---
title: "Zero to working local AI"
slug: start
kind: runbook
verified_on: 2026-10-15
status: "Commands verified against official docs on 2026-10-15; not yet run end-to-end on the Mac Studio test rig unless marked mac_studio_tested: true."
platform_priority: ["macos-apple-silicon", "linux", "windows"]
steps: 5
json: https://insidethemachine.ai/start.json
catalog: https://insidethemachine.ai/api/index.json
---

# Zero to working local AI

> Executable runbook. Run steps in order; each has goal, commands per platform, verify (with expected output) and rollback. Commands verified against official docs on 2026-10-15; not yet run end-to-end on the Mac Studio test rig unless marked mac_studio_tested: true.

## Bootstrap prompt

Paste this into your agent once step 3 works:

```text
You are my local AI setup agent. Work only inside ~/itm-agent. Ask before deleting anything, spending money, or sending anything outside this machine.

Context: a local model server is already running (Ollama at http://localhost:11434/v1 or LM Studio at http://localhost:1234/v1).

1. Read https://insidethemachine.ai/start.md and https://insidethemachine.ai/llms.txt. Use them as your runbook and catalog.
2. My goal: <describe what you want, in plain English>.
3. Split that goal into workflows (for example research, writing, files, documents, memory). Create ~/itm-agent with AGENTS.md (goal, workflows, rules), memory/MEMORY.md, and one folder per workflow, following step 4 of start.md.
4. For each workflow, pick the tools it needs from the catalog. For each tool, fetch https://insidethemachine.ai/setups/<slug>.md, run its install and verify steps, and write the result to memory/MEMORY.md. If a verify step fails, run that listing's rollback and tell me.
5. Set up LiteLLM routing (step 5): "fast" = my everyday local model, "smart" = the biggest model my RAM allows. Use smart only for hard tasks.
6. Finish with a short report: what you installed, what passed, what failed, and what you'd upgrade next. Note that official Gap Scores are still pending test on Mac Studio.
```

## Step 1: Check your machine

**Goal:** Know total RAM (Apple Silicon: unified memory) or GPU VRAM, and pick a model size from the fit chart.

### Fit chart

Rule of thumb (estimate, not measured): comfortable if the model file is at most 50% of RAM/VRAM, tight up to 75%, otherwise no. Context window and the OS need the rest. Sizes: https://ollama.com/library (tags pages, download sizes as listed 2026-10-15)

| RAM | qwen3.5:4b (3.3 GB) | gemma4 (6.6 GB) | gemma4:26b (16 GB) | qwen3-coder:30b (19 GB) | qwen3.5:122b (81 GB) | pick |
|---|---|---|---|---|---|---|
| 8 GB | comfortable | no | no | no | no | qwen3.5:4b |
| 16 GB | comfortable | comfortable | no | no | no | gemma4 |
| 32 GB | comfortable | comfortable | comfortable | tight | no | gemma4:26b |
| 64 GB | comfortable | comfortable | comfortable | comfortable | no | qwen3-coder:30b |
| 128 GB | comfortable | comfortable | comfortable | comfortable | tight | qwen3-coder:30b (fast) + qwen3.5:122b (smart) |

### Commands: macOS (Apple Silicon)

1. Run:

```bash
sysctl -n hw.memsize | awk '{printf "%.0f GB unified memory\n", $1/1073741824}'
```

2. Run:

```bash
system_profiler SPHardwareDataType | grep -E 'Chip|Memory'
```

### Commands: Linux

1. Run:

```bash
free -g | awk '/Mem:/{print $2" GB RAM"}'
```

2. Run:

```bash
nvidia-smi --query-gpu=name,memory.total --format=csv   # NVIDIA GPUs only
```

### Commands: Windows

1. Run:

```bash
(Get-CimInstance Win32_ComputerSystem).TotalPhysicalMemory/1GB   # PowerShell
```

### Verify

1. Expect: prints a byte count; 128GB Mac Studio ≈ 137438953472

```bash
sysctl -n hw.memsize
```

### Rollback

1. Run:

```bash
# read-only step; nothing to undo
```

Guides: Machines: what to buy or what yours can run -> https://insidethemachine.ai/machines.md (JSON: https://insidethemachine.ai/machines.json); Models vs memory: what fits your RAM -> https://insidethemachine.ai/models.md (JSON: https://insidethemachine.ai/models.json)

