Those who made the jump, how is the experience? I'm currently using Bitwarden but plan on switching sometime soon so currently evaluating my options. The main concerns I have with protonpass are claims about bad auto complete on Android and browser, and how this is part of the proton ecosystem of products and doesn't get enough attention from devs according to some.
I'm trying to decide whether to get a hyper subscription instead of continuing my ollama pro. How is the usage compared to plans of a similar price point?
I'm writing this because I see a lot of Sri Lankan undergraduates and devs get discouraged by token/subscription prices they have to pay to get some real work done. Subsidized $20 subscriptions are no longer viable if you use agentic coding/ automations for your work daily. The only usable plans and subscriptions you probably hear are Codex and Claude max plans. Pretty much both of them are above 100 USD range per month. If you're getting paid in LKR, that's a lot of money unless your employer is willing to pay for you. It's also important to realize that the subsidized AI era (meaning companies eating the cost for you) is coming to an end. Which basically means that prices are going to increase unless these companies become profitable. This is also the reason why these companies nerfed a lot of their subscriptions and introduced weekly/monthly limits.
But with the massive shift in the industry and the way things are going it's important (especially if you're in a technology field) to learn how to use agentic tools, security implications, best practices, etc. if you want to survive the next 5-10 years without getting laid off or losing your job to someone who can output your monthly work within a week. So for the past few months I have been experimenting with different AI plans, harnesses, PAYG APIs and pretty much all the popular providers out there, including obscure ones like umans ai. If you're interested in getting productive with AI within a budget around 10-20 USD max per month, keep reading. My findings will help you a lot.
Before anything, there are a few things you guys should realize,
Models you use do not matter anymore. It doesn't matter whether you're using Fable, GPT, or something like DeepSeek V4 Pro. Because you can pretty much get the same performance and work done in any of these frontier models and usually open weight ones cost way less than Anthropic or OpenAI. Don't trust the benchmarks, look up and see how people use them for work.
It's mostly about the harness and how you engineer your workflow. Promoting a web UI like Chatgpt can only get you so far. You should learn to use a harness like OpenCode or pi agent. The reason I'm recommending these is because they are open source and easily configurable. Especially pi, it's such a great tool that you can turn it into something that fits only your workflow. Also there are desktop apps for both like OpenCode Desktop if you prefer that.
Security is still important. Ignore the noise. Don't let AI run wild in your machine, go through your sensitive files like ssh keys, password, etc. You should learn to containerize or use things like microvms. Unless you want it to accidentally leak your personal stuff, getting your data deleted and learning the basics of security is mandatory. You don't want to be the guy who dropped the production database at work (this has actually happened 😂)
Learn about LLM caching, Skills, MCPs, and how they work under the hood. At least on the surface level. pi.dev blog and opencode have really good material on this. This will help you reduce costs and learn how to be actually productive without outputting slop.
If you work for the corporate be careful which models you use and what privacy and data retention policies they have. You don't want to leak sensitive documents / NDA work to some unknown LLM.
If you made it this far, great! Now we can actually talk about how to reduce the cost.
For Sri Lankans, cost wise, the best options we have are OpenCode Go and Cline Pass subscriptions. Both are 10 USD and usually OpenCode Go is more than enough for monthly coding (For working/studies) IF YOU LEARN ABOUT 4. ABOVE.
If you prefer direct token pricing DeepSeek API offer the cheapest rates for their models but they retain all your data.
You can use both subscriptions with any of the harnesses I mentioned, opencode go or pi. The cost and how much you can use these subscriptions will depend on what models you utilize for which tasks. If you throw Kimi K3 at everything you will burn your limits within a day. The trick is to use a powerful model for planning/reviews along with models like DeepSeek V4 Flash for actual work and things like scouting.
It's important that you learn about LLM caches because most of your costs will come from breaking the cache. Like when you switch models midway during a session. So read up and learn how they work first! It's important.
There are so many things I could mention like how system prompts and skills work, pitfalls of mcp, but I'll let you guys research on those on your own. I hope you got the gist of what I tried to explain with this wall of text.
TLDR,
If you want to get productive with AI and work within a budget, learn your tools and get a subscription like opencode go / cline pass.
I'm curious about how good Neuralwatt Subscription plans are compared to open router and the alike. Basically my workflow usually goes like planning with a powerful model (Deepseek V4 Pro / GLM 5.2) and then implementing it with less powerful workers (Deepseek V4 Flash / Kimi K2/3) and running reviewer subagents for each task (with fresh context) and iterating until the work is done. Usually this costs me less than $40 per month.
I'm curious if I will be able to have a similar workflow around the same budget with Neuralwatt subs. What do you guys think and how many tokens do you usually consume per month?
I feel like opencode is becoming worse. Startup time (on nvme) is very bad. I don't have any plugins or anything it takes about a good 1-5 seconds to start. I remember it was pretty instant before. I'm not sure what's causing this. With each update I feel like performance slightly got worse. On top of that default prompts are sometimes a bit too much and compaction most of the time does not preserve what matters. After going back and forth between opencode and pi I'm pretty sure these issues are real.
Also on very long sessions it sometimes becomes very slow. Like unusably slow. It could be Ghostty leaking memory since it's pretty common but still. I'm hopeful for the v2 though.
What's the standard approach with pi for denying access to certain files/directories similar to opencode.json and other permission systems in different harnesses?
I usually disallow .ssh and .env with opencode for example
I'm curious as to what models people actually use with the Clould coding plan since ollama doesn't have a fixed request limit or a proper explanation on which models take more usage and such.
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I mostly write code by hand still and the tasks I'm lazy to do are usually handed over to deepseek v4 flash or kimi k2.5 and I feel like I burn through my weekly limits pretty quickly.