- Problem: AI UI generators are high-fidelity by default → teams bikeshed aesthetics before structure is right.
- Idea: use ASCII as an intentionally low-fidelity “layout spec” to lock hierarchy/flow first.
Why ASCII:
- forces abstraction (no colors/fonts/shadows)
- very fast to iterate (seconds)
- pasteable anywhere (Slack/Notion/GitHub)
- editable by anyone
Workflow:
- describe UI → generate ASCII → iterate on structure/states → feed into v0/Lovable/Bolt/etc → polish visuals last
It also facilitates discussion:
- everyone argues about structure/decisions, not pixels
- feedback is concrete (“move this”, “add a section”), not subjective
More advanced setups could integrate user/customer support feedback to automatically propose changes to a spec or PRD, enabling downstream tasks to later produce PRs.
It's exciting to see all these AI funds and credits, does anyone has a list or website of active funds available? We recently got granted 15k on OpenAI, but would love to get more ;)
In April we will be launching our mobile app; a next-gen non-linear TV news aggregator for mobile devices, that converts articles on topics that interest our users into short-form video using AI and trusted media content.
Sign up for our mobile app beta testing program at https://nius.tv.
The way we synthesize our videos is a little different from Baidu in practice, but similar on a high level.
For video synthesis we use an event-driven architecture using K8s jobs, and a custom job scheduler built in-house that manages all the steps of the video creation process.
Additionally, because GPUs are expensive and knowing that we synthesize our stories in batches through the day, we built a k8s-cluster-builder, that spins up and down the k8s cluster based on pending stories. This saves us a tremendous amount of cost per month, as the cluster only runs when there is actual work for it. As a bootstrapped project, costs are really important.
Our current video synthesis pipeline is comprised of 20+ steps, this includes speech-synthesis, image generation, speech+text aligners (for captions), language models, text generation, among others.
From my experience building the product, and despite the fact that AI is expensive (training wise), it is feasible to build AI-powered products using off-the-shelf DL technology without having large resources.
Hello, I am the sole developer, designer and founder of the project.
We are a two-person company that has bootstrapped the project so far.
I hope you like it. Do not hesitate to ask any questions.
Some technical nuggets:
- We spin up and down a K8s cluster based on load
- We use Tacotron and WaveGlow
- We use a "tweaked" version of Pix2Pix
- We use forced-aligners
- We have a speech-to-text DL model
- We have around ~30 discrete parts including microservices and cloud functions in an event-driven architecture
In broad terms, we manually select news from RSS feeds and then an automated system takes the text out from the news or blog post and generates a video.
I am building an end-to-end text-to-video product that converts text/blog news into TV-like news using multiple AI techs including GANs, Transformers, variational autoencoders, motion graphics, kubernetes, among other techs.
It has taken me some months, but I have CI/CD in place, some traction in twitter, and subscribed users on our home page.
I have bootstrapped the product so far. I'm the solo dev, designer, and creator.
Hi there, I'm the founder, sole dev and designer at NIUS.TV.
I have been working for the last 6 months, 26 hours a day in the product and tech. Now that things are somewhat stable and the vid production pipeline is not breaking due to audio-transcription alignments and other AI-related-corner-cases I wanted to share this with the HN community.
There is still tons of work to do, bugs to iron out, and there are a ton of ideas in the TODO list (exciting stuff).
The goal is to offer an ad-free product that doesn't sell your data while informing you about the topics that matter to you the most. A product that is private, where you can select topics you want to follow, with no social media bluff.
ATM, we are experimenting with Twitter, and we already have a WebApp working at NIUS.TV -- check it out, it is pretty cool. We hope to have an iOS and Andriod version by the end of the year -- fingers crossed.
It will basically go through all the links it finds, that can be millions of links. You can also tell it to ignore certain links using regular expressions or via tales java apis.
"... and vector('king') - vector('man') + vector('woman') is close to vector('queen') [3, 1]. You can try out a simple demo by running demo-analogy.sh..."
I downloaded the code but I dont understand how to get queen as a result. I tried submitting king - man + woman, but the bin dont understand it.
For those who want to use a java based solution, I invite you to check out my open source block tolerant (IP Blocking) web scraper that runs on top of aws and rackspace, called Tales. Tales is designed to be easy to deploy, configure, and manage. With Tales you can scrape 10s or even 100s of domains concurrently.