I made a short fantasy battle video set on Black Mist Peak at dawn.
The scene follows a young girl and a mysterious white cat as they face an unknown threat in the mist.
Would love to hear your thoughts on the mood, character design, and overall fantasy vibe.
A short diving video I made, focusing on smooth motion, graceful body lines, and the moment of entering the water.
Still experimenting with this style — what do you think of the overall vibe?
Just made this short Chinese martial arts sparring scene between a master and his disciple.
Tried to capture the feeling of training, respect, and fast kung fu movement. Would love to hear what you think about the action and cinematic vibe!
I wanted to test out some high-speed sports action, so I put together this women's badminton match.
Tried my best to capture the intense rallies and that satisfying smash! Would love to hear your thoughts on the fluidity and overall vibe. How did the racket swings turn out?
I’ve been experimenting with some dynamic movements and decided to put together this quick baseball animation test.
My main focus here was trying to get the sports action and pacing to feel right. I'm still tweaking my workflow, but I’d love to get some feedback from you all.
How does the motion, speed, and overall vibe feel to you?
I made a silly little short where Barry gets into a full-on battle with a seagull over some fries.
I used GPT-image-2 for the visuals and Seedance 2 to help bring it to life as a video.
Would love to know what you think — especially about the comedy timing, pacing, and overall vibe!
I made a short video of someone practicing punches in a rainforest.
I was going for a mix of calm nature vibes and intense training energy. The rain, trees, and jungle atmosphere made it feel a lot more cinematic than I expected.
Still learning and experimenting, so I’d love to know what you think about the mood, pacing, and visuals.
It’s silly, fast-paced, and hopefully fun to watch. I was trying to make it feel like a tiny action/crime scene, but with donuts instead of diamonds.
Would love to know what you think — especially about the pacing and visual style!
Hi everyone,
This time I tried making a short video about a cat going surfing.
The idea started as a random silly thought, but I wanted to see if I could turn it into something fun and cinematic. I focused mostly on the timing, the movement, and making the whole thing feel lighthearted rather than too serious.
I’m still practicing video editing and visual storytelling, so I’d love to hear any feedback on the pacing, shots, or overall vibe.
Hope it gives you a quick laugh!
I made a short video about a cockroach showing up at the worst possible moment.
The idea is simple: one tiny bug causes way more chaos than it should. I wanted it to feel dramatic, silly, and a little absurd.
Would love to hear what you think, especially about the pacing and whether the joke lands.
I watched an online tutorial recently and wanted to try making something similar myself.
This is my attempt at a short rooftop storyboard video. The idea is pretty simple: two people on a rooftop, looking at the city and imagining what they could build in the future.
I’m still learning, so any feedback on the flow, composition, or pacing would be really helpful.
You’ve been wearing that dark little face since the very first day you came home.
Who exactly are you making that grumpy face for?
I only used your whiskers to make a wish!
Is that really worth giving me the cold shoulder every single day?
And it’s not like my wish wasn’t for you —
I wished that you could eat canned food every day!
But you still got mad at Mommy.
Not grateful at all!
Okay, fine… Mommy may have also made a tiny wish for herself on the side.
Like living in a bigger house, buying more bags, eating fancy meals…
Ahem, wishes are just wishes.
prompts：
A luxurious realistic 3D render of a premium travel guide map for [CITY NAME], viewed from an elegant isometric top-down angle. The map shows [CITY NAME] as a raised, three-dimensional city silhouette with realistic terrain, rivers, coastline, mountains, parks, urban blocks, and surrounding districts if applicable. At the top of the image, large refined title text reads: “Three-Day [CITY NAME] Travel Guide”. The city’s most iconic landmarks are accurately placed on their corresponding locations as highly detailed miniature 3D models, each model extremely detailed and highly realistic, representing famous architecture, cultural sites, scenic spots, museums, shopping streets, transportation hubs, and skyline landmarks of [CITY NAME]. White glowing road lines and railway lines connect the major districts, attractions, and transportation routes, forming a clean and elegant travel route network across the map. Around the map are scattered premium 3D travel decorative elements: passport, boarding pass, suitcase, camera, sunglasses, compass, airplane ticket, small airplane, hotel key card, coffee cup, travel itinerary cards, location pins, and miniature route markers. High-end editorial design, premium travel magazine style, luxury tourism branding, ultra-realistic materials, cinematic lighting, soft shadows, refined color palette of deep navy, warm gold, ivory, silver, and subtle blue accents, sharp details, realistic miniature models, polished surface, elegant composition, depth of field, global illumination, photorealistic 3D rendering, 8K quality.
The idea was to see if AI can create something close to a real fashion campaign video, with consistent outfits, cinematic lighting, controlled camera movement, and a polished brand feel.
The workflow included image generation, shot selection, image-to-video, camera motion, transitions, and final editing.
What I’m testing:
My take: AI is already useful for fashion concepts, campaign mockups, and social content, but it still struggles with exact clothing details, hands, body movement, and keeping everything consistent across shots.
Curious what you think — would this be usable for a small clothing brand promo?
A lot of people describe AI Agents as “LLMs that can call tools.”
But once you actually map out the runtime flow, it becomes clear that an Agent is not just:
User query → LLM response
There is usually a much longer execution chain behind it.
