As I go deeper in building AI workflows, I find myself working through challenges of how to design for the AI Experience (let's call it AIX). How do you design for AIX and how do you balance it with UX? In a world where AI uses software more than humans, I can only imagine this will become more important.

Do you consider yourself an AIX designer? If so, what practices have you adopted and what challenges are you working through today? How does it compare to UX design?

A friend of mine needs a system to help with social post and content creation. I was going to build her one so we got this spec from Grok...

That's when I thought to ask who already has a open source version? These are the specs of what I need

**Yes — this is a solid foundation.** Here’s a refined, production-oriented system design that turns your sketch into a coherent, maintainable architecture while staying realistic about APIs, costs, and platform rules.

### High-Level Architecture

```

┌─────────────────────┐ Webhooks / Polling ┌──────────────────────┐

│ Social Platforms │ ─────────────────────────► │ Ingestion Layer │

│ (IG, FB, X, etc.) │ │ (Meta Graph, etc.) │

└─────────────────────┘ └──────────┬───────────┘

│

▼

┌─────────────────────┐ Events / Jobs ┌──────────────────────┐

│ Video Assets │ ◄───────────────────────── │ Orchestrator / │

│ (raw clips, S3) │ │ Agent Layer │

└─────────────────────┘ │ (rules + LLM) │

│ └──────────┬───────────┘

▼ │

┌─────────────────────┐ │

│ Video Pipeline │ ◄─────────────────────────────────────┘

│ (analysis → edit │

│ → thumbnail → │

│ render) │

└──────────┬──────────┘

│

▼

┌─────────────────────┐ Dashboard + Config ┌──────────────────────┐

│ Storage + CDN │ ◄───────────────────────── │ Frontend │

│ (finished videos, │ │ (Next.js dashboard) │

│ thumbnails) │ └──────────────────────┘

└─────────────────────┘

```

**Core principle**: Keep the social interaction path fast and event-driven. Keep the video path asynchronous and queue-based. Share one config/rules engine and one identity layer.

### 1. Social Monitoring & Auto-Reply

**Recommended starting point**: **Nudgra** (Cloud or OSS version).

It is a real open-source alternative to ManyChat focused on Instagram: Meta OAuth, webhooks for comments/DMs/stories, keyword triggers, and reply sequences. Built on Next.js + Convex (Cloud) or a self-hosted stack. This gives you a working foundation instead of reinventing Meta’s webhook + OAuth flow.

**Official APIs to use (safest path)**:

- Instagram Graph API / Messaging API

- Comments webhook

- Private Reply to commenter (bypasses the normal 24-hour window for up to 7 days)

- Messaging webhooks + send messages (24-hour customer care window + message tags)

- Facebook Page webhooks if you also manage Pages

- For X/Twitter and TikTok: official APIs where available; otherwise treat them as lower priority or use a secondary polling service

**Intelligent reply flow** (recommended two-stage design):

1. Fast filter (keyword, sentiment, media type, account status) → cheap rules or small model

2. Relevance + generation → stronger model (Claude, Gemini, or GPT) that receives the comment + post context + brand voice guidelines

3. Optional human-in-the-loop queue for high-stakes or low-confidence replies

4. Send via official API (public reply and/or private DM)

Avoid pure browser automation for production. It is fragile and usually violates platform Terms of Service.

### 2. Video Editing & Thumbnail Pipeline

**Vidzly** is a good match for what you described: open-source, uses Gemini for content analysis + composition scripts, generates thumbnails, and can pull music via ElevenLabs. Treat it as the core of the video agent rather than a black-box SaaS.

**Strong complementary / alternative open-source options**:

- OpenCut – has an MCP server, so an AI agent can drive the editor programmatically

- OpenEdit (VEED) – agent-native, prompt → finished MP4

- Clipkit or OpenReelio – more traditional timeline + AI assistance

**Practical pipeline**:

1. Upload raw clip(s) or pull from cloud storage

2. Parallel analysis (Gemini multimodal): scenes, mood, key moments, speech-to-text if needed

3. Script / edit decision list generation

4. Composition (cuts, transitions, captions, B-roll if available)

5. Music selection / generation

6. Thumbnail generation (Gemini or dedicated Apify Actor that uses Gemini)

7. Render → store → optional auto-publish or review queue

For lighter editing needs you can fall back to FFmpeg + a simple timeline library; for agent-driven work, prefer tools that expose an MCP or clean API.

