Post
3019
We have received the majority of components for our first small commercial batch of SO-ARM101 robotic arms. Feetech STS3250 servo drives, parallel gripper, and depth camera.
- 1 reply
Cahlen Humphreys PRO
cahlen
AI & ML interests
☠️💻
Recent Activity
updated a dataset about 2 hours ago
cahlen/ramanujan-machine-results published a dataset about 2 hours ago
cahlen/ramanujan-machine-results liked a model about 10 hours ago
mistralai/Devstral-2-123B-Instruct-2512Organizations
reactedto branikita's post with 🔥 5 days ago
reactedto branikita's post with 🔥 7 days ago
Post
5592
Progress update on our Robonine mobile robotic platform. We've wired up the full electronics stack - Raspberry Pi, motor controllers, and a camera module - and added Bluetooth joystick and keyboard control with WiFi-based management. A 7 kg counterweight keeps the platform stable. Powered by a powerbank and ready to roll.
- 1 reply
reactedto salma-remyx's post with 🔥 8 days ago
Post
3481
We built an OpenClaw 🦞 skill that sends daily ranked research recommendations to Slack using the Remyx AI CLI.
You define Research Interests (topics, HF models, GitHub repos, blogs etc), Remyx ranks new arXiv papers and repos to find the most relevant resources, and an OpenClaw cron job posts a formatted digest to your team's #research channel every weekday morning.
The tutorial covers the full setup end-to-end: installing the CLI, creating interests, connecting OpenClaw to Slack, installing the Remyx skill, and scheduling the cron. About 15 minutes start to finish.
Tutorial: https://docs.remyx.ai/tutorials/daily-research-digest-slack
Would love to hear how folks are tracking research for their projects. If you give this a try, let us know what you think!
You define Research Interests (topics, HF models, GitHub repos, blogs etc), Remyx ranks new arXiv papers and repos to find the most relevant resources, and an OpenClaw cron job posts a formatted digest to your team's #research channel every weekday morning.
The tutorial covers the full setup end-to-end: installing the CLI, creating interests, connecting OpenClaw to Slack, installing the Remyx skill, and scheduling the cron. About 15 minutes start to finish.
Tutorial: https://docs.remyx.ai/tutorials/daily-research-digest-slack
Would love to hear how folks are tracking research for their projects. If you give this a try, let us know what you think!
reactedto umarbutler's post with 🔥 8 days ago
Post
4810
Isaacus, the AI research company building legal superintelligence, is hiring!
We're looking for passionate engineers who love to build and tinker and want to have an impact on the world. Specifically, we're hiring:
• ML engineers (Australia).
• Data engineers (Australia).
• Full-stack engineers (Australia).
• DevRel engineers (Australia, San Francisco, and London).
• DevOps engineers (Australia, San Francisco, and London).
If you'd like to be a founding employee at one of the few VC-backed LLM research labs in the world, receive generous equity compensation, and work alongside other highly motivated, highly skilled engineers, get in touch: https://isaacus.com/careers
We're looking for passionate engineers who love to build and tinker and want to have an impact on the world. Specifically, we're hiring:
• ML engineers (Australia).
• Data engineers (Australia).
• Full-stack engineers (Australia).
• DevRel engineers (Australia, San Francisco, and London).
• DevOps engineers (Australia, San Francisco, and London).
If you'd like to be a founding employee at one of the few VC-backed LLM research labs in the world, receive generous equity compensation, and work alongside other highly motivated, highly skilled engineers, get in touch: https://isaacus.com/careers
reactedto Jiaqi-hkust's post with 🚀 8 days ago
Post
5797
🛰️ Introducing Awesome-Remote-Sensing-Agents: The Largest Curated Collection of Intelligent Remote Sensing Agents
We are excited to share our new repository Awesome-Remote-Sensing-Agents – a comprehensive, community-driven collection of 100+ papers at the intersection of remote sensing and intelligent agents (LLMs, VLM, multi‑agent systems, etc.).
🔗 GitHub Repository: https://github.com/PolyX-Research/Awesome-Remote-Sensing-Agents
Our repository organizes this rapidly growing field into a structured, easy‑to‑navigate resource for researchers, practitioners, and enthusiasts.
