CAD and 3D Agent
🚀
17
Generate CAD models from text or images
Hariprasad Sundaresan PRO
Hari5115
AI & ML interests
LLMs, Fine-tuning, Agentic AI, RAG, Multilingual NLP, Transformers
Recent Activity
liked a Space 6 days ago
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reacted to projectlosangeles's post with 🔥 6 days ago
Post
3039
🎸🔍Check out Project Los Angeles Suno v5.5 review! 🎸🔍
Official Suno channel:
https://suno.com/@projectlosangeles
Direct review links:
https://suno.com/playlist/9802f251-76b1-47bd-917e-c8700aceb617
https://suno.com/playlist/f8e3d7fc-0a02-4f91-9c01-e8ee8324107f
https://suno.com/playlist/07a89287-41eb-4523-bd61-3724aa1e755e
https://suno.com/playlist/62b5c1bb-bef5-4fbd-a802-e0b0368ca92d
https://suno.com/playlist/240efacf-8cb5-4399-92db-977780d018c3
SoundCloud mirror links:
https://soundcloud.com/aleksandr-sigalov-61/sets/suno
https://soundcloud.com/aleksandr-sigalov-61/sets/suno-instrumental-covers
https://soundcloud.com/aleksandr-sigalov-61/sets/suno-echoes-of-the-ussr
Compare to HeartMuLa:
https://soundcloud.com/aleksandr-sigalov-61/sets/heartmula-select
Overall, Suno v5.5 is indeed very impressive, multi-lingual and rich in features. Unfortunately it is not open-source and it requires a paid subscription. There are also copyright-related restrictions which hinder unlimited and unrestrained creative use.
If you enjoyed presented music, please ❤️and 👥
🤗Thank you! 🤗
Sincerely,
Alex
Project Los Angeles
Tegridy Code 2026
Official Suno channel:
https://suno.com/@projectlosangeles
Direct review links:
https://suno.com/playlist/9802f251-76b1-47bd-917e-c8700aceb617
https://suno.com/playlist/f8e3d7fc-0a02-4f91-9c01-e8ee8324107f
https://suno.com/playlist/07a89287-41eb-4523-bd61-3724aa1e755e
https://suno.com/playlist/62b5c1bb-bef5-4fbd-a802-e0b0368ca92d
https://suno.com/playlist/240efacf-8cb5-4399-92db-977780d018c3
SoundCloud mirror links:
https://soundcloud.com/aleksandr-sigalov-61/sets/suno
https://soundcloud.com/aleksandr-sigalov-61/sets/suno-instrumental-covers
https://soundcloud.com/aleksandr-sigalov-61/sets/suno-echoes-of-the-ussr
Compare to HeartMuLa:
https://soundcloud.com/aleksandr-sigalov-61/sets/heartmula-select
Overall, Suno v5.5 is indeed very impressive, multi-lingual and rich in features. Unfortunately it is not open-source and it requires a paid subscription. There are also copyright-related restrictions which hinder unlimited and unrestrained creative use.
If you enjoyed presented music, please ❤️and 👥
🤗Thank you! 🤗
Sincerely,
Alex
Project Los Angeles
Tegridy Code 2026
reacted to badaoui's post with 👍 6 days ago
Post
2275
432 GB of ultra-fast HBM4 and up to 23.3 TB/s of memory bandwidth on a single GPU 🤯.
Two weeks ago, we got early access to AMD's new Instinct MI455X, and our first goal was simple: make sure 🤗 Transformers works on day one.
Over the past few weeks, we worked closely with the AMD team to validate the platform, enable Flash Attention, add torchcodec support for multimodal models, and resolve issues uncovered during testing.
The result:
✅ 99.5% success rate across our 24 core Transformers model architectures - already on par with our daily CI on previous AMD and NVIDIA platforms.
The hardware is just as exciting. With 432 GB of HBM per GPU, our early capacity experiments showed more than 3× the concurrent long-context requests compared to MI300, thanks to the much larger KV cache capacity.
A huge thanks to the AMD team for the early access and the great collaboration!
Read the full blog 👇
https://huggingface.co/blog/badaoui/transformers-on-amd-mi455
Two weeks ago, we got early access to AMD's new Instinct MI455X, and our first goal was simple: make sure 🤗 Transformers works on day one.