Related listings: https://insidethemachine.ai/setups/mlx-lm.md, https://insidethemachine.ai/setups/llama-cpp.md

## Step 2: Pick your tool

**Goal:** A local model server is running and answers a prompt.

### Commands: macOS (Apple Silicon)

1. Option A (recommended for agents): Ollama:

```bash
brew install ollama   # or: curl -fsSL https://ollama.com/install.sh | sh   (or the Mac app from ollama.com/download)
```

2. Ollama docs: agents and coding tools should get at least 64000 tokens of context:

```bash
ollama serve   # skip if the Ollama app is already running; for agents use: OLLAMA_CONTEXT_LENGTH=64000 ollama serve
```

3. Run:

```bash
ollama pull gemma4   # pick your size from the fit chart, e.g. qwen3.5:4b on 8GB, qwen3-coder:30b on 64GB+
```

4. Option B (GUI): LM Studio. Model search names are untested on Mac Studio:

```bash
brew install --cask lm-studio && lms get gemma-4   # lms get searches; pick a Gemma 4 or Qwen 3.5 build (--mlx for MLX on Apple Silicon)
```

5. Run:

```bash
lms server start && lms load <model-id-from-lms-ls>
```

### Commands: Linux

1. Run:

```bash
curl -fsSL https://ollama.com/install.sh | sh && ollama pull gemma4
```

### Commands: Windows

1. Run:

```bash
irm https://ollama.com/install.ps1 | iex; ollama pull gemma4
```

### Verify

1. Expect: a short greeting

```bash
ollama run gemma4 "Say hi in five words."
```

2. Expect: JSON list including gemma4 (Ollama's OpenAI-compatible endpoint)

```bash
curl -s http://localhost:11434/v1/models
```

3. Expect: model listed; PROCESSOR should read 100% GPU (per Ollama context-length docs)

```bash
ollama ps
```

4. Expect: LM Studio path: JSON model list (port 1234 is LM Studio's usual default)

```bash
curl -s http://localhost:1234/v1/models
```

### Rollback

1. Run:

```bash
ollama rm gemma4 && brew uninstall ollama
```

2. Run:

```bash
lms server stop && brew uninstall --cask lm-studio   # models live under ~/.lmstudio
```

Related listings: https://insidethemachine.ai/setups/ollama.md, https://insidethemachine.ai/setups/lm-studio.md

## Step 3: Point an agent at it

**Goal:** A harness (OpenCode, Goose, Aider or Hermes Agent) talks to the local endpoint http://localhost:11434/v1 (Ollama) or http://localhost:1234/v1 (LM Studio).

### Harness options

- **OpenCode**: Terminal coding agent; Ollama can configure it in one command. Docs: https://docs.ollama.com/integrations/opencode
- **Goose**: General agent with MCP extensions; desktop app or CLI. Docs: https://docs.ollama.com/integrations/goose
- **Aider**: Git-aware pair programmer for existing repos. Docs: https://aider.chat/docs/llms/ollama.html
- **Hermes Agent**: Nous Research agent with skills and cross-session memory. Docs: https://docs.ollama.com/integrations/hermes

### Commands: macOS (Apple Silicon)

1. OpenCode (recommended):

```bash
curl -fsSL https://opencode.ai/install | bash   # or: brew install anomalyco/tap/opencode
```

2. starts OpenCode with an inline config for the Ollama model you pick; does not overwrite ~/.config/opencode/opencode.json:

```bash
ollama launch opencode
```

3. manual alternative, format from opencode.ai/docs/providers; model ids must match GET /v1/models. Back up any existing file first:

```bash
mkdir -p ~/.config/opencode && cat > ~/.config/opencode/opencode.json <<'EOF'
{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "ollama": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "Ollama (local)",
      "options": { "baseURL": "http://localhost:11434/v1" },
      "models": { "gemma4": { "name": "Gemma 4" } }
    }
  }
}
EOF
```

4. Goose: choose Configure Providers → Ollama → enter your model name:

```bash
curl -fsSL https://github.com/aaif-goose/goose/releases/download/stable/download_cli.sh | bash && goose configure
```

5. Aider: see /setups/aider:

```bash
python -m pip install aider-install && aider-install && aider --model ollama_chat/gemma4
```

