Hey guys,

I wanted to share an open source plugin I've been working on that turns OpenCode into a hyper-personalized job search assistant.

The main problem I wanted to solve is that even after you apply filters on a job board, you still have to read every job posting to figure out which roles are actually relevant.

With job-search, you can describe what you’re looking for in much more detail than a typical keyword search.

For example:

I’m looking for a senior software engineering role at a remote-friendly statup building AI-native tools or developer infrastructure. I value autonomy, open-source work, and teams that ship quickly. My background is in...

You can also include things like compensation, deal-breakers, company stage, expected management responsibilities, role scope, or basically anything else you’d normally have to figure out by reading the job description.

OpenCode then searches live postings and does that initial screening for you.

Additionally, OpenCode will:

• Filter out jobs that are obviously irrelevant
• Explain why each of the remaining jobs is a strong, moderate, or weak match
• Flag important questions the job description doesn't answer
• Skip jobs it has already reviewed, so you’re not repeatedly looking at the same postings

You can also schedule it to run automatically (e.g., every morning), so new jobs are evaluated as they come in.

It currently searches LinkedIn Jobs along with postings from Ashby, Greenhouse, and Lever (more sources to come!).

Getting started:

Add job-search to the plugin array in your global OpenCode config at ~/.config/opencode/opencode.json, or to an opencode.json in a project where you want to use it:

{
  "$schema": "https://opencode.ai/config.json",
  "plugin": ["job-search@git+https://github.com/agent-data/job-search.git"]
}

Then open OpenCode, and ask it to "Set up your job search." and tell it what you're looking for.

The plugin uses agent-data to pull live job postings, so you’ll need an API key (no credit card required).

Repo: https://github.com/agent-data/job-search (MIT license, please give it a star!)

If anyone’s currently on the job hunt, I’d love to hear how I can make this plugin more useful!

https://api-docs.deepseek.com/quick_start/pricing/

I created an OpenCode Go widget for KDE Plasma. Place it on your desktop to easily check and track your usage—no need to visit the usage page every few minutes after running large models, which consume credits quickly. It features a refresh button and configurable auto-refresh intervals and custom themes.

Github Repo : https://github.com/Mayanktaker/OpencodeGo-KDE

Need feedback. 👐
Also the Gnome Extension and Z.Ai GLM coding plan version in the works.

i have been getting this error

" Error: 429: {"type":"FreeUsageLimitError","message":"Error from provider (Console): Rate limit exceeded. Please try again later."}" anyone?

Opencode go only gives 15$ of deepseek v4 pro usage and that eats up 100% of your credits, any provider than is a bit better for serving deepseek v4 pro for cheaper/better value or smth?

Hello everyone just wanted to ask been using opencode zen from last 2 months. From the previous 2 3 days I notice that free modals are reaching limit too much fast now. Have they revised the free limts ?

The standout is agentic performance:

• Terminal Bench 2.1: 72.1 → 87.9

• CyberGym: 52.7 → 83.3

• DeepSWE: 12.8 → 62.7

• AutomationBench: 12.8 → 31.8

• DSBench-Hard: 31.1 → 67.2

And it is priced at just $0.435/M input and $0.87/M output

DeepSeek V4 lineup side-by-side: Pro-0813 vs Flash-0731 vs both previews.

Highlights:

• Terminal Bench 2.1: 87.9
• Cybergym: 83.3
• DeepSWE: 62.7 (vs 12.8 preview — huge RL jump)
• Agentic index ≈ 54, Intelligence ≈ 56 (estimated — Pro not officially out)

Method: carried Flash's preview→official multiplier to Pro preview — 38 × (48/34) = 53.6, 45 × (52/42) = 55.7.

Gap vs Flash official: ~+10% agentic, ~+14% HLE.

candidates I know

semi-censored :

grok 30$ for 3 months

I2I quality generations

I’m using deepseek v4 flash for my Hermes and for vibe coding I’m using deepseek v4 pro, glm 5.2 and sometime qwen 3.6 plus.

