We support flexible compute sizing in Bult.ai so apps only use what they actually need.
XS to XL sizes cover everything from prototypes to databases, with per-minute billing and the ability to resize anytime from the Canvas UI. Compute options: XS: 0.1 vCPU, 256 MB RAM, $2.7 per month. Ideal for prototypes S: 0.25 vCPU, 512 MB RAM, $5.4 per month. Suitable for small apps M: 0.5 vCPU, 1 GB RAM, $12 per month. Designed for web applications L: 1 vCPU, 2 GB RAM, $24 per month. A good fit for APIs XL: 2 vCPU, 4 GB RAM, $48 per month. Optimized for databases
Custom sizes are also available for advanced workloads available on request. Happy to answer questions about sizing or use cases.
1. Recently, several small bloggers have talked about ProblemHunt: a few from the USA, a few from Spain, and one from France. And we noticed an obvious thing: traffic from these countries, although not much, has started to grow.
2. But the most important thing is that people from these countries have started sharing problems more actively. For example, in the last month alone, France has already submitted 4 problems, three of which were published yesterday and today.
If your launch does not go as planned, do not judge it too quickly. Avoid the instinct to immediately add more features or pivot the product.
Instead, pause and evaluate what already exists. Check whether the core features are clearly communicated, fully polished, and genuinely solve the intended problem. Often, the issue is not the idea, but the execution, positioning, or user experience.
Refine what you have. Improve clarity, usability, onboarding, and messaging. Then relaunch with focus and confidence.
Many products fail not because they were wrong, but because they were unfinished, unclear, or rushed.
I m currently working on Clugg OS, an early-stage AI-powered study tool for students (grades 6 12).
The core problem I m trying to solve is something I see repeatedly: Students often spend hours studying but still feel:
unsure if they re studying the right things
anxious before exams
overwhelmed by unstructured notes and syllabi
Instead of focusing on more content, I m experimenting with:
turning existing notes into revision-focused quizzes
helping students create clear, realistic study plans
keeping the experience simple and non-intimidating
This is still very early, and I m intentionally keeping the scope narrow while learning from real usage.
I d really value community input on:
If you ve built for students or education what mistakes should I avoid early?
How do you balance AI automation with user trust?
What signals helped you know you were solving a real problem?
Not here to sell genuinely here to learn and improve what I m building.
Thanks for reading, and happy to answer questions.