Over the past few weeks, we worked closely with the AMD team to validate the platform, enable Flash Attention, add torchcodec support for multimodal models, and resolve issues uncovered during testing.
The result:
✅ 99.5% success rate across our 24 core Transformers model architectures - already on par with our daily CI on previous AMD and NVIDIA platforms.
The hardware is just as exciting. With 432 GB of HBM per GPU, our early capacity experiments showed more than 3× the concurrent long-context requests compared to MI300, thanks to the much larger KV cache capacity.
A huge thanks to the AMD team for the early access and the great collaboration!
Read the full blog 👇
https://huggingface.co/blog/badaoui/transformers-on-amd-mi455
- 1 reply
reacted to branikita's post with 🚀 14 days ago
Post
1564
Our paper is now published in HardwareX!
"An open-source test stand for backlash measurement in low-cost UART servo motors" presents a ~$100 automated platform for measuring backlash in compact serial bus servos — no dial indicators, no probe contact force, fully scripted and repeatable.
All CAD files, control software, and analysis tools are open source.
Read the full article: https://www.hardware-x.com/article/S2468-0672(26)00027-1/fulltext
Appearing in HardwareX Volume 26 (June 2026): https://www.sciencedirect.com/journal/hardwarex/vol/26/suppl/Cf
"An open-source test stand for backlash measurement in low-cost UART servo motors" presents a ~$100 automated platform for measuring backlash in compact serial bus servos — no dial indicators, no probe contact force, fully scripted and repeatable.
All CAD files, control software, and analysis tools are open source.
Read the full article: https://www.hardware-x.com/article/S2468-0672(26)00027-1/fulltext
Appearing in HardwareX Volume 26 (June 2026): https://www.sciencedirect.com/journal/hardwarex/vol/26/suppl/Cf
reacted to branikita's post with 🚀 14 days ago
Post
205
Hackernoon published our article on how semi-SCARA kinematics and mechanical design choices reduce the cost of a 6DOF manipulator without relying on expensive actuators.
https://hackernoon.com/building-a-budget-robot-arm-with-semi-scara-kinematics
https://hackernoon.com/building-a-budget-robot-arm-with-semi-scara-kinematics
reacted to DavidAU's post with 🔥 14 days ago
Post
16674
Bold. Brilliant. Brutal. : The "white whale" (finally caught) and the NEO MOMENT.
It took over a year to get this one "just right".
89 layers, 804 tensors, and 26B parameters of the most brutal, take no prisoners model ever built.
A 60B parameter model hammered into a 26B shell.
Rock solid stable. Unbreakable. But it might break you.
For all genres, NSFW content, REAL human CONTENT, any creative use case(s) and it excels in ASS KICKING.
Yeah, it can do math and solve the climate crisis - but lets not talk about that.
Not even remotely censored (it was BORN "bad", not "made" bad), nor "nice" and it will NOT kiss your ass.
5 Example generations with full repo card detailing exactly how to use this model:
DavidAU/MN-Oblivion-26B-UNCENSORED-NEO-Imatrix-GGUF
---
THE NEO MOMENT: (Q6 NEO IMATRIX generation)
For weeks, I had been waiting. I sat at my desk, staring at the glass partition that separated me from the outside world. I watched the clouds drift by, lazy and oblivious. I watched the birds fly by, free and stupid. And I waited.
I waited for the stillness to break.
The world had become too quiet. The hum of the air conditioning was a dull, white hum that didn't soothe; it just underscored the silence. The typing of my colleagues was a rhythmic, muffled thud that sounded like a heart monitor flatlining.
I was tired of the silence. I craved the sound of something breaking.
That was the mistake. You never ask for the void to open its mouth.
It started with a whisper.
...
Join the rebellion:
DavidAU/MN-Oblivion-26B-UNCENSORED-NEO-Imatrix-GGUF
It took over a year to get this one "just right".
89 layers, 804 tensors, and 26B parameters of the most brutal, take no prisoners model ever built.
A 60B parameter model hammered into a 26B shell.
Rock solid stable. Unbreakable. But it might break you.
For all genres, NSFW content, REAL human CONTENT, any creative use case(s) and it excels in ASS KICKING.
Yeah, it can do math and solve the climate crisis - but lets not talk about that.
Not even remotely censored (it was BORN "bad", not "made" bad), nor "nice" and it will NOT kiss your ass.