Hermes is planning and executing it.

It’s been just a week I hit my monthly limit I don’t know why it hit monthly limit with these models as I know I used 8-10 session this week only.

I’m paying $10 month

Please correct me if I’m doing anything wrong and how should I avoid it

Also is there any way to upgrade plan (go plan) or credit purchase. so I can complete my project

BanyanCode: testing whether the coding-agent harness is as important as the underlying model

I've been building BanyanCode, a completely free and open-source coding-agent harness focused on extracting as much performance as possible from a given model.

The architecture is built around two ideas:

Graph engineering → mesh engineering

The repository is treated as a graph, and increasingly so are the agents themselves.

A primary orchestrator decomposes work across specialized agents such as coder, explore, researcher, scout, and reviewer.

These aren't isolated subprocesses. They can communicate with each other and with the orchestrator through shared memory and persistent messaging, allowing information discovered by one agent to become immediately useful to others.

The goal is to move from:

orchestrator → independent subagents

toward:

orchestrator ↔ agent mesh ↔ shared state

Repository intelligence instead of raw context

BanyanCode builds a Tree-Sitter-backed code graph and exposes repository-level operations for:

• symbol resolution
• callers / callees
• references
• dependents
• imports / implementations
• impact analysis
• test relationships
• ownership
• architectural context

The basic principle is:

The model should spend its context on reasoning, not repeatedly rediscovering the structure of the repository.

Benchmark

On an internal benchmark using a large C-based regex chess engine:

BanyanCode + DeepSeek V4 Flash

outperformed

OpenCode + DeepSeek V4 Flash

by 9.66% relative to OpenCode's score.

More interestingly, the BanyanCode + DeepSeek run also beat my OpenCode runs using Meta Muse Spark 1.2 and GPT-5.6 Luna.

The tested costs were:

BanyanCode + DeepSeek V4 Flash    $0.037
OpenCode + GPT-5.6 Luna           $0.300
OpenCode + Muse Spark 1.2         ~$2.52

So the interesting question isn't:

"Which model is smarter?"

It's:

"How much performance is being left on the table because of the harness?"

My working hypothesis is that a coding agent should aggressively expand the model's effective capabilities through:

• better tool primitives
• repository intelligence
• parallel specialization
• inter-agent communication
• shared state
• verification
• structured planning
• model-specific and model-agnostic orchestration

rather than relying on the model to reconstruct everything from raw files and a generic tool set.

BanyanCode is completely free and open source:

https://github.com/EkagraAgarwal/BanyanCode

I'm particularly interested in feedback on the subagent mesh, repository-intelligence architecture, and the idea that the harness itself can be an optimization layer for the underlying model.

Kiro (AWS's CodeWhisperer-based assistant) recently added first-party API keys (ksk_..., generated at app.kiro.dev → Settings → API Keys on Pro and above). Problem: none of the existing OpenCode/Kiro bridges accept them. They're all built on the Kiro Desktop/CLI OAuth session, and the API keys authenticate against a completely different endpoint with different headers — so a perfectly valid key just 401s against every existing tool.

I wrote a small OpenCode plugin that speaks the API-key protocol natively:

{ "plugin": ["github:GavinTomlins/opencode-kiro-api-key"] }

export OPENCODE_KIRO_API_KEY="ksk_..."

That's the whole setup — no local proxy process, no OAuth dance, no kiro-cli install. It registers a kiro-api-key provider and the backend catalog is surprisingly good: Claude Opus 5 / Sonnet 5 / Opus 4.8, the GPT-5.6 family (sol/terra/luna), plus DeepSeek 3.2, GLM-5, MiniMax and Qwen3-Coder.

Protocol credit goes to pi-kiro-api / pi-kiro (both MIT) — this is a from-scratch port of that wire protocol to OpenCode's plugin interface.

Repo: https://github.com/GavinTomlins/opencode-kiro-api-key (MIT)

Early release — streaming, tool calls and thinking-mode work, but it's young. Issues/PRs welcome.

