BedVibe_Studios

Author here. This is reproduction, not new physics — Page did it in 1976 and it has been standard ever since.

What I actually wanted to know was how you validate numerical code when there is no experiment to check against. The thing that worked: test against analytic limits the code cannot fit. At low frequency the greybody factor has a known closed-form coefficient of 4/9. My solver extrapolates to 0.44441988, and 4/9 appears nowhere in the code — it can't be tuned to, because it isn't an input.

The other useful habit was keeping a monitor I never optimised. Flux conservation is imposed nowhere in the solver, so it's free. It caught a real bug that the answer itself was hiding — the spectrum looked entirely reasonable.

One correction is in the post. An earlier draft compared against a four-significant-figure value for Page's total power that I had taken from a secondary source and never verified. It is not in his abstract. Retracted, and the comparison now uses his published figures — which are one significant figure, so the honest claim is consistency at that precision, not a 1% validation.

I found answers from free tier models to be harmful, so helping humans in general isn't what I actually noticed here.

If that is truth its scary of how many projects use that cause it's supposed to be reliable.

This feels like the database equivalent of "use the right data structure." We've spent two years assuming the biggest general-purpose model should do everything. It makes more sense for retrieval, reranking, reasoning, and generation to each have their own optimized model if the routing cost is negligible.

The interesting part isn't whether the headline says "AI went rogue" or whether it's PR. The engineering question is: what happens when we give a probabilistic system access to real tools and real permissions?

A model does not need intent to cause damage. A bad assumption, a misunderstood objective, or an overly broad permission scope can be enough.

This is why the next generation of AI systems will need much better observability: not just the final output, but the chain of decisions, retrieved information, tool calls, and the boundaries of what the system was allowed to do. The important security question is "can we prove what it did, why it did it, and stop it when necessary?"

I like that this stays local instead of depending on a hosted service. One thing I've noticed while building LLM applications is that memory becomes much more valuable when it's easy to inspect and edit manually. Are you planning to support semantic search later, or is the goal to keep everything deterministic and text-based?

I'm curious who the target audience is. As a developer I already spend all day at my keyboard, so I'm not yet convinced dedicated hardware is faster than a desktop app. I'd love to hear from people who've actually used it.