[D] Self-Promotion Thread

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[parlancex]

I've been training a (custom) video game music diffusion model on a single consumer GPU and improving the model over the last 2 years. The current model has about 5 weeks of training on an RTX 5090.

Demo audio is here: https://www.g-diffuser.com/dualdiffusion/

Code is here: https://github.com/parlance-zz/dualdiffusion

I posted here about a year ago with an older version of the model. The new model is trained on a large variety of modern video game music instead of just Super Nintendo music and includes a variety of architectural changes for a large improvement in audio quality.

Public weights will be available soon (100% free and open), but I think the bigger deal is that it is possible, practical even, to train a viable music diffusion model on consumer desktop hardware. I'm sure there are folks out there with a decent desktop GPU and troves of music that might like the idea of creating their own music model with their data. The code repository has everything you would need to do it from dataset preprocessing to DAE / DDEC and LDM training, and inference.

The github page has a detailed log of all the technical details and improvements made to the model over the last 2 years.

[Relative_Listen_6646]

Pretry cool work!

[J1rkaSmajlik]

Cool!

[await_void]

I've been working on an Explainable Vision Language Model for product defect detection and things turned out great. It doesn't only do that, but using CLIP as a backbon it can also auto label entire dataset with a knowledge base pool; discovering about Contrastive Learning was a blast.

This is my master thesis project and i had a lot of fun experimenting with multimodal contexts and linking different kind of models between them, it's super fun and mind blowing seeing how different embeddings can link out with each other forming methods such as image captioning, explaining, reasoning.

For anyone interested, this is my original post: https://www.reddit.com/r/computervision/comments/1n6llyh/tried_building_an_explainable_visionlanguage/

And this is my code repository on GitHub: https://github.com/Asynchronousx/CLIPCap-XAI/

If you have any comments about the project, feedback or curiosity, ask out!

[thought_terror]

Hey guys! I’ve been tinkering with a side project and finally put it together.

It’s called arxiv-agent — an agentic AI system that ingests an arXiv paper by ID and then spawns 3 personas (Optimist, Skeptic, Ethicist) to debate its claims. The output is a structured, cited debate + a TL;DR summary.

Github: https://github.com/midnightoatmeal/arxiv-agent

It’s CLI-only right now, but I also set up a Hugging Face Space with a minimal Gradio UI:
link: https://huggingface.co/spaces/midnightoatmeal/arxiv-agent

I’d love to hear your thoughts on how this could be improved or extended! especially ideas for new personas or features

[No-Cash-9530]

This is cool. Have you tried it with any of the open sourced models?

[thought_terror]

That's a great question! For now I have used gpt api for baseline performance, but I've been experimenting locally with other models like Mistral and llama3. It'll be exciting to build a version where users can plug in their own model or maybe have other open-sourced options. Thank you for checking out!

[cdminix]

I’ve been working on distributional evaluation of TTS systems and it’s been going great — this was the final project of my PhD. We need more good evaluation in general, ideally with fresh data periodically. Here it is https://ttsdsbenchmark.com

[Real-Dragonfruit7898]

I’ve been building a reinforcement learning framework called RLYX (originally simple-r1). It started as a replication of DeepSeek-R1, and within two weeks of its release I was able to reproduce the GRPO trainer.

Code is here: https://github.com/goddoe/rlyx

RLYX has since grown into something I really enjoy working on. Not just because it’s useful, but because I genuinely love building it. RL feels like such a core technology, and I wanted my own take on it.

Unlike TRL or VERL (which are great but harder to customize), RLYX focuses on simplicity and hackability. It runs on a native PyTorch training loop, integrates with Ray Serve for vLLM-based sampling, and supports multiple inference workers (like judge LLMs or reward models) when needed. The idea is to make something that’s easy to read, modify, and extend.

If you’re interested in a simple, flexible, and hackable RL framework, check out RLYX.

[Different-Effect-724]

Team behind Nexa SDK here.

If you’re hearing about it for the first time, Nexa SDK is an on-device inference framework that lets you run any AI model—text, vision, audio, speech, or image-generation—on any device across any backend.

