jmort

No options?

OpenPCC is Apache 2.0 without a CLA to prevent rugpulls whereas edgeless is BSL

I haven’t been able to find their source code. Pretty important for the transparency side of it. Have you seen it?

It should be able to support connecting via an OpenPCC client, then!

It does work generically. Like Apple, we initially targeted inference, but it under the hood just an anonymous, attested HTTP server wrapper. The ComputeNode can run an arbitrary workload.

Yep, you could run an anonymization workload inside the OpenPCC compute node. We target inference as the "workload" but it's really just attested HTTP server where you can't see inside. So, in this case your client (the wearable) would send its data first through OpenPCC to a server that runs some anonymization process.

If it's possible to anonymize on the wearable, that would be simpler.

The challenge is what does the anonymizer "do" to be perfect?

As an aside, IMO homomorphic encryption (still) isn't ready...

I think a technical solution is necessary, rather than a legal/regulatory one

I think we should have an opt-out standard via a subsonic signal, like DO NOT TRACK in browsers. Then, it's on the vendors to intentionally ignore a clear signal.

To that end, I've been working on opening sourcing https://dontrecord.me as a side project. Putting together a fork of Whisper that will follow the opt-out signal, too. If anyone one wants to help, please connect.

I don't see a privacy policy and their desktop app is closed source. So, not encouraging.

[full disclosure I am working on something with actual privacy guarantees for LLM calls that does use a transparency log, etc.]

Also https://inflammatix.com/

Probably https://www.epa.gov/lead/why-cant-i-use-hot-water-tap-drinki...

https://cooking.stackexchange.com/questions/21471/is-hot-tap... has some more sources

Can’t tell much from the docs — How does this compare to fly.io?

The points in this post reminds me of the “configuration clock”[1]. Hardcoded -> configure file -> rules -> DSL -> Hardcoded. Maybe it should be the “configuration ouroboros”. Provides one explanation for why this config becoming code situation occurs over and over and what to watch out for.

[1] http://mikehadlow.blogspot.com/2012/05/configuration-complex...

Can you say a bit more about "performant" or point me to some information? I haven't found any yet. I'm processing millions of protobufs per second and would love to get away from batch jobs to do some incredibly basic counting -- this seems like a fit conceptually...If its a fit, any recommendations on the best way to get those protobufs off a kafka stream and into pipelinedb would be great, too!

[Gyroscope cofounder here] We've experienced similar things and that was the motivation for this post -- ticketing systems and startups seem contradictory. What other processes/stack does your company use?

Does YC explain why they don’t use SAFEs somewhere?

Thanks! Really an excellent tool! I'd originally written a shiny app, but this was much better!

Other than the plot (thanks, gballan), it would be nice to adjust the width of layout cells, especially for the code cells. It would also be cool to adjust the spacing between cells to reduce whitespace when another cell in the same row takes up a huge amount but others do not.

I was not clear how to place markdown text in between or after tables and sliders but might have just missed how to do that.

Thanks again!

Hey all, I wrote this little Blab after struggling to visualize the impact of budgeting and investments in the long term. People kept telling me to save and invest, but I wanted to see empirically why this is true.

Features I'd like to add:

  - Better taxation rules
  - Specific asset classes and investment accounts with 
    associated taxation, withdraw limits, and contribution limits
  - Investment allocation strategy with a changing risk profile 
    and tax conscious account allocation
  - Social security income
  - Highlighting the "important" factors 
    (i.e., the ones that impact the plot the most)
  - A probabilistic simulation to get a better result estimate 
    (i.e., confidence intervals)