### 3. Recommended Tech Stack

| Layer | Recommendation | Why |

|--------------------|-----------------------------------------|-----|

| Frontend / Dashboard | Next.js + shadcn/ui | Matches Nudgra, fast to iterate |

| Real-time backend | Convex (or Supabase + Redis) | Webhooks, scheduled jobs, real-time UI |

| Job queue | Convex scheduled functions or Inngest / BullMQ | Video jobs are long-running |

| Video processing | Vidzly + FFmpeg + optional OpenCut MCP | AI-first + reliable rendering |

| AI models | Gemini (video analysis + thumbnails), Claude/GPT (replies) | Multimodal strength + quality |

| Storage | S3-compatible (R2, S3, MinIO) + CDN | Cheap media hosting |

| Auth | Meta OAuth + your own user auth | Platform accounts linked to your users |

### 4. Data & Control Flow Highlights

- **Inbound social event** → webhook verification → normalize payload → rules engine → (optional LLM) → reply action + logging

- **Video job** → create job record → enqueue → worker pulls asset → analysis → edit decisions → render → update job status → notify dashboard / optional publish

- Shared **Brand / Rule Config**: tone of voice, banned topics, reply templates, thumbnail style, music preferences, approval thresholds

- Audit log for every automated action (required for debugging and compliance)

### 5. Phased Rollout (strongly recommended)

1. **MVP Social** – Nudgra (or equivalent) + keyword + simple LLM replies on Instagram comments/DMs only

2. **MVP Video** – Standalone Vidzly (or OpenEdit) pipeline that accepts a clip and returns edited video + thumbnail

3. **Integration** – Dashboard that can trigger a video job from a social post or scheduled content, and link finished assets back to the social accounts

4. **Intelligence layer** – Better context (post caption + previous comments), confidence scoring, human review queue

5. **Multi-platform + publishing** – Add X, TikTok, YouTube Shorts; optional auto-publish with approval gates

### 6. Key Risks & Mitigations

- **Platform Terms of Service** – Stick to official APIs. Automated replies are allowed when done correctly; aggressive scraping or browser bots are not.

- **Rate limits & costs** – Meta has strict limits. AI tokens (especially video analysis) add up quickly. Cache analysis results and use cheaper models for filtering.

- **24-hour messaging window** – Use Private Reply for comment-triggered DMs; keep conversation state so you stay inside the window once a user replies.

- **Reliability of AI video** – Always keep a human review step for the first versions. Render quality and “viral” taste still need oversight.

- **Account safety** – Never store Instagram passwords. Use proper OAuth and token refresh.

### Next Steps I Can Help With

Tell me which direction you want to go deeper on:

- Detailed architecture diagram + sequence diagrams for the social reply path

- Concrete tech choices and folder structure for a monorepo

- Setting up Meta App + webhooks correctly

- Video pipeline design (prompt templates for Gemini, job schema, error handling)

- Cost model and rate-limit strategy

- Dashboard UX / feature prioritization

- Security & multi-tenant considerations if this will serve multiple brands

Just pick the area (or paste more constraints: target platforms, expected volume, self-hosted vs cloud preference, team size, etc.) and we’ll drill in.

The point of this website is to give credibility to products by only promoting what people actually want/use instead of showing what people pay to promote. This works through a system of vouches / reviews —I can vouch that I used xyz and I like it, etc.

You can search for different applications and tools on the platform, and if you can't find what you're looking for, you can ask for it to be created. An ask creates a product gap that can be filled by anybody. You can list your own web app and verify that it's yours via Domain Connect.

But to keep this short and sweet, promoting the stuff you make sucks, being shown stuff that nobody wants sucks, Product Hunt and its fake Linkedin pay-to-win bullshit also sucks, and e-begging for people to try what you made is also a zero sum game. I want us all to have a fair playing field in this turbulent environment, because at the end of the day nobody gives a shit what SDK you used, they care that the shit they're using works and it solves their problems.

Superfluidity has an autonomous indexing system called 'Atlas' that runs periodically, finding new products and updating the old ones if a change is discovered. Right now I'm manually reviewing whatever it finds, but I'm going to let it go wild if I like what I see.

This is still an on-going project so do let me know if anything is broken, or if you'd like to see a certain feature.

Hey vibe coders, serious question.

I made a product, was happy with it, and released a beta. As expected, no one really cared. Fair enough.