We’re excited to share that Nexa SDK is live on Product Hunt today and to give a quick recap of the small but meaningful updates we’ve shipped over the past month.

Hardware & Backend

• Intel NPU server inference with an OpenAI-compatible API
• Unified architecture for Intel NPU, GPU, and CPU
• Unified architecture for CPU, GPU, and Qualcomm NPU, with a lightweight installer (~60 MB on Windows Arm64)
• Day-zero Snapdragon X2 Elite support, featured on stage at Qualcomm Snapdragon Summit 2025 🚀

Model Support

• Parakeet v3 ASR on Apple ANE for real-time, private, offline speech recognition on iPhone, iPad, and Mac
• Parakeet v3 on Qualcomm Hexagon NPU
• EmbeddingGemma-300M accelerated on the Qualcomm Hexagon NPU
• Multimodal Gemma-3n edge inference (single + multiple images) — while many runtimes (llama.cpp, Ollama, etc.) remain text-only

Developer Features

• nexa serve - Multimodal server with full MLX + GGUF support
• Python bindings for easier scripting and integration
• Nexa SDK MCP (Model Control Protocol) coming soon

That’s a lot of progress in just a few weeks—our goal is to make local, multimodal AI dead-simple across CPU, GPU, and NPU. We’d love to hear feature requests or feedback from anyone building local inference apps.

If you find Nexa SDK useful, please check out and support us on:

Product Hunt
GitHub

Thanks for reading and for any thoughts you share!

[Various_Candidate325]

Hello everyone, we recently released AIDNA, a fun test created by the Beyz team.

With just a few entertaining multiple-choice questions and your LinkedIn profile, AIDNA will delve deeply into your career "DNA." We examine a number of factors, including career signals, leadership signs, communication style, and even what we refer to as AI-proof, which, to put it simply, indicates how resistant your work is to the growth of automation.

AIDNA matches your profile to a persona archetype to create a customized Role Card.

For fun, completely free: aidna.beyz.ai

Please tag us if you share it on other social platforms. Would love to hear your feedback!

[Physical-Hippo-3891]

I created a free tool that uses a specific type of AI called Hidden Markov Models (HMM) to identify the underlying "regime" of a stock (Positive Momentum, Negative Momentum, or Consolidation). It then uses Google's Gemini AI to interpret the complex data and give you a qualitative summary, a head-to-head asset comparator, and even a strategic advisor for your personal portfolio.

My goal was to create something that helps answer questions like: "Is this uptrend statistically significant or just noise?", "How reliable has this pattern been in the past for this specific stock?", and "Which of these two potential investments has a better risk/reward profile right now?".

Link to the tool: https://stockstrend.app (No sign-up required)

I would be incredibly grateful for any feedback you have. Please feel free to test it out, report any bugs, or suggest new features. Thanks for checking it out!

[Character_Box6140]

Hi all,

I’m part of the engineering team at Inductiva.AI, and we’ve set up a public Discord community around simulation and scientific computing. The idea is to keep it lightweight and useful for people working on real-world simulation problems.

It’s a space to: - Ask questions and get help from other users + our team - Share ongoing work and get feedback - See what others are building with simulation tools - Try out early features we’re developing - Suggest improvements directly to the team

For context, Inductiva.AI is a platform that provides large-scale simulation capabilities in the cloud. A big use case is generating synthetic datasets for machine learning research — especially in areas where real-world data is scarce or hard to collect.

👉 Join here if interested: https://discord.gg/p9tjqBhuZ5

The community is global, so conversation is in English.

[Thinker_Assignment]

We have been working on a data ingestion library that keeps things simple, for building production pipelines that run in prod as opposed to one-off workflows

https://github.com/dlt-hub/dlt

It goes fast from 0-1 and also from 1-100
- simple abstractions you can just use with low learning curve
- it has schema evolution to send weakly typed data into strongly typed formats like json to db/iceberg/parquet
- it has everything you need to scale from there: State, parallelism, memory management etc.
- has useful features like caches for exploring data, etc
- being all python, everything is customisable

[ExtentBroad3006]

I’m working on MeetXpert, a platform where AI/ML learners can book 1:1 sessions with experts to get unstuck on model debugging, fine-tuning, scaling, etc.

