A Terraform Provider Network Mirror.
My idea is this project to grow into a more generic proxy cache for packages/artifacts for other ecosystems: npm, pypi, maven, nuget, container images.
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A Terraform Provider Network Mirror.
My idea is this project to grow into a more generic proxy cache for packages/artifacts for other ecosystems: npm, pypi, maven, nuget, container images.
- Easily parsing UDS, ISO-TP protocol CAN frames
- Parsing CAN-DBC files
- Building CAN Frame Payloads based on messages in a DBC file.
You can create multiple PocketBase instances for different environments and projects, all with controllable pricing.
We're working on more features, including: - Choosing the PocketBase version to deploy - Editing hook files from the UI - Server monitoring - Creating an on/off Node.js/Deno/Bun project to avoid complex logic in hooks
I’m working on a video game, purely for fun. Here is a work in progress build: https://muffinman-io.itch.io/space-deck-x It is a combination of a shoot-em-up and deck building. You fly and shoot until you get to the boss, when you get your deck out to fight them. That genre combination is definitely too ambitious, but I think it is fun to play and I’m enjoying making it. I have a bunch of ideas how to combine the two…
Back in 2020, while federating more than 100 service meshes across on-prem and AWS for a big hybrid cloud project, I had an idea: what if we could "split" the CAP theorem in a way that flips its limitations, enabling massive scaling far beyond traditional consensus protocols? Fast-forward five years:I started prototyping with libp2p, but the networking layer was always just a means to an end; the real goal is that CAP inversion/split for extreme distributed scaling. I think the timing is perfect given current geopolitical pushes.
Super curious to hear thoughts from folks. Any pitfalls I'm missing? Open to feedback or collaborators.
The main problem we're tackling is the quality of automated content for large catalogs. Instead of just spinning existing keywords, we use Vision AI to analyze product images directly. This allows us to generate accurate, accessible alt text and detailed descriptions based on what the product actually looks like.
To avoid the "generic AI" feel, the app also crawls your existing store content to build a custom brand voice profile, ensuring new content matches your established tone.
Key features: • Vision-based generation: Analyzes images for context-aware descriptions and alt text (WCAG 2.1 AA compliant). • Brand Voice Intelligence: Learns from your previous writing style. • Bulk Processing: Handles up to 500 products per batch with real-time tracking.
We have a free tier (20 credits/month) if you want to give it a spin. I'd love to hear your feedback on the "Vision AI" output quality versus standard text-generation tools!