being reductive, causality is the key term for a world model. the rollout is the key action to demonstrate causality. lots of nice LLMs can generate a 3d representation of a "visual world". These days the focus is on sensory data (image, video, etc.), however world models can be more than just 3d spatial representations - they could be tabular. Again being reductive, its how do you predict the next state from the current state (the rollout).
It was my fault, I copied the command and ran it from a search result provided by my OpenClaw that was running on a non-frontier model (I can’t remember which - something small and free).
I’m pretty confident it’s gone, this happened about 6-7 weeks ago. Have been running recurring checks for processes, Malwarebytes, and a PiHole to monitor traffic.
My sense is yes. Running a small, non-frontier model in a "loose harness" has less guardrails under the hood. However I have no evidence besides my outcomes!
An analogy might be: AltaVisa 20+ years ago vs. Google + Chrome today. There are more layers to filter or warn of malicious links.
The Max version gets more details right. The bike frame looks good, the chain, the wings are appropriately styled instead of “arms”, and the knee is bent, etc. Obviously we’re hitting marginal returns now, but I see differences.
[Take a look at my portfolio site](https://reebz.com), please view on desktop. This is about 3 weeks of effort to date. It is unfinished, but you get the idea.
Just like SaaS boilerplate from the decade prior, there is LLM boilerplate (since it’s trained on the internet).
So if you put in enough elbow-grease anything is (still) possible!
There are certainly simpler solutions, but I love maximum flexibility to pickup and go from my desktop to iPad to iPhone anytime I want with full terminal access
The influencer economy trades on hype, on frenzy, and ultimately, eyeballs. The more the better.
They want you feel like you’re missing out. They want you to switch. Being boring is far more productive. Pin your versions. Stick to stable releases and avoid the nightlies.
Significant noise created from 4.6 to 4.7 Opus transition has caused some to interpret this as signal. Excluding certain genuine and real bugs, the noise about perceived quality falling dramatically was noise. Influencers doing influencing turned it into “signal”. The reality was that if you had strong planning and spec driven development it ranged from manageable to non-existent.
The vast majority of the people I know and work with have not switched off CC or their Max sub.
Claude 4.7 is the clear winner to me for manager and formal report updates.
As an ex-senior exec (hundreds of staff), the bolded timeline impact is a particular nuance that I would expect a Lead/Director to format for a VP+ audience. Interesting none of the other models did that. My eyes immediately went to impact statement, then worked back to context to grasp the whole situation.
Many commentators are, mostly fairly, criticizing the repo due to issues raised on benchmarks. This is reasonable, however many are going further to bash the repo likely due to the authors.
For me, I see a silver lining. I'll be implementing mempalace for a few small agents to have memory portability that's managed locally.
I think the benchmarker who ran independent tests in GitHub issue #39 summed it up best:
To be clear about what this all means for our own use case: we still think there's a real product here, just not the one the README is selling. The combination of a one-command ChromaDB ingest pipeline for Claude Code, ChatGPT, and Slack exports, a working semantic search index over months or years of conversation history, fully local, MIT-licensed, no API key required, and a standalone temporal knowledge graph module (knowledge_graph.py) that could be used independently of the rest of the palace machinery,is genuinely useful, and we're planning to integrate it into our Sandcastle orchestrator as a claude_history_search MCP tool exactly along those lines.
