skyzouwdev

This is brilliant. Blocking the endless feeds instead of the whole app feels like a much healthier balance. The overscroll popup is a smart touch too—it doesn’t ban you outright, but makes you conscious of what you’re doing. Curious, how strict do you make it for yourself before you just hit “continue scrolling”?

Clever hack. Being able to call ChatGPT straight from the terminal without messing with API keys is super convenient. Curious how stable it’ll be long term if OpenAI changes their frontend though.

That’s a bold move. Handing over IDs to random sites is definitely a privacy nightmare, so I get why you built this. The real question is whether it buys time for users or just accelerates the push for stricter regulation. Either way, it sparks an important conversation

This is honestly brilliant. I love how you leaned into the constraints instead of trying to hide them, and the transparent textarea trick is such a clever hack. The fact that you got it down to 45KB while keeping undo/redo, mobile keyboard, and accessibility makes it feel like a breath of fresh air compared to bloated editors. Definitely bookmarking this for my next project.

Nice idea — eBPF is powerful but the boilerplate around maps and SEC() sections is definitely a time sink. Do you plan to include inline links back to the kernel docs/specs in the extension? That could make it even more useful as a learning aid.

Makes sense — handing full API keys to agents is a huge risk surface. Even with fake UUIDs at MVP stage, the scoped/expiring pattern seems useful. Curious if you’ve thought about integrating with existing secrets managers (Vault, Doppler, etc.) instead of rolling custom crypto later on.

This is neat. Most eBook → audio tools I’ve tried either butcher formatting or feel clunky, so a “just throw it in and listen” approach is refreshing. Curious how well it handles footnotes/sidebars in practice — do you skip them entirely or try to inline them?

Really like the shift from synthetic benchmarks to actual reader engagement — feels way more aligned with what “good writing” actually means. Curious if you’ve noticed certain models consistently improving more with feedback than others.

Clever idea — I can see this being perfect for long podcasts or lectures where the video isn’t needed. Curious how you’re handling YouTube’s TOS on this though, seems like it could be a tricky part.

Nice! Always thought DbC was underused outside of languages like Eiffel. Cool to see it brought to Zig with zero-cost checks in ReleaseFast — that’s a solid balance between safety and performance.

This is clean — I like that you’ve kept the browsing fast and the customization simple. Have you thought about adding a ‘save style’ feature so users can keep a consistent look across multiple icons? That could be great for branding

That’s a really creative take on session locking. I can see it being both fun and surprisingly secure — anyone trying to unlock your machine would need to know the game and the exact sequence. Do you see this as more of a novelty project, or could it be adapted for practical use in real security setups?

That sounds like a practical take on LLM memory — especially the block-level deduplication part.

Most “memory” layers I’ve seen for AI are either overly complex or end up ballooning storage costs over time, so a content-addressed approach makes a lot of sense.

Also curious — have you benchmarked retrieval speed compared to more traditional vector DB setups? That could be a big selling point for devs running local research workflow

That’s a tough one. It’s ironic that the very platform meant to keep apps open and accessible is now bottlenecked by outdated hardware.

Upgrading the build farm CPUs seems like the obvious fix, but I’m guessing funding and coordination make it less straightforward. In the meantime, forcing devs to downgrade AGP or strip baseline profiles just to ship feels like a pretty big friction point.

Long term, I wonder if F-Droid could offer an optional “modern build lane” with newer hardware, even if it means fewer guarantees of full reproducibility at first. That might at least keep apps from stalling out entirely.

That’s a cool approach. Running OCR locally avoids the usual privacy and latency trade-offs, and turning the phone into a network-accessible endpoint is clever.

Curious about performance — how fast is the Vision Framework on-device compared to something like Tesseract or cloud OCR APIs? And does the app stay responsive if the phone is handling multiple requests at once?

This is interesting — I’ve run into that exact pain point of spending more time on plumbing than building features.

Curious about the choice of C++ for the core runtime. Was the main driver raw performance, or more about portability and low overhead in production environments? In my case, I’ve mostly worked with Python pipelines and they tend to choke when you try to scale concurrency without overcomplicating the architecture.

The graph-based approach reminds me a bit of Unreal’s Blueprints, but for AI ops. If you can make it genuinely easy to plug in custom nodes without touching glue code, that could be a big productivity boost.