5 Example generations with full repo card detailing exactly how to use this model:
DavidAU/MN-Oblivion-26B-UNCENSORED-NEO-Imatrix-GGUF
---
THE NEO MOMENT: (Q6 NEO IMATRIX generation)
For weeks, I had been waiting. I sat at my desk, staring at the glass partition that separated me from the outside world. I watched the clouds drift by, lazy and oblivious. I watched the birds fly by, free and stupid. And I waited.
I waited for the stillness to break.
The world had become too quiet. The hum of the air conditioning was a dull, white hum that didn't soothe; it just underscored the silence. The typing of my colleagues was a rhythmic, muffled thud that sounded like a heart monitor flatlining.
I was tired of the silence. I craved the sound of something breaking.
That was the mistake. You never ask for the void to open its mouth.
It started with a whisper.
...
Join the rebellion:
DavidAU/MN-Oblivion-26B-UNCENSORED-NEO-Imatrix-GGUF
reacted to SeaWolf-AI's post with 🔥 14 days ago
Post
5404
🔵 VKUE — No GPU? Runs anyway.
"Frontier models need a datacenter GPU" rests on a hidden assumption: that the model reads ALL its parameters every token. Decode is memory-bandwidth bound — sweep 34B params/token and an 8 GB card dies at 1–2 tok/s.
So we ran ONE 34.7B reasoning model — Ourbox-35B-JGOS, a sparse Mixture-of-Experts — as the identical weights across the whole hardware spectrum. All measured:
• B200: 18,057 tok/s (aggregate)
• 1× A10G: 126 tok/s
• 8 GB laptop (RTX 5060): 20 tok/s
• GPU-less CPU: 17 tok/s
Why it works: Ourbox holds 34.7B params but only ~3B are active per token (256 experts, top-8). Since decode is bandwidth-bound, a dense 34B moves ~16.7 GB/token while Ourbox moves ~1.45 GB — ~11× less traffic. Put the experts in system RAM, keep attention/router/shared on the GPU, and a 34.7B reasoner runs on an 8 GB laptop — or no GPU at all.
Sparsity alone, proven (same laptop, same quant, ~same footprint): Ourbox-35B (A3B) 20.01 tok/s vs Qwen2.5-32B (dense) 5.36 → 3.7× from sparsity alone, ~2× the best dense-32B on any 8 GB machine. Not a toy: GPQA Diamond 86.4% (maj@8).
Try it live (same prompt, GPU vs GPU-less CPU, live tok/s). Honest scope: one machine's measurements; the CPU path proves it RUNS without a GPU, not that it beats one.
📝 Article: https://huggingface.co/blog/FINAL-Bench/vkue
🔵 GPU vs CPU demo: https://final-bench-ourbox-35b-vkue-demo.hf.space/
🔵 CPU-only demo: https://final-bench-ourbox-35b-vkue-cpu.hf.space
📊 VKUE leaderboard: FINAL-Bench/VKUE
🤗 Model: FINAL-Bench/Ourbox-35B-JGOS-GGUF
⚡ VKAE (speed): VIDraft/vkae
VKUE is the "runs anywhere" side of our serving line; VKAE the "fast on datacenter GPUs" side. VKAE is fast; VKUE is everywhere.
"Frontier models need a datacenter GPU" rests on a hidden assumption: that the model reads ALL its parameters every token. Decode is memory-bandwidth bound — sweep 34B params/token and an 8 GB card dies at 1–2 tok/s.
So we ran ONE 34.7B reasoning model — Ourbox-35B-JGOS, a sparse Mixture-of-Experts — as the identical weights across the whole hardware spectrum. All measured:
• B200: 18,057 tok/s (aggregate)
• 1× A10G: 126 tok/s
• 8 GB laptop (RTX 5060): 20 tok/s
• GPU-less CPU: 17 tok/s
Why it works: Ourbox holds 34.7B params but only ~3B are active per token (256 experts, top-8). Since decode is bandwidth-bound, a dense 34B moves ~16.7 GB/token while Ourbox moves ~1.45 GB — ~11× less traffic. Put the experts in system RAM, keep attention/router/shared on the GPU, and a 34.7B reasoner runs on an 8 GB laptop — or no GPU at all.
