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founded 3 years ago
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Hi all, I'm relatively new to this instance but reading through the instance docs I found:

Donations are currently made using snowe’s github sponsors page. If you get another place to donate that is not this it is fake and should be reported to us.

Going to the sponsor page we see the following goal:

@snowe2010's goal is to earn $200 per month

pay for our 📫 SendGrid Account: $20 a month 💻 Vultr VPS for prod and beta sites: Prod is $115-130 a month, beta is $6-10 a month 👩🏼 Paying our admins and devops any amount ◀️ Upgrade tailscale membership: $6-? dollars a month (depends on number of users) Add in better server infrastructure including paid account for Pulsetic and Graphana. Add in better server backups, and be able to expand the team so that it's not so small.

Currently only 30% of the goal to break-even is being met. Please consider setting up a sponsorship, even if it just $1. Decentralized platforms are great but they still have real costs behind the scenes.

Note: I'm not affiliated with the admin team, just sharing something I noticed.

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cross-posted from: https://lemmy.zip/post/66993290

I released v0.3.0 of my LCARS Star Trek Niagara Launcher Theme project.

For anyone unfamiliar, Niagara Launcher is an Android launcher/home screen replacement with a clean vertical app list instead of the usual grid of icons. I like it because the layout is already kind of minimal and interface-y, so I thought it would be a fun base for making a phone feel more like a Star Trek LCARS panel.

Niagara Launcher: https://niagaralauncher.app/

My project started as me trying to make Niagara Launcher look more like LCARS, but it has slowly turned into a more general LCARS wallpaper/icon/theme generator. This update is probably the first one where it starts to feel like a proper system instead of just a pile of cool-looking experiments.

The big change is that palettes are now shared across the wallpaper generator, icon generator, and gallery. I added a lot more categorized palettes too, including:

  • Classic LCARS
  • Trek faction-inspired palettes
  • Pride/identity palettes
  • Regional/flag-inspired palettes
  • Accessibility modes
  • Retro computing colors
  • Space/nature themes
  • General mood/function stuff

There is also a custom palette editor now, so you can directly change the LCARS block colors.

The other big thing is that wallpaper panel rhythm and color mapping are separate now. Before, the shape/layout pattern also determined which palette colors got used, which made some palettes come out weird. Now the rhythm controls the panel proportions, while color mapping controls how the colors are distributed.

So rainbow, pride, and flag-style palettes can actually behave more like themselves instead of getting shoved through the old LCARS role-color pattern.

The icon generator has matching color-mapping options now too, and the gallery got cleaned up with better filters, no weird caps, and live local-font previews for installed Trek/decorative fonts.

Live site: https://yearbook-enzyme.github.io/LCARS-Star-Trek-Niagara-Launcher-Theme/

GitHub release: https://github.com/Yearbook-enzyme/LCARS-Star-Trek-Niagara-Launcher-Theme/releases/tag/v0.3.0

Still very niche, still very Star Trek, but it is getting a lot more useful now.

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Support this channel on Patreon ► https://www.patreon.com/zoranhorvatGenerative AI can write code, but it cannot develop software on its own. Here is why the...

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I maintain LocalEmu, a free and open-source (Apache 2.0) AWS emulator. It started as a fork of the archived LocalStack Community edition. The goal is to keep a genuinely free, open local AWS emulator alive and maintained.

What it does:

  • Emulates 132 AWS services on a single endpoint (localhost:4566)
  • Pure-Python core, with real Docker engines for Lambda, EC2, RDS, ECS, EKS, and OpenSearch
  • Point your existing AWS CLI, boto3, Terraform, CDK, or Pulumi at it, zero config
  • No account, no auth token, no telemetry. Persistent state across restarts
  • Optional fidelity knobs: IAM policy enforcement, throttling, latency injection, Lambda cold starts

Why I built it: kill the multi-minute deploy loop, drop the dev/test AWS bill to zero, and stop keeping real credentials on dev machines.

It's for fast local dev, testing, and learning, not production, and not bit-for-bit parity with the real cloud.

Repo: https://github.com/localemu/localemu Site: https://localemu.cloud/

Happy to answer questions, and feedback is very welcome.

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For a script I'm making, I'm looking for a way to either get the build output directory Gradle uses, or change it to something different.

I'm aware you could edit one of the gradle files in a project, but my script requires not altering the android app project (It only uses gradlew / gradle.bat that comes with it to build)

Is there some way to change gradle's build output directory, or if not, a way to get it from the command line?

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I built Soundmoji, a small Android Auto beta that replaces emoji in read-aloud messages with short local sounds, then lets the rest of the text continue through TTS.

Free APK: https://soundmoji.nanocorp.app/downloads/soundmoji-v1.0.0-beta-release.apk

Landing page: https://soundmoji.nanocorp.app/

If anyone here has Android/AA dev experience, I’d value blunt feedback on permissions, UX, and edge cases. Support link for anyone who wants to keep it alive: https://buy.stripe.com/3cI7sM91ydTh22mboTePY10

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The failure analysis in First Proof’s Appendix A describes something qualitatively different from the hallucination patterns studied in factual QA: models producing proofs that are fluently wrong, where the wrongness is concentrated in a small number of unjustified load-bearing claims rather than spread across obviously false individual facts. I have tried in this paper to give that pattern a precise enough description to be studied systematically. The taxonomy has four modes (F1: citation fabrication, F2: premise smuggling, F3: silent reformulation, F4: local-to-global gap), and my empirical audit of eight Flash proofs finds that F2 accounts for the failure in every case—even though it is the mode least targeted by existing mitigation proposals.

The obvious question this raises is whether it is possible to build a system that doesn’t produce these failures in the first place, as opposed to detecting them after the proof has been written. A prevention-oriented system would need to enforce, during generation, that every load-bearing claim in the proof is either derived from stated premises, grounded in a retrieved and verified source, or explicitly flagged as unverified before the output is returned. The failure modes described here are, I think, a reasonable specification of what such a system would need to prevent.

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TLDR; The title of this post.

Feel free to reach out for clarity instead of reading the code/docs.

I was working on a “react-like syntax for webcomponents”, I wanted to create something robust and flexible for secure data storage and management.

I started off with an approach for asynchronous state management so that components outside the shadow-root could receive updates. (The events are also encrypted to secure against things like browser extensions.)

https://positive-intentions.com/docs/projects/dim/async-state-management

It then made sense to be able to persist that data so it can work between page releoads.

https://positive-intentions.com/docs/projects/dim/bottom-up-storage

The result looks and works like the following when used in a project.

https://positive-intentions.com/docs/projects/dim/encrypted-store

The Dim framework seems like a dead-end. I wanted to try it out on my existing React projects. So I created the equivalent React hooks.

https://positive-intentions.com/docs/projects/dim/use-dim-store-react

I find it to be performant and I want to push the scale of the approach, so I am in the process of testing it out on my projects. A notable use-case there is storing encrypted files at rest.

IMPORTANT: Im not trying to promote “yet another ui framework”, this is an investigation to see what is possible. You should not use this in your own code. It is not reviewed, audited or production-ready. It is not on npm. Shared for testing, feedback and demo purposes only.

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Software Freedom Conservancy publish Recommendations When Using LLM-backed Generative AI Systems for FOSS Contributions, which reflect the extremely difficult dilemmas that these systems pose for FOSS contributors.

They are approaching LLM-gen-AI from a variety of perspectives and this is practical assistance to minimize damage caused by using proprietary systems, whether FOSS contributors reject LLM-gen-AI or choose (voluntarily or by employer mandate) to use them.

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