neuphonic/neutts-air
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AI & ML interests
Voice Models, NLU, HCI-Images, OS Local LLMs
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In-the-Flow Agentic System Optimization for Effective Planning and Tool
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AI for Scientific Discovery Won't Work Without Fixing How We Collaborate.
My co-author @cgeorgiaw and I just published a paper challenging a core assumption: that the main barriers to AI in science are technical. They're not. They're social.
Key findings:
🚨 The "AI Scientist" myth delays progress: Waiting for AGI devalues human expertise and obscures science's real purpose: cultivating understanding, not just outputs.
📊 Wrong incentives: Datasets have 100x longer impact than models, yet data curation is undervalued.
⚠️ Broken collaboration: Domain scientists want understanding. ML researchers optimize performance. Without shared language, projects fail.
🔍 Fragmentation costs years: Harmonizing just 9 cancer files took 329 hours.
Why this matters: Upstream bottlenecks like efficient PDE solvers could accelerate discovery across multiple sciences. CASP mobilized a community around protein structure, enabling AlphaFold. We need this for dozens of challenges.
Thus, we're launching Hugging Science! A global community addressing these barriers through collaborative challenges, open toolkits, education, and community-owned infrastructure. Please find all the links below!
Paper: AI for Scientific Discovery is a Social Problem (2509.06580)
Join:
hugging-science
Discord: https://discord.com/invite/VYkdEVjJ5J
My co-author @cgeorgiaw and I just published a paper challenging a core assumption: that the main barriers to AI in science are technical. They're not. They're social.
Key findings:
🚨 The "AI Scientist" myth delays progress: Waiting for AGI devalues human expertise and obscures science's real purpose: cultivating understanding, not just outputs.
📊 Wrong incentives: Datasets have 100x longer impact than models, yet data curation is undervalued.
⚠️ Broken collaboration: Domain scientists want understanding. ML researchers optimize performance. Without shared language, projects fail.
🔍 Fragmentation costs years: Harmonizing just 9 cancer files took 329 hours.
Why this matters: Upstream bottlenecks like efficient PDE solvers could accelerate discovery across multiple sciences. CASP mobilized a community around protein structure, enabling AlphaFold. We need this for dozens of challenges.
Thus, we're launching Hugging Science! A global community addressing these barriers through collaborative challenges, open toolkits, education, and community-owned infrastructure. Please find all the links below!
Paper: AI for Scientific Discovery is a Social Problem (2509.06580)
Join:
hugging-science
Discord: https://discord.com/invite/VYkdEVjJ5J
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🤖 What if building your own robot arm costs less than £220?
For years, robotics has been locked behind high prices and complex systems.
So we decided to change that.
Today, we’re open-sourcing Ark-Bot — a fully 3D-printed, 6-DOF robot arm that works seamlessly with our Python robotics library, Ark.
And yes… It’s only £215.86 to build.
🧠ArkBot Specs 🧠
1️⃣ Reach: 1 meter
2️⃣ Weight: 2.6 kg
3️⃣ Payload: 1.8 kg 💪
4️⃣ DOF: 6
5️⃣ Input Voltage: DC 12V
🤟Fully 3D-printable & open-source
🤟Integrated with Ark — no ROS required
📹 We’ve also released a video showing the full assembly process — because robotics should be something everyone can learn, build, and improve on.
👩🎓 With Ark-Bot, anyone — from students to AI researchers — can experiment with embodied AI, robot learning, and control algorithms on real hardware, affordably.
If you could control a 1-meter robot arm from your laptop for under £220…
👉 What would you build first?
🔗https://github.com/Robotics-Ark/ark_bot
🎥 https://www.youtube.com/watch?v=Kuk4pC0EaEw&feature=youtu.be
For years, robotics has been locked behind high prices and complex systems.
So we decided to change that.
Today, we’re open-sourcing Ark-Bot — a fully 3D-printed, 6-DOF robot arm that works seamlessly with our Python robotics library, Ark.
And yes… It’s only £215.86 to build.
🧠ArkBot Specs 🧠
1️⃣ Reach: 1 meter
2️⃣ Weight: 2.6 kg
3️⃣ Payload: 1.8 kg 💪
4️⃣ DOF: 6
5️⃣ Input Voltage: DC 12V
🤟Fully 3D-printable & open-source
🤟Integrated with Ark — no ROS required
📹 We’ve also released a video showing the full assembly process — because robotics should be something everyone can learn, build, and improve on.
