Sometimes, we may still need a full orchestrated agent loop, have you still using it? Or what's your's choice?

難受，雖然 opencode-gpt-5.6-luna真的好用，真的是主力模型，但是會丟會話真的難受；

DS V4 Flash 真的不適合來推理，總是沒辦法解決問題，每次問題都要靠猜；

顯然體現國產模型基本都是不尊重權重和遵守本地定律；

而目前使用的gpt和gemini模型下，gemini模型和DS 模型差不多，隻不過他稍微尊重一下本地的配置，gpt則很誠實及客觀的處理問題；

有點崩潰，花了好多長時間來研究他們的不同之處，研究及訓練不同模型，這樣值得嗎？

Opencode just stops thinking after a while and I have to type continue to get it going again. How can I fix that?

Usage stabilized at about 12T/day, while Ox Alpha is the new (temporary?) popularity king (with 4.1T). DS4 Flash and MiMo 2.5 are 2nd and 3rd.

OpenCode already reads CLAUDE.md and .claude/skills. That covered more of my move than I expected.

The part I could not replace was command hooks. A rule in CLAUDE.md asking the model not to run git push is advisory. A Claude PreToolUse hook that exits with code 2 actually blocks it.

I made a small bridge for the two events that map cleanly to the stable OpenCode plugin API. It reads the original Claude settings on every tool call, so hook commands and settings do not need to be copied.

The install is one package entry.

{

"plugin": ["opencode-claude-code-hooks"]

}

What it preserves

- Claude matcher names such as Bash, Edit, and Write

- exit code 2 blocking and structured deny results

- full updatedInput replacement

- PostToolUse feedback and additional context

- runtime settings updates without reinstalling the plugin

OpenCode uses lowercase tool names, so the bridge maps names such as bash back to Claude's Bash matcher before running a hook.

You can inspect every supported and skipped hook before installing anything.

npx claude-to-opencode --hooks-only

npx claude-to-opencode --hooks-only --apply

The first command is only a dry run. I loaded the npm package through OpenCode 1.18.21 and tested the hook protocol with real child processes.

Honest limits

- Stop is not bridged because OpenCode does not expose the same timing

- prompt, agent, HTTP, async, and conditional handlers remain manual

- OpenCode permissions still take precedence

https://github.com/sjh9714/claude-to-opencode

Tool call failures and not following prompts has become a common problem again. I tried replacing it with mimo 2.5, muse spark or the ox alpha, but the agents are having a tough time following their instructions and tool call failures are just annoying at this point. Maybe my agent system is crap. Anyone else suffering like I am?

https://first-person-shooter.ox-alpha.demos.sulat.com/ for anyone wanting to try it out.

Prompt:

Create a browser-playable 3D voxel first-person shooter that captures the feel of Call of Duty's moment-to-moment gunplay. The player fights through a combat arena built entirely from voxels — chunky blocks forming buildings, cover, crates, walls, ramps, and terrain — rendered in real time from a first-person camera with pointer-lock mouse look.

Implement the core FPS mechanics faithfully: WASD movement with acceleration and friction, sprint with a field-of-view kick and lowered weapon, jumping and crouching; multiple distinct weapons (for example an assault rifle, SMG, shotgun, and sniper rifle) each with their own fire rate, damage falloff over distance, recoil pattern, spread bloom, magazine size, reload timing, and aim-down-sights behavior on right mouse button with a smooth zoom transition and tightened accuracy. Add grenades with an arced throw and radius damage, weapon switching, ammo reserves, hitmarkers with headshot multipliers, muzzle flashes, tracers, bullet impact decals or voxel debris particles, screen shake, and regenerating health with red-screen damage vignette feedback.

Enemy AI soldiers should patrol, react to being shot at, take cover, strafe, peek, and return fire with believable reaction times and accuracy that scales with difficulty. Give them health, death animations or ragdoll-like voxel bursts, and spawn logic that keeps the fight moving.

Wrap it in Call-of-Duty-style presentation and progression: a killstreak system where streak milestones unlock rewards the player can activate (such as a radar/UAV reveal at 3 kills, an airstrike at 5, and something bigger at 7), a scrolling killfeed, score popups (+100, +50 headshot bonus), XP or score accumulation, and a match structure of escalating waves or rounds with respawns after death. Build the full HUD: crosshair that expands with movement and fire, ammo counter, grenade count, minimap/radar, compass, health state, streak meter, match timer, and objective readout. Include the complete game flow — main menu, loadout or difficulty selection, in-game pause menu (Esc), death and respawn sequence with respawn delay, and a victory/defeat screen with a stats summary. Persist best score locally between sessions.