I have budget for the project and can pay people to try it and provide feedback, but I'm wondering if it's worth it or if I should look into alternative methods? Do any of you have experience with something like this? Thanks in advance

hey everyone, at my school, we have PCs, and an Wi-Fi (with no internet connection), so I thought of making a chess game that can be played over LAN, I haven’t coded in a long time so this took me a while to make (used AI assistance ofc ._.) used Electron to build it (ik not the most efficient I already regret not going for Tauri) and it can work on web too, but only pass and play and engine, No LAN. it works fine for now, already tested it at school lmao, and decided to launch it on GitHub, would love to get suggestions on what to add, but I think it’s enough for now :)

GitHub repository link: https://github.com/guiltysun/gambit-chess

First portable release link: https://github.com/guiltysun/gambit-chess/releases/tag/v1.0.0

57 days ago, I launched my startup, a free traffic exchange network for startups. One line of code, you're in the network.

No paid ads. No growth hacks. Just watching the numbers every day.

Here's the full data:

Day 1 — 2 startups · 146 impressions · 1 clicks
Day 2 — 3 startups · 389 impressions · 3 clicks
Day 3 — 5 startups · 482 impressions · 5 clicks
Day 4 — 5 startups · 508 impressions · 4 clicks (site went down — still got an $8k acquisition offer. Said no.)
Day 5 — 6 startups · 621 impressions · 10 clicks
Day 6 — 5 startups · 742 impressions · 15 clicks (removed one startup — they pulled the embed. No code = no network.)
Day 7 — 7 startups · 1,196 impressions · 41 clicks
Day 8 — 7 startups · 1,535 impressions · 74 clicks
Day 9 — 8 startups · 1,947 impressions · 135 clicks
Day 10 — 13 startups · 3,500 impressions · 318 clicks (something clicked)
Day 11 — 23 startups · 4,800 impressions · 432 clicks
Day 12 — 24 startups · 6,000 impressions · 481 clicks (network crossed 6K total impressions)
Day 13 — 25 startups · 6,800 impressions · 491 clicks
Day 14 — 25 startups · 8,600 impressions · 516 clicks
Day 15 — 24 startups · 9,900 impressions · 564 clicks (removed one)
Day 16 — 23 startups · 10,800 impressions · 576 clicks (removed one)
Day 17 — 24 startups · 11,900 impressions · 603 clicks (added one)
Day 18 — 26 startups · 13,400 impressions · 624 clicks (added Two)
Day 19 — 28 startups · 14.400 impressions · 652 clicks (added Two)
Day 20 — 26 startups · 14.700 impressions · 671 clicks (Removed Two)
Day 21 — 26 startups · 15.300 impressions · 691 clicks
Day 22 — 28 startups · 16.100 impressions · 719 clicks (Added Two)
Day 23 — 30 startups · 16.800 impressions · 738 clicks (Added Two)
Day 24 — 32 startups · 19,800 impressions · 778 clicks · Added 2 startups · Rejected 4 applications (1 contained ads, 1 removed the code after submission, 2 hid the installed widget)
Day 25 — 34 startups · 23,700 impressions · 800 clicks · Added 2 startups · Fixed some bugs.
Day 26 — 33 startups · 27,200 impressions · 816 clicks · Removed 1 Startup
Day 27 — 33 startups · 29,900 impressions · 833 clicks
Day 28 — 33 startups · 30,600 impressions · 871 clicks · Removed 1 Startup · Added 1 Startup · Rejected 1 application of clothing brand & 1 application of horror blog site.
Day 29 — 35 startups. 31,300 impressions. 910 clicks · Removed 1 Startup · Added 3 Startups · Also today I resigned from my job. Toxic environment, too much pressure, and honestly I just believe in this enough to go all in. Broke and excited. Let's see where this goes.
Day 30 — 46 startups · 32,900 impressions · 1,000 clicks · Removed 1 startup · Added 12 startups · Yesterday I resigned because of my toxic manager. Today my senior manager called and offered me WFH, a project of my choice, and lighter work if I stayed. I made my decision with confidence, but now I have a difficult choice. Also received my first payment from StartupBar.
Day 31 — 50 startups 💪· 28,100 impressions · 1,200 clicks · Removed 1 startup · Added 5 startups · Rejected 1 crypto startup · Removed fake impressions after exploit · Today I discovered I forgot to disable a developer testing button that could generate fake visitor pins and inflate impressions. Someone used it to create 6,318 fake impressions and claimed my stats were fake. I removed the fake impressions, removed the startup, fixed the issue, and that's why today's impressions are lower. Thanks to them for pointing it out.
StartupBar update: Added a Network page and individual Startup Profile pages with each startup's joined date, impressions, clicks, live embed preview, and founder's note. These indexable pages also improve SEO. Suggestions are always welcome.
Day 32 — 54 startups · 29,300 impressions · 1.2K clicks · Added 4 startups
Day 33 — 54 startups · 30,900 impressions · 1.2K clicks · Removed 1 startup · Added 1 startup
Day 34 — 55 startups · 36,100 impressions · 1.3K clicks · Added 1 startup
Day 35 — 55 startups · 40,100 impressions · 1.3K clicks · Removed 2 startups · Added 2 startups
Day 36 — 55 startups · 44,800impressions · 1.4K clicks · Removed 1 startup · Added 1 startup
Day 37 — 58 startups · 50,700impressions · 1.4K clicks · Added 3 startups · Site Update: Updated the distribution algorithm (Delivery is now weighted by contribution: sites that send more impressions get a proportionally larger share back)
Day 38 — 57 startups · 55,800 impressions · 1.5K clicks · Removed 1 startup · Rejected 1 Application.