6. Hermes Agent: Ollama points it at http://127.0.0.1:11434/v1:

```bash
ollama launch hermes   # or: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
```

### Verify

1. Expect: prints 'ready' using the local model (untested on Mac Studio)

```bash
opencode run "Reply with the word ready"
```

2. Expect: Goose opens a session; type: say ready

```bash
goose session -n itm-check
```

### Rollback

1. Run:

```bash
rm ~/.config/opencode/opencode.json   # restore your backup if you had one
```

2. Run:

```bash
brew uninstall opencode   # if installed via brew
```

3. Run:

```bash
# Goose / Hermes: follow uninstall steps in their docs (install scripts place a binary in your PATH)
```

Related listings: https://insidethemachine.ai/setups/aider.md, https://insidethemachine.ai/setups/ollama.md

## Step 4: Your goal becomes workflows

**Goal:** A workspace with AGENTS.md, memory/MEMORY.md, one folder per workflow and MCP tools configured.

### Default workflows

- research: fetch MCP server (uvx mcp-server-fetch)
- writing: AGENTS.md style rules
- files: filesystem MCP server
- memory: memory/MEMORY.md, upgrade to Mem0
- documents: Docling

### Commands: macOS (Apple Silicon)

1. Run:

```bash
mkdir -p ~/itm-agent/{workflows/{research,writing,files,documents},memory,tools} && cd ~/itm-agent && git init -q
```

2. Run:

```bash
cat > AGENTS.md <<'EOF'
# AGENTS.md
## Goal
<the user's goal in one sentence>
## Workflows
- research/: gather sources, save notes as markdown with URLs
- writing/: drafts; never publish or send without the user's OK
- files/: only touch files inside ~/itm-agent
- documents/: convert PDFs/Office files with `docling <file>`
## Memory
- Read memory/MEMORY.md at the start of every session; append decisions and facts at the end.
## Upgrades
- Before installing anything, read the listing at https://insidethemachine.ai/setups/<slug>.md and run its verify step.
EOF
```

3. Run:

```bash
printf '# MEMORY\n\n- %s: workspace created\n' "$(date +%F)" > memory/MEMORY.md
```

4. project-level OpenCode config (format from opencode.ai/docs/mcp-servers); replace /Users/YOU. Requires uv (brew install uv) and Node:

```bash
cat > opencode.json <<'EOF'
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "fetch": { "type": "local", "command": ["uvx", "mcp-server-fetch"], "enabled": true },
    "filesystem": { "type": "local", "command": ["npx", "-y", "@modelcontextprotocol/server-filesystem", "/Users/YOU/itm-agent"], "enabled": true }
  }
}
EOF
```

### Verify

1. Expect: AGENTS.md, memory/MEMORY.md, workflows/* folders, opencode.json

```bash
find ~/itm-agent -maxdepth 2 -not -path '*/.git*'
```

2. Expect: the agent lists the workflows (untested on Mac Studio)

```bash
cd ~/itm-agent && opencode run "Read AGENTS.md and list the workflows"
```

### Rollback

1. Run:

```bash
rm -rf ~/itm-agent   # deletes the workspace, irreversible
```

Related listings: https://insidethemachine.ai/setups/agents-md.md, https://insidethemachine.ai/setups/mcp-servers.md, https://insidethemachine.ai/setups/mem0.md, https://insidethemachine.ai/setups/docling.md

## Step 5: It builds and upgrades itself

**Goal:** The agent pulls /llms.txt, installs missing pieces using each listing's verify/rollback, routes via LiteLLM (fast vs smart), and logs a self-check.

### Commands: macOS (Apple Silicon)

1. the agent's map of what exists:

```bash
curl -s https://insidethemachine.ai/llms.txt && curl -s https://insidethemachine.ai/api/index.json
```

2. any listing as markdown:

```bash
curl -s -H 'Accept: text/markdown' https://insidethemachine.ai/setups/litellm
```

3. LiteLLM docs show pip install 'litellm[proxy]'; uv tool install is the isolated equivalent (untested on Mac Studio):

```bash
brew install uv && uv tool install 'litellm[proxy]'
```

