AI. I can start with a written prompt, upload reference images, or import an existing model that needs new textures or cleaner geometry.

Text to 3D and Image to 3D are the obvious starting points, but Meshy covers more of the workflow than I expected. Multi-image to 3D accepts up to four views of the same object, which gives the model more information about its proportions. AI texturing can generate PBR materials, while remeshing lets me adjust the topology and polygon count.

Character assets can also be auto-rigged and paired with animation presets. For users who prefer a more guided process, 3D Agent provides a chat-like workflow that can begin with an idea, photograph, or sketch.

Finished models can be exported as FBX, OBJ, GLB, or STL. Meshy also connects with Blender, Unity, Roblox Studio, and several 3D printing tools. There is an API for teams that want to add generation, texturing, remeshing, rigging, or animation to another product.

It is a broad set of features. Still, none of that tells me whether the models look good once they start rotating. So I began with the simplest option.

Creating my first 3D game character from text

Text to 3D works much like an AI image generator. I describe the asset, and Meshy builds the initial geometry. Once the shape is ready, I inspect it from every angle before generating textures.

For my first test, I asked for a stylized fantasy knight. Characters give the model several chances to mess up. The armor must remain separate, the proportions need to make sense, and the cape cannot melt into the back.

Here is the prompt I used:

Create a full-body stylized fantasy knight for a third-person adventure game. The character wears dark blue and silver plate armor with a short brown leather cape, heavy boots, metal gloves, and a small glowing blue crystal on the chest. Give the knight broad heroic proportions and a simple helmet with a narrow visor. Keep the character symmetrical, centered, isolated, and standing in a neutral A-pose with no weapon, platform, background, or additional objects. Use clean game-ready geometry, clearly separated armor pieces, and detailed PBR materials for metal, leather, and fabric.

I deliberately kept the knight in a neutral A-pose because I wanted something that could be rigged later. I also removed the weapon. Swords and shields look good in a concept image, but they can hide the body, fuse with the hands, or make automatic rigging harder.

The first mesh was cleaner than I expected. The silhouette worked from the front and side, and the major armor pieces did not collapse into one shape. Meshy also carried the design around the back.

The texture stage made a much bigger difference.

The metal, leather, and fabric had noticeably different surfaces. The blue crystal gave the chest a clear focal point, and the armor no longer looked like several flat colors painted over the same material.

I would not call it production-ready without checking the topology. The armor gaps, joints, hands, and cape deformation all need closer inspection before rigging.

For a prototype, background character, or starting mesh, this was already useful. Building the knight manually would involve modeling each part, cleaning the topology, unwrapping the mesh, and painting several materials.

That was a good first result, but characters have lots of organic shapes where small errors can blend in. I wanted to try something less forgiving.

Animating the model

Once your character model is ready, click the Animate button to open Meshy AI’s animation workspace. From there, you can browse a library containing more than 600 motion presets. These include a wide range of movements suitable for games and other 3D projects, such as walk cycles, combat actions, and attack animations.

Before applying an animation, click the Rig button to automatically rig your character.

Follow the on-screen instructions and align each marker with the corresponding part of the model’s body. Correct marker placement helps the system build a cleaner and more accurate skeleton.

After confirming the marker positions, Meshy AI will process the model and generate a fully rigged character. You can then preview different animation presets and choose the motion that works best for your project.

For this example, a walk cycle or attack animation is a good way to demonstrate the quality and flexibility of the generated rig.

Here’s the sample output:

The rig came out clean, and the presets made it easy to test different movements. In just a few minutes, I turned a static model into an animated character, a process that would normally take much longer and require several tools. It’s a great example of how generative AI makes 3D character creation faster and more accessible.

Creating and animating this character cost me 35 credits, or about $1.12 based on the $8 price for 250 additional credits. In total, I spent 75 credits, or around $2.40, to generate all the assets featured in this article.

At that rate, the platform offers excellent value for rapid prototyping.

Testing Meshy with a hard-surface game prop

For the second test, I chose a medieval treasure chest. Hard-surface objects make mistakes easy to see. A chest has straight edges, repeated metal bands, a centered lock, and a symmetrical shape. Bent surfaces or drifting hardware become obvious once it rotates.

Here is the prompt:

Create a stylized medieval treasure chest for a fantasy role-playing game. Use thick dark oak planks, reinforced iron bands, a large brass lock, metal corner guards, and small scratches from years of use. The lid should be closed and the chest should have a slightly exaggerated shape suitable for a stylized game. Keep it centered and isolated with no floor, coins, background, text, or extra objects. Use clean low-poly geometry and detailed PBR textures for wood, iron, and brass.

The chest held its shape surprisingly well. The wood, iron, and brass were easy to distinguish, while the scratches added age without covering it in random damage. The lock stayed centered, and the metal bands continued over the lid.

