tdubey

I tried and as a individual (single/one at a time) image editor is it fantastic, beats Lightroom by a mile and the node editor gives me the ability to "emulate" panels from CaptureOne (and OFX plugin support means I can just LLM my own if I need).

Library management is where Lightroom (and honestly CaptureOne) still wins, I couldn't find a good way to manage a 100k photo library.

I created one project and tried importing the whole library, but it took more than 8 hours and didn't make meaningful progress (~10%?) so I stopped (compared to 15hr for the whole thing in Lightroom).

I also did a project for about 1K photos and while the import worked, the library management itself was lacking. I could not find ways to do image stacks for bursts, group by metadata, etc. The obvious filters are there, but I think it needs more work.

Open up https://www.liveatc.net/ in another tab and you have a complete experience. Really fun!

Are there benchmarks for how this performs on charts, or maybe more accurately, plots? I've yet to find a model that can digitize a plot into X,Y points with some accuracy in my use case of digitizing old datasheets.

Hilariously, the Arctic Blast screenshot seems to be the Audacity audio editor with Emacs overlaid! https://ianyepan.github.io/images/arctic-blast-emacs.png

Is there improved guidance on migrating from tmux to zellij?

I've attempted to move over a few times, and while this is certainly user skill, it just felt too different from screen/tmux. Perhaps I should bite the bullet and force myself to get used to a new paradigm...

Disclosure, I work for Datadog:

https://updog.ai/status/amazonaws

Looks fine for now.

I'd posit this is more knowing your user. Lots of photo editors/multimedia types use Macs, so it would only help GIMP if they offered Mac keybinds out of the box, or even mimicked the keybinds Adobe Photoshop has.

Loki by Grafana Labs is nice (https://grafana.com/oss/loki/). There was a time (3+ years ago) where the product was changing pretty rapidly and much of the documentation was on git, so we had a few headaches doing minor version bumps, but I believe its much more mature now.

Hi HN.

I am teaching myself some more full-stack dev and try out AI assisted coding, I decided to build Terminal Tinder, a way for your machine to learn what colorschemes you like, and maybe generate some novel ones for you.

Would love for people to try it out and hopefully find some fun or use out of it!

Open to feedback, bugs, PR, etc here: https://github.com/tanishq-dubey/TerminalTinder and thanks!

You solve a lot of the same problems that other tech companies solve, just with different constraints. An example I like to use is: "Facebook can afford to lose a 'like' on a post -- whether that is through data loss or just eventual consistency -- while we cannot afford to lose a penny (exaggerating a bit here) since there are people making live decisions on that penny"

Those sort of problems are more on the product side, along with the regular slew of product development tech problems. On the infrastructure/platform side (where I work) it is remarkably close to "regular tech". Same types of distributed systems problems, database issues, CI/CD pipelines, engineering efficiency, etc. The "new" part here is working with the finance specific areas such as physical data centers and networking to clients, or domain specific items such as data retention for compliance.

The firm I work at developed an internal library (open source soon hopefully!) that does all our templating in Python, example:

  import os

  """
  Imports redacted, but this imports our library
  """

  NAME = "my_app"
  PORT = 5000
  DEPLOY_ENV = os.environ["DEPLOY_ENV"]

  deploy_all(
      create_stateful_service(
          NAME, http_port=PORT, cpu=2000, memory=500
      ),
      create_ingress(
          NAME,
          service_name=NAME,
          service_port=PORT,
          hostnames=["myapp.internal.com"],
      ),
  )

In the background, this converts a bunch of objects to YAML, leveraging the fact that objects in Python are just dictionaries. It then takes these objects and sends them to the K8S API. Really improved our DevEx since you can do complex tasks like "define a deployment, deploy it, and then check server status" because it is all just Python at the end of the day.

Edit: formatting

Cortex was just released, and while it doesn’t solve HA right away, it does make it easier: https://cortexmetrics.io