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Viloi AI is a platform that mainly focuses on humanizing texts and while it doesn’t have it’s own chatbot yet, there are recent models of ChatGPT, Claude, Gemini and Grok AI available in it with a single very cheap subscription. Our most special feature is its Humanizer which I am very confident in, that it is indeed worth calling one of the best in my opinion. It has many modes and also can work on 4 different languages. Although it can not be the same opinion for everyone, I still ask you to try it out for free and if you like it, its up to you to buy any subscriptions.

Moreover, we had Turnitin Checks before, but now its under maintenance again and will soon be released again with better functionalities and efficiency.

Incase any of you try it and find any bugs, you can do us a favor and let us know in our Discord Server. You can find it through our website.

Thank you.

Do u guy’s know these ai alternatives b/c I’m so tired of seeing their daily primium buy message

any recommendations on team AI chats + long running work? ive been looking into https://duet.so but wanna also check out other apps. how are you using ai cohesively in your company?

I’ve tried quite a few AI companion apps recently, including LustCrush AI, Replika, and Character AI, and I feel like most platforms still focus too heavily on visuals, quick roleplay, or NSFW features.

But honestly, the moments that actually feel real usually come from long-term consistency.

Things like:

remembering conversations from days ago

bringing up small personal details naturally

reacting differently depending on your mood

initiating conversations instead of waiting for prompts

developing personality over time

I think the hardest part of building a believable AI companion isn’t realism anymore. It’s emotional continuity.

If you could design the perfect AI companion experience, what would it actually do differently?

Meet Aiko — My AI Anime Waifu

Aiko is an AI chatbot with persistent memory, vision, voice input/output, web-search via simple tool calling, and 3D avatar animation — all running on models under 4B parameters. Technically, she could run on a home PC.

I submitted Aiko as my entry for the Hugging Face Build Small Hackathon, and after approval, got some free credits from their sponsors. So right now, all models run via llama.cpp servers on the Modal platform.

The source code is free and fully open source — do whatever you want with it. Note: the codebase is currently wired to Hugging Face Spaces (Gradio) and my specific Modal endpoints, so some modifications are needed to port it to a local machine and run on a normal OS.

• Live: HuggingFace Space Demo
• Video: YouTube
• Repo: https://github.com/OppaAI/Aiko-demo

Why I built her

None of the AI companions or chatbots on the market really suited my taste. What I really wanted was a good TTS that could speak both English and Japanese with a beautiful anime girl voice — something I could use to help learn Japanese.

For the memory architecture, I recycled the system I originally built for my AI autonomous robot project (submitted to NVIDIA earlier this year). Figured it’s better to put it to good use than let it sit there doing nothing.

Stack

• 🧠 Brain: Ministral-3 3B Instruct (GGUF) via llama.cpp
• 🔊 Voice: MioTTS 2.6B, custom-cloned voice (may try FishTTS S2 Pro — it supports emotional voices including laughing, angry, surprised, shouting, and even moaning… hehe)
• 🎙️ Ears: faster-whisper large-v3-turbo for speech-to-text (may try Nemotron 3.5 streaming model — supposedly faster with streaming support)
• 👁️ Eyes: MiniCPM-V 4.6 (1.3B) for image/video inference via webcam or uploaded images/videos
• 💾 Memory: SQLite-vec vector DB with hybrid semantic + keyword search, using an Ebbinghaus decay curve so frequently-recalled memories persist and unused ones fade after a grace period
• 🔍 Web Search: Self-hosted SearXNG instance on Modal, called via LLM tool/function calling
• 🔧 Tools: Weather, time/timezone, currency conversion, crypto prices, and anime lookups — all via LLM-driven function calling with regex-based intent detection as fallback — May try for more complicate tasks in the future
• 🎭 Avatar: VRoid 3D model rendered via Three.js/three-vrm (using the default model for now — may build my own once I figure out 3D modeling and animation)

I’m looking for a Chatbot to add to my Shopify store, I’m looking to a good one can help with improve trust and hopefully help with the conversion rate.

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Would love to get your opinion and recommendations…

Okay so I’ve been trying to find this one bit. I’m trying to quit using ai in general and I just want to find my chats with this one specific bot first. The bot was something about the user having been in an accident and losing their memories and then char pretends to be user’s fiance (husband?) as some sort of revenge because user had sent char to prison for a decade or so.

Just wanted to share a look at a structural frontend refactor we’ve been working on over the weekend.

For a long time, the frontend of our workspace assistant was built entirely on a monolithic, top-down Streamlit Python script. Streamlit was an absolute lifesaver for rapid backend-driven prototyping, but as our layout complexity grew, we completely hit a wall with its linear execution model. We couldn’t handle complex, asynchronous sidebar interactions, dynamic widescreen layouts, or granular component state-swapping without triggering awkward global page redraws.

To fix that, we spent the last couple of days completely decoupling the frontend and rebuilding the layout from scratch.

Our Current Architecture:

• Frontend: Flutter & Dart. We migrated to a modular widget system using Riverpod (StateNotifierProvider) to isolate local state management across our custom side panels, user profiles, and view configurations.
• Backend / Gateway: Python backend handling token parsing, managing database sessions, and handling active chat histories.
• Streaming Logic: Communication between the Flutter client and the Python architecture is managed via Server-Sent Events (SSE) to push raw text and model reasoning deltas in real-time.

I’ve attached a screenshot showing how the widescreen desktop profile layout is behaving right now. https://imgur.com/djSLcjP

It’s been an incredibly fast learning curve jumping from linear Python scripting into the world of nested Dart widgets and compilation trees, but the rendering performance and interface freedom have been completely worth the headache.

Open to any questions on how we’re structuring the data model pipelines or handling the real-time Riverpod state notifications!

Ik it feels like a dumb question but I’m just curious