brody_slade_ai

I’ve been working on Vanta, a scalable AI hardware solution powered by 2–8 NVIDIA RTX 4090s, delivering up to 1.32 petaflops FP32 in a compact form factor.

It’s built for startups, developers and researchers to prototype, fine-tune and run models up to 70B parameters locally. So you can own your computer instead of renting.

- A 2-GPU setup costs $9k and breaks even in 9 months vs. cloud rental at $0.69/hr (ex: RunPod).

- The 8-GPU at $40k saves $12k in year one compared to $48k in cloud costs.

This can handle different AI framework: TensorFlow, PyTorch, ONNX, CUDA-optimized libraries, VLLM, SGLANG, llama.cpp...

I can get it built in a day and shipped out quick. Let me know what you think!

If you look into Digits and Vanta, Digits is better for FP4 inference, while a 4090 setup is more cost effective per FP32 FLOP, ideal for training large models and general compute

8x GPUs can handle parallel tasks better than Digits' single chip and scale easier. And 192GB of memory also exceeds its 128 GB unified memory for massive workloads

We’ve got 4090s on deck, stock’s solid, can get a rig built in 1 day and shipped fast

I’ve been working on Vanta, a scalable AI hardware solution powered by 2–8 NVIDIA RTX 4090s, delivering up to 1.32 petaflops FP32 in a compact form factor.

It’s built for startups, developers and researchers to prototype, fine-tune and run models up to 70B parameters locally. So you can own your computer instead of renting.

- A 2-GPU setup costs $9k and breaks even in 9 months vs. cloud rental at $0.69/hr (ex: RunPod).

- The 8-GPU at $40k saves $12k in year one compared to $48k in cloud costs.

This can handle different AI framework: TensorFlow, PyTorch, ONNX, CUDA-optimized libraries, VLLM, SGLANG, llama.cpp...

I can get it built in a day and shipped out quick. Let me know what you think!

I'm building a pre-built AI setup to test various LLMs and have successfully integrated it into my business. I want to share it with everyone here. This already integrates Qwen2.5-Coder 32B, Llama 3.1 70B 405B, FLUX.1 [dev] and Hermes-3-Llama-3.1-8B. It's highly customizable and can be used as a cloud AI, similar to Runpod when it's not in use. Tech specs:

- AMD Ryzen Threadripper PRO 5955 WX

- 2 x RTX 4090

- 128GB RAM

- SSD NVMe 1TB

I'm building a pre-built AI setup to test various LLMs and have successfully integrated it into my business. I want to share it with everyone here. This already integrates Qwen2.5-Coder 32B, Llama 3.1 70B 405B, FLUX.1 [dev] and Hermes-3-Llama-3.1-8B. It's highly customizable and can be used as a cloud AI, similar to Runpod when it's not in use.

Tech specs:

- AMD Ryzen Threadripper PRO 5955 WX

- 2 x RTX 4090

- 128GB RAM

- SSD NVMe 1TB

I'm building a pre-built AI setup to test various LLMs and have successfully integrated it into my business. I want to share it with everyone here.

This already integrates Qwen2.5-Coder 32B, Llama 3.1 70B 405B, FLUX.1 [dev] and Hermes-3-Llama-3.1-8B. It's highly customizable and can be used as a cloud AI, similar to Runpod when it's not in use.

Tech specs:

- AMD Ryzen Threadripper PRO 5955 WX

- 2 x RTX 4090

- 128GB RAM

- SSD NVMe 1TB

Hey there! I'm Brody from Autonomous Labs. We're backing it up with a 2-year warranty. We've sold over 417k units and gotfeedback from 1,000+ users.

We just launched the Ergochair Ultra 2, and it's got the same awesome warranty. I think you'll love it. Try it out and let me know how it goes

Yes we're using E2EE via TLS/SSL, storing everything locally, and keeping it all anonymous with no logins

We have built this one with a privacy-driven approach and ofc we still have paid plans for that, which ensures that we don't collect user data with the "free options". You can explore the AI agent and pay to use your favourite one for a customizable experience

For individuals, we have an online Anon solution with privacy by design to help keep your data safe up to some extent. Series B+ companies might want to try our on-premise solution that keeps data locked down tight within their own space. No worries about your data leaving your control.