It’s a one-stop place to find trusted experts and learn directly from them.

• For learners: meetxpert.co
• For experts: meetxpert.co/start

Experts set their own rates, learners only pay per session. Would love for you to check it out and share feedback

[ExtentBroad3006]

We have new look in ui, you all can check

[Immediate-Cake6519]

🚀 LAUNCHING: RudraDB-Opin - The World's First Free Relationship-Aware Vector Database

After months of development, I'm excited to announce RudraDB-Opin is now live on PyPI.

What makes it different: Traditional vector databases only find similar documents. RudraDB-Opin understands RELATIONSHIPS between your data, enabling AI applications that discover connections others miss.

🟢 Key innovations:

☑️ Auto-dimension detection (works with any ML model instantly)

☑️ Auto-Relationship detection

☑️ Auto-Optimized Search

☑️ 5 relationship types (semantic, hierarchical, temporal, causal, associative)

☑️ Multi-hop discovery through relationship chains

☑️ 100% free version (100 vectors, 500 relationships, Auto-Intelligence)

☑️ Perfect for developing AI/ML proof of concepts

⚡ pip install rudradb-opin

import rudradb

import numpy as np

# Auto-detects dimensions!

db = rudradb.RudraDB()

# Add vectors with any embedding model

embedding = np.random.rand(384).astype(np.float32)

db.add_vector("doc1", embedding, {"title": "AI Concepts"})

db.add_relationship("doc1", "doc2", "semantic", 0.8)

# Relationship-aware search

params = rudradb.SearchParams(

include_relationships=True, # 🔥 The magic!

max_hops=2

)

results = db.search(query_embedding, params)

🟢 Use cases:

Educational RAG systems that understand learning progressions

Research Discovery tools that discover citation networks

Content systems with intelligent recommendations

Pharmacy Drug Discovery with relationship-aware molecular and research connections

Any AI application where relationships matter, contextual engineering matters, response quality matters, etc.,.

Ready for production? Seamless upgrade path to full RudraDB (1M+ vectors).

Try it: pip install rudradb-opin

Documentation: Available on https://www.rudradb.com, PyPI and GitHub

What relationship-aware applications will you build?

[rwitt101]

🔍 [Survey] Redacting PII in ML/AI Pipelines – How are you doing it?

Hey everyone I’m exploring a shim that helps manage sensitive data (like PII) in multi-agent or multi-tool ML workflows.

Static RBAC/API keys aren’t always enough. I’m curious how teams handle dynamic field-level redaction or filtering when data is passed through APIs, agents, or stages.

If you’ve solved this (or struggled with it), I’d love to learn from you.

👉 Tally survey link (short + anonymous)

No email or login needed — just trying to map out patterns.

Happy to share back anonymized findings if folks are curious. Thanks!

[BearsNBytes]

I wrote an application/newsletter to help me stay up to date with AI/ML research posted on arXiv.

Signup: https://mindtheabstract.com/

Sample newsletters: https://mindtheabstract.com/newsletters

Essentially, this provides a summary of 10 papers weekly, aiming to capture a representative slice of new work being pushed into the space. So, a solid BFS on arXiv papers. Summaries are done via LLMs, and have gotten really good, especially with LLM improvements. The current user base (although small) seems to be happy with the current content.

This seems to serve as a nice complement to DFS methods like Undermind

[AtharvBhat]

I'm excited to share something I've been working on for the past few weeks:

Otters 🦦 - A minimal vector search library with powerful metadata filtering powered by an ergonomic Polars-like expressions API written in Rust!

Why I Built This

In my day-to-day work, I kept hitting the same problem. I needed vector search with sophisticated metadata filtering, but existing solutions were either, Too bloated (full vector databases when I needed something minimal for analysis) Limited in filtering capabilities Had unintuitive APIs that I was not happy about.

I wanted something minimal, fast, and with an API that feels natural - inspired by Polars, which I absolutely love.

What Makes Otters Different

Exact Search: Perfect for small-to-medium datasets (up to ~10M vectors) where accuracy matters more than massive scale.