👩🎓 With Ark-Bot, anyone — from students to AI researchers — can experiment with embodied AI, robot learning, and control algorithms on real hardware, affordably.
If you could control a 1-meter robot arm from your laptop for under £220…
👉 What would you build first?
🔗https://github.com/Robotics-Ark/ark_bot
🎥 https://www.youtube.com/watch?v=Kuk4pC0EaEw&feature=youtu.be
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Make your ZeroGPU Spaces go brrr with ahead-of-time compilation
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Smol course has a distinctive approach to teaching post-training, so I'm posting about how it’s different to other post-training courses, including the llm course that’s already available.
In short, the smol course is just more direct that any of the other course, and intended for semi-pro post trainers.
- It’s a minimal set of instructions on the core parts.
- It’s intended to bootstrap real projects you're working on.
- The material handsover to existing documentation for details
- Likewise, it handsover to the LLM course for basics.
- Assessment is based on a leaderboard, without reading all the material.
To start the smol course, follow here:
smol-course
In short, the smol course is just more direct that any of the other course, and intended for semi-pro post trainers.
- It’s a minimal set of instructions on the core parts.
- It’s intended to bootstrap real projects you're working on.
- The material handsover to existing documentation for details
- Likewise, it handsover to the LLM course for basics.
- Assessment is based on a leaderboard, without reading all the material.
To start the smol course, follow here:
smol-course
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We released a new competition! https://www.kaggle.com/competitions/grocery-items-multi-class-object-detection/overview
Join to:
💡 Learn from others
🤔 Develop your Sim2Real skills using simulation
📸 Generate custom data on the cloud to improve your model
✨ and more!
Join to:
💡 Learn from others
🤔 Develop your Sim2Real skills using simulation
📸 Generate custom data on the cloud to improve your model
✨ and more!
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PawMatchAI — Now with SBERT-Powered Recommendations! 🐶✨
⭐️ NEW: Description-based recommendations are here!
Just type in your lifestyle or preferences (e.g. “I live in an apartment and want a quiet dog”), and PawMatchAI uses SBERT semantic embeddings to understand your needs and suggest compatible breeds.
What can PawMatchAI do today?
📸 Upload a photo to identify your dog from 124 breeds with detailed info.
⚖️ Compare two breeds side-by-side, from grooming needs to health insights.
📊 Visualize breed traits with radar and comparison charts.
🎨 Try Style Transfer to turn your dog’s photo into anime, watercolor, cyberpunk, and more.
What’s next?
🎯 More fine-tuned recommendations.
📱 Mobile-friendly deployment.
🐾 Expansion to additional species.
My goal:
To make breed discovery not only accurate but also interactive and fun — combining computer vision, semantic understanding, and creativity to help people find their perfect companion.
👉 Try it here:
DawnC/PawMatchAI
If you enjoy PawMatchAI, please give the project a ❤️ — it really helps and keeps me motivated to keep improving!
#ComputerVision #SBERT #DeepLearning #MachineLearning #TechForLife
⭐️ NEW: Description-based recommendations are here!
Just type in your lifestyle or preferences (e.g. “I live in an apartment and want a quiet dog”), and PawMatchAI uses SBERT semantic embeddings to understand your needs and suggest compatible breeds.
What can PawMatchAI do today?
📸 Upload a photo to identify your dog from 124 breeds with detailed info.
⚖️ Compare two breeds side-by-side, from grooming needs to health insights.
📊 Visualize breed traits with radar and comparison charts.
🎨 Try Style Transfer to turn your dog’s photo into anime, watercolor, cyberpunk, and more.
What’s next?
🎯 More fine-tuned recommendations.
📱 Mobile-friendly deployment.
🐾 Expansion to additional species.
My goal:
To make breed discovery not only accurate but also interactive and fun — combining computer vision, semantic understanding, and creativity to help people find their perfect companion.
👉 Try it here:
DawnC/PawMatchAI
If you enjoy PawMatchAI, please give the project a ❤️ — it really helps and keeps me motivated to keep improving!
#ComputerVision #SBERT #DeepLearning #MachineLearning #TechForLife
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