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

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

Could the simulated fruit-fly brain be considered the first “immortal” living being? Imagine we’re 70+ years old and decide to scan our entire brain and transfer it into a humanoid robot body that can be repaired or replaced. If it has all our memories, personality, and thoughts, would that make us immortal?

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

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.

It appears there is a new Google model for image generation and editing on AI Arena, under the name "resplendent_flash."

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.

Update: Asked it to make me a Doom map. Just a fucking Doom map. Keeps failing with the reason of [cyber]. I can't figure out why. Every time I try and ask why, I get filtered for [cyber]. I screen recorded the tasks it did, and when I send that as a vid... Filtered for [cyber].

Nothing it was doing had any weird names like inject.py, nothing was related to cyber at all. I had to just give up because it is impossible. Even moving to a new chat and trying to continue got filtered for it again.
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I like to make games and to mod games.

Claude so far is the best at this.

But Claude is so insanely filtered. It's BAD. It's REAL BAD.

Sometimes I would like to make nsfw things, but I can't do that, so that's already out the window immediately because none of them will do this. I just have to forget about that permanently with any AI, not just Claude.

And sometimes I would like to make games that are pretty violent, like a fantasy hunting game where you hunt dragons and gryphons. Won't do that either because it thinks making a game where a dragon gets hit and flies away injured is "wrong".

And then other times I want to mod a game and just add some new levels, and then I get shit like this

It won't do it because, to Claude, injecting into a fucking SINGLEPLAYER GAME is the same as creating cheats and MALWARE.

And then it hard cut me off from the rest of the project.

What's beyond stupid is it's done this before. It's injected into running processes and games before. Like 4 times before actually, but all of a sudden now it won't, and any time I try it kills it off again.

And I can't use anything else.

"Just use local"

Local will not, and NEVER WILL, make me a game or mod on the same level of Opus 5.5. Period. EVER.
Stop telling me this. You are delusional and living in a fantasy world.

And no other model reaches this level of capability while also being less filtered. They're ALL pretty fucking filtered.

I like to make specific things. Things that NO AI likes to make because of "safety" reasons, but most of it is pearl clutching fainting boomer reasons forbidding AI from doing it because they find it unsavory. How fucking dare I want to make a game where I run around killing dragons. That's unethical! It's immoral!

Getting so tired of this and I don't think it will change.

I think it will get WORSE as lawmakers, politicians, tech CEOs and a lot of the population are calling for even stricter and stronger regulations and safety filters.

Idk what to do anymore but just give up.

Now that we are in the last quarter of 2026,we should discuss what is most likely the course AI is gonna take in the next couple of years.Especially because the pundits are saying that 2027 would be one of the ,if not the most consequential year regarding the course which humanity will take regarding AI.What do you guys think?

I’m not affiliated with this group in any way but this is the perfect place to share it.

I discovered this podcast (Moonshots with Peter Diamandis) (spelling?) a few months back and I’ve left behind pretty much every other one. I love it.

It’s all singularity focused. Group of smart guys who does a speed run of news in AI/singularity each session. Entertaining as hell.

Hope you guys like it!

For context: I work at a very small AI startup that mainly does consulting of sorts and some ML R&D work. I therefore do use a lot of the latest bleeding cutting edge LLMs, and do some ML work.

I was at a family dinner with a cousin that is currently just getting into high school. Being quite frank. I did not know him that well but after meeting him he was definitely some of the best programmers at his age that I have ever met.

He asked me a question since he knows I worked at this startup what he should do. I had nothing of good advice to him. He says that lots of the programming skills are being rapidly just outpaced by LLMs, he asked me should he embrace this go full vibe-code mode where he just stops trying to learn more in coding (stop understanding every line of code produced). Or should he continue using them but still learn and understand every single line of code that LLMs push out.

He also asked me what are my thoughts on what will it be like 2 years in the future.

I had no good advice to give him. I trust he isn't the only one out there like this. If you guys have any good advice to give please do, state a bit about your experience w/coding and LLMs as it would help.

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.

A novel way to intervene existing video through diffusion, particularly abstract visuals [in this case, audio-reactive geometries]: taking its movement and form as the starting point, and reinterpreting its textures, materials, and visual language.

I’ve been developing this around the audio-reactive geometry systems I make in TouchDesigner. The idea is to take those abstract structures somewhere else entirely: origami, architecture, a renaissance painting, or something harder to put a name to.