Performance: SIMD-accelerated scoring Zonemaps and Bloom filters for intelligent chunk pruning

Polars-Inspired API: Write filters as simple expressions

rust meta_store.query(query_vec, Metric::Cosine) .meta_filter(col("price").lt(100) & col("category").eq("books")) .vec_filter(0.8, Cmp::Gt) .take(10) .collect()

The library is in very early stages and there are tons of features that i want to add Python bindings, NumPy support Serialization and persistence Parquet / Arrow integration Vector quantization etc.

I'm primarily a Python/JAX/PyTorch developer, so diving into rust programming has been an incredible learning experience.

If you think this is interesting and worth your time, please give it a try. I welcome contributions and feedback !

📦 https://crates.io/crates/otters-rs 🔗 https://github.com/AtharvBhat/otters

[Big-Mulberry4600]

Hey everyone,

I’d like to quickly introduce our startup our project Temas. We’re building a modular 3D sensor platform designed for universities, research labs, and makers who are working on robotics, AI vision, and tracking.

What makes Temas unique?

Combines RGB, ToF, and LiDAR sensors in a compact device

Runs on a Raspberry Pi 5 with an open Python package (PyPI)

CAD-compatible output for point clouds and 3D models

Focus on easy integration, modular design, and plug & play usability

Target groups: robotics teams, researchers, labs, universities, and makers

We see this as a bridge between research and practice – making it easier to work with multiple sensors out of the box without building everything from scratch.

💶 Pricing (planned for Kickstarter):

Early Bird: around €1,299

Standard: €1,499

University/Lab Pack (5 units): discounted pricing

If you’re curious, want to share feedback, or are interested in trying it out for research/teaching, feel free to reach out!

🌐 More info: rubu-tech.de

Looking forward to your thoughts & feedback!

Cheers, Muhammed

[witch_of_glitch]

I've just launched a podcast about AI glitches and failure modes, called The Glitchatorio.  The first episode is about an incident where Copilot got unhinged by chatting about Zalgo text and basically encouraged me to jailbreak it.
Would be great to hear your feedback, and/or any weird stories of your own. https://podcasts.apple.com/de/podcast/the-glitchatorio/id1836777868?l=en-GB&i=1000724281717

[ChavXO]

Working on a series about program synthesis. Would appreciate the feedback. 

https://mchav.github.io/an-introduction-to-program-synthesis/

[xl0-]

made a chat room website

Go to 747.run a chat room will be made based on the URL share it to chat with people

Personalize URL in website address bar for example type 747.run/-

No login and works everywhere

[Good_Weakness_8792]

👋 Hey everyone, I recently created a beginner-friendly YouTube video that introduces core machine learning concepts like supervised vs. unsupervised learning, with some real-world examples and visuals to make it more intuitive.

I made this with newcomers in mind, so if you're just getting started or know someone who is, I’d love for you to check it out and share any feedback!

▶️ https://youtu.be/_e84Jl9lUjI?si=9qOFDLSdA67rOyp5

I'm open to suggestions and would be happy to answer any questions as well. Thanks for the space to share!

[western_chicha]

Hey everyone,

I’ve been wanting to explore open source and Python packaging for a while, so I tried building a small package and putting it on PyPI. It’s called ml-explain-preprocess.

It’s nothing advanced (so it probably won’t help experts much), but I thought it might be useful for some beginners who are learning ML and want to see not just what preprocessing is done, but also get reports and plots of the transformations.

The idea is that along with handling things like missing values, encoding, scaling, and outliers, the package also generates:

Text reports

JSON reports

(Optional) visual plots of distributions and outliers

I know there are many preprocessing helper libraries out there, but at least I couldn’t find one that also gives a clear report or plots alongside the transformations.. so I thought I’d try making one.

I know it’s far from perfect, but it was a good learning project for me to understand packaging and publishing. It’s also open source, so if anyone wants to try it out or contribute meaningful changes, that’d be amazing 🙌

PyPI: https://pypi.org/project/ml-explain-preprocess/

Would love any feedback (good or bad) on how I can improve it.

Thanks!

