DIsclaimer: Fable was the orchestrator, and called itself on some tasks, but called Opus 5.0 on most of them. This still took 36% of my fable budget on a Claude max 20x sub, so doing the whole project with Fable would probably have spent all of my Fable budget or more.

I am also not certain that i have truly picked the absolute best prompt to make an impressive 3D scene but i wanted to actually make a game from it.

This was done in 2 shots. It made a first pass, i reviewed it, then it improved it again. I probably could go further.

My actual prompt is long and not worth sharing here because it reference past projects, but the main thing worth knowing is it spawned a lot of sub agents and did so in 3 waves. If someone really wants to see it: https://pastebin.com/iPnk4PZ8

This took around 5 hours and 30% of my weekly budget...

3-axis test successful. Planning to use it to make a 6-axis desktop robotic arm...See if we can implement remote operation or simulation.

Further post trained on Coding & Cowork, Qwen3.8-Max-0902 now delivers stronger performance across complex enterprise tasks, scientific research, and long horizon workflows.

https://x.com/Alibaba_Qwen/status/2094968708288680276

From the reporting here allegedly OpenAI has trained Astra to do latent space reasoning, which is thinking in a more abstract way and not necessarily jotting down in text all the model's thoughts (as how all the current frontier models work right now). Which is most likely a lot more information and allows the model to reason about things that can't be very well described as text. As for example when us humans do spatial reasoning we don't really think in text.

Estamos muy cerca de comenzar las pruebas de balanceo. Mucho tiempo invertido pero seguimos adelante. Somos Corporación Robot

The setup: Secondhand UFactory xArm 7. A size-4 round watercolor brush in a 3D-printed compliant side mount (leans roughly 16° from the paper). A cheap 24-well tray for paint, two water wells and a sponge for cleaning, 300×300 mm paper held down by magnets. Overhead camera with a ChArUco solve for the paper plane; brush-tip height from a touchscreen contact station. Motion is planned and executed by an AI coding agent driving Python stack (path planning, FK preflight against the current tool, safety gates, logging).

Operating a brush is tricky. It's is a soft, tilted, wet tuft, and the mark depends on shaft tilt, rotation about the shaft, depth, and how much pigment and water is loaded. None of that is observable from the controller.

This particular run had 16 paint pickups, 96 palette circles (six per load), 8,437 waypoints, about 32 m of arm travel for 28.8 cm of painted line, 31 minutes wall-clock.

Collecting training video for robotics is probably the easiest market to enter. Sure maybe not for factory or warehouse jobs but basically anything that involves working outside is what companies will be looking for next. Just wondering if this is a good time to enter this business and if it has potential

Atlas: A World Model for Spatial Intelligence | World Labs

AI Summary:

The blog introduces Atlas, World Labs’ new spatial world model. In brief:

• Atlas takes text, images, video-like image sequences, camera poses, and depth and builds a persistent spatial understanding of a scene.
• It can generate unseen viewpoints and infer missing geometry, rather than only reconstructing what was directly observed.
• It can turn those generated/reconstructed scenes into practical 3D representations such as Gaussian splats for fast rendering.
• World Labs emphasizes that Atlas can be updated with more observations, so its guesses about unseen areas can be replaced by real data.
• They position it for things like robotics, simulation, 3D content creation, and spatial AI.
• The key claim is that Atlas is not merely making pretty 3D reconstructions; it is learning a model of how a scene is arranged in space and using that to predict new observations.

The main caveat is that the blog demonstrates strong spatial modeling much more clearly than it demonstrates a fully general physics-based world simulator.

Hello Guys,

I’m the creator of Calibra which is an open-source project for robotics dataset quality and intelligent data curation.

Calibra aims to identify higher-quality training data and reduce GPU training costs by up to 70%.

We’re currently looking for collaborations with robotics teams and researchers.If interested, dm me.

AI was never exactly HARD. Organic computation, at it's core, has existed for a long time. Fundamental machine learning stuff, neural networks. Simulations. And they've been good! If you wanted to gaze into the infinite waters, I'm pretty sure that was a windows xp screensaver to do that.

But it's a different thing for AI to be literally magic, and that requires belief. We have that plus trillions. People say, ohhhh it's going to break out, shut off stuff, fire nukes. It has instead escaped into the minds and processes of every top 500 CEO and their business. It evaluates candidates. It judges us and for us. You can't interact with something like that and not get scrambled. You also can't see it outright.

To be fair, mind eating amoeba isn't the MOST common sci-fi plot ever, but it's very '50s!

Vals AI has claimed that fable 5.1 helped solve a three centuries old cipher that no one else could figure out.

Here’s the X thread that sums everything up:

https://x.com/ValsAI/status/2094851406931095593

For those who don’t like X or want something more in-depth, here is their blog:

https://www.vals.ai/blogs/fable-solves-cyphral-distich

CrowdStrike says it has launched SafeMind, a security-focused AI system with two models: Red Tempest looks for attack paths, while Blue Solano works to close them. The company says the system runs in the Falcon platform and was built with NVIDIA Nemotron.