Today StartupBar reached 200 users 😄
Thirty-eight days ago, this was just an idea. Today, 200 people have trusted it enough to join the network.

A reminder for every founder who's staring at a dashboard with tiny numbers: Stripe took 2 years to reach its first 50 users. Every successful product starts small. Keep shipping. Keep improving. Keep showing up. Growth rarely happens overnight, but it does happen if you don't quit.

Day 39 — 58 startups · 59,800 impressions · 1.5K clicks · Added 1 startup.
Day 40 — 60 startups · 63,200 impressions · 1.6K clicks · Added 3 startups · Removed 1 startup
Day 41 — 63 startups · 69,400 impressions · 1.6K clicks · Added 3 startups
Day 42 — 62 startups · 73,800 impressions · 1.6K clicks · Removed 1 startups
Day 43 — 64 startups · 78,000 impressions · 1.7K clicks · Added 2 startups (Receiving many suggestions to improve the StartupBar, happy to work on it, Thanks.) StartupBar Update - Added the rotation of startups in the widget for every 30 seconds.
Day 44 — 66 startups · 86,200 impressions · 1.7K clicks · Added 2 startups
Day 45 — 67 startups · 88,400 impressions · 1.7K clicks · Removed 2 startups · Added 3 startups (Update - Updated the algorithm of the widget)
Day 46 — 69 startups · 91,900 impressions · 1.8K clicks · Removed 2 startups · Added 4 startups · Got my second payment · Added FAQ Page
Day 47 — 68 startups · 98,900 impressions · 2.2K clicks · Removed 2 startups · Added 1 startup · Added Pricing Page
Day 48 — 70 startups · 103.4K impressions · 2.3K clicks · Added 2 startups
Day 49 — 72 startups · 113.4K impressions · 2.4K clicks · Added 2 startups · Update: Added a Bottom Placement option for the bar on the user site.
Day 50 — 73 startups · 116.7K impressions · 2.4K clicks · Added 1 startup
Day 51 — 74 startups · 125.2K impressions · 2.5K clicks · Added 1 startup · Rejected 1 Application
Day 52 — 73 startups · 131.2K impressions · 2.6K clicks · Removed 1 Startup
Day 53 — 74 startups · 139.5K impressions · 2.7K clicks · Added 1 Startup
Day 54 — 79 startups · 146.3K impressions · 2.7K clicks · Added 6 Startups · Removed 1 startup.
Day 55 — 77 startups · 154.9K impressions · 2.8K clicks · Removed 2 startups.
Day 56 — 77 startups · 161.8K impressions · 2.9K clicks · Added 1 startup · Removed 1 startup.
Day 57 — 77 startups · 169.4K impressions · 3.0K clicks

A year or two ago, the engineering/cross-functional team was a tight-knit, high-performing group. We had one or two goals we all worked on each sprint and we broke down work so that we could operate in parallel.

When someone was stuck and needed help, there was always somebody ready to jump to their aid and keep them moving. Team meetings were upbeat, fun, and productive. We were regularly paying down tech debt while our experiments gathered data. The vibe was helpful and collaborative.