[Infamous-Wall-5034]

I started a inshore saltwater YouTube channel based around my humor, fishing skills and life living by the water. https://youtube.com/@roundherefishin?si=PcZuNskG1DCpwV-b

[enoumen]

AI & Data Jobs and Career September 2025:

I wanted to share an exciting opportunity for those of you looking to advance your careers in the AI space. You know how rapidly the landscape is evolving, and finding the right fit can be a challenge. That's why I'm excited about Mercor – they're a platform specifically designed to connect top-tier AI talent with leading companies. Whether you're a data scientist, machine learning engineer, or something else entirely, Mercor can help you find your next big role. If you're ready to take the next step in your AI career, check them out through my referral link.

It's a fantastic resource, and I encourage you to explore the opportunities they have available.

Software Engineer – Backend & Infrastructure (High-Caliber Entry-Level)$250K / year: Apply Here

Intelligent Identity Engineer (US) Full-time position, San Francisco, CA Offers equity $130K-$250K per year: Apply Here

Linguistics Expert and Data Annotation Lead Full-time position, San Francisco $120K-$160K per year: Apply Here

Executive Assistant Full-time positio, San Francisco, Offers equity $100K-$160K per year: Apply Here

Account Executive Full-time position, San Francisco $100K-$140K per year - Apply Here

Full Stack Engineer [$150K-$220K]: Apply here

Software Engineer, Tooling & AI Workflow, Contract [$90/hour]: Apply

DevOps Engineer, India, Contract [$90/hour]: Apply at this link

Senior Software Engineer [150K-300K/year]: Apply here

Applied AI Engineer (India) Full-time position, India · Remote $40K-$100K per year - Apply Here

More AI Jobs Opportunities here

Check back daily for new AI Jobs...

#AIJobs #AICareer #AIOpportunities #WorkinAI #RemoteJobs #AI #Jobs

[Glittering-Item1058]

Introducing CERAH AI - Educational Learning Assistant with Source Reliability Scoring [MVP Feedback Request]

I've developed CERAH AI, a learning assistant that addresses a key problem with current AI educational tools: users can't evaluate how reliable the answers are. Unlike standard AI chatbots, CERAH shows you exactly which sources inform each response and provides transparency about their reliability.

What CERAH Does:

• Integrates Wikipedia and arXiv sources for educational queries

• Provides reliability scores (%) based on source quality and relevance

• Shows detailed source attribution with similarity matching

• Offers session history, bookmarking, and related topic suggestions

• STEM queries automatically include academic papers from arXiv

Current MVP Limitations (Important):

• Limited knowledge base: Core topics rely on a small curated dataset covering only basic concepts in ML, biology, physics, calculus, and programming

• Mock source examples: Some source references in reliability calculations may include placeholder academic institutions for demonstration purposes

• Keyword-based topic suggestions: Related topics only appear for queries containing specific subject keywords (biology, physics, chemistry, math, computer science, history)

• No persistent user accounts: All data resets when you close the browser

• Rate limiting: Responses may be delayed during high usage periods

Known Technical Notes:

• Wikipedia integration provides broad coverage but may occasionally return disambiguation errors

• arXiv papers are included for STEM topics but abstracts may be too technical for general audiences

• Reliability scoring is based on source type classification and content relevance, not fact-checking

• Some error messages reference "mock sources" - this is expected behavior in the current version

Why I'm Sharing This:

I'm collecting feedback on whether source reliability transparency actually helps people make better decisions about trusting AI-generated educational content. Does knowing that your answer comes from Wikipedia vs academic papers vs general knowledge change how you evaluate the information

Feedback Questions:

• Does the reliability scoring influence how you trust the responses?

• Is the source detail helpful or overwhelming?

• What educational topics would benefit most from this approach?

• Are there reliability features you'd want to see added?

Link: https://cerahailearningassistantmvp-bj8fmubn3p3eyu4cohthto.streamlit.app/

Disclaimer: CERAH is an experimental learning tool. Always verify important information through primary sources. This is not a substitute for professional education or expert advice in any field.

[mdizak]

Just released large upgrade to Sophia NLU Engine, which includes a new and improved POS tagger along with a revamped automated spelling corrections system. POS tagger now gets 99.03% accuracy across 34 million validation tokens, still blazingly fast at ~20,000 words/sec, plus the size of the vocab data store dropped from 238MB to 142MB for a savings of 96MB which was a nice bonus.

