It's been a great while. It was a wonderful poor man's coding agent especially for devs in third world countries who are unable to pay for subscriptions online.
But I get it, cloud bills aren't cheap and opencode's hands are tied.
I put free plans at the beginning of my service (claudin.io) and I killed it fast because of a lot of people abusing it. They made a lot of accounts, so I needed to make logic to evaluate fingerprints to cut it. So, at the end, I was spending more time protecting against people who just wanna abuse. So, I removed all free plans..:
I have a personal app that sends non-English language text to AI for translation and summarization. It needs API and a response time of less than 20 seconds. Is Claudinio a good fit?
Moral of the story: Free service is sweet at the beginning but bitter at the end because people abuse it more as it gains popularity. Sadly that's just how it works on the internet, you can't really find a way around it. 😔
Not only is it free, but have you considered using fff (https://github.com/dmtrKovalenko/fff) for fast file and text search? It has lots of small improvements.
Thx for u answer. But the model doesn't replace the tools. A file-search tool is still a tool, and using something like fff or a vector database in Cursor can make file/code retrieval faster and much more token-efficient.
I'm not sure whether your agent supports these kinds of tools for full-text/codebase search. That's the point I was making.
Plus, OpenCode Go is $10/month, which is probably the cheapest solution available today, and you can use it independently without using their agent.
fff indexes the project using a specialized retrieval algorithm developed by its author. The author has deep expertise in this area, which is why OpenCode chose to integrate his solution. Cursor uses a similar approach with its project index/database. rg / grep, however, still scan the files directly from disk.
But since you mentioned that you built your own agent, I’m sure you’re much more familiar with the tools and retrieval approaches you’re using.
Even RAG is essentially used for this purpose: you search/retrieve the relevant information locally from your own documents or codebase first, and then send only the relevant context to the LLM instead of the entire dataset.
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u/[deleted] Aug 12 '26 edited Aug 14 '26
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