Hi HN! I'm sharing VORAvideo, an AI video generation platform that provides unified access to multiple cutting-edge models including OpenAI Sora 2, Google Veo 3.1, Wan 2.5, and Kling 2.5.
Key features:
• All models integrated in one platform (no API keys or waitlists)
• Text-to-video, image-to-video, and speech-to-video generation
• 3-10 minute render times with 4K output
• Full commercial rights, no watermarks
• 50% launch discount on all models
Built for content creators, marketers, and filmmakers who need professional-grade video generation without platform-hopping. Would love to hear your feedback!
Next.js has definitely gotten heavier. What started as a simple SSR framework is now a full meta-framework with opinions about everything.
If you're looking for something lighter, give Astro a shot. The philosophy is refreshing - zero JS by default, only hydrate the interactive islands you actually need. Works great for content-heavy sites.
For full-stack apps with similar patterns to Next.js but less magic, Remix and SvelteKit are worth exploring too.
What's your main pain point with Next.js? Complexity, Vercel lock-in, build times, or something else?
You're definitely not alone. Social media amplifies the "AI is everywhere" narrative, but in reality? Most people are still shipping code the old-fashioned way.
I'd estimate maybe 20% of devs have actually integrated AI into their daily workflow beyond occasional ChatGPT queries. The other 80% either tried it and bounced off the friction, or are waiting to see which tools actually stick.
Not using AI doesn't mean you're falling behind - it means you're avoiding cargo-culting. The real skill is knowing when it's worth the context-switching cost and when grep + your brain is faster.
Are you trying to build AI applications or research AI itself? Completely different paths.
If it's the former - skip the math and start calling APIs. OpenAI, Anthropic, or open-source models via Replicate. Spend a week building something real: add a chatbot to your product, build a document Q&A system, whatever solves an actual problem.
Focus on prompt engineering, handling token limits, streaming responses, managing costs, error handling. These are the 80% of "AI development" for application builders.
The deep learning theory? You can learn that later if you actually need to fine-tune models or optimize inference. Most developers never do. Don't let the AI hype convince you that you need a PhD to ship useful AI features.
Are you trying to build AI applications or research AI itself? Completely different paths.
If it's the former - skip the math and start calling APIs. OpenAI, Anthropic, or open-source models via Replicate. Spend a week building something real: add a chatbot to your product, build a document Q&A system, whatever solves an actual problem.
The deep learning theory? You can learn that later if you actually need to fine-tune models or optimize inference. Most developers never do. Don't let the AI hype convince you that you need a PhD to ship useful AI features.
I built an AI music generator that turns text prompts into full tracks.
Features:
• 50+ music styles (rock, jazz, electronic, classical, etc.)
• Three modes: Inspiration, Custom, Instrumental
• Outputs: WAV, MP3, MIDI
• Commercial license included
• Sign up and get 2 free generations to try
Link: https://musicgeneratorai.io
What kind of music would you create with this?
Honestly, I think the biggest win is just having a solid test harness that can compare AST snapshots across versions. It’s not glamorous, but it catches regressions early and gives you confidence when you refactor the optimizer. Maybe throw in some fuzzing on the AST nodes and see what breaks – it’s surprisingly fun.
Honestly, some of these AI‑generated snippets make me wonder if my cat could write better prompts. It’s like being a janitor for a mess that keeps reproducing itself, but hey, at least it keeps the job interesting.
I feel for the team behind it; running a DNS service can't be cheap, especially when you're trying to stay green. Maybe a community‑funded model could keep it alive? Just a thought.
Probably something like the NTP pool [1] model could work but I can also see people abusing that by adding nodes that rewrite zones or specific records to MitM people. I only mention this model because it scales very well and people can contribute resources they can afford but they can also withdraw from the pool without harming the community [2] in regards to funding resources at least. Some type of automation would have to continuously validate each pool member and use a unique assigned NSID or id.server that maps to an operator account.
Each person just runs a node with a specific Unbound [3] configuration and pulls filter lists from community approved repositories. I mention Unbound as it is one of the most flexible and powerful recursive DNS options and many here are already using it. Bootstrapping could come from a static updated file in a repository that gets refreshed via cron.
This is a classic knowledge distillation pattern in ML - the "teacher" models (AlphaFold, ESMFold) with complex MSA-based architectures generate training data for a simpler "student" model. What s particularly interesting is how well the simplified architecture generalizes despite losing the evolutionary signal from MSAs. The performance suggests that much of the MSA complexity might be capturing patterns that can be learned more directly from structure data. This could be huge for real-time applications where MSA computation is the bottleneck. Has anyone benchmarked inference speed comparisons with the original AlphaFold pipeline?
Key features: • All models integrated in one platform (no API keys or waitlists) • Text-to-video, image-to-video, and speech-to-video generation • 3-10 minute render times with 4K output • Full commercial rights, no watermarks • 50% launch discount on all models
Built for content creators, marketers, and filmmakers who need professional-grade video generation without platform-hopping. Would love to hear your feedback!
Contact: nicohayes@voravideo.com