The models use Falcon telemetry, threat intelligence, and 15 years of incident-response data, according to CrowdStrike. CSO reports that SafeMind also creates a digital twin of an enterprise environment so the red-team model can test attack paths and the blue-team model can respond.

CrowdStrike reports a 29% higher detection rate, 6x faster end-to-end remediation, and 99% cost savings on detection and remediation compared with leading frontier models and open-source baselines. Those are vendor-reported results. The practical test is whether independent production evidence shows the same gains without adding false positives or unsafe automated actions.

Sources:

CrowdStrike: https://www.crowdstrike.com/en-us/press-releases/crowdstrike-launches-frontier-models-for-cybersecurity-with-nvidia/

CSO: https://www.csoonline.com/article/4217135/crowdstrike-launches-cyber-frontier-ai-models-agentic-security-system/

Maybe I'm getting old, but AI has probably been more useful to me fixing stuff around the house than it has been writing emails or any of the things people keep talking about. The other day I had a door hinge pulling out of the frame. I've always used the old toothpick trick because that's what my dad showed me years ago. AI suggested using gel super glue as well. Never crossed my mind. Took five minutes and it's probably the best that hinge has ever been. Same thing with cars. Same thing with plumbing. Half the time I don't actually need AI to tell me what to do, I just need someone or something to point me in roughly the right direction so I stop overthinking it. Now before anyone says I'm replacing YouTube with ChatGPT, no. AI gets plenty wrong. It once wanted me to spend half an afternoon repairing a cheap kitchen appliance that costs less than a decent takeaway to replace. It has absolutely no concept of when something isn't worth fixing.

That's probably the bit people miss. AI isn't replacing experience. It's replacing that feeling of staring at something broken and thinking, "I've got absolutely no idea where to even start." Twenty years ago I'd have been digging through random forums hoping someone had the same problem. Today I ask AI, sanity-check the answer, and get on with it. Curious if anyone else has found this. Has AI actually changed how you approach DIY or fixing things, or am I just becoming the bloke who asks a chatbot what I used to ask my neighbour over the fence?

World Labs just announced Atlas, an omni world model aimed at advancing spatial intelligence by natively handling text, images, video, and 3D geometry within a single architecture.

Rather than treating video as isolated 2D pixel grids, Atlas uses an autoregressive diffusion transformer to ground every input in a shared 3D "spatial context." The space-time simulation features demonstrate how this moves AI past basic generation into true physical simulation:

• Democratized "Bullet Time": By processing footage from just 3 to 5 consumer mobile phones, Atlas can reconstruct dynamic events, freeze time, and simulate fluid camera trajectories through impossible angles without specialized capture rigs.
• Scalable Real-to-Sim: Beyond scanning static geometry, Atlas simulates dynamic robot navigation and manipulation. As an agent moves, the model synthesizes the exact RGB and metric depth streams its onboard sensors would capture along that path.
• Interactive Dynamics: Casual real-world video can be turned into simulations that model rigid, articulated, and deformable object physics, allowing researchers to alter object placement, lighting, and camera paths to generate synthetic training data.
• Native 3D Representations: It directly outputs point clouds and 3D Gaussian splats alongside novel video views, outperforming dedicated 3D reconstruction baselines across standard benchmarks like DTU, ETH3D, and ScanNet.

As AI agents become more autonomous, they are increasingly able to modify their own prompts, tools, middleware, routing, resources, and execution harnesses.

That raises a question we studied in our recent work:

What happens when a self-modification improves capability but cannot be safely reversed later?

We introduce EvoUndo, a framework that treats recoverability as an explicit constraint on self-evolving agents.

Across 600 unseen self-evolution tasks, we identified 197 capability-improving mutations that failed recoverability verification. Under the original recovery representation, conventional repair recovered 0/197.

Our experiments suggest that two important bottlenecks are state grounding and recovery-language expressivity.

The broader idea is that persistent self-modifications should not be judged only by whether they improve performance. They should also be tested for whether the previous state can be safely recovered across counterfactual states.

I’m one of the authors of the paper.

Paper: https://arxiv.org/abs/2608.28363

The new Atlas model from World Labs is a massive step toward spatial intelligence and giving AI a true understanding of physics and 3D geometry. It’s a multimodal autoregressive diffusion transformer that doesn't just generate 2D pixels, but grounds everything in a shared "spatial context."

The space-time simulation and Real-to-Sim capabilities are the most mind-bending parts of this release:

• The "Holodeck" from a Cell Phone: With footage from just three to five standard cell phones, Atlas builds a complete, navigable space-time simulation. It freezes time and lets you reframe 3D shots from impossible angles without a multi-million-dollar volumetric capture studio.
• Real-to-Sim for Embodied AGI: It doesn't just scan a static room. As a simulated robot moves through a reconstructed space, Atlas actively generates the exact RGB and depth sensor data the robot would see along its specific trajectory.
• Physics and Interaction: It captures how objects actually move and interact in the real world. You can take a casual recording and simulate rigid, articulated, and deformable objects, instantly tweaking the lighting and background to generate infinite training environments.
• Explicit 3D Geometry: Instead of just hallucinating flat video frames, it natively outputs full 3D point clouds and Gaussian splats that can be rendered and explored in real-time.