u/Dry-Reveal4114

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:

• clothing consistency
• model/style consistency
• fabric and motion quality
• cinematic look
• whether it feels like a real promo instead of random AI clips

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:

1. The user sends a query

2. The Agent receives the request and retrieves relevant context from chat history or memory

3. The LLM identifies the user’s intent, breaks down the task, and generates an execution plan

4. The Agent decides whether it needs to call Skills or external tools

5. If needed, the request is routed to the right Skill

6. The Skill reads context, checks configuration, and prepares the calling strategy and parameters

7. The LLM may help complete or refine the tool-call parameters

8. The Agent sends the request through the Skill

9. MCP handles tool discovery, tool selection, and the actual API call

10. The tool result returns to MCP, then goes back through the Skill for parsing, validation, and formatting

11. 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?

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:

https://gptproto.com/?s=vdt

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:

• Stability
• Pricing
• Model availability
• Documentation
• Ease of configuration in Cherry Studio
• Support for OpenAI-compatible API formats

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!

Everyone, please take a moment to experience this level of cuteness!!

Just look at this absolutely adorable little cat tummy~

Who else gets it? I’m trying so hard to control the urge to give it a tiny little bite!

That night, I was walking through the neighborhood as I usually did, when I heard a soft "meow." I turned to see two cats huddled in a corner, looking both fearful and hopeful. I stopped and shared some cat food from my bag with them. They hesitated at first, but eventually took the food, their eyes filled with quiet trust.

I hadn’t planned to take them home, but they stayed close, following me all the way to my door. I hesitated for a moment, then let them in. A few days later, they settled into their new home, becoming the quiet companions I hadn’t realized I needed. Every evening, as I walked outside, they were there, trailing behind me like shadows — not asking for anything, just being there, silently keeping me company.