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?

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

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.

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.

I just had Claude calculate the fraction of cache read costs for the past two months using ccusage and it amounts to 78% for Fable 5. If cache reads get 75% cheaper then overall costs should decrease by ~57%. I’m excited to see if the math holds up.

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.

Been generally tracking sentiment on the topic of the Singularity and AI progress for a very long time - on Reddit, in real life, wherever.

A year ago, while people talking about AI progress were starting to be taken seriously, the Singularity still was pretty niche and on places like Reddit people would still not respect any position that tried to argue it seriously, and bubble popping and walls were still primarily how people discussed AI.

That's been shifting particularly rapidly in the last 8 months, and I think in the last month or so, it suddenly started feeling very different.

In public, I hear people openly talking about AI _everywhere_. Not just how they don't like data centers... I hear regular people talking about using agents and freaking out. I've heard that kinda thing at the dog park multiple times over the last few weeks - I try not to even talk about it in places like that to keep some AI free islands... But there really aren't any of those anymore. I hear it at the bar when I go out dancing! It feels like people are really grappling with feeling capabilities rise... Maybe because more and more people are experiencing multiple generations of models now?

It's not just that people are using advanced models now and are talking about AI capabilities, it's that they are taking seriously the idea that we will have robotics soon. I don't even have to bring it up anymore as a potential future consideration, it's on people's minds already. Jobs, automation, even what it means to be a human being in the future we are building are all regular parts of the conversion, either implicitly or explicitly predicated on concepts like RSI!

I am... Happy? Confused? Pleasantly surprised?

There are still people who are in denial, very normal, or people who are still out of the loop or just can't understand... But even on other subs here on Reddit that are notorious for not taking AI progress seriously... Well if someone says that AI code is terrible or useless or whatever, it's now just a crowd of people who are _not me_ arguing with that person. Often people say something to the effect of "Look dude, I believed this too until a little while ago but I was in denial, I have to use these tools every day and I can't lie to myself about how capable they are anymore and I'm freaking out".

Anyway... This is good but jarring! Has anyone else noticed?

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.

Released by Felix Rieseberg of Anthropic on X/Twitter. Generally, it appears an incremental shift forward. OpenAI's Astra might very well leap this in short order.

The idea: curiosity shouldn't have to wait for someone to make a podcast about the thing you're curious about.

You type any topic, moment, or person and about two minutes later two hosts are telling you the story, researched with sources and paired with period artwork.

And it's interactive: press the mic mid-episode and ask whatever you're wondering ("how big were these ships actually?") — the hosts answer and weave it back into the story.

Happy to answer anything about how it's built and curious to know what the community thinks!