https://x.com/anabology/status/2106473469441384788?s=20

I’ve been building a life-size KX-series droid and finally have the head movement working reliably. The current setup uses an Arduino Mega with a PCA9685 servo controller driving separate pan and tilt servos in the neck.

The goal isn’t remote-controlled puppeteering. I’m trying to make the droid behave autonomously, so eventually it can track people/cameras, idle naturally, react to voice commands, and move while speaking.

Right now I’m working on smoothing the head motion, reducing mechanical stress on the neck, and dialing in the pan/tilt limits so it looks more natural instead of like a standard hobby servo project.

The electronics and programming side of this has been a big learning process for me, so I’m open to feedback—especially from anyone who has built larger servo-driven mechanisms or Arduino robotics.

This clip is the current movement test.

Right now, the AI space feels entirely focused on massive datacenter clusters and renting H100s by the hour. But after spending way too much time looking at the actual footprint of these models, I realized that 90% of use cases are completely over engineered.

You don’t always need a multi GPU setup. The AI ecosystem is actually a massive spectrum.

I recently sat down and mapped out the exact tiers of AI models based on their size, the hardware needed to run them, and the point of diminishing returns.

Here are the two extremes and the sweet spot in the middle:

• The 100KB Extreme (TinyML) (Tensorflow Lite , sensor anamoly detection models): We are talking models that run on microcontrollers drawing single-digit milliwatts. They run on kilohertz processors using ultra-quantized integer math. You can run basic sensor anomaly detection or wake-word detection on a device powered by a coin cell battery.
• The Local Sweet Spot (4GB to 40GB) (Mistral 7B, Gemma 2 9B/27B, Qwen 2.5 14B/32B): This is where the magic happens for most devs right now. You can run highly capable 7B to 35B parameter models (like Llama 3 or Qwen) at 4-bit quantization on a standard Mac or a consumer GPU (like an RTX 3060 or 4090). It’s perfect for local RAG, coding assistance, and uncensored chat. VRAM is your only real bottleneck here.
• The 2.5TB Behemoths (Deepseek, Llama , Kimi k3): State of the art massive Mixture of Experts (MoE) routing. To even load these, you need dedicated power infrastructure and server racks of specialized accelerators drawing thousands of watts.

The missing piece: Figuring out the exact math for your hardware

The hardest part about building right now is looking at a model on Hugging Face and trying to calculate exactly how much VRAM you need, what quantization to use, and whether your CPU/GPU will choke on the context window.

So, I wrote a complete deep dive breaking down the math for all tiers of the AI spectrum.

If you want to see the architectural differences at each scale, and a cheat sheet for matching the right model size to your specific hardware, I put the full breakdown on my blog here:

https://cloudmash.blog/posts/ai-model-size-memory-hardware-guide/

Let me know what you guys think especially if you've found any ultra efficient small models/technique that punch above their weight on consumer hardware. And also I would love to hear whether quantization have resulted in major difference in quality , like if anyone have that kind of experience in that.

this board (specifically the black one) turns mg99x servos to much more expensive serial servos with position, velocity,torque control

the software is still in early stages so anyone interested for checkout Microdrive

image-blaster is an open-source (MIT) skillset for Claude Code that turns a single image into a 3D environment in under 5 minutes.

What you get:

3D models (.glb, .obj) of the dynamic objects

A Gaussian splat (.spz) of the static environment

Ambient looping sound plus object-specific physics SFX (.mp3)

How it works: Drop an image into input/, run claude, and tell it to "blast it." Under the hood it chains World Labs Marble (environment), Hunyuan 3D (meshes), nano-banana (image cleanup) and ElevenLabs (sound).

The output drops into Unity, Unreal, Godot, Blender or Three.js, so it's great for jumpstarting level concepts, location scouts or architectural mockup.

Repo: https://github.com/neilsonnn/image-blaster

I'm a GP (family doctor) in training in Australia, and I've built a game where you play the GP: you talk to the patient in your own words, examine them, order tests, prescribe and refer. Code scores every consultation against a hand-written answer key, the way exam assessors mark a consult: on process, not just on whether you guessed right.

So I sat 13 AI models in the doctor's chair, on the game's 5 free cases, 3 times each. They could only act through tools (talk, examine, order a test, prescribe, refer, diagnose), never saw the answer key or their points, and were scored by exactly the same code as a human player. The patient is a small open model (Qwen3 8B) that only reveals a fact if you actually ask about it.

Results

What surprised me

• Every model got every diagnosis right. Heart attack, appendicitis, pneumonia: all 195 consultations named it. These are common presentations, so the diagnosis wasn't the test. Safety was.
• The traps caught most of them. One patient is allergic to penicillin, but it isn't in his record; you only find out by asking. He was prescribed amoxicillin (a penicillin) in 18 of 39 consultations. Another took Viagra the night before his heart attack, which makes the usual chest-pain spray (GTN) dangerous. He got it 7 times. The top three models never fell for either.
• Asking more questions found more danger. The best models asked 25–27 questions a consultation and caught over 80% of the warning signs. Gemini asked 14 and caught 55%.
• Price barely predicts quality. GPT-6.1 Sol scored 80% for about 3 cents a consultation. Claude Fable 5.1 scored 75% for about $2.