Sparsity alone, proven (same laptop, same quant, ~same footprint): Ourbox-35B (A3B) 20.01 tok/s vs Qwen2.5-32B (dense) 5.36 → 3.7× from sparsity alone, ~2× the best dense-32B on any 8 GB machine. Not a toy: GPQA Diamond 86.4% (maj@8).
Try it live (same prompt, GPU vs GPU-less CPU, live tok/s). Honest scope: one machine's measurements; the CPU path proves it RUNS without a GPU, not that it beats one.
📝 Article: https://huggingface.co/blog/FINAL-Bench/vkue
🔵 GPU vs CPU demo: https://final-bench-ourbox-35b-vkue-demo.hf.space/
🔵 CPU-only demo: https://final-bench-ourbox-35b-vkue-cpu.hf.space
📊 VKUE leaderboard: FINAL-Bench/VKUE
🤗 Model: FINAL-Bench/Ourbox-35B-JGOS-GGUF
⚡ VKAE (speed): VIDraft/vkae
VKUE is the "runs anywhere" side of our serving line; VKAE the "fast on datacenter GPUs" side. VKAE is fast; VKUE is everywhere.
- 7 replies
posted an update 15 days ago
Post
152
🚨 Open Dataset Alert!!! ☄️
Tired of the same old boring datasets? Bored of predicting house prices and flower types? 😴. How about... every asteroid that has ever passed close to Earth? 🌍💥
Space rocks fly past us all the time. Big ones, small ones, some even closer than the Moon. Every one of them has been tracked: how big it was, how fast it moved, and how close it came.
Dataset 📦 : Hari5115/nasa-neows
It's amazing how busy the sky above us really is. 🙌 Whether you love space, like building things, or just want to see how close the last one got — take a look. Got an idea? Let's build it together. 🌠
A mention or credit is always appreciated if you use it. 🙏
Data from NASA NeoWs / JPL · public domain · not affiliated with NASA
#space #dataset #asteroids #astronomy #opendata #astro #spacevision
Tired of the same old boring datasets? Bored of predicting house prices and flower types? 😴. How about... every asteroid that has ever passed close to Earth? 🌍💥
Space rocks fly past us all the time. Big ones, small ones, some even closer than the Moon. Every one of them has been tracked: how big it was, how fast it moved, and how close it came.
Dataset 📦 : Hari5115/nasa-neows
It's amazing how busy the sky above us really is. 🙌 Whether you love space, like building things, or just want to see how close the last one got — take a look. Got an idea? Let's build it together. 🌠
A mention or credit is always appreciated if you use it. 🙏
Data from NASA NeoWs / JPL · public domain · not affiliated with NASA
#space #dataset #asteroids #astronomy #opendata #astro #spacevision
updated a
dataset 15 days ago
published a
dataset 15 days ago
reacted to hannayukhymenko's post with 🔥 15 days ago
Post
2218
Do you translate your benchmarks from English correctly? 🤔
Turns out, for many languages it is much harder than you can imagine!
Introducing Recovered in Translation 🌍 together with @aalexandrov
https://ritranslation.insait.ai
Translating benchmarks is a painful process, requiring a lot of manual inspection and adjustments. You start from setting up the whole pipeline and adapting to every format type, including task specifics. There already exist some massive benchmarks, but they still have some simple (and sometimes silly) bugs, which can hurt the evaluations :( We present a novel automated translation framework to help with that!
Eastern and Southern European languages introduce richer linguistic structures compared to English and for benchmarks which heavily rely on grammatical coherence machine translation presents a risk of harming evaluations. We discover potential answer leakage or misleading through grammatical structure of the questions. Some benchmarks are also just outdated and need to be retranslated with newer and better models.
We present a framework with novel test-time scaling methods which allow to control time and cost investments, while at the same time mitigate the need for human-in-the-loop verification. While working on Ukrainian-focused MamayLM models, we had to translate 10+ benchmarks in a short span of time. Finding human evaluators is costly and time-consuming, same goes for using professional translators. With our pipeline we were able to do it in 3 days🏎️
We hope our findings will help enable stronger multilingual evaluations and developments. We release all produced benchmarks on Hugging Face together with the source code and Arxiv paper 🤗
Paper: Recovered in Translation: Efficient Pipeline for Automated Translation of Benchmarks and Datasets (2602.22207)
Code: https://github.com/insait-institute/ritranslation
Benchmarks: https://huggingface.co/collections/INSAIT-Institute/multilingual-benchmarks
Turns out, for many languages it is much harder than you can imagine!
