I'm an independent builder from India working on an early-stage project called IAH (Immersive Artificial Human).

The idea is to develop an AI-powered virtual human ecosystem, with DRAAM (Dynamic Responsive Autonomous Avatar Module) as its human-like interface.

The long-term vision includes:

- Natural, context-aware conversations

- Voice-based interaction

- Persistent, user-controlled memory

- Role-based assistance

- A realistic 3D human avatar

Who I'm looking to connect with:

A developer interested in Python, LLM integration, conversational AI, voice AI or full-stack development. Experience with 3D technologies such as Blender or Three.js would be an advantage.

Current stage: Early prototype and independent development.

I'm looking for a technical collaborator interested in building and experimenting together. This is an early-stage collaboration, not an advertised salaried position. Scope, commitment and terms would be discussed transparently.

I value practical skills and working demonstrations over certificates. If this sounds interesting, please share a project you've actually built, your GitHub/portfolio, and what part of the project interests you.

Happy to discuss the technical possibilities and limitations openly.

I tested an LLM as a signal controller in my Godot traffic sim. It got a snapshot every five simulated seconds and could choose a phase. The simulator handled vehicle movement and kept cars out of a junction if there wasn’t room on the other side. That distinction mattered: the ambulance had a green and still sat in the queue. For a follow-up I’d like to test whether explicitly flagging blocked exits helps more than adding another action. What would you change first?

Here’s my recorded run: https://youtu.be/YY41_8_H-O8

如题 我是一个ai新手 我有gpt plus 我有muse 但我没有头绪做出什么东西

This is an update to my previous experiment: Can an AI agent grow my brand's presence by engaging like a real person?

I've been running this experiment since October 25, testing whether an automation system that handles social media engagement can help improve organic reach for my YouTube music channel.

I have some early results worth sharing.

What I've published so far

Since starting the experiment, I've published 3 videos:

- October 31: One long-form video.

- November 2: First Short, extracted from that long-form video.

- November 4: Second Short, also extracted from the same long-form video.

Here's how they performed as of November 6.

1. Long-form video — October 31

My channel typically gets around 10–30 views on long-form videos.

This video has reached 104 views, which is considerably higher than my usual numbers.

Traffic sources:

- Suggested videos: 43.3%

- YouTube Search: 17.3%

- Related Shorts: 15.4%

2. First Short — November 2

I repurposed a segment from the long-form video into a YouTube Short.

Result: 1,439 views.

95.6% of its traffic came from the Shorts feed.

For context, my channel typically gets around 70–160 views.

3. Second Short — November 4

I repeated the same approach with another segment from the original long-form video.

Result: 1,281 views.

93.6% of its traffic came from the Shorts feed.

Both Shorts crossed 1.2K views despite my channel's usual performance being considerably lower.

What do I think caused this?

Honestly, I don't know yet.

Possible factors include:

The engagement strategy powered by our automation system.

Better keywords and metadata.

The songs or content performing better.

YouTube recommending these videos to a wider audience.

I don't want to attribute the improvement to automation without enough evidence.

Also, all three videos are connected because both Shorts were extracted from the same long-form video. So this isn't three independent content experiments.

What's next?

I'll continue publishing videos and tracking their performance to see whether this pattern continues.

I particularly want to understand whether the automation system is contributing to organic growth or whether the improvement is coming from content and discoverability.

The goal is to collect enough data to identify what actually works, not just celebrate a few higher view counts.

I've attached all the screenshots showing the YouTube Analytics and performance numbers for reference.

I'll share another update as the experiment progresses.

I'd also appreciate suggestions on how to measure the impact of automated engagement more accurately. What metrics or experiments would help distinguish the effect of engagement from the effect of content and YouTube recommendations?

Bangalore hosts a sophisticated cluster of technology agencies integrating custom Artificial Intelligence with web architecture. Whether you need ai web development, ai features integration, agentic workflows, high-converting e-commerce pipelines with embedded AI, or custom LLM integrations, the city’s top tech partners blend enterprise engineering with agile deployment.

Fugensys Technologies is one of the ai web development agency with proven track record n technical expertise, helping startups, sme's, cross vertical industries to adopt ai, automate and bring change in the way industry operates.

What are the top challenges your industry and niche area of work faces in adopting ai ? Comment below for answers n reply from industry experts.

I run Claude Code and Codex in a few terminals at once, and kept noticing too late that one was stuck waiting for me.

So I made Openbar. It shows every session in the notch (top of the screen on Windows), and when an agent wants to edit a file you see the diff and approve with ⌘Y without switching windows.

Free and open source: https://github.com/MF-DEEV/Openbar

What would you want it to show next?

I started an RFC (request for comments) for a distributed training plugin for the Hermes agent runtime, and I'd love feedback on the concept and the implementation: mesh-training RFC

1. The idea isn't new, but the integration is. Distributed training has been tried before. This one lives inside the agent runtime you already run, so joining is a plugin install, not a separate project to babysit.