📚 What’s Inside?
We’ve carefully curated papers across 6 key application domains:
🌿 Ecological Monitoring – forest fires, biodiversity, climate science
🚨 Emergency Response – flood mapping, wildfire tracking, disaster geolocalization
⛏️ Geological Exploration – mineral mapping, lithological recognition, geologic reasoning
🌊 Marine Supervision – ocean science, autonomous surface vehicles
🌾 Precision Agriculture – crop disease detection, land use simulation
🏙️ Urban Governance – change detection, urban planning, embodied navigation
🤝 Join the Community!
We warmly welcome contributions to keep this list up‑to‑date:
📝 Add missing papers via Pull Request
🏷️ Propose new or refined categories
🔗 Report broken links or outdated entries
💬 Discuss via GitHub Issues or contact the authors
We are excited to share our new repository Awesome-Remote-Sensing-Agents – a comprehensive, community-driven collection of 100+ papers at the intersection of remote sensing and intelligent agents (LLMs, VLM, multi‑agent systems, etc.).
🔗 GitHub Repository: https://github.com/PolyX-Research/Awesome-Remote-Sensing-Agents
Our repository organizes this rapidly growing field into a structured, easy‑to‑navigate resource for researchers, practitioners, and enthusiasts.
📚 What’s Inside?
We’ve carefully curated papers across 6 key application domains:
🌿 Ecological Monitoring – forest fires, biodiversity, climate science
🚨 Emergency Response – flood mapping, wildfire tracking, disaster geolocalization
⛏️ Geological Exploration – mineral mapping, lithological recognition, geologic reasoning
🌊 Marine Supervision – ocean science, autonomous surface vehicles
🌾 Precision Agriculture – crop disease detection, land use simulation
🏙️ Urban Governance – change detection, urban planning, embodied navigation
🤝 Join the Community!
We warmly welcome contributions to keep this list up‑to‑date:
📝 Add missing papers via Pull Request
🏷️ Propose new or refined categories
🔗 Report broken links or outdated entries
💬 Discuss via GitHub Issues or contact the authors
repliedto their post 8 days ago
lol. too scary for gemini.
reactedto unmodeled-tyler's post with 🚀 8 days ago
Post
3444
Here's a demo of Vessel Browser in action!
Minimax M2.7 was challenged with navigating to a large Ecom site, curate a selection of 5 different products, and add them all to the cart with included reasoning behind choices. (or try it yourself - open source, MIT license, and BYOK!)
Vessel is a browser that I've been working on which is designed specifically for agents with human-in-the-loop visibility. It comes with a local MCP server allowing any harness that supports custom MCP to control the browser. Additionally, you can BYOK to 8+ different providers (including custom OAI compatible endpoints and local models).
One of my favorite features of the browser is persistent, bi-directional highlighting - meaning that both you AND the agent can highlight anything on the screen and the agent receives the context.
Vessel Browser is unique in that it surfaces available tools contextually to the agent, meaning the agent doesn't have to decide between 80+ tools at any given time, but rather is focused on a subset of tools most applicable to the current state.
Give it a try!
https://github.com/unmodeled-tyler/vessel-browser
Minimax M2.7 was challenged with navigating to a large Ecom site, curate a selection of 5 different products, and add them all to the cart with included reasoning behind choices. (or try it yourself - open source, MIT license, and BYOK!)
npm i @quanta-intellect/vessel-browserVessel is a browser that I've been working on which is designed specifically for agents with human-in-the-loop visibility. It comes with a local MCP server allowing any harness that supports custom MCP to control the browser. Additionally, you can BYOK to 8+ different providers (including custom OAI compatible endpoints and local models).
One of my favorite features of the browser is persistent, bi-directional highlighting - meaning that both you AND the agent can highlight anything on the screen and the agent receives the context.
Vessel Browser is unique in that it surfaces available tools contextually to the agent, meaning the agent doesn't have to decide between 80+ tools at any given time, but rather is focused on a subset of tools most applicable to the current state.
Give it a try!
https://github.com/unmodeled-tyler/vessel-browser
- 2 replies
posted an update 8 days ago
Post
160
Speaking of just being able to make shit these days.