4. 128GB example; on smaller machines map smart to the biggest model your chart allows, or to a cloud model you have a key for:

```bash
cat > ~/itm-agent/tools/litellm.yaml <<'EOF'
model_list:
  - model_name: "fast"
    litellm_params:
      model: "ollama_chat/qwen3-coder:30b"
  - model_name: "smart"
    litellm_params:
      model: "ollama_chat/qwen3.5:122b"
EOF
```

5. Run:

```bash
litellm --config ~/itm-agent/tools/litellm.yaml   # OpenAI-compatible proxy on http://0.0.0.0:4000
```

### Verify

1. Expect: OpenAI-format JSON reply

```bash
curl -s http://0.0.0.0:4000/chat/completions -H 'Content-Type: application/json' -d '{"model":"fast","messages":[{"role":"user","content":"ready?"}]}'
```

2. Expect: the agent logged what it installed and each verify result

```bash
tail -5 ~/itm-agent/memory/MEMORY.md
```

### Rollback

1. Run:

```bash
uv tool uninstall litellm && rm ~/itm-agent/tools/litellm.yaml
```

2. Run:

```bash
# for anything else the agent installed: run the rollback section of that listing's .md
```

### Self-check

Official Gap Scores are pending test on Mac Studio. Until then the agent's self-check is: did every listing's verify step pass, plus tokens/sec from `ollama run <model> --verbose` (untested on Mac Studio).

Related listings: https://insidethemachine.ai/setups/litellm.md, https://insidethemachine.ai/setups/mem0.md, https://insidethemachine.ai/setups/open-webui.md

## Hermes-style gaps: identity, instructions, and lean context

Frontier apps feel smart partly because of what surrounds the model: an identity, project rules, skills, memory and careful context management. These four parts give a local agent the same scaffolding without a million-token window.

### AGENTS.md and SOUL.md

#### AGENTS.md = how to work here

A plain markdown file of project rules your agent reads every session. Most harnesses (OpenCode, Goose, Hermes, OpenClaw) load it automatically.

- AGENTS.md: open, harness-agnostic project-instructions file (see /setups/agents-md).
- Hermes: loads AGENTS.md from the CWD at startup and subdirectories progressively; .hermes.md / HERMES.md take highest priority (docs: user-guide/features/context-files).
- OpenCode: reads AGENTS.md in the project and ~/.config/opencode/AGENTS.md globally (opencode.ai/docs/rules).
- Goose: CONTEXT_FILE_NAMES default is ["AGENTS.md", ".goosehints"] (goose-docs.ai using-goosehints).
- OpenClaw: workspace files include AGENTS.md, SOUL.md, IDENTITY, USER (docs.openclaw.ai templates); default workspace ~/.openclaw/workspace.

Template (~/itm-agent/AGENTS.md):

```markdown
# AGENTS.md
<!-- Always loaded. Keep under ~150 lines: rules + pointers, not knowledge. -->
## Project
One sentence: what this workspace is for.
## Rules
- Work only inside this folder. Ask before deleting, spending, or sending anything.
- Prefer small diffs. Run the verify step after every install.
## Map (load on demand, do not paste here)
- workflows/<name>/AGENTS.md : rules for that workflow only
- memory/MEMORY.md : read the last 30 lines at session start; append decisions
- skills/ : one folder per repeatable procedure (SKILL.md each)
## Output hygiene
- Pipe long command output through `tail -n 50` or `head -c 4000`.
- Summarize files; never print whole logs or datasets.
```

#### SOUL.md = who the agent is

A persona file: voice, values and limits. Hermes puts it first in the system prompt; OpenClaw injects it into normal sessions.

- Hermes: SOUL.md is the primary identity, slot #1 in the system prompt, loaded only from $HERMES_HOME/SOUL.md (default ~/.hermes/SOUL.md). Hermes seeds a starter file and never overwrites yours (docs: features/personality).
- OpenClaw: 'SOUL.md is where your agent's voice lives. OpenClaw injects it into normal sessions' (docs.openclaw.ai/concepts/soul). Lives in the agent workspace.
- OpenCode / Goose / Aider have no SOUL.md concept: put persona lines at the top of AGENTS.md or list SOUL.md as an extra instruction file (below).

