- 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.
The problem with ASCII-driven development for me is that emoji ruin the alignment. It’d be nice if they could be forced into monospaced. Emoji aren’t ASCII so maybe that’s the problem too.
Seems like there's more going on with it than that, it's also affecting the lines that don't have emoji. It kind of looks like it assumes every vertical bar takes up two characters so a space before the bar is missing. Except not always.
Example 2 has five boxes in a row each with a number 1 to 5 in them, and each box is missing a single space before the second vertical bar... I think the problem might be centering, where it needs to distribute 3 spaces on either side of the text, divides by 2 to get 1.5, then truncates both sides to 1, instead of doing 1 on one side and 2 on the other. Doesn't quite fit with how many are missing in [PRODUCT IMAGE] right above that, though.
(Also I'm just eyeballing it from mobile so I may be wrong about exact counts of characters)
Those are called emoticons, not emoji. "Emoji" came about specifically to distinguish the single-character ones (unicode or proprietary) from what we did before.
Companies I’ve helped start have been acquired by OpenAI (2025) and private equity firms (2023). I have over a decade of experience in conceiving and developing AI products, and I've led zero-to-one initiatives at multiple startups backed by Y Combinator, Techstars, MIT delta v, and others.
Location: San Francisco, CA
Remote: Yes
Willing to relocate: Open to discussion
I’m currently open to new challenges and opportunities. If you know anyone looking for a consultant, VP, staff, or a software engineer with experience in AI, I’d love to connect.
Thanks _hfqa! We think there's massive potential here. It's a big platform shift, and spreadsheets weren't really impacted by the mobile or cloud compute waves, so it's a space long-overdue for disruption. (The last shift was back when Google Sheets took spreadsheets to the browser 17 years ago!!)
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.
- 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.