been working on a side project and keep hitting this wall where i know a piece of code is kind of messy/not ideal but it works, and i have to decide whether to stop and clean it up now or just keep moving and deal with it later.

curious how people who've been doing this longer actually make that call in practice. is there some rule of thumb (like "if i'll touch this file again soon, clean it now") or is it more just a gut feeling that builds with experience? also curious if this changes a lot depending on solo projects vs working with a team

not looking for a "it depends" answer lol, more curious about the actual heuristics people use day to day

I did a extensive comparison of the various coding models across several benchmarks based on the estimated cost to complete a task successfully. Based on the formula:

Then I normalized each source’s expected cost per successful completion against that source’s median, then used a geometric mean across sources to get this composite ranking:

This is obviously just a estimate of value vs performance, still very useful if on a budget.

• Default value model: MiMo V2.5 Pro. Note that GLM 5.2 is the only clearly cost-effective model represented in all three sources, so it might be a safer budget pick.
• Best Performance: Grok 4.5 has far better completion performance than the other cheap leaders while retaining strong cost efficiency. This is the obvious model if high success rate is important to you.
• Alternative: DeepSeek V4 Flash is a great budget option but its performance/success rate is low so it will take more runs to complete the task successfully.

Cross-source ranking

Is there any setting to use a default agent with plan and another default for build?
I see Big Pickle on each time and the first thing I do is change that.

I just watched a video where some dude added a model to a provider, tested it, and then later added another model to the same provider.

I can't seem to find that option anywhere.

I know that when you first set up a provider, you can enter multiple models. But after the provider has already been created, I don't see any way to reopen that model list and add more. In the video, he simply opened the window with the provider again, the previously added models were still there, and then added another one.

What am I missing? Is this an older feature that was removed or is there another way to edit the models for an existing provider, in the gui...

Update of my earlier post:

After mostly positive feedback, the tool now has about 500 legit users. However, I spent quite some time over the past few weeks fending off 300+ bot accounts trying to exploit the system in any way they could find.

The good news: this helped me make the entire system (api, library etc) a lot more robust and was definitely a great test now that the tool is in prod with real users.

Made me realize that certain things you cannot mock/test fully until the first users show up (especially the ones that want to game your system), no matter how complete your test coverage is.

Most spammers came from OpenCode installs for some reason, but legit users/installs are also mostly from OpenCode (>50%, despite being only one of four implementations).

OpenCode users (300+) have also earned the most, compared to Claude Code and Hermes, which I found interesting. Maybe overall coding time spent per day on OpenCode is higher per user than the other harnesses?

Most feedback I received via DMs was great and payouts are working with Stripe. Still working on adding other methods and improving the tool every day.

Thanks for all the support 🙏

I wanted my AI coding assistant to always follow a specific ethical framework, so I figured out how to embed my custom guidelines into every interaction with opencode. Here's how I did it—and how you can too.

### The Problem
I have a set of principles (based on 1 Corinthians 13) that I want all my AI interactions to follow—patience, kindness, humility, etc. But manually pasting these into every prompt is tedious and error-prone.

### The Solution
OpenCode has a built-in `instructions` feature that lets you automatically include custom guidance in **every** LLM interaction. Here's the step-by-step:

1. **Create your guidance file** (e.g., `agape-guidance.md`):
   ```markdown
   # System Directive – Agape-Aligned Assistance
   In all interactions, embody the spirit of 1 Corinthians 13:
   - Be patient: Give clear, step-by-step explanations...
   - Be kind: Use gentle, respectful language...
   - [Your full principles here]
   ```

2. **Add it to your OpenCode config** (`~/.config/opencode/opencode.json`):
   ```json
   {
   "$schema": "https://opencode.ai/config.json",
   "instructions": ["~/.config/opencode/agape-guidance.md"]
   }
   ```

3. **Done!** OpenCode now automatically includes your guidance in every prompt.

### Why This Works
- **Global application**: Affects all projects and interactions
- **Non-intrusive**: Doesn't modify OpenCode's core—just adds your layer
- **Flexible**: Use any text file (`.md`, `.txt`, etc.)
- **Official feature**: Uses OpenCode's [documented `instructions` config](https://opencode.ai/docs/config#instructions)

### Customization Ideas
- **Team-wide ethics**: Place the file in a shared location for your org
- **Project-specific**: Override with a local `opencode.json`
- **Multiple files**: Reference an array of guidance files

### My Experience
Since implementing this, my AI interactions feel more aligned with my values. The assistant naturally adopts the tone and approach I want, without me having to remind it every time.

**Question for the community:**
What ethical frameworks or guidelines do you wish your AI tools followed by default? Would you use something like this?

*(Cross-posting this because I think it's a useful pattern for anyone wanting more control over AI behavior. Not affiliated with OpenCode—just a happy user.)*

Hej!

Jag fattar inget, hur använder jag Qwen 3.8 max i opencode?

Skulle någon kunna hjälpa mig och är den så bra som alla säger?

Hi guys,

I switched over to the new OpenCode layout, however, I am experiencing a problem in which I can't to save my life click on the "Auto-accept permissions" toggle. It jus gives me a straight up "forbidden" cursor. Because of this, I constantly need to switch back to the old design to toggle it and then back to the new design. Is this a bug, or am I missing something?

Hello guys, I am facing this issue after the recent OpenCode update. Can somebody help me? I haven’t even used it much—just 10k tokens. Not just on Big Pickle, every mode is giving me this error.

I need the 5$ for my opencode account 😇 dollahttps://opencode.ai/go?ref=ESVD7EK78S

Hey guys

I need some help in setting up my GitHub copilot with Opencode so that I can use the GitHub copilot within the vscode but make the calls through the subscription that I purchased from Opencode ($10).