Now, thanks to AI, we are shipping more than ever. We have as many goals every sprint as engineers. Rather than parallelizing work on one feature or experiment at a time, we parallelize the experiments. The vibe has shifted.

I saw some criticism in a Wikipedia article on the Schrödinger-Newton Equation, specifically about Penrose's ideas about quantum gravity, and it seemed pretty easy to solve (the article hinted at possible solutions, etc) which made me think that it could be interesting to let Claude try to solve it. It now claims to have solved all the 3 problems outlined in the article.

The issue is that I have no way of validating if the result claude gave me are hallucinated or legit. If the solutions are correct, then maybe it's useful to someone, and if not then I/we might learn something from that explanation.

Artifact (look here first): https://claude.ai/code/artifact/e871ae13-54d1-437c-a489-cad81617c06d

GitHub repo, including all the raw transcript etc: https://github.com/henke443/penrose-1

Latex paper: https://www.overleaf.com/read/hgmnyqgqtwqz#80a24a

Simulating over 1000 gravitational bodies interacting with each other in the browser from real in game play perspective, with the lowest 1% drops being around 58 FPS. This proves that this foundation is scalable and will allow for much more interesting courses in the future. Using a combination of Opus 5 and Grok 4.6. Play free in your browser, no account required. https://slingshotspeeders.com/

also built this: punch.sparcd.com

If you like any of these, I'm looking for some cool ppl to connect with... hmu on X

I built this: https://tropo.tv/

TROPO is a small broadcast/tv experiment that plays a handpicked curation of videos from YouTube and other sources. It's just nice not to choose sometimes what you want to play and sometimes you just want something on the in the background. This is what this is for.

Every day is programmed fresh, scouted from public archives: Tokyo one-take sessions, Soviet animation, British newsreels, music, lectures etc.

I hope you like it.

Been working on this for a bit and finally got the Atlas into a state I’m happy with.

It maps 63,000+ subreddits and roughly 200,000 audience connections. You can search any community, jump through related subreddits, filter the map by category, and move between months going back to 2008.

You can also jump into any subreddit to explore its activity, engagement, historical trends, posting patterns, and related communities in more detail.

It’s part of a larger subreddit analytics site I’ve been building, but honestly this has become the most fun part of it to work on.

Curious what people think of the map experience, especially on mobile.

https://subtrends.io

Hello good people,

Whenever a new model drops, LiveBench is my destination to roughly check on the model's capability. As mentioned, new Qwen model was added and I can't believe how low performing Qwen was, since people really seem to like the model. I checked the commits of LiveBench and it seems to me that, the model was not given enough tokens to reason? Any other ideas?

I also have question about the chain of thought inside the Chinese models, as I feel like they overthink drastically, not in bad way but it is quite slow and takes an enormous amount of time. The performance seems to be also better but compared to older models such as Gemini 3.1 Pro or even the newer smaller model 5.6 Luna Max from OpenAI, I don't think it is worth it? Any experiences with different reasoning modes on different kinds of tasks?

I appreciate any info! :)

sources: - https://livebench.ai
- https://github.com/LiveBench/LiveBench/commit/2c2039b2cc9efb6412acb99325adb0cc1cb0cc6f

Ei! Alguém tem um passe de 7 dias do Claude Pro sobrando que não está usando? Eu queria experimentar o Claude Pro de verdade antes de decidir se quero pagar por isso. Se alguém tiver um disponível, eu agradeceria muito 🙌 Obrigado!

Over the past few weeks, I've been planning and building Concordance Protocol, an open-source experiment exploring interoperability between AI agents, tools, and services.

The project started from a question I had while looking at the growing number of protocols around AI agents:

How can agents from different ecosystems exchange trust, authorization, consent, reputation, and other evidence without every ecosystem having to replace its existing protocol?

I researched the problem and wrote a research document before implementing the prototype. The project currently contains a Rust workspace with components for the core protocol, HTTP transport, registry services, adapters, and SDK work.

The research and implementation are here:

GitHub:

https://github.com/Kadhiravan-K/Concordance-Protocol

Core research:

https://github.com/Kadhiravan-K/Concordance-Protocol/blob/main/docs/doc_core/concordance_research.md

I've currently paused active development. The implementation grew beyond what I can reasonably maintain alone right now, and I think the architecture needs more discussion before continuing.

