Amanda Silmon

I'd like to share an opinion on the current situation in AI ops, not the coding/tech side.

Right now the market has a pretty clear pattern. Big labs like OpenAI, Anthropic and xAI are building their own desktop AI and computer-use apps. The trick is they still treat this segment as Growth. Maturity is far away. So the biggest players burn money acquiring customers and giving them huge subsidies on tokens vs real cost.

Today the world is getting a huge AI bill, mostly paid by investors. We're running on debt. If there is no real breakthrough, math doesn't lie, there will have to be a cost-effective compromise. Using heavy models for coding is justified. Using them for the simplest things in operations is not, economically.

I used a few different tools to vibe code my saas. I used lovable, chatgpt, gemini to code my saas. The thing is, there is a huge problem of these AI's of forgetting when you chat for a longer time.

This has happened to me a lot of times. One solution I found is that you can start a new chat to overcome this problem. But, here the problem arises to explain the AI all the context which you had in the previous conversation.

Curious, has anyone else ever ran into this problem? How did you manage to overcome this?

I ve recently launched an Android application and I m working on improving the streaming experience. I m particularly interested in reducing buffering and handling different network speeds more efficiently.

For developers who have worked on video-based apps, which tools or services would you recommend for adaptive bitrate streaming, CDN delivery, video encoding, and performance monitoring? I d like to choose a setup that can remain reliable as the user base grows.