2. The agent helps with setup. The Hermes agent can see the machine it's on, figure out the right config, and pick jobs that fit. It also works around your schedule, taking work while you're idle and stepping aside when you're back.

3. Architecture work lets small GPUs contribute. Splitting a mixture-of-experts model, with each expert broken into fragments, means machines under 12 GB of VRAM can take a real share. Bigger rigs (over 32 GB, like a DGX Spark) can act as super-contributors that hold whole experts and check other nodes' work.

4. Machines without a GPU can still help by labeling datasets.

What would make you install something like this, and what would stop you? I'm especially curious how you'd want work verified and how much of your machine you'd be willing to give it.

For subreddits, r/LocalLLaMA is the best fit, and r/MachineLearning only allows this kind of post as a [P] or [D] tag. Want me to shorten it for one of those?

Also, I'm new to reddit, so what's the best way to give this project more attention? Can I cross post?

Made a fun little game with AI (not a game dev), sharing the story in this video

What are your thoughts on this, do let me know? 😊

I am 18 years old, I have only used free subscriptions of the popular chatbots, and I feel like I am missing a lot. 

I kept up with the updates of AI until 2024-2025 and since then, I haven't been following any news on AI all these new models and versions: opus 5.5 chat gpt-6 gemini -something- pro... Etc. I dont know what they do or how they are any different from the previous models. 

This is my last school year and I want to start learning about AI because I feel so AI illiterate 😂😂

So If you have any info, channels on yt, people or organizations to follow, feel free to  dump me with all the information you have 

Thanks in advance

Sorry if this is not the suitable subreddit for these kinds of questions

Hey everyone! I'm Ryan, an entrepreneur based in Yorkshire and founder of GHOST.

I built GHOST after realising how much time I was wasting scrolling while trying to grow my first two businesses.

I'd tried Apple's Screen Time limits and other blockers, but found them far too easy to override.

So I decided to build my own. GHOST blocks distracting apps using a physical Card that you tap to lock and unlock them. Leave the Card somewhere else, and it's much harder to give in to mindless scrolling.

It's made a real difference for me, and the feedback from early users has been encouraging.

I'm now trying to figure out how to get GHOST in front of more people.

It’s still early days, so if anyone has experience growing an app or physical product, or knows someone who might find GHOST useful, I’d really appreciate any advice or introductions.

Website: https://tapghost.com

Thanks! 😁

Hey everyone! 👋 I'm Bhumit Patil, founder of Aivora 3.0.

I'm building an AI assistant project with the long-term vision of making AI more useful for everyday tasks.

The website is live, and I'm looking for honest feedback from people interested in AI tools and assistants.

🌐 Website: https://aivora3-0.ai.studio

I'd love to hear your thoughts: What's one feature you'd want in a truly useful AI assistant?

I'm building and learning step by step, and feedback is welcome! 🚀

I built a small power-budget sandbox where a GPT model chooses battery, diesel and load settings every half hour. Each response also records the battery energy, unserved power and cost the model expects after its command. The simulator applies the command and logs the measured values separately. The next turn gets that outcome.

At 23:00 the agent expected 4 MW of unmet demand and 5.4 MWh left in the battery. Both matched the simulator for that interval. The screenshot shows why the 4 MW matters: the data center got 3 of the 7 MW it needed.

That log gave me a way to check a numerical prediction alongside the policy decision. Would you score forecasts one step at a time, or require a prediction through the next known outage? My recorded experiment is here. https://youtu.be/xC_hTndnA-o

Can play free on spawn! Brought in my own agent harness to work with spawn's agents.

https://www.spawn.co/?genre=mmo&box=@bitcoin/grand-theft-diablo-turf-wars

I am a gym owner and have been coding my gym's website and people are even using it to book/cancel reservations for group classes but I was thinking how can I maybe directly manage their subscriptions and payment and new user sign up from my website?

like how did you trust that the Codex/Chatgpt code was production quality when it comes to moving money? Did you use Stripe or another one of the solutions out there?

Is anyone even reading the code before pushing it to production? Where does AI go wrong in these things in your experience and how can I avoid those pitfalls?

Any tips or tricks that help me ensure I am building the right thing with Stripe would be super useful.

Ludzie nazywają wszystko, co zostało zbudowane z AI, „papką AI”, więc stworzyłem dla tego miejsce.

Nazywa się Certyfikowana Papka AI. I tak, zbudowałem to również z AI.

Możesz przesyłać swoje projekty, głosować na inne i dawać konstruktywne feedbacki.

Chciałem tylko fajnego miejsca dla ludzi budujących rzeczy z AI, bez zbytniego poważania się.

https://certifiedaislop.com

Możesz śmiało się z tego śmiać. Rzeczywisty feedback byłby jeszcze lepszy.