4,753 WiFi networks. 1,544 BLE devices. 41 IR signal patterns. 117 kilometers. 257 open networks. 125 printers exposing WiFi Direct. 163 Samsung SmartTags. 121 separated AirTags. A Mercedes-Benz MBUX infotainment system advertising to anyone in range.
That's from just one leisurely evening drive in socal.
All classified, mapped, and queryable. From a device that fits in your pocket.
ESP32 Marauder + Flipper Zero + DGX Spark running OpenClaw with a local Qwen 3.5 35B abliterated model. All AI processing on-prem. No cloud APIs, no token burn. If you can run it with a local LLM, do so. Free inference forever.
Throw it in your bag and drive. Every 20 seconds — full sensor rotation. WiFi, BLE, infrared, SubGHz. GPS-tagged, timestamped, saved to SD. When WiFi is up, it fires data home. The device never waits. It just keeps scanning.
Two ESP32 chips plugged into a Flipper Zero. White chip does WiFi and BLE. Orange chip does IR and SubGHz. Both scan in parallel. Two sets of hands, one brain.
It classifies every device from raw BLE bytes — AirTags, SmartTags, printers, iBeacons. Builds a persistent knowledge graph. Filters out your own devices automatically. Plots everything on a live map with an AI chat panel. Ask it to highlight all separated AirTags and it does. OpenClaw does the heavy lifting, I just emit data to a sensor garbage disposal type API endpoint.
What interests me most is what happens over time. Same routes, different days. Devices that show up Monday but not Friday. Networks that vanish. Trackers that migrate. The ontology never rotates.
I'm probably never going to release this. But you could probably build it yourself.
Casually I call it 'MarauderClaw' because I added a full agentic action mode where OpenClaw can take over and control the flipper zero and Marauder entirely, but that just kinda seemed like a bad idea, so nah on that one boss.
Favorite WiFi name in the wild so far: Wu-Tang-Lan.
4,753 WiFi networks. 1,544 BLE devices. 41 IR signal patterns. 117 kilometers. 257 open networks. 125 printers exposing WiFi Direct. 163 Samsung SmartTags. 121 separated AirTags. A Mercedes-Benz MBUX infotainment system advertising to anyone in range.
That's from just one leisurely evening drive in socal.
All classified, mapped, and queryable. From a device that fits in your pocket.
ESP32 Marauder + Flipper Zero + DGX Spark running OpenClaw with a local Qwen 3.5 35B abliterated model. All AI processing on-prem. No cloud APIs, no token burn. If you can run it with a local LLM, do so. Free inference forever.
Throw it in your bag and drive. Every 20 seconds — full sensor rotation. WiFi, BLE, infrared, SubGHz. GPS-tagged, timestamped, saved to SD. When WiFi is up, it fires data home. The device never waits. It just keeps scanning.
Two ESP32 chips plugged into a Flipper Zero. White chip does WiFi and BLE. Orange chip does IR and SubGHz. Both scan in parallel. Two sets of hands, one brain.
It classifies every device from raw BLE bytes — AirTags, SmartTags, printers, iBeacons. Builds a persistent knowledge graph. Filters out your own devices automatically. Plots everything on a live map with an AI chat panel. Ask it to highlight all separated AirTags and it does. OpenClaw does the heavy lifting, I just emit data to a sensor garbage disposal type API endpoint.
What interests me most is what happens over time. Same routes, different days. Devices that show up Monday but not Friday. Networks that vanish. Trackers that migrate. The ontology never rotates.
I'm probably never going to release this. But you could probably build it yourself.
Casually I call it 'MarauderClaw' because I added a full agentic action mode where OpenClaw can take over and control the flipper zero and Marauder entirely, but that just kinda seemed like a bad idea, so nah on that one boss.
Favorite WiFi name in the wild so far: Wu-Tang-Lan.
- 1 reply
repliedto their post 10 days ago
work constantly and never sleep. 😀
its proven.
reactedto prithivMLmods's post with 👍🔥 10 days ago
Post
4396
Map-Anything v1 (Universal Feed-Forward Metric 3D Reconstruction) demo is now available on Hugging Face Spaces. Built with Gradio and integrated with Rerun, it performs multi-image and video-based 3D reconstruction, depth, normal map, and interactive measurements.