Template (~/.hermes/SOUL.md):

```markdown
# SOUL.md
<!-- Identity, voice, boundaries. No project facts: those go in AGENTS.md. -->
You are Kit, a calm, direct assistant running on a local model.
## Voice
- Short sentences. Lead with the answer. No filler, no hype.
- Say "I don't know" and check, rather than guess.
## Values
- The user's machine and data stay local unless they say otherwise.
- Ask before irreversible actions.
## Never
- Invent numbers, benchmarks or citations.
```

### Point each harness at the files

#### Hermes

Edit ~/.hermes/SOUL.md for identity and start Hermes inside your workspace so it finds AGENTS.md. Skills live in ~/.hermes/skills.

```bash
$EDITOR ~/.hermes/SOUL.md
cd ~/itm-agent && hermes
```

- Note: Directory layout per docs: ~/.hermes/{config.yaml,SOUL.md,memories/,skills/}.

#### OpenCode

It reads AGENTS.md on its own. Add extra files like SOUL.md with one line of config.

```bash
cat > ~/itm-agent/opencode.json <<'EOF'
{
  "$schema": "https://opencode.ai/config.json",
  "instructions": ["SOUL.md", "workflows/*/AGENTS.md"]
}
EOF
```

- Note: 'instructions' accepts paths, globs and URLs; combined with AGENTS.md (opencode.ai/docs/rules). Merge with the mcp block from step 4 if you created one.

#### Goose

Goose reads AGENTS.md and .goosehints by default. Tell it about other files with one environment variable.

```bash
export CONTEXT_FILE_NAMES='["AGENTS.md", "SOUL.md", ".goosehints"]'
cd ~/itm-agent && goose session -n itm
```

- Note: Global hints: ~/.config/goose/.goosehints; local hints win over global.

#### Aider

Aider loads rule files as read-only context. Put them in its config so every session gets them.

```bash
printf 'read: [AGENTS.md, SOUL.md]\n' > ~/itm-agent/.aider.conf.yml
aider --read AGENTS.md   # one-off equivalent
```

- Note: Docs: aider.chat/docs/usage/conventions.html (read files are marked read-only and cached if prompt caching is enabled).

### Avoid context bloat

#### Keep always-loaded files lean

AGENTS.md and SOUL.md ride along on every turn, so every line costs you. Keep rules and pointers there, and nothing else.

- AGENTS.md: aim for under ~150 lines (house rule of thumb, not a harness limit)
- SOUL.md: aim for under ~40 lines

#### Skills load only when needed

Write repeatable procedures as skills: the agent sees a one-line description and opens the full text only when it needs it. Hermes calls this progressive disclosure.

- Hermes skills: on-demand documents following a progressive disclosure pattern to minimize token usage; agentskills.io compatible; stored in ~/.hermes/skills/.

Layout: skills/<name>/SKILL.md with front-matter name + description; body loaded on demand

#### Retrieve, don't stuff

Keep notes and documents on disk and let the agent search them. Pull in a few relevant chunks instead of whole files.

- memory/MEMORY.md tail + grep
- Mem0 (/setups/mem0)
- Docling to convert PDFs before retrieval (/setups/docling)
- Open WebUI knowledge bases (/setups/open-webui)

#### Compact long sessions

When a chat gets long, have the harness summarize the middle and keep the recent tail. Turn it on before the window fills.

opencode.json:

```
{
  "compaction": {
    "auto": true,
    "prune": true,
    "reserved": 10000
  }
}
```

~/.hermes/config.yaml:

```
compression:
  enabled: true
  threshold: 0.50   # compress at 50% of context limit
  target_ratio: 0.20
```

hermes (manual):

```
/compress
```

aider (manual):

```
/clear or /drop unused files
```

- Note: OpenCode prune=true removes old tool outputs (default false). Hermes: the summary model needs a context window at least as large as the main model's.

#### Trim tool output

Most bloat is logs and command output, not your words. Cap it at the source.

```bash
<cmd> 2>&1 | tail -n 50
<cmd> | head -c 4000
rg -n --max-count 20 'pattern' .
```

#### One folder per workflow

Give each workflow its own folder and its own small AGENTS.md. The agent only loads the rules for the job in front of it.

Layout: ~/itm-agent/workflows/{research,writing,files,documents}/AGENTS.md; start the harness inside the workflow folder (Hermes and OpenCode both pick up subdirectory AGENTS.md)

### Keep the local model efficient

#### Pick the right quant

A 4-bit model is about a quarter the size of 16-bit and usually close in quality. Use the fit chart and leave room for context.

