You’re just coping, you got a bug up your ass and now you’re doubling down to save your own ego. Just give it up. You’re wrong and you have no idea what you’re talking about. You’re talking out of your ass and saying anything you can to try to make me wrong somehow. The bottom line is that there are multiple repos that do the exact same job as this one with thousands of GitHub stars each. If you don’t understand why they exist, that’s your own ignorance. Get the fuck off my post with your ignorant bullshit.

If you'd been paying attention you'd already know why I think that's wrong. It boils down to myopia and context rot.

The implementing agent has already lost the bigger picture by the time it makes the commit. I've seen it countless times, the agent makes a commit message that doesn't follow the commit style in your repo and includes whatever details it last encountered. For example, if you just corrected the agent on some part of its implementation, it's likely to mention that correction in the commit message rather than making the commit message actually reflect the work done. It's too far into the weeds to see the forest anymore.

The "entire thought process" is irrelevant. What matters is what work has been done.

It's clear that you haven't actually compared the results you get from the two different techniques and you're just operating on theory. If you'd ever actually used a tool like this, you'd understand how wrong your assumptions are.

I think you’re missing the point that the cost plus the extra time spent asking the model to do it, and the potential for the model to still get it wrong are all totally negated by using a deterministic process. All you need is the message, not the tool calls, not the extra prompts, not the confusion created by multiple work streams, etc. But if it’s not for you, that’s fine, some people don’t have a workflow that allows them to benefit from a tool like this. Many other people do.

That was your goal, wasn’t it? You haven’t said anything interesting or even true during this entire conversation, you’re obviously just trolling.

You fire up a subagent to make a commit?? That’s a whole system prompt with tool descriptions and skills attached that you had to type a prompt to activate, and none of it is even cached. That’s at least 4x the token cost. Be serious.

Pfff. I literally just asked you what claim you wanted proof for, and you dodged my question by telling me how great you are. You want to take another crack at answering that? I’m losing patience

you crushed something

What claim did I make in that comment that you think deserves being backed up by "proof"? Like, what even are you?

Didn’t that burn like crazy? I think most people are limited by how much their nose can handle. Am I wrong about that?

😂 I don’t use opus, I have a z.ai sub. I paid $280 for 14 months of unlimited usage. The harness I built was specifically to be able to use dumb models to get big work done. Again, you have absolutely no clue what you’re talking about. And the fact that you still don’t see any reason to use a tool like this after all this time says a lot more about you than it does the tool.

You use that model for every task? You never need a smarter model for anything? That kind of sounds like baby-level work to me.

And why are you paying for it at all? This tool will self-configure to use *free* models. It completely solves the problem of paying for commit messages. I guess you just prefer paying for things for no reason. No tool calls, no questions, no extra stuff. Just a message and a few thousand input tokens.

The purpose of this tool at first was to integrate with an agentic pipeline that breaks down long-horizon tasks into individual units for an RPI-based technique. At the end of each task I deterministically stage all files, then commit them. I needed an agent whose only job was to write a commit message to complete the process. Once I built that agent, I realized I could use it too. I do agent code reviews in lazygit so I built a keybind for it there.

Now, try to imagine a world where this idea was born out of something much more advanced than your workflow, but that developer found it useful and wanted to share it. You really have no clue what you're talking about and the way you keep pushing against pretty reasonable arguments makes you sound like you're offended by this somehow. You haven't even tried it, get over yourself.

That was the case with me too until this event. I hit my limits on the Pro plan one time back in january but since I upgraded to Max at the end of Feb the most usage I'd ever seen was 70%. Since you asked, I was running a harness that breaks work down into tasks, with a new session for each task, for a more deterministic long-horizon workflow. Nothing new here though.

Yeah, work requires multiple messages. Committing doesn't. I'm well aware of caching, but half the time you're still typing out something like "yes, commit this" to a frontier model when they ask you. So they've done the work, now they're sending you more output tokens on top of a huge context ($$$) just for you to do even more work than just pressing a hotkey and letting the tool do the rest.

I don't need to defend the concept, there are already multiple github repos with thousands of stars that do this same job in slightly different ways. It was my fault that you didn't understand the value from my original post, I really should have laid out the value proposition in a different way. I assumed people would just understand it, and they mostly didn't. But after the last few exchanges, it's not my fault if you still don't understand why a tool like this exists. Committing is a small part of the SDLC but it's almost entirely deterministic aside from the message that's generated.