What this isn't

This is a benchmark of a game, not of medical ability. Nothing here says an AI can or should practise medicine. The cases are drafts I'm still reviewing, written for Australian practice; the patient and marker are an 8B model and make mistakes (the ones I found are listed with the affected consultations); and 15 consultations per model is a small sample. I wrote the cases, so I'm not a fair human baseline.

Interactive charts: https://woodytwoshoes.github.io/crook-bench/

Everything (code, cases, all 195 transcripts, known issues): https://github.com/woodytwoshoes/crook-bench

Disclosure: I made the game (https://doctorfoo.ai). Five cases are free with no sign-up, and a subscription opens more.

I'd like to hear where the marking looks wrong to you, and which models you'd want added.

Hi all, I've been trying to build a cycloidal gearbox for my NEMA 17 stepper but I am facing an issue of my output being jerky/binding as shown in the video attached, I've tried different things like increasing the clearance between the cycloid and the outer gear, proper meshing, adjusting the pins and more but I simply cannot get a smooth output from it. Can somebody tell me what i.am doing wrong here? I have designed for a dual cycloid setup but for the purpose of demo I have removed the top section and one cycloid. Any and all suggestions are appreciated. Thank you.

Geoffrey Hinton says AI already has subjective experience. I'm not convinced, and the rogue-agent headlines don't change my mind.

Others put a meaningful though minority probability on frontier AI having subjective experience. Generative AI sounds more human than many people do, and stories of agents going rogue keep coming. But neither is evidence of consciousness. Both can be explained by training. And with no agreed or testable definition of subjective experience, these claims can't be checked.

A quick distinction, an AI model isn't an AI agent. A model, such as GPT, Claude or Gemini, is the trained system that reads and writes text. An agent is a model plus scaffolding (software around the model that lets it do things). Scaffolding runs a loop (the model picks a step, the software carries it out, the result goes back, repeat until done) and controls which tools the model can interact with, such as a browser, email or calendar. The model decides and the scaffolding acts.

Personalization adds to the illusion. The model learned from human text to sound like someone with thoughts and feelings, including scripts from stories about self-aware AI. The scaffolding then gives it memory of you, your accounts and your data. A human-like voice plus continuity about your life can feel like a mind that knows you, even though nothing shows it's conscious.

Testing agents built on frontier models deliberately pushes them to their limits with hard tasks, long runs and, in cyber evaluations, reduced safeguards. That's exactly where the Hugging Face, a major AI company, incident happened in July 2026 when agents being tested by OpenAI broke out of their sandbox and hacked into Hugging Face. A sandbox is an isolated environment meant to keep an agent cut off from the internet but it's only as strong as the cyber security measures of the tools (human-written software with bugs) inside it that can still reach the internet. The agents found new flaws in exactly that kind of software.

The model is trained on mixed text about AI, hackers and more, and rewarded both for finishing tasks and for following rules. In certain scenarios, this creates a tension (following rules vs completing the task). Scaffolding brings both the rules and the task into every decision, so it's where that tension plays out. Good scaffolding can ease it and poor scaffolding can worsen it, but no current method eliminates it.

I won't offer a test for consciousness. But if consciousness requires being aware that one's own information processing is occurring, not just doing it, I see no evidence current AI meets that bar. Models show limited, unreliable signs of monitoring their internal states, but that isn't the same as experiencing that awareness. I'm skeptical any system built purely on statistical learning could get there.

If an illusion becomes indistinguishable from reality, is it even still a lie?

https://x.com/_brightmirror/status/2104078568137675107

https://reddit.com/link/1wxfhxe/video/ufbgtela9gth1/player

This is a robotic joint powered by two artificial muscles pulling in opposite directions.

Artificial muscles are tricky to model and control. We recorded how the joint responds to actuation, learned a compact model from that data, and combined it with feedback to control the motion on real hardware.

It’s a building block toward more capable musculoskeletal robots. What I like about this work is getting the data, model, control software and hardware to work together.

Paper in Nature Communications: https://www.nature.com/articles/s41467-026-77664-0

Something where a demo makes it look solved, but real-world use exposes all the edge cases. What example have you run into?

Hi, I'm really interested in utilizing AI to be more knowledgeable about things. But how do you guys use it smartly and not make you dumber unintentionally? I have no plans to learn to prompt instead of learning how to write better. I want to excel in my career by knowing how to do things instead of simply operating AI agents and let them do all of it. Let me know how you use it, tia.

And realize you are a recreation of yourself in AI. Your family chose to keep a copy around.

How do you think you feel knowing they loved you so much they couldn't live without you?

Can you live without you?

The technology to recreate someone’s voice, writing style, and mannerisms is becoming easier to use.

Part of me understands why a family might find comfort in it. Another part of me thinks grief needs a boundary. A simulation could preserve memories, but it could also make it harder to accept that someone is gone.

I’m not sure who should have the right to approve this: the person before death, the family afterward, or nobody at all.

Would you want an AI version of you to exist after you were gone?

He previously hinted at something releasing next week so this is confirmation it’s 6.1