wcdolphin

I’ve been hacking on something in this vein and would love your feedback. What if you could reuse your CI env by using Github Actions as your sandbox. You can reuse the caching, any oidc based roles and self host via runs-on.com for cost and performance. We expose a claude code web experience of interactive low latency chat. I have a working prototype I’m happy to share if you think it would be interesting.

Have you found any tools you think are easier/better? I’d love to learn more about the space.

Is this telemetry enabled internally at Google? Enabling it internally would be an interesting litmus test of what is required to make it privacy/security compliant.

1. Can you share information about the specific hardware/CPUs used and where you are hosted? 2. Running untrusted workloads is a huge security challenge. Can you share the technology you are using for isolation and how you have approached mitigation of security threats?

This new release seems to continue the trend of new AWS hardware generations increasing performance while also increasing cost, as opposed to the prior trend of increased performance and decreased cost.

The "Flex" variants appear to be pushing the "burstable" machine types (T4,T3,etc) to be positioned for use in production workloads.

Is there a timeline for a clustered, HA and horizontally scaled version?

The vertical scaling and performance seem extremely interesting, but for our use cases we need HA.

From a business perspective I’m interested to hear about the plans. Will there be a per core licensing fee of some sort?

Would you mind sharing more details of your experience using MypyC? What domain are you working in, and what kind of effort and speedups did you see?

Has anyone migrated a large code base between MyPy and Pyright? In particular would love to hear about experiences with Django. Mypy’s performance and the join based type merging are quite limiting. At the same time, the ecosystem support around MyPy seems strong.

Is anyone else using MyPyC in production and can share their experience? Did you attempt the compile it all approach, or incrementally add? What do compile times look like at scale?

Would love to buy you a coffee and hear about your experience and the challenges and benefits.

Has anyone used Cast.ai or Spot by netapp as a comparison?

Very cool. I love the idea of one tool rather than two, I’ll check it out!

Interesting. Now that isort has support for black-compatibility, what’s the main difference with usort?

I really wanted the M1 upgrade story to be a slam dunk, but it seems like docker on arm64 is a real thorn of incompatibility and poor performance with amd64 images.

I’m not super well versed, but I thought BitTorrent’s main contribution was essentially the chunking and distributed hash table. There is perhaps a hood analog of the different layers of a docker image.

I’d love for others with more knowledgeable to chime in, since this feels close to the logical end state for non-user-facing distribution. At a protocol level, content basically becomes a combination of a hash/digest and one or more canonical sources/hubs. This allow any intermediaries to cache or serve the content to reduce bandwidth/increase locality, and could have many different implementations for different environments to take advantage of local networks as well as public networks in a similar fashion as recursive DNS resolvers. In this fashion you could transparently cache at a host level as well as eg your local cloud provider to reduce latency/bw.

The “Trusted by” page has some pretty impressive flagship companies (GitHub, Slack, Square, AirBnb). Is anyone familiar with the role in which Planetscale is used at those companies? (Unless that is advertising users of vitess vs the hosted offering).

If anyone has experience using self hosted GH Actions at scale, I’d love to buy you a virtual coffee and hear about pros/cons for a parallelized CI flow currently running in Circle. Main motivation for switching would be simplification of tooling and increasing performance with better cache reuse and running within AWS for faster network access to ECR.

This tool seems interesting, but I haven’t yet figured out how to configure it to work with Django’s dynamic nature. If anyone has had good success I’d love to hear more!

Very creative, extremely complex, what a fun hack. TL;DR exploit the fact that AWS keeps your lambda process running (and in memory) beyond the time you are billed for in order to build an ephemeral cache. Creates one lambda per slice of cache (think redis slots) and provides a proxy to hide the (substantial) complexity from clients, keeping those lambdas warm, etc. I could see this being useful for cost hacking if the request volume was on the order of one request per key every few minutes.

You’ll like Kotlin’s concurrency model then! It not only allows concurrency, but also parallelism, with rich language support. It is really pretty great, check it out!

I wasn’t able to find the results or summary, did I miss it in my skim? Somehow one day a week fasting sounds more manageable than daily intermittent fasting to me. Could be an interesting way to kick off the week on Monday :)

Out of curiosity, are you using the durable, NATS-Streaming server, or just the ephemeral NATS-server? I’d be interested to hear more about the performance differences you see.

“Fuel Economy: 20 mpg, mixed driving (est.)” Crazy to think of how little progress we’ve made on that end, assuming the reporting is accurate.

Thank you for going out of your way to tell the truth. It is so easy and common for our industry to self-inflate egos with grandiose stories of exits. The truth has much more to teach us, so thank you.

I have come to believe that privacy and permissions logic tends towards the complexity of your product. Unless you’d consider having much of your application logic be in the data store via procedures or something like VoltDB’s jar based user defined functions, I’d be wary of putting permissions logic in the DB.