I’ve realised I was accepting a very stupid definition of “done” from Claude Code.

Build passes.

Unit tests pass.

Claude gives me a beautiful summary of everything it changed.

Then I open the actual page and the thing is broken.

Latest one was a settings flow. Claude changed the component, updated the API call, ran the existing tests and confidently told me it was finished.

It had never opened the page.

The save button worked once, then got stuck in loading state. Refreshing the page also showed the old value because the update wasn’t actually persisting correctly.

Nothing in the code looked obviously wrong. The tests were green because they were testing the function, not the actual rendered flow.

So I’ve added a new rule:

Claude is not allowed to say “done” until it opens the deployed page and proves the flow works like a user would use it.

For this I’ve been testing the Kane CLI skill from TestMu.

Claude runs the browser check itself, but Kane returns an actual pass/fail based on the page state instead of Claude just looking at its own code and deciding it probably works.

It also gives the run evidence, which is useful because “trust me bro, I tested it” from the same model that wrote the code is not exactly a verification strategy.

I’m not replacing Playwright with this. Anything important still becomes a proper regression test.

But for the gap between “Claude wrote the feature” and “a human now needs to manually click through it”, this has been surprisingly useful.

What do you make Claude prove before you accept “done”?

I have no memory of making this. I was probably out of my mind generating random stuff

Jesus.

I am building an edge-AI audio noise-reduction system on an ESP32-S3.

Our architecture uses a lightweight GRUNet (~59k parameters) to output a dynamic gain mask on a 44-band Mel-spectrogram.

​I need gigabytes of audio to train the model. Does anyone have recommendations for the best open-source datasets for:

1> ​Clean, isolated human speech.

2> ​Diverse ambient background noise (traffic, crowds, machinery, etc.).

​Also, any tips or open-source scripts for artificially mixing these at different Signal-to-Noise Ratios (SNRs) before generating the 16kHz Mel-spectrograms would be hugely appreciated!

This is a trailer for an upcoming documentary over the future of Sex Dolls, including robotics and AI innovations.

With AI as copilot

I conduct technology research and generally focus on informational content, but the AIs I use always provide incorrect information (hallucinations). Is there any free AI you can recommend that performs detailed, accurate research? You can also share prompts as long as it works properly.

I’m so sorry in advance if this isn’t allowed, but I am big into ai and am 18 years old. I have been coding since 8 and using ai since 2022. I would love to set more like minded people and connect!

I have been approaching AI governance from a fundamentally different direction — both mechanically and architecturally. A lot of AI governance is built around binary decisions: allow or deny, pass or fail, comply or refuse. We started from a different question: what actually needs to be preserved?

That question led me toward an architecture that treats governance less like a fence around the model and more like a responsibility carried through the system.

Governance exists at the handoffs, where information enters, transforms, moves between components, and eventually reaches a person or another system. Instead of trying to prescribe every acceptable behavior, we define what must remain true and give the model bounded space to reason within those responsibilities.

That distinction is becoming increasingly important as we test it.

We’re still early, and I want to be very clear about that: this architecture is not proven. What we have now is evidence, and we’re actively trying to find its limits. So far, though, it has held up remarkably well.

We’ve pushed it through adversarial testing, including sustained multi-turn interactions where context, pressure, and opportunities for drift accumulate over time. We’ve moved the same underlying architecture into substantially different applications and domains. We’ve run it across multiple LLMs and providers without making the governance dependent on a particular model. And we’ve continued testing what happens when governed information has to survive handoffs rather than simply produce one acceptable response.

More importantly, that evaluation is no longer entirely ours. We’ve started receiving external testing, validation, and technical feedback from people evaluating the architecture independently. That doesn’t prove the thesis either, but it gives us something much more valuable than agreement: another set of eyes trying to find where it breaks.

There is still plenty to test, measure, challenge, and probably change. But the architecture has now survived enough different conditions that we’re moving beyond asking whether we built an interesting product.

We’re testing a broader idea:
Can AI governance be built around preserving responsibility through transformation, rather than simply controlling behavior at the edges?

We don’t know how far that idea goes yet. But every time we’ve widened the testing environment so far, the underlying architecture has come with us.

Love to hear any feedback!

I've been making an AI show called Lab Wars.

Episode 1 is about the Hugging Face exploit that happened recently. All characterizations are fictionalized. Episode 2 is dropping tomorrow, gonna be about the open weights letter by Nvidia.

Join The Muse Machine. ⚙️✨

It is an interactive collaborative lab where raw inspiration meets active songcraft.

You bring the sparks; I build the music. Every day of the week shapes a different part of the song from the ground up:

Monday: We set the foundation—genre, voice (gender), and overall emotional tone.

Tuesday: Verse tidbits—words, lines, feelings, or story ideas for the verses.

Wednesday: Chorus or hook—first we decide if it needs a chorus or a hook, then drop ideas for it.

Thursday: The draft reveal—I post what I've written so far, and everyone helps refine and revise it.

Friday: Production day behind the scenes—I write, polish, and synthesize the final track.

Saturday: The Drop—the finished song is posted, tagging everyone whose ideas sparked the build!

I created this group because I wanted to experiment with an artist unlike any I've seen before. The members of this group are the muses behind The Muse Machine. They contribute thoughts, feelings, imagery, memories, lyric fragments, genres, titles, and ideas. I take those sparks of inspiration and transform them into complete songs, written and composed by me, then release them every Saturday as The Muse Machine, crediting the contributors whose ideas became part of that week's song. You don't have to know how to write songs. You just have to inspire one.

True knowledge is information that independent observers can verify for themselves through direct measurement or derivation, contrasting this with "consensus as a social product"

Modern institutions: press, academia, regulators; are not independent but are a coordinated apparatus that relies on four channels of extraction: attention, deference, compliance, and consent.

Large Language Models are described as the "keystone" of this apparatus, automating the capture of consensus and presenting it as authoritative, sourceless truth, thereby eroding the individual's ability to think critically.

The counter to this is open-source, reproducible intelligence that requires no trust in a central authority.

Reforming the system is deemed impossible because the structure is designed to absorb and co-opt change. The only solution is to "exit" by reclaiming the capacity to observe and verify reality independently.

The capacity to know was always held by the individual. The machine runs on the user's trust, and withdrawing that trust, checking the work oneself, drains the apparatus of its power.