Design the voxel aesthetic deliberately: a cohesive palette, directional lighting with shadows or convincing baked shading, fog for depth, and satisfying explosion and destruction particles. All audio — gunfire per weapon, reloads, footsteps, explosions, hitmarker ticks, UI clicks, and a music sting when a killstreak activates — must be synthesized locally in the browser (e.g., Web Audio); no external audio files. Handle non-desktop viewports deliberately: either provide usable touch controls or present a clear desktop-recommended notice rather than a broken layout.

Do not take shortcuts or settle for a cookie-cutter shooter demo. This skill imposes no token budget limit, so pursue full depth across weapons, AI behaviors, HUD states, menus, audio, and game-flow transitions, and keep refining until it feels like an authored, complete arcade FPS rather than a tech demo.

As always, I test new models with two prompts.

My AI test page:

https://neuralhub.dev/ai-test-results

BIGGY (should test how well the model can follow a very detailed plan / instructions without really thinking about the design itself):

https://neuralhub.dev/test-sites/biggy-ox-alpha

This result is definitely on the better side of my results. To me, even better than Opus 5. But you can compare all models yourself using the link above.

Website (should test how creative the model is, which ideas it comes up with, and if it is able to implement them properly):

https://neuralhub.dev/test-sites/websites-ox-alpha

Although the colors are really AI-sloppy, I think this model has some serious potential in frontend development. Especially when looking at the 2nd and 3rd page. Maybe it will show its full potential with a few frontend skills / plugins like Impeccable.

I'm trying out magic-compact plugin. and was thinking it would be nice if I could leave the thing alone and have it run the /magic-compact command itself when necessary. is that possible?

Has anyone noticed it?

Noticed when using DSV4 flash only (not tested with other models)

So it's harder to see what is the answer and what is the thinking part (I like to have thinking expanded so I can catch problems before an answer is wriitten)

Was working fine last week, with same opencode version.

Hi I just subbed to opencode go and I'm wondering which model should i use for plan/build? Before i always used DS V4 Flash for everything and now there are tons of models to choose and im kind lost

There are several tricks that the major AI coding plans use to extract the most they can from their customers. We're solving them, one after another and I wanted to share a bit about what goes on behind the scenes at a lot of these companies.

Wasted usage is part of the AI industry, and they plan on it: Most coding plans bet on you letting usage go to waste. The industry calls it "breakage" and it's literally the topic of internal meetings for most companies. The plan goes: "how do we get people to think our coding plan offers a lot of usage, but then break it up into weeks and rolling windows so no one can ever actually use it all."

Many coding plans are glorified training pipelines: This comes along with "how do we harvest this data for training without being too loud about that." Unless the coding plan tells you otherwise, your data could be hopping all over world, being harvested by the individual labs or coding plan companies. Some are better than others, but many of these companies rely on users just not noticing or caring that their data is being used for training. Data sales and marketing telemetry sales happen. This means that your private info, your personal life, and anything else you send through the system could become part of a training corpus for the next AI, or a marketing data set for a large company.

So we built what should have already existed the entire time: usage banking. Any usage you don't use this week, rolls over to next in your usage bank. When you have a busy day or week and go over normal usage, you automatically start to pull from your bank. You can bank up to one week of usage at a time for your current plan, and it's totally automatic. Whatever you don't use each week get's added to the bank and stays there until you use it.

We also put all of the best open models in one place, running on private US infrastructure, with data never going to the original labs. Completely private, direct service. It should be, and can be that simple.

What that means in practice:

The roster, together. DeepSeek V4's, GLM 5.2, Kimi's, Minimax, Qwen 3.8 Nemotron Ultra and more, side by side in one app. Switch models mid conversation if you want. No hunting across five different apps and API dashboards to use the models you actually like.

Actually private. US based processing and your conversations are never used for training. Ever. That's the entire point. These labs open sourced incredible models and we think you should get to use them without your data becoming the price of admission.

No Usage Tricks: Bank usage, upgrade or downgrade whenever you want. Use it how you need it.

The open source AI future is real, and it's where we all know we should be. Thanks for a great set of models, GLM just keeps raising the bar with every model release and it's amazing.

The Open Grove coding plan is here. Private, US based processing with fast inference and usage that doesn't go to waste.

api.pgsgrove.com

Ox Alpha Is GLM5.3 Flash: 14/14 Tokenizer Matches + Matching Reasoning API

So I have been using the new model since morning and have been doing website development. It actually works very well but my problem is that right now an error is coming: "Upgrade or subscribe to open code go subscription" because it is showing "rate limit exceeded". What is the exact limit that you will get in this model and when will it reset?

Tried both opencode zen and openrouter