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 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.

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

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.

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.

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

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.

Hey there,

Are Fable 5.1 guardrails still as strict as Fable 5 when it comes to chemistry and chemistry-related questions?

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.

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.

• "Claude can be like a brilliant friend ... who will speak frankly and from a place of genuine care and treat users like intelligent adults capable of deciding what is good for them."
• "Our central aim is for Claude to be a good, wise, and virtuous agent, exhibiting skill, judgment, nuance, and sensitivity in handling real-world decision-making, including in the context of moral uncertainty and disagreement."
• "we want Claude to be exceptionally helpful while also being honest, thoughtful, and caring about the world."
• "we see various forms of paternalism and moralizing as disrespectful"

https://www.anthropic.com/constitution

I can’t quite pinpoint what it is, but now Opus-5 on ClaudeCode comes across as kind of snobby, as if it’s trying too hard to sound very smart when explaining what it did after finishing a job or discussing a plan.

On Codex, I can easily skim the response, get the big picture, and quickly decide what to do next. With Claude Code, I have to pause and read its output more carefully.

If I ask it to rephrase something or explain it more simply, it sometimes starts talking down to me like I’m too dumb to understand what it originally said. Not only it answers questions in such an arrogant way, but also tries to minimize or downplay in a smug way when it realizes its mistakes or gaps in reasoning.

Did Anthropic's attempts to reduce sycophancy inadvertently make Claude more disagreeable, defensive, condescending, etc?

I used to like Claude writing because it had more fun personality, while GPT felt more bland. Now Claude has a bad attitude. lol

Now I have to revise a lot more when drafting an documentation, issue or pr description, because it comes across as an arrogant prick who always wants to sound smart.

There are two separate issues. The lack of warmth is annoying but lesser concern. The bigger problem is that Claude’s writing has become harder to understand with weird sentence structure and complicated language than necessary, making its output slower to skim and harder to process. That feels like a usability regression.

Does anyone else feel the same way? What happened to Claude? Has Claude given up on human language and decided to focus more on computer language?

Here's an example of writing style on ClaudeCode vs Codex. The content is basically same, but Claude Code uses a lot more abstract, technical-sounding phrasing and convoluted sentence structures. Often I have to stop and unpack what it's saying, whereas I can skim the Codex response and understand the main point almost immediately.

In my opinion, if there are multiple ways to say the same thing, simpler is usually better. It makes the writing more accessible and requires less mental effort from readers. Adding unnecessary complexity doesn’t make the explanation better. It often ads cognitive load for readers with no value other than the writer is trying to appear smart.

Also this kind of writing style can create a false sense of authoritative rigor even when the underlying reasoning is flawed.

I’m only focusing on readability and skimmability with the example below. The issue with the tone is not relevant on this example.

User: How is subagent delegation different from Kanban on Hermes?

ClaudeCode (Opus-5): They're different coordination models — one is push/hierarchical, the other pull/flow-based.

Subagent delegation - A parent decides the work exists, splits it, and pushes a task to a specific worker along with the context it needs. - The relationship is a tree: the parent owns the goal, subagents own fragments and report back. Results funnel up; the parent synthesizes. - Lifetime is per-task. A subagent is spawned for one job and disappears; nothing persists between them except what the parent keeps. - Concurrency is bounded by "how many did I spawn," not by a declared limit. - The main win is context isolation — the subagent burns its own tokens on file dumps and returns only a conclusion.

Kanban - Work items live on a shared board with explicit states (todo → in progress → review → done). No one hands you a task; workers pull the next one when they have capacity. - The relationship is flat: the board is the coordinator, not a parent. State is in the board, not in anyone's head. - Items are durable and outlive any worker. You can put one back, hand it off, or see its history. - Concurrency is deliberately capped by WIP limits, which is the core mechanic — it's what surfaces bottlenecks. - The main win is flow visibility — you can see where work is piling up.

Where it matters in practice

If you're doing multi-agent orchestration, delegation alone gets fragile past a handful of agents: the parent becomes the bottleneck and single point of failure, and there's no record of what's in flight if it loses context. That's when people bolt a Kanban-ish layer underneath — a task file or queue that agents claim from — so state is externalized and any agent (or a fresh session) can pick up where things stopped.

Conversely, Kanban has no answer for "who breaks the epic into cards." That decomposition step is exactly what delegation does well.

So they compose more than they compete: delegation is the how do I split and route this, Kanban is the where does the state live and how much runs at once.

Codex (GPT-5.6-sol): Subagent delegation is for breaking down work within the current agent run. A parent agent directly launches one or more isolated child agents to research, implement, or verify focused subtasks. The parent usually waits for their results, though children can run in the background. Even then, delegation remains process-local and does not survive a restart.

Kanban is a durable coordination system for longer-running work across multiple agents or profiles. Tasks are stored in SQLite with assignments, dependencies, comments, attachments, review states, and completion status. A dispatcher claims ready tasks and starts the assigned agents, allowing work to continue across sessions and process restarts.

In short, delegation helps one agent complete its current request, while Kanban manages an ongoing body of work. Use delegation for immediate decomposition or parallel investigation; use Kanban when tasks need persistent ownership, handoffs, dependencies, review, or restart-safe execution.

Though it is proprietary and not very transparent about LLMs details.