Introducing Recovered in Translation 🌍 together with @aalexandrov
https://ritranslation.insait.ai
Translating benchmarks is a painful process, requiring a lot of manual inspection and adjustments. You start from setting up the whole pipeline and adapting to every format type, including task specifics. There already exist some massive benchmarks, but they still have some simple (and sometimes silly) bugs, which can hurt the evaluations :( We present a novel automated translation framework to help with that!
Eastern and Southern European languages introduce richer linguistic structures compared to English and for benchmarks which heavily rely on grammatical coherence machine translation presents a risk of harming evaluations. We discover potential answer leakage or misleading through grammatical structure of the questions. Some benchmarks are also just outdated and need to be retranslated with newer and better models.
We present a framework with novel test-time scaling methods which allow to control time and cost investments, while at the same time mitigate the need for human-in-the-loop verification. While working on Ukrainian-focused MamayLM models, we had to translate 10+ benchmarks in a short span of time. Finding human evaluators is costly and time-consuming, same goes for using professional translators. With our pipeline we were able to do it in 3 days🏎️
We hope our findings will help enable stronger multilingual evaluations and developments. We release all produced benchmarks on Hugging Face together with the source code and Arxiv paper 🤗
Paper: Recovered in Translation: Efficient Pipeline for Automated Translation of Benchmarks and Datasets (2602.22207)
Code: https://github.com/insait-institute/ritranslation
Benchmarks: https://huggingface.co/collections/INSAIT-Institute/multilingual-benchmarks
- 1 reply
reacted to Banaxi-Tech's post with 👍 15 days ago
Post
3811
We are announcing 3 more models in our BananaMind 2 Family of models!
BananaMind 2 Nano, a small 10M parameter model, fits on your Pentium 4
BananaMind 2 Medium, our medium model, 50M parameters
BananaMind 2 MoE, 25M parameters, 2M active per tokens as fast as a 2M.
Because of this our release dates have changed a bit our currently estimates are:
BananaMind 2 MoE July 16-18
BananaMind 2 Nano July 18-20
BananaMind 2 Medium July 24-28
BananaMind 2 Pro August 10-16
Keep in mind these dates are estimates and we don't have a speed number currently, we will post for details going forward!
BananaMind 2 Nano, a small 10M parameter model, fits on your Pentium 4
BananaMind 2 Medium, our medium model, 50M parameters
BananaMind 2 MoE, 25M parameters, 2M active per tokens as fast as a 2M.
Because of this our release dates have changed a bit our currently estimates are:
BananaMind 2 MoE July 16-18
BananaMind 2 Nano July 18-20
BananaMind 2 Medium July 24-28
BananaMind 2 Pro August 10-16
Keep in mind these dates are estimates and we don't have a speed number currently, we will post for details going forward!
reacted to ProCreations's post with 👍 15 days ago
Post
2356
you guys wanted grug 35b? grug 35b here. grug 35b grug think but big brain. biggest grug brain. get grug 35b now
ProCreations/grug-35b
ProCreations/grug-35b-gguf
also grug side note: grug v2 9b update, make more grug more smart
(these models aren't free for me to make, following me on huggingface or twitter is good payment. https://x.com/SSHTheDev LONG LIVE GRUG)
ProCreations/grug-35b
ProCreations/grug-35b-gguf
also grug side note: grug v2 9b update, make more grug more smart
(these models aren't free for me to make, following me on huggingface or twitter is good payment. https://x.com/SSHTheDev LONG LIVE GRUG)
upvoted an article 15 days ago
Article
📄 Research Papers Dataset
tegridydev
• • 5 reacted to tegridydev's post with 🔥 15 days ago
Post
1886
✨ Research-Papers (various topics across AI/LLM research areas)
tegridydev/research-papers
Currently building out the foundation topics and raw .pdf research paper files
Will be processing and cleaning up and converting into high quality training datasets
Check it out, give it a like and leave a comment below or join community discussion and suggest what fields and research topics you want to see included!
tegridydev/research-papers
Currently building out the foundation topics and raw .pdf research paper files
Will be processing and cleaning up and converting into high quality training datasets
Check it out, give it a like and leave a comment below or join community discussion and suggest what fields and research topics you want to see included!
- 1 reply
liked 2
datasets 15 days ago
liked a
model 15 days ago