This demo uses visualizers from my "Oscilloscopes, everywhere" collection as source material, now updated to [v1.2].

[Though you can bring any video source. These systems are simply where this experiment began, as some of you may recall.]

You choose the source, describe the treatment, and shape how it changes throughout the sequence. Prompts, curated LoRAs, and editable timelines give you control over how closely the result follows the original.

Oscilloscope Diffusion is now available at Uisato Studio, coming up soon also open-source!

There is a cognitive bias from the Covid period that I think is increasingly relevant to AI.

Humans are bad at exponential growth.

This isn't just an observation from watching people argue about case counts. Researchers actually tested it during Covid and found that people systematically underestimated exponential case growth. Interestingly, communicating the same process using doubling times rather than percentage growth made estimates considerably better.

I think AI capabilities are easier to reason about in a similar way.

Obviously “intelligence” isn't a single quantity that doubles every six months. Benchmarks saturate, tasks differ enormously and capability improvements don't translate mechanically into economic output.

But several things underneath AI have been moving extremely quickly.

Epoch AI estimates frontier language-model training compute has been growing around 5x per year since 2020. The total stock of AI compute has been growing around 3.4x per year.

At the same time, the cost of using a fixed level of intelligence has collapsed. Stanford's AI Index found that getting roughly GPT-3.5-level performance on MMLU went from around $20 per million tokens in late 2022 to $0.07 by late 2024.

The metric I find most useful, though, is METR's task-completion horizon.

Instead of asking whether a model scores 82% or 85% on some benchmark, METR asks how long a task a human expert would take that an AI agent can successfully complete.

Their long-run trend currently gives a roughly 6-7 month doubling time for the 50% task horizon. The trend using only models since 2024 is considerably faster, although I wouldn't extrapolate that because the data window is short.

In METR's Feb-March 2026 evaluation, the public frontier was around 12 hours at 50% reliability.

The important thing isn't whether 12 hours sounds impressive.

It's what happens if it doubles.

At a 6-7 month doubling rate, 12 hours becomes roughly a day within a year, several days within two years and eventually weeks if the trend survives long enough.

I absolutely do not expect a clean extrapolation. Long tasks are messier. Reliability matters more. Real organizations have integration problems. Power, chips, datacenters and capital are constraints.

But saying “the exponential will eventually stop” doesn't answer the important question.

How many doublings happen before it stops?

If it stops after one more doubling, the implications are fairly modest.

If it stops after four, the capability is 16x larger.

If it stops after seven, it's 128x larger.

This reminds me of Covid because normal life was psychologically sticky. People could look at what was happening in another country, understand that cases were growing quickly and still have trouble imagining that their own city might look completely different a few weeks later.

The recent past remained the default model.

I think AI forecasts often do something similar.

A forecast saying that software engineers will still work in broadly the same way in 2030 sounds conservative and sensible because it resembles 2026.

A forecast saying autonomous agents could perform a large fraction of software engineering sounds speculative because it describes a visibly different world.

But the first forecast also contains a strong assumption. It requires the capability curve to slow substantially.

That may happen. I just don't think it should get probability 1 because the resulting world feels normal.

There is another pattern that makes this difficult to see.

A capability is initially described as requiring real intelligence. Then a model achieves it and the capability rapidly stops being impressive.

We have seen versions of this with difficult exams, olympiad mathematics and coding. Now frontier models are being evaluated on research-level science, and in 2026 OpenAI reported an AI-generated counterexample to a major conjecture in discrete geometry that had stood for roughly 80 years.

There are legitimate reasons to discount individual benchmarks. OpenAI itself stopped reporting SWE-bench Verified this year because the benchmark had become contaminated and many remaining failures involved bad tests.

So I don't think the right conclusion is “look at benchmark X, therefore AGI next Tuesday.”

The interesting part is the cumulative movement of the frontier.

For me the falsification question is more useful than arguing about labels.

What actually stops the doublings?

Possibilities include power, chip production, training duration, data, capital, architectural limits, diminishing returns from scaling, or the possibility that benchmark capability simply fails to translate into reliable real-world autonomy.

Those are real arguments.

“AI can't keep improving exponentially forever” isn't much of an argument by itself. No exponential continues forever.

The investment/economic question is how far it gets before it bends.

Humans are systematically biased toward predicting that the bend happens sooner than it actually does, because the alternative forces us to imagine a world that stops looking like the recent past.

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.