I especially liked that the larger metal parts had actual depth. The detail did not exist only in the texture, so the chest reacted better when I moved the light around it.

I would still clean some edges and inspect the underside before using it as a hero asset. But as a prop inside a level, it was convincing.

This is where Meshy started to make practical sense to me. A game environment may need dozens or hundreds of secondary objects. Generating a first version, lowering the polygon count, and cleaning only the visible problems could save a lot of repetitive modeling time.

Turning a high-quality image into a 3D model

Text prompts leave plenty of room for interpretation. Image to 3D gives Meshy a much clearer target because the reference already defines the subject, proportions, colors, and surface details.

I made this test harder by uploading a detailed image of a roaring dinosaur. It had textured skin, visible teeth, an open mouth, and an aggressive pose. This was not an easy product photo.

I initially had doubts. Meshy still had to invent the back, opposite side, and parts hidden by the camera angle. A single image cannot provide an exact reconstruction.

I switched to the Image-to-3D mode and uploaded the reference photo. I set the model to Meshy 6 and hit the Generate button.

Here’s the final textured output:

Meshy preserved the large head, open jaw, muscular legs, and forward-leaning pose well enough to make the model immediately recognizable. The texture followed the original colors and carried much of the skin detail across the visible areas.

The unseen side impressed me more. Meshy generated a complete back and opposite profile instead of a hollow cutout. Those areas were more generic because the system had to guess, but they remained consistent with the reference.

To talk about the limitations. The mouth, teeth, claws, and other thin parts would need careful inspection. These details can look fine in a rendered preview while containing geometry that is difficult to animate or print. The pose also limits how easily the dinosaur could be rigged compared with a neutral stance.

Even with those issues, the result was a strong starting point. I could take it into Blender for cleanup, export it as a GLB for an interactive web scene, or use it as a visual prototype before building a production version.

For objects where accuracy is more important, I would use Multi-image to 3D and provide front, side, rear, and three-quarter views. Giving the model more visual information should reduce how much it needs to invent.

The best ways to use Meshy AI

After these tests, I do not see Meshy as a replacement for a complete 3D workflow. I see it as a way to get past the empty scene and reach the first usable version much faster.

Game prototyping is the clearest use case. A small team could generate temporary characters, props, and buildings while testing an idea. Those assets can establish scale and atmosphere before the final art is ready.

Retexturing may be just as useful for experienced artists. If the geometry already works, there is no reason to generate a new mesh. Meshy can apply a different PBR material or visual direction to an uploaded model, which is useful when testing several art styles.

Auto-rigging and animation can help with previs, prototypes, and short scenes. I would still test deformations carefully, especially around shoulders, hands, clothing, and tails. Automatic rigging saves time, but it cannot know every way a custom character is supposed to move.

Image to 3D also fits product previews and AR. A store could turn photos into interactive web assets, while a designer could place an early concept inside a virtual scene. I would use multiple references because dimensions and small details need more accuracy than a fictional prop.

Meshy supports STL export and provides remeshing and repair tools for 3D printing. I would not send an AI-generated object straight to a printer without checking wall thickness, intersecting geometry, and watertightness, but it can provide a useful base for a physical prototype.

How much does Meshy AI cost?

Meshy has a free plan with 100 credits per month. A complete Meshy 6 Text to 3D or Image to 3D generation currently costs 20 credits, including the mesh and texture stages. That gives free users enough credits to run several tests before paying.

The Pro plan costs $20 per month and includes 1,000 credits. Premium costs $40 per month with 3,000 credits, while Ultra costs $100 per month with 10,000 credits. The Studio plan starts at $60 per seat each month and requires at least two seats. Enterprise pricing is customized.

Paid plans add private assets, unlimited downloads, API access, Multi-image to 3D, faster queues, and more than 600 animation presets. Free-plan outputs use a CC BY 4.0 license, so anyone planning commercial work should review the current licensing terms before publishing or selling an asset.

The free plan is enough to see whether the workflow fits. Serious iteration will consume credits quickly, especially when I generate several versions, test textures, remesh the result, and try different references.

Final thoughts

Alright, I started this test with one question: can Meshy create 3D assets that are useful beyond a good-looking preview?

For the knight, treasure chest, and dinosaur, I think the answer is yes, with some reasonable limits. The outputs were clean enough for prototypes, background assets, visual development, and starting meshes. I would still inspect topology, joints, thin parts, and hidden surfaces before using them in production.

I have always enjoyed playing with 3D modeling, but creating every object from scratch takes a lot of time. Meshy gives me a faster place to begin. It does not remove the need for experienced 3D artists, and I do not think that should be the goal. Someone still needs to judge the result, fix the geometry, and decide whether it fits the project.

What do you think about the models Meshy generated here? If you have tried another AI 3D tool that produced better results, let me know in the comments.

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