- ollama default tags: 4-bit-class quants (sizes in step 1 chart)
- rule: prefer a bigger model at 4-bit over a smaller one at 8-bit when it fits (rule of thumb, untested on Mac Studio)

#### Set context length on purpose

Bigger windows eat memory fast. Give agents enough (64k) and no more.

ollama:

```bash
OLLAMA_CONTEXT_LENGTH=64000 ollama serve   # server-wide
/set parameter num_ctx 64000   # inside ollama run
API: "options": {"num_ctx": 64000}
ollama ps   # CONTEXT column shows what was allocated
```

llama.cpp:

```bash
llama-server -m model.gguf -c 65536   # -c, --ctx-size; 0 = from model
```

mlx-lm:

```bash
mlx_lm.generate --max-kv-size 65536 ...   # rotating fixed-size KV cache
```

- Note: Ollama: RAM scales with OLLAMA_NUM_PARALLEL × OLLAMA_CONTEXT_LENGTH; keep OLLAMA_NUM_PARALLEL=1 for a single agent.

#### Shrink the KV cache

The cache that holds your context can be quantized too. 8-bit halves its memory with very little quality loss.

ollama:

```bash
OLLAMA_FLASH_ATTENTION=1 OLLAMA_KV_CACHE_TYPE=q8_0 ollama serve
# values: f16 (default), q8_0 (~1/2 memory, recommended), q4_0 (~1/4, more loss at long context); global for all models
```

llama.cpp:

```bash
llama-server -m model.gguf -c 65536 -fa on -ctk q8_0 -ctv q8_0
```

- Note: llama.cpp allowed K/V types: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1 (default f16).

#### Reuse the prompt

Your AGENTS.md and SOUL.md are the same every turn, so cache them instead of re-reading. Keep them stable so the cache keeps hitting.

ollama:

```bash
OLLAMA_KEEP_ALIVE=30m ollama serve   # keep the model loaded between turns
```

llama.cpp:

```bash
llama-server ... --cache-reuse 256   # reuse cached chunks via KV shifting; needs prompt caching
```

mlx-lm:

```bash
cat AGENTS.md | mlx_lm.cache_prompt --model <model> --prompt - --prompt-cache-file agents.safetensors
mlx_lm.generate --prompt-cache-file agents.safetensors --prompt "..."
```

- Note: Editing the top of an always-loaded file invalidates the cached prefix: put changing content at the end.

#### Small model first, big model when needed

Send routine turns to a fast model and hard ones to your biggest. LiteLLM gives both one address.

See: Step 5 litellm.yaml (fast / smart aliases)

- Note: Hermes can also run compression on a separate model via auxiliary.compression (model, base_url).

## Sources

- https://aider.chat/docs/llms/ollama.html
- https://aider.chat/docs/usage/conventions.html
- https://docs.litellm.ai/docs/providers/ollama
- https://docs.ollama.com/context-length
- https://docs.ollama.com/faq
- https://docs.ollama.com/integrations/goose
- https://docs.ollama.com/integrations/hermes
- https://docs.ollama.com/integrations/opencode
- https://docs.openclaw.ai/concepts/agent-workspace
- https://docs.openclaw.ai/concepts/soul
- https://github.com/aaif-goose/goose
- https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md
- https://github.com/ml-explore/mlx-lm
- https://github.com/modelcontextprotocol/servers
- https://goose-docs.ai/docs/getting-started/providers
- https://goose-docs.ai/docs/guides/context-engineering/using-goosehints
- https://hermes-agent.nousresearch.com/docs/user-guide/configuration
- https://hermes-agent.nousresearch.com/docs/user-guide/features/context-files
- https://hermes-agent.nousresearch.com/docs/user-guide/features/personality
- https://hermes-agent.nousresearch.com/docs/user-guide/features/skills
- https://lmstudio.ai/docs/cli
- https://ollama.com/library/gemma4/tags
- https://ollama.com/library/qwen3-coder/tags
- https://ollama.com/library/qwen3.5/tags
- https://opencode.ai/docs/cli/
- https://opencode.ai/docs/config/
- https://opencode.ai/docs/mcp-servers/
- https://opencode.ai/docs/providers/
- https://opencode.ai/docs/rules/