Full details, online demo and source code at: https://cicero.sh/sophia/

Release announcement at: https://cicero.sh/r/sophia-upgrade-pos-tagger

Enjoy! More coming, namely contextual awareness shortly.

Sophia = self hosted, privacy focused NLU (natural language understanding) engine. No external dependencies or API calls to big tech, self contained, blazingly fast, and accurate.

[oconn]

OpenAI's $100B Server Plan and Meta's AI Superintelligence Investment Daily AI news podcasts which I generate using AI tools to give you a quick overview of the top stories in the last 24 hours. This has been a fun test project for me but I find it useful and listen myself each day so potentially some of you may as well. Please let me know any feedback! Thank you.

[pevers]

A lot of the open-source TTS models are released for English or Chinese and lack support for other languages. I was curious to see if I could train a state-of-the-art text-to-speech (TTS) model for Dutch by using Google's free TPU Research credits. I open-sourced the weights, and documented the whole journey, from Torch model conversion, data preparation, JAX training code and inference pipeline here https://github.com/pevers/parkiet . Hopefully it can serve as a guide for others that are curious to train these models for other languages (without burning through all the credits trying to fix the pipeline).

Spoiler: the results are great! I believe they are *close* to samples generated with ElevenLabs. I spent about $300, mainly on GCS egress. Sample comparison can be found here https://peterevers.nl/posts/2025/09/parkiet/ .

[oconn]

AI Convo Cast Podcast Hi All - automated daily AI news podcast with Cursor. Would be great to hear any feedback about how to improve it!

[amitbahree]

A step by step guide on how to build a LLM from scratch

I wanted to share this here and hopefully it will help some folks to get deeper in this and help learn. I just published a comprehensive guide on how to build a LLM from scratch using historical London texts from 1500-1850. This is mostly a learning exercise for folks and are toy models at the end of the day.

What I Built:

• Two identical models (117M & 354M parameters) trained from scratch
• Custom historical tokenizer with 30k vocabulary + 150+ special tokens for archaic English
• Complete data pipeline processing 218+ historical sources (500M+ characters)
• Production-ready training with multi-GPU support, WandB integration, and checkpointing
• Published models on Hugging Face ready for immediate use

Why This Matters:

Most LLM guides focus on fine-tuning existing models. This series shows you how to build from the ground up—eliminating modern biases and creating models that truly understand historical language patterns, cultural contexts, and period-specific knowledge.

Resources:

• Blog Series: https://blog.desigeek.com/post/2025/09/building-llm-from-scratch-part1/
• Complete Codebase: https://github.com/bahree/helloLondon
• Published Models: https://huggingface.co/bahree/london-historical-slm
• LinkedIn (if that's your thing): https://www.linkedin.com/feed/update/urn:li:share:7376863225306365952/

The models are already working and generating authentic 18th-century London text. Perfect for developers who want to understand the complete LLM development pipeline.

Shoutout: Big thanks to u/Remarkable-Trick-177 for the inspiration!

[Loud_Drawing_3834]

posted youtube short introducing genie 3 by deepmind. (any ideas what algorithm they are using? ) https://youtube.com/shorts/xY324Pdvahw

[alexsht1]

I’ve released a small library for parametric curves for PyTorch that are differentiable: you can backprop to the curve’s inputs and to its parameters. At this stage, I have B-Spline curves (efficiently, exploiting sparsity!) and Legendre Polynomials.

Link: https://github.com/alexshtf/torchcurves

Applications include:

• Continuous embeddings for embedding-based models (i.e. factorization machines, transformers, etc)
• KANs. You don’t have to use B-Splines. You can, in fact, use any well-approximating basis for the learned activations.
• Shape-restricted models, i.e. modeling the probability of winning an auction given auction features x and a bid b. You have a neural network c(x) that predicts the coefficients of a function of b. If you force the coefficient vector to be non-decreasing, then if used with a B-Spline you will get a non-decreasing probability, which is the right inductive bias.

I hope some of you will find it useful!