A typical flow looks something like this:
The user sends a query
The Agent receives the request and retrieves relevant context from chat history or memory
The LLM identifies the user’s intent, breaks down the task, and generates an execution plan
The Agent decides whether it needs to call Skills or external tools
If needed, the request is routed to the right Skill
The Skill reads context, checks configuration, and prepares the calling strategy and parameters
The LLM may help complete or refine the tool-call parameters
The Agent sends the request through the Skill
MCP handles tool discovery, tool selection, and the actual API call
The tool result returns to MCP, then goes back through the Skill for parsing, validation, and formatting
Finally, the Agent combines the original context and tool results, then asks the LLM to generate the final response for the user
So in my view, an AI Agent is not just a smarter chatbot. It is more like an execution system that can understand intent, decompose tasks, plan steps, call capabilities, and integrate results.
I used gptproto to help organize the diagram and think through the workflow, but the main point here is the architecture itself.
Curious to hear your thoughts:
- Is this close to how you think about Agent architecture?
- Would you add or remove any steps?
- How do you usually structure Skills, tools, memory, and MCP in your own Agent systems?
I generated this car commercial video using AI tools.
I’m still experimenting with the workflow and trying to improve the realism, camera movement, lighting, and overall consistency.
Would love to hear your honest feedback — what works well, and what could be improved?
I’ve been testing GPT-5.5 for a few days and comparing it with Claude Opus 4.7.
My quick impression:
GPT-5.5 feels better for coding and technical workflows.
Claude still feels better for writing and careful explanations.
For coding, GPT-5.5 gave me more useful first drafts. The code was usually cleaner, and it was faster when I asked follow-up questions or debugging help.
I also tried using it for technical diagrams. I asked for a microservices architecture diagram with Chinese labels, and the result was actually usable as a rough first draft.
Claude Opus 4.7 still feels more polished for long-form writing, editing, and tone. I wouldn’t say GPT-5.5 replaces it for that.
So for now, my split would be:
I tested them through an API aggregator to switch models more easily, but I’m still curious about the privacy side.
For anyone else testing these models: are you seeing similar results?
I’m fairly new to using AI APIs for work, but recently I’ve needed to call different models more frequently.
A friend recommended an API aggregator platform to me, and I’ve been testing it because it provides an OpenAI-compatible API endpoint. It seems convenient since I can use one API format to access different models, but I don’t have enough experience to judge whether a platform like this is actually good or reliable compared with other options.
For people who have used AI API aggregators or multiple API providers before, how do you usually evaluate them?
Things I’m currently trying to understand:
- How important is latency?
- How do you test reliability or uptime?
- What does good pricing transparency look like?
- Should I worry about data privacy when using an API aggregator?
- Is OpenAI-compatible API support usually enough for tools like Cherry Studio or Open WebUI?
- How do I know whether the model list is accurate?
- Are rate limits usually clearly documented?
- Should I test the API with curl/Postman before using it in an app?
- What are common red flags with third-party API platforms?
Also, are there any similar platforms you would recommend comparing against?
I’m not looking for a “best provider” answer. I mainly want to learn how experienced developers evaluate these tools before relying on them for real work.
Hi everyone,
I’m new to using LLM APIs and recently started learning because of a project requirement.
At first, I was confused by the basic setup process — API keys, Base URLs, model names, and OpenAI-compatible endpoints. I’ve been testing things in Cherry Studio because it makes API configuration a bit easier for beginners.
My friend recommended an API aggregation platform called gptproto, but I’m still not sure how to evaluate whether a platform like this is reliable.
For people who regularly use LLM APIs, how do you usually choose an API provider or aggregator?
What factors matter most to you?
- Stability and uptime
- Pricing
- Model availability
- Documentation
- OpenAI-compatible API support
- Ease of setup in Cherry Studio or similar apps
- Latency
- Privacy and data retention policies
- Transparency around model versions
Are there any API aggregators you would recommend for beginners? Are there any you would avoid?
I’d also appreciate any beginner-friendly resources for learning how API keys, Base URLs, and model configuration work in practice.
Hi everyone, I’m still a beginner when it comes to APIs. Recently, I started learning how to use large language model APIs because of a project requirement.
At the beginning, I felt really confused. Although some friends recommended a few API aggregation platforms to me, I didn’t really understand what things like an API Key, Base URL, or model name meant, or how to configure them properly.
I started learning API configuration with Cherry Studio. Since it allows users to configure APIs directly and has a relatively intuitive interface, I began by entering the API Key, setting the Base URL, selecting a model, and testing whether the API call worked.
I also recorded the whole process and made it into a short tutorial, which is especially suitable for beginners like me who are just starting to learn about APIs or Cherry Studio.
In the tutorial, I mainly cover:
- Where to enter the API Key
- How to set the Base URL
- How to configure the model name
The API service I used was recommended by a friend:
If you are also stuck on configuring an API in Cherry Studio, you can refer to this tutorial.
Since I’m still new to this, I’d also like to ask for some advice:
Could you help me take a look at the API website my friend recommended? Is it reliable and suitable for beginners? Also, are there any better API aggregation platforms you would recommend?
For example, I’d like to know which platforms are better in terms of:
If you have any better API aggregation websites, learning resources, or practical tips for beginners, I’d really appreciate it if you could share them with me!