🤗 Demo: prithivMLmods/Map-Anything-v1
🤗 Model: facebook/map-anything-v1
🤗 Hf-Papers: MapAnything: Universal Feed-Forward Metric 3D Reconstruction (2509.13414)
🤗 Demo: prithivMLmods/Map-Anything-v1
🤗 Model: facebook/map-anything-v1
🤗 Hf-Papers: MapAnything: Universal Feed-Forward Metric 3D Reconstruction (2509.13414)
posted an update 12 days ago
Post
3017
It’s wild to me how you can just make shit now.
You can take a weekend with a raspberry pi 5, a pi camera, a 3d printer, and a smidgen of custom fine tuning (wakeword, whisper, tinybert, and pipertts) and you have physical device as a talking personal assistant.
What a time to be alive.
Edge ai, physical ai, ai augmented animatronics… tiny models. Tiny agents.
Going to be a wild year.
You can take a weekend with a raspberry pi 5, a pi camera, a 3d printer, and a smidgen of custom fine tuning (wakeword, whisper, tinybert, and pipertts) and you have physical device as a talking personal assistant.
What a time to be alive.
Edge ai, physical ai, ai augmented animatronics… tiny models. Tiny agents.
Going to be a wild year.
- 6 replies
reactedto alvdansen's post with 🔥 25 days ago
Post
1335
Releasing Flimmer today — a video LoRA training toolkit for WAN 2.1 and 2.2 that covers the full pipeline from raw footage to trained checkpoint.
The standout feature is phased training: multi-stage runs where each phase has its own learning rate, epochs, and dataset, with the checkpoint carrying forward automatically. Built specifically with WAN 2.2's dual-expert MoE architecture in mind.
Data prep tools are standalone and output standard formats — they work with any trainer, not just Flimmer.
Early release, building in the open. LTX support coming next.
http://github.com/alvdansen/flimmer-trainer
The standout feature is phased training: multi-stage runs where each phase has its own learning rate, epochs, and dataset, with the checkpoint carrying forward automatically. Built specifically with WAN 2.2's dual-expert MoE architecture in mind.
Data prep tools are standalone and output standard formats — they work with any trainer, not just Flimmer.
Early release, building in the open. LTX support coming next.
http://github.com/alvdansen/flimmer-trainer
reactedto rajkumarrawal's post with 🔥 about 2 months ago
Post
3682
I submitted a "FlashLabs Chroma 1.0: A Real-Time End-to-End Spoken Dialogue Model with Personalized Voice Cloning" Paper by Tanyu Chen, Tairan Chen, Kai shen , Zhenghua Bao, Zhihui Zhang, Man Yuan, Yi Shi From 
FlashLabs to Daily Papers on 
huggingface .
Chroma 1.0 enables real time spoken dialogue with personalized voice cloning through discrete speech representations and interleaved text audio token scheduling.
Chroma 1.0 , the world’s first open source, real time speech to speech model with voice cloning.
FlashLabs Chroma 1.0: A Real-Time End-to-End Spoken Dialogue Model with Personalized Voice Cloning (2601.11141)
Chroma 1.0 enables real time spoken dialogue with personalized voice cloning through discrete speech representations and interleaved text audio token scheduling.
Chroma 1.0 , the world’s first open source, real time speech to speech model with voice cloning.
FlashLabs Chroma 1.0: A Real-Time End-to-End Spoken Dialogue Model with Personalized Voice Cloning (2601.11141)
reactedto philschmid's post with 🔥 12 months ago
Post
5728
Gemini 2.5 Flash is here! We excited launch our first hybrid reasoning Gemini model. In Flash 2.5 developer can turn thinking off.
**TL;DR:**
- 🧠 Controllable "Thinking" with thinking budget with up to 24k token
- 🌌 1 Million multimodal input context for text, image, video, audio, and pdf
- 🛠️ Function calling, structured output, google search & code execution.