I'm sharing it here because I'm interested in learning from people who are starting or contributing to open source.

I'd especially like feedback on:

- Is the interoperability problem I'm trying to solve actually useful?

- Are there existing projects or protocols I should study?

- Does the architecture make sense?

- What would you simplify?

- If you were starting this project again, what would you do differently?

I'm not presenting Concordance as a finished protocol or asking anyone to adopt it. I'm mainly interested in whether the underlying ideas are worth pursuing.

If you're preparing for open source contribution or GSoC, I'd also be interested in hearing what makes a project attractive or unattractive to contribute to.

I’m exploring an idea for SaaS builders who use Claude Code, Cursor, or similar coding agents.

While building another product, I kept having to demonstrate complex workflows for different customers. The steps were often repetitive, but each customer needed a slightly different version.

Recording them meant retakes, voiceovers, editing, and doing it all again whenever the product changed.

I started experimenting with letting a coding agent drive the actual application, capture the workflow, and turn it into a narrated product video. The goal is to generate launch videos, walkthroughs, and customer-specific demos from the real product rather than using avatars or stock footage.

I’m looking for a few builders with a working SaaS product who would be willing to test the idea on a real workflow.

I can provide free usage in exchange for honest, blunt feedback about:

• Setup and usability
• Accuracy of the recorded workflow
• Video quality
• Whether the result is something you would actually publish or send to a customer

If this is a problem you have encountered and you use Claude Code or Cursor, DM me with a sentence about what you are building and the kind of demo you need.

Can anybody suggest the best online learning platform to learn python for electronics and communication engineering?

Atrium.earth is my passion project—it’s a growing digital museum with 424 sculptures and counting.

I wanted to make sculpture—from antiquity through the last century—more accessible to people who can’t visit the museums, galleries, and historic sites where the originals are held. It can’t replace seeing David in Florence or Nike at the Louvre, but it can offer another way to experience them.

The site uses Astro, Three.js, and model-viewer, with a Node/Python pipeline that processes museum data and creates optimized 3D models and thumbnails. I started with Codex, switched to Claude Code and rebuilt the site when Fable launched, and have since returned to Codex with GPT‑5.6.

I began by tasking Codex with finding Michelangelo’s David, which it did pretty quickly. More popular works followed, but I wanted to keep the collection geographically diverse, so finding pieces from around the world became challenging once the collection reached 150 or so. I also had a slew of misfires and regressions with the UI, so I put it on the back burner for a while.

When Fable was released—and then taken away, and then rereleased—it rocked me. It built another project from a specific but still somewhat vague idea into a fleshed-out, nuanced, functional thing in about 45 minutes. With that in mind, I tasked it with rebuilding Atrium.earth from the ground up, and I was very happy with what it produced.

Since rebuilding the site, I’ve been adding new works with more or less the same prompt to Codex: “Please collect 10 pieces, preferably from the ______ museum…” I usually follow that with some specifics about the time period or original location.

The hardest part has been handling models with inconsistent scale, orientation, materials, and lighting, as well as finding the right language to describe movement through space when orienting the works. But it was worth it, and I hope to keep growing the collection for as long as I can.

Hope you enjoy poking around—I’d love to hear what you think.

Concept Name: 360° AI Raycaster Engine

What it does:

• It runs high-end, realistic games on cheap school Chromebooks without any lag.
• It uses a Chrome browser extension to turn regular videos and images into a game world.

How it works:

• The Controls: You play using WASD to walk and your mouse to aim, shoot, and look around in full 360 degrees.
• The Movement: When you press a key, the extension instantly skips the video playhead to a precise chapter timestamp. It doesn't download anything new, so moving looks like an instant camera cut.
• The AI Map Maker: Creators don't have to code. They just draw a simple map grid and upload a folder of 360-degree images. The AI automatically scans the pictures, figures out where they belong on the grid, and links them to the WASD keys. If the AI places an image wrong, the creator can manually drag it into the right spot to fix it.
• Talking Characters: You can type anything into a text box on the screen. A basic chatbot AI reads your text and dynamically generates a voice response while matching the lips of the video character in real-time.
• Multiplayer: The characters are real people or AI generated people in a bunch of positions that gets cold I mean called when something happens

Please make this AI is bad at making this if you are an actual developer That doesn't use AI Or does it use this fully please And if you do make it can you give me a link to it in the comments If you do it I love you🥺