Luna is unusable garbage. When I see people in threads talking about how useful Luna is for all of their tasks and it's so cheap and fast, it reads to me like people relating anecdotes about how tasty the garbage scraps they're eating out of the dumpster behind McD's are. Sol Max is the threshold of "just barely usable", and even then it's certainly not what the people are hyping it up to be.
> But GPT also offers a $100 plan with five times the usage limits. And how long would that last me?
Sol usage is basically unlimited on the $100 plan if you're human-in-the-loop, reviewing the code, and not doing some useless Gas Town crime against humanity.
I’ve been slowly converting my org to use opus 5.5 as default over sonnet and the difference is night and day. More usage, because it does shit better the first time. Less cost, because it does shit better the first time. It’s literally been the thing that’s kicked our org in the pants to actually adopt agentic methodology at scale.
It just gets worse as your codebase grows with more code and more files especially. It's like a human developer who is under intense time pressure. If the codebase is large, it might miss an existing function and implement a second one... or it might miss the fact that a database table already exists to solve its current problem and create another one. The solution is to keep the codebase as simple and small as possible and name everything as intuitively as possible with proper separation of concerns. Same principles which were useful before AI are still useful with AI.
> Inability to change the response style
In recent months, I’ve been noticing a new pattern in sentence structure more and more often. Here are some examples:
There’s a difference between "I read the text" and "I understood the meaning of the text".
We too often mistake "I’m used to this" for "I like this".
The path from "I’m afraid of making a mistake" to "I’m ready to try" can take years.
It’s worth taking the time to setup a style guide for your agent to remove llm-isms like this. Somewhat paradoxically, you can even have the agent help write it. Just include a couple of snippets of “bad” phrasing from previous chats, add some commentary about why it’s bad, and what to replace it with, and put that in your system prompt. I could provide examples but the whole point is that you can tailor it to exactly how you want it. This has the added bonus of being a quick sanity check for model quality. If you start noticing llm-ism appear, then likely the model is not following your instruction’s and the thread should be discarded/you need to switch to a better model
Most of these issues are vastly improved or mitigated by adding clear instructions in AGENTS.md.
I follow a specific process with agents, and it works reasonably well:
1) describe my intended feature or change and then say “please recapitulate my intention and write an implementation plan”
2) read the plan carefully and refine it, make any corrections
3) tell the agent to go, but I check in frequently as it’s going. I watch the stream of thought and make course corrections or pull it out of rabbit holes
4) carefully review the result. Checking that tests actually fail, removing unnecessary changes
5) add instructions in AGENTS.md if I’ve found a new behavior that needs redirection
Unlike the OP, this makes using an agent actually sound useful.
My summary of the article: I use a really cheap LLM, and suffer like everyone else using GPT 3.1. This is definitely a throwback to the days not so long ago when LLMs were completely incompetent.
Idk how you are running out of budget so fast. My usage is basically unrestricted tokenmaxxing ("/goal", flagship models at xhigh reasoning, etc) and my monthly burn is single digit thousands of dollars (maybe ~$350/day). Granted, my employer foots the bill so I don't care at all. But the return-per-spend is easily worthwhile and I'm not worried about the cost at these ratios.
Nerves of steel? Not even close.
I think 90% of the article is the author complaining about (predictably) bad small-model behavior. The large models are much better!
How people are getting anything done at all with less than 5-6 claude max and 4-5 codex pro max plans and codex-lb and better ccflare juggling weekly limits at all times, I will never know.
> But GPT also offers a $100 plan with five times the usage limits. And how long would that last me?
Sol usage is basically unlimited on the $100 plan if you're human-in-the-loop, reviewing the code, and not doing some useless Gas Town crime against humanity.
It’s worth taking the time to setup a style guide for your agent to remove llm-isms like this. Somewhat paradoxically, you can even have the agent help write it. Just include a couple of snippets of “bad” phrasing from previous chats, add some commentary about why it’s bad, and what to replace it with, and put that in your system prompt. I could provide examples but the whole point is that you can tailor it to exactly how you want it. This has the added bonus of being a quick sanity check for model quality. If you start noticing llm-ism appear, then likely the model is not following your instruction’s and the thread should be discarded/you need to switch to a better model
I follow a specific process with agents, and it works reasonably well: 1) describe my intended feature or change and then say “please recapitulate my intention and write an implementation plan” 2) read the plan carefully and refine it, make any corrections 3) tell the agent to go, but I check in frequently as it’s going. I watch the stream of thought and make course corrections or pull it out of rabbit holes 4) carefully review the result. Checking that tests actually fail, removing unnecessary changes 5) add instructions in AGENTS.md if I’ve found a new behavior that needs redirection
My summary of the article: I use a really cheap LLM, and suffer like everyone else using GPT 3.1. This is definitely a throwback to the days not so long ago when LLMs were completely incompetent.
Nerves of steel? Not even close.
I think 90% of the article is the author complaining about (predictably) bad small-model behavior. The large models are much better!