- 🏦 $0.15 1M input tokens; $0.6 or $3.5 (thinking on) per million output tokens (thinking tokens are billed as output tokens)
- 💡 Knowledge cut of January 2025
- 🚀 Rate limits - Free 10 RPM 500 req/day
- 🏅Outperforms 2.0 Flash on every benchmark
Try it ⬇️
https://aistudio.google.com/prompts/new_chat?model=gemini-2.5-flash-preview-04-17
**TL;DR:**
- 🧠 Controllable "Thinking" with thinking budget with up to 24k token
- 🌌 1 Million multimodal input context for text, image, video, audio, and pdf
- 🛠️ Function calling, structured output, google search & code execution.
- 🏦 $0.15 1M input tokens; $0.6 or $3.5 (thinking on) per million output tokens (thinking tokens are billed as output tokens)
- 💡 Knowledge cut of January 2025
- 🚀 Rate limits - Free 10 RPM 500 req/day
- 🏅Outperforms 2.0 Flash on every benchmark
Try it ⬇️
https://aistudio.google.com/prompts/new_chat?model=gemini-2.5-flash-preview-04-17
- 1 reply
reactedto MonsterMMORPG's post with 🔥 12 months ago
Post
2995
FramePack Full Tutorial: 1-Click to Install on Windows - Up to 120 Second Image-to-Videos with 6GB > https://youtu.be/HwMngohRmHg
Tutorial video : https://youtu.be/HwMngohRmHg
FramePack from legendary lllyasviel full Windows local tutorial with a very advanced Gradio app to generate consistent videos from images with as long as 120 seconds and as low as 6 GB GPUs. This tutorial will show you step by step how to install and use FramePack locall with a very advanced Graido app. Moreover, I have published installers for cloud services such as RunPod and Massed Compute for those GPU poor and who wants to scale.
🔗 Full Instructions, Installers and Links Shared Post (the one used in the tutorial) ⤵️
▶️ https://www.patreon.com/posts/click-to-open-post-used-in-tutorial-126855226
🔗 SECourses Official Discord 10500+ Members ⤵️
▶️ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388
🔗 Stable Diffusion, FLUX, Generative AI Tutorials and Resources GitHub ⤵️
▶️ https://github.com/FurkanGozukara/Stable-Diffusion
🔗 SECourses Official Reddit - Stay Subscribed To Learn All The News and More ⤵️
▶️ https://www.reddit.com/r/SECourses/
🔗 MSI RTX 5090 TRIO FurMark Benchmarking + Overclocking + Noise Testing and Comparing with RTX 3090 TI ⤵️
▶️ https://youtu.be/uV3oqdILOmA
🔗 RTX 5090 Tested Against FLUX DEV, SD 3.5 Large, SD 3.5 Medium, SDXL, SD 1.5, AMD 9950X + RTX 3090 TI ⤵️
▶️ https://youtu.be/jHlGzaDLkto
Packing Input Frame Context in Next-Frame Prediction Models for Video Generation
FramePack, to train next-frame (or nextframe-section) prediction models for video generation. The FramePack compresses. Input frames to make the transformer context length a fixed number regardless of the video length.
Paper : https://lllyasviel.github.io/frame_pack_gitpage/pack.pdf
Project Page : https://github.com/lllyasviel/FramePack
Tutorial video : https://youtu.be/HwMngohRmHg
FramePack from legendary lllyasviel full Windows local tutorial with a very advanced Gradio app to generate consistent videos from images with as long as 120 seconds and as low as 6 GB GPUs. This tutorial will show you step by step how to install and use FramePack locall with a very advanced Graido app. Moreover, I have published installers for cloud services such as RunPod and Massed Compute for those GPU poor and who wants to scale.