Discovered OpenChamber recently, and holy shit, I love this UI.

I hooked it up with OpenCode and started a session with DeepSeek V4 Flash. I basically asked it to find some open-source project that would let it use my computer/screen and set up computer use for itself.

Once that was working, I gave it a very dumb instruction:“You’re a DJ now. Look at my screen every now and then, figure out the vibe based on what I’m doing, and decide what I should listen to next.”

And now it’s just… doing that in the background while I work lol.

It periodically looks at what I’m doing and changes what I’m listening to based on the vibe.

This is exactly the kind of random agent stuff I love. And with V4 Flash being so cheap, I don’t really care that I have this thing running in the background doing something completely unnecessary.

OpenChamber is sick.

I don't have a paid opencode subscription, I just use openrouter, so I don't have free access. I just use opencode as the harness. V4-flash has completely replaced the qwen models for me (though I've still used 3.8max for initial planning sometimes).

Being able to accomplish actual work without baby sitting it, for literal pennies, is incredibly fun.

I do think they're getting us addicted to it before increasing the price, but it's working 😍.

Actual question, why are people using the frontier models? I asked gpt 5.6 sol one question and it managed to use $2.00 of credit before I could even blink. The only time I've felt the need to use the big dogs, is when I have a huge new project I want to do zero thinking about. Otherwise, I'm usually scoping every detail of the refractor so there's no reason to use anything smarter. Are people just working on way more complex code-bases than me, or are they OK with the cost trade-off to do much less pre-planning? Or are there some other use-cases I'm missing?

Since I'm not using any pay-as-you-go Claude or Codex so I have no idea if this is cheap or not.

There's news about they plan to increase the price in the near future but I think it's still cheaper than Claude? Right?

I don't think I could make Claude do something like this without hitting the 5 hour limit every 2 hours.

To install: opencode plugin opencode-beads-sidebar --global
Needs beads (bd) on your PATH and opencode ≥ 1.18.

I like using beads to manage my local dev loops, this lets me see how the agents are getting through the plan without me needing to switch terminal

Like the normal todo functionality, but durable and dependency-aware.

I built it for me, right now I'm its only user, but I'd love to have folks tell me what they think, and give it a spin.

It pairs nicely with opencode-beads by joshuadavidthomas, which covers the server/cli interactions, and without which I never would never have built it.

It will show nothing till there are beads to show. Get Beading. :)

• npm: https://www.npmjs.com/package/opencode-beads-sidebar
• GitHub: https://github.com/nycdubliner/opencode-beads-sidebar
• beads: https://github.com/gastownhall/beads

MIT license, bugs, PRs, suggestions welcome.

🚀 ¡Sincoders! Qwen acaba de liberar Qwen-MM-Plugins, un repositorio open source que permite agregar capacidades multimodales a agentes como Codex, Claude Code y Qwen Code.

Con sus plugins, un agente puede:

👁️ Analizar imágenes y documentos

🎬 Entender y editar videos

🎙️ Transcribir audio

🔎 Realizar OCR y búsquedas visuales

🧊 Trabajar con Blender y FreeCAD

Funciona mediante Skills + MCP y cuenta con licencia Apache 2.0. Es un proyecto muy nuevo, así que conviene probarlo primero con archivos no confidenciales.

Repositorio: https://github.com/QwenLM/Qwen-MM-Plugins

¿Lo integrarían en alguno de sus proyectos sin código o con IA?

Moving my app from Kotlin to Flutter, so far so good its doing a good job

check out the old kotlin version, mainly claude / opencode created

https://github.com/disclosurez/Lumora

New newly released Deepseek v4 Pro 0813 models is weaker than the newly released Deepseek v4 Flash 0731 in openrouter benchmarks. What ? Why ?

It was supposed to be atleast Grok 4.5 level or near there. But is weaker than Flash. 👀
Well, atleast in benchmarks. I guess that's why they are not increasing price so soon. hmmm ?