We are utilizing the open source Llama3.1 405B model from Meta, which has shown impressive benchmark results compared to cloud-based alternatives. You are right, some AI models have their own built-in safeguard layers. You can try some Anon agent without built-in safeguard.

we use end to end encryption via TLS/SSL, comply with GDPR and CCPA and strict access controls to prevent unauthorized access. Conversations stay locally on the device, so you can defo view your previous data and searches

thanks! Hope you have a great time with Anon. Come back here some time and let me know how your chat goes

This AI model is stateless, so it doesn't store any info about previous conversations. It processes input in real-time, in memory only and purges any temporary data as soon as it's done.

it comes from my concerns that today's chatbots gather user data and use them for training, even sell them without user awareness. That's why I want to build a AI, where users can feel confident using it and about their privacy

Thanks. Glad you found this one helpful

Traditional models: collect data and store it on central servers. Anon AI models: Conversational data still go to an LLM, currently hosted by us. However, we've implemented several measures to ensure user data security: - Our AI model is stateless, meaning it doesn't store any info about previous conversations. - We use ephemeral processing, where user input is processed in real-time and only retained for the duration of the request. - The AI model processes user input in memory only, without writing any data to disk. - Any temporarily stored data is automatically purged at the end of the process. As we scale with model providers, we'll thoroughly vet them to ensure they meet our security standards. Thanks for pointing this out, and I hope this helps address your concerns!

We're a team of engineers with a proven track record, having developed our first AI-powered product - a smart desk designed to boost productivity back in 2015 (https://www.youtube.com/watch?v=P56pkB-iEI4). Our vision is to create a portfolio of AI products, with AnonAI being one of those products.

Yeah you can delete your conversation history whenever you want. Since we store data locally only, you have full control over it. Delete it, and it's gone for good. We don't retain any copies on our servers.

Besides our focus on privacy, what sets us apart is we'ew using some of the latest AI models out there. Our tech stack includes

- Llama 3.1 405B by Meta (outperforming GPT4 by 13%) - Llama 3.2 Vision by Meta (we're one of the first to use it!) - FLUX.1 [dev] by Black Forest Labs

Agree. That’s why I want to build a truly private AI, where users are confident to use without worry about their privacy. At first, we hosted stateless AI models using ephemeral processing to handle real time convos. We designed and implemented a protocol to ensure E2EE via TLS/SSL, GDPR/CCPA compliant with conversations stored locally on-device. We also provide an anonymous mechanism, no login required. Currently, we hosted open source flagship models of meta, Llama 3.1 405B, Llama 3.2 Vision and FLUX.1[dev] The product has just launched and is free to use with unlimited prompts. Paid plans are for AI agents. We still have a long way to go and would love to hear your feedback.

I've been exploring open source data analytics software and it's been a game-changer. I mean the flexibility and cost savings are huge perks. I've been looking into Apache Spark and KNIME, and they both seem like great options

I've used a few different tools for synthetic data generation, but some of the ones I've found to be most useful include SDV, Faker and DataSynth.

Looking forward to it! All the best :)

I've got a product, but I'm not sure if it's a good fit for Simple Lister. Can you provide any guidance or support to help me determine whether my product is a good fit for the platform, and if so, how I can optimize my submission to get the most out of it?

I've had my fair share of struggles with data anonymization. I've tried various techniques, from simple data masking to more complex approaches like pseudonymization and generalization.

However, I've found that these traditional methods often fall short in preserving the intricate relationships and structures within the data. That's why I was excited to discover synthetic data generation, which has been a game-changer for me. By creating artificial data that mirrors the statistical properties of the original, I've been able to share data with others without compromising sensitive information. I've used synthetic data generation for various projects and it's been a valuable tool in my toolkit.

Kalman Filter helped me understand the mathematical concepts that make it a powerful tool for estimating values from noisy data

I made a simulation that forecasted a greenhouse's temperature and humidity to help me understand the idea. I began by going over the basics of Gaussians and normal distribution once more. After that, I used NumPy and SciPy to develop the Kalman Filter in Python. To represent the system, I defined noise matrices (Q and R), a state transition matrix (F), and a control input matrix (B).

Cursor is indeed an amazing tool. I use it daily as well. Have you tried using it with any other LLMs, or are you sticking with the Claude Sonnet 3.5 and OpenAI O1 combo?

Btw have you explored any AI frameworks like JAX or Haiku?

The context switching between frontend and backend is cool. I have become so much more productive.

As for Elevenlabs, how do you find the voice synthesis quality?

When it comes to coding, it's a defo yes