🔗 Full Instructions, Installers and Links Shared Post (the one used in the tutorial) ⤵️
▶️ https://www.patreon.com/posts/click-to-open-post-used-in-tutorial-126855226
🔗 SECourses Official Discord 10500+ Members ⤵️
▶️ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388
🔗 Stable Diffusion, FLUX, Generative AI Tutorials and Resources GitHub ⤵️
▶️ https://github.com/FurkanGozukara/Stable-Diffusion
🔗 SECourses Official Reddit - Stay Subscribed To Learn All The News and More ⤵️
▶️ https://www.reddit.com/r/SECourses/
🔗 MSI RTX 5090 TRIO FurMark Benchmarking + Overclocking + Noise Testing and Comparing with RTX 3090 TI ⤵️
▶️ https://youtu.be/uV3oqdILOmA
🔗 RTX 5090 Tested Against FLUX DEV, SD 3.5 Large, SD 3.5 Medium, SDXL, SD 1.5, AMD 9950X + RTX 3090 TI ⤵️
▶️ https://youtu.be/jHlGzaDLkto
Packing Input Frame Context in Next-Frame Prediction Models for Video Generation
FramePack, to train next-frame (or nextframe-section) prediction models for video generation. The FramePack compresses. Input frames to make the transformer context length a fixed number regardless of the video length.
Paper : https://lllyasviel.github.io/frame_pack_gitpage/pack.pdf
Project Page : https://github.com/lllyasviel/FramePack
reactedto thomwolf's post with 🚀 12 months ago
Post
8238
If you've followed the progress of robotics in the past 18 months, you've likely noticed how robotics is increasingly becoming the next frontier that AI will unlock.
At Hugging Face—in robotics and across all AI fields—we believe in a future where AI and robots are open-source, transparent, and affordable; community-built and safe; hackable and fun. We've had so much mutual understanding and passion working with the Pollen Robotics team over the past year that we decided to join forces!
You can already find our open-source humanoid robot platform Reachy 2 on the Pollen website and the Pollen community and people here on the hub at
pollen-robotics
We're so excited to build and share more open-source robots with the world in the coming months!
At Hugging Face—in robotics and across all AI fields—we believe in a future where AI and robots are open-source, transparent, and affordable; community-built and safe; hackable and fun. We've had so much mutual understanding and passion working with the Pollen Robotics team over the past year that we decided to join forces!
You can already find our open-source humanoid robot platform Reachy 2 on the Pollen website and the Pollen community and people here on the hub at
We're so excited to build and share more open-source robots with the world in the coming months!
- 1 reply
reactedto nyuuzyou's post with 🔥 12 months ago
Post
5664
🇷🇺 Russian Forum Messages Dataset - nyuuzyou/ruforum
Collection of approximately 58 million Russian forum messages featuring:
- Complete message content from Russian online forums spanning 2010-2025
- Comprehensive metadata including unique message IDs and timestamps
- Full text content preserving original user discussions and interactions
- Monolingual dataset focused exclusively on Russian language content
This dataset offers a unique textual archive of Russian online conversations suitable for text generation, sentiment analysis, and language modeling research. Released to the public domain under CC0 1.0 license.
Collection of approximately 58 million Russian forum messages featuring:
- Complete message content from Russian online forums spanning 2010-2025
- Comprehensive metadata including unique message IDs and timestamps
- Full text content preserving original user discussions and interactions
- Monolingual dataset focused exclusively on Russian language content
This dataset offers a unique textual archive of Russian online conversations suitable for text generation, sentiment analysis, and language modeling research. Released to the public domain under CC0 1.0 license.
reactedto hesamation's post with ❤️ 12 months ago
Post
10631
Google published a 69-page whitepaper on Prompt Engineering and its best practices, a must-read if you are using LLMs in production:
> zero-shot, one-shot, few-shot
> system prompting
> chain-of-thought (CoT)
> ReAct
LINK: https://www.kaggle.com/whitepaper-prompt-engineering
> code prompting
> best practices
> zero-shot, one-shot, few-shot
> system prompting
> chain-of-thought (CoT)
> ReAct
LINK: https://www.kaggle.com/whitepaper-prompt-engineering
> code prompting
> best practices
reactedto daavoo's post with 👀 12 months ago
Post
2970
Wondering how the new Google Agent Development Toolkit (ADK) compares against other frameworks? 🤔You can try it in
https://github.com/mozilla-ai/any-agent
any-agent 🚀 https://github.com/mozilla-ai/any-agent
agent = AnyAgent.create(
AgentFramework("google"),
AgentConfig(
model_id="gpt-4o-mini"
)
)
agent.run("Which Agent Framework is the best??")
- 1 reply