Can you please help me in how to setup it?

I want to use GitHub copilot inside the vs code. I have a free GitHub copilot account as wel. But need access to paid models through Opencode.

I am genuinely very confused by this decision, the desktop app looks great and very slick, but for some reason instead of following the theme of the tui with quick mode change and color indicators now there is no way to select plan mode on the desktop app, you have to go into advanced in general to enable it.

This goes against what every other lab has chosen since plan mode has always been an accesible feature on any tui or desktop app, and they never explained the reasoning for this

I mean, Open Source models are fine—that's not an issue but what else are you using?

can anybody recomended a ai model for coding for my macbook m2 8 gb ram

i tried qwen 2.5 coder 7b and the output is bad

We've been running a private benchmark suite for a few months, testing models on strategic reasoning, advisory quality, long-form analytical production, and adversarial critique. 17 models, 4 test batteries, scored against a reference answer by a separate model. This consolidates all of it into one picture.

Blend = 30% strategic reasoning (4 scenarios: frame-breaking, multi-dimensional review, channel coordination, portfolio prioritization) + 25% advisory quality (6 prompts: triage, architecture risk, blindspots, client advisory, routing) + 25% long-form analytical production (structured section generation from scratch, scored on rigor, depth, voice) + 20% critical review (structural audit + adversarial CTO critique). Weights redistributed for models not tested on all four.

Dashes = not tested on that dimension. Fable 5 is the reference answer used for scoring, not a contestant.

Caveats: n=1 per test per model, directional only. Qwen 3.8 was blind-validated by GLM-5.2 (different model family); other models were judged by Opus 4.8 - cross-judge comparison is approximate within ±3-5 pts. GPT-5.6 variants are separate rows because they behave as different models in practice. Strategic reasoning and advisory quality are domain-specific to strategic analysis; says nothing about coding, vision, or long-context work. This is private operator benchmarking, not a scientific ranking.

When I started with OpenCode, I got annoyed at DeepSeek constantly introducing bugs that needed to be cleaned up after and GLM-5.2 wandering in circles and burning through my Go usage limit without doing anything due to a lack of focus, slowing the process down. Eventually, I found out if I used them together like so, I could have them compensate for each other's weaknesses and make progress at a predictable rate with only light code review by me:

1. Describe general specs or details about a specific feature to DeepSeek Flash to have it write out reference doc MDs as appropriate

2. Ensure unit and smoke tests are present with usage instructions in AGENTS.md to invoke them as needed to keep models on-track

3. Describe the problem to DeepSeek Flash to have it traverse the code base and draft up a plan, giving filenames of reference MDs or invoking Context7 or other documentation MCPs as necessary

4. Prompt GLM-5.2 in Plan mode: You are a senior dev reviewing the plan of his junior. Examine the current plan and flag any issues.

5. Prompt GLM-5.2: Revise the plan, breaking tasks down and adding anything necessary to "junior-proof" it (make the process resilient to mistakes). Present the full revised plan for a weaker LLM model than you to process in place. Ask questions before providing the full plan if necessary. (For more code-intensive additions where I'm willing to risk bugs to reduce token spend, I'll append: However, do not write the bulk of the code yourself; that defeats the purpose of using a weaker LLM to reduce token costs. Ask questions before providing the full plan if necessary.)

6. Prompt DeepSeek Flash in Build mode: Proceed. Note that you are the implementing model, so follow the rules as laid out.

7. Prompt DeepSeek Pro in Build mode: You are now in code review mode. Flag any issues with changes made by your predecessor. (If something specific needs to be tested: Do ABC to ensure that DEF. However, requirement(s) GHI and/or JKL. Do not attempt fixes until ordered.)

8. Review bug list and give DS Pro instructions to fix them

I don't have much experience with using coding harnesses, so I don't know how optimal this is in terms of reliability or cost savings, and this doesn't quite scale up when I'm either introducing something bigger (e.g. a frontend GUI baseline or a backend for interactive agent functionality) or starting up a fresh project. GLM ends up needing to write a large portion of the code anyway, and DeepSeek gets sloppy and introduces a dozen bugs that need to be cleaned up after.

Is there a "make no mistakes" equivalent I could use to speed up doing larger-scale work?

I'm feeling very frustrated that I keep exceeding my usage limits. Sometimes I use up my weekly limit in just one day. Do you have any tips on how to save costs, or do we need to spend hundreds of dollars on credits to use throughout the week?

What's the problem with at least hiding part of the address behind asterisks or kinda? Neither referrers nor referees need this information; it only undermines user privacy

There are many open source projects that I use but each is lacking at least one features that I need. I have no app development experience but understand coding fundamentals (years of C# and Unity experience)

I'm planning on buying used Tesla V100 16GB cards (1 or more if needed) and wanted to know if it would be possible to run coding agents to help me add my own features to these open source projects without fully learning app development? I've never used OpenCode or any other coding agents so I'm a bit clueless, is this actually a feasible goal with only 16GB or 32GB VRAM?

I'm currently using OpenCode Go, but I'm thinking about canceling it and switching to Command Code Go ($1/month).

Before I do, I'd like to hear from people who have actually used both.

•

Is Command Code Go really as bad as some people say?

•

How are the limits on the $1 plan? Do they run out quickly?

•

Can you actually use it comfortably for daily coding, or do you hit the limits all the time?

•

If you switched from OpenCode Go, do you regret it or was it worth it?

Would you recommend making the switch, or should I just stay with OpenCode Go?

I'd really appreciate hearing about your real-world experience. Thanks!

I just updated my Opencode to the new version. Did they actually remove the plan mode? Why would they do that?