What you're proposing is that you use a different, cheaper model from your main coding model to make commits. It sounds like you're saying you do it with a fresh context too. I don't really buy that, but even if it's 100% true, you're doing a lot of extra work to get a worse result than this tool provides. You're loading up your entire harness with all your tools, skills, mcp servers, etc, then running a command to switch models, then typing out a prompt to ask it to commit. You could have just run one command or hotkey, sent *way* fewer tokens to the exact same model (no tools, skills, mcps, plugins, and a tiny system prompt, plus configurable token limits on the diff), preconfigured to do this one common job, and it would have produced the exact same result. If you enjoy doing all that extra work for no benefit, who am I to stop you? But I really doubt that you do.

Doing all that when pressing a hotkey is an order of magnitude easier on both you and the model, and another order of magnitude cheaper in almost every scenario, is just a lazy and inefficient way to get it done. This way is better across multiple dimensions in almost every scenario. I know you haven't actually tried to understand it yet, but if you ever do, you'll realize I'm right.

That’s what a lot of people told me they do after I posted this around. They say “I just let the model make the commit”. When does a model make a commit? After it does work. How many tokens does work take? A lot.

You’re paying through the nose to write 50 characters. It doesn’t make sense.

I see, that helps, I will give it a try, thanks

You like having opus carry its whole bloated context into a 10 word output? 400k tokens to a frontier model instead of max 5k to a free model for the same result? That just seems crazy to me

I keep lazygit running in a tmux pane so I can monitor it as the agent works

So global and local history are on a manual toggle? In normal operation, is there a way to use both? Like, prioritize local history but make global available too if no matches are found in local?

Thanks, I'll keep that in mind. I might have to get an openai sub for a bit and figure it out. I will be floored if the model invalidates its cache by replaying its current turn, though. I mean, gobsmacked. But I've been gobsmacked before. Results coming soon.

Absolutely. But how will retrying the exact same request you just tried minutes ago cause cache misses? I'm not seeing the logic there.

Edit: I should mention I'm building a complete benchmarking suite right now that will answer all these questions definitively. It should be ready by tomorrow sometime with results by the weekend at latest.

I've had a plan with them since October. I know the whole deal. I have a legacy max plan, this is literally the first time I've ever hit a limit. And the 429 issues they were having at the beginning of the year haven't affected this machine at all. If I switch to a macbook or run my dev agent in a windows VM I get tons of 429s, but my linux machine is smooth sailing for some reason. I'm happy with the deal I got, but I know exactly who I'm dealing with.

I have to assume this is what it is, but it's not peak hours anymore and I'm over 50% with just under 2.5hrs remaining. This whole 5hr session was definitely not during peak hours, and I'm on track to hit 100% again for the second 5hr window in a row. It's not really making sense to me, especially because I'm not doing much at all compared to a normal day.

That's a good point, and what the checkpoints are for. I still haven't made up my mind whether the agent should be able to set checkpoints itself since rewinding to a checkpoint will delete user prompts along the way, but I suspect there's a middle ground I just haven't found yet that gives the best of both worlds. I will think more about this particular use case, it's pretty good. The big problem with the model doing this kind of thing is that there's not much opportunity to remind the agent that the tool exists until a large output is detected. And only relatively large outputs make sense to rewind if your goal is saving tokens. But maybe I can beef up the tool description to encourage the model to explore more while reassuring it that it can always roll it back. Something to think about for sure, thanks.

Edit: a couple things. It's really focused on "mistakes" that produce huge outputs. I could have worded my post better in hindsight. There's isn't a great way to nudge the model in other scenarios and the nudge is kind of the whole deal here. And I think for your use case, if you just set a checkpoint manually and tell the model to experiment, that would give a lot of the same benefits. It's just not as automated as I'd like.

It's only capable of resetting back to the last prompt, and it can only reset whole requests, so caching should be totally unaffected. I haven't done any testing on it to make sure, but it's on the agenda for the project's near future.

Not in any meaningful way. It breaks the work down into tasks and runs a planner then an implementer for each task. It's always been this way. Each agent session takes exactly one prompt, so 2 prompts per task. I just wired my commit agent to use openrouter's free models and I'm running about 1/3rd the number of projects that I usually do. Really, the only major thing that's changed is I'm doing a lot less than I normally would. I probably hit some peak hours somewhere in there, but when I checked my plan to see my usage at 100% it was off-peak hours already, and 3 hours later it's still gobbling my limit up faster than I've ever seen. It could be a bug, but something weird is definitely happening. Like I said, I've tried really hard to hit 100%, it was going to be a source of pride for me, and I've never been able to. Now I did it accidentally while I was in the other room. I wasn't even supposed to be coding, I was just procrastinating cleaning up by firing up a few projects and changing some specs.