sangramrout's picture
AURA-1 chip design archive: docs, blog, research folders, tiny-gpu work, GDS viewer
7858a76 verified
|
Raw History Blame Contribute Delete
29.2 kB

Documentation Index

Fetch the complete documentation index at: https://docs.wafer.ai/llms.txt Use this file to discover all available pages before exploring further.

Agent Setup

Fast open-source LLMs for Linzumi, Claude Code, Codex, Conductor, OpenClaw, Hermes Agent, Cline, Roo Code, Kilo Code, OpenHands, and other coding agent harnesses.

Features, model availability, rate limits, and pricing may change as we iterate. Questions? Email [emilio@wafer.ai](mailto:emilio@wafer.ai).

Set up with: Linzumi · Claude Code · Conductor · Codex · OpenClaw · Hermes Agent · Cline · Roo Code · Kilo Code · OpenHands · LibreChat

Wafer builds AI that optimizes AI. We take open models and make them dramatically faster. Wafer Serverless gives you pay-as-you-go access to fast open models — including GLM-5.2, glm5.2-fast, GLM-5.1, Kimi-K2.6, and more — served at multiples of the speed of generic inference providers.

Wafer Serverless is built for Linzumi, Claude Code, Codex, Conductor, OpenClaw, Hermes Agent, Cline, Roo Code, Kilo Code, OpenHands, and other agent harnesses. Wafer exposes both an OpenAI-compatible endpoint and an Anthropic-compatible Messages endpoint, so tools like Claude Code work out of the box. Linzumi also exposes Wafer directly inside its coding-job flow.

Get a Wafer Serverless API key for fast open-source models through a standard API endpoint.

Get started: https://app.wafer.ai/signup

Connection Details

Use your Wafer Serverless API key with these values:

OpenAI-compatible endpoint https://pass.wafer.ai/v1
Anthropic-compatible endpoint https://pass.wafer.ai/v1/messages
Send your API key as Authorization: Bearer <key> (wfr_… Serverless keys work here)
Request-scoped ZDR Add Wafer-ZDR: required on direct API calls

See Models below for the model strings to pass on the OpenAI-compatible endpoint.

**Claude Code** uses the Anthropic Messages endpoint. Set `ANTHROPIC_BASE_URL=https://pass.wafer.ai` and `ANTHROPIC_API_KEY` to your Wafer key — Claude Code will hit `/v1/messages` automatically. To make sure Claude Code talks to a Wafer model, set `ANTHROPIC_DEFAULT_OPUS_MODEL`, `ANTHROPIC_DEFAULT_SONNET_MODEL`, `ANTHROPIC_DEFAULT_HAIKU_MODEL`, and `CLAUDE_CODE_SUBAGENT_MODEL` to one of the Wafer model IDs in [**Models**](#models) (e.g. `GLM-5.1`). Linzumi is built in: select **Wafer** in Linzumi's **Start coding job** screen instead of pasting an endpoint or API key. All other harnesses (OpenClaw, Cline, Roo Code, etc.) use the OpenAI-compatible endpoint at `https://pass.wafer.ai/v1`.

For raw curl requests, request fields, and direct API usage, see API Reference.

What's Included

With a Wafer Serverless API key you get:

  • Pay-as-you-go access to Wafer-hosted models, billed per token
  • Access through a standard OpenAI-compatible API and an Anthropic-compatible Messages API using your Wafer API key
  • Works with Linzumi, Claude Code, Codex, Conductor, OpenClaw, Hermes Agent, Cline, Roo Code, Kilo Code, OpenHands, and other agent harnesses
  • Reasoning-capable models support explicit thinking on/off controls in Chat Completions
  • New fast models as we release them

Models

model string Family Max context tokens ZDR support Reasoning Notes
GLM-5.2 Z.AI flagship 1048576 Yes Yes 1M-context general model
glm5.2-fast Z.AI fast tier 1048576 Yes Yes Low-latency GLM-5.2 tier
GLM-5.1 Z.AI flagship 202752 Yes Yes
Kimi-K2.6 Moonshot Kimi K2.6 262144 No Yes Serverless-only
Kimi-K3 Moonshot Kimi K3 1048576 Yes Yes Self-hosted
kimi-k3-fast Moonshot Kimi K3 (fast) 1048576 Yes Yes High-TPS tier of Kimi-K3
DeepSeek-V4-Flash-0731-Fast DeepSeek V4 Flash (fast) 1048576 Yes Yes High-TPS tier of DeepSeek V4 Flash
Qwen3.5-397B-A17B Qwen3.5, 397B MoE 262144 No See models API Serverless-only

The Max context tokens value is the hard cap enforced by the backend — requests where prompt tokens exceed this value return a 400. If your harness has a Context Window Size setting (Cline, Roo Code, Droid, etc.), set it to the exact integer above. Leave ~2–4k of headroom for the model's response when filling context.

Pass any model string above to any OpenAI-compatible harness configured against https://pass.wafer.ai/v1. Model names are case-insensitive — GLM-5.1 and glm-5.1 both work. If you pass a model name that doesn't match any available model, the API returns a 404 with the list of available models.

Call GET https://pass.wafer.ai/v1/models for the live catalog, capabilities, and pricing before hard-coding model-specific behavior.

For Claude Code and other Anthropic-compatible harnesses, set ANTHROPIC_DEFAULT_*_MODEL to one of the model IDs above. See Set Up Claude Code.

API Capabilities

Wafer Serverless supports request-scoped privacy enforcement and advanced completion controls:

Getting Started

Go to [app.wafer.ai/signup](https://app.wafer.ai/signup), then mint a Serverless API key from the app. No card is required to create the key. Your API key is shown once after it is minted in the app. You can create multiple named keys per account — see [API Keys](/serverless/api-keys). Select Wafer directly inside Linzumi, or use the key in Claude Code, Codex, Conductor, OpenClaw, Cline, Roo Code, Kilo Code, Hermes Agent, OpenHands, or any other supported harness.

Set Up Linzumi

Linzumi has a built-in Wafer agent provider for coding jobs. You do not need to paste https://pass.wafer.ai/v1, configure WAFER_API_KEY, or add a custom OpenAI-compatible provider inside Linzumi.

Install the macOS app from [linzumi.com](https://linzumi.com), sign in to your workspace, and make sure your machine appears under **Computers** in the sidebar.
Linzumi runs coding jobs on a connected computer, so pick the runner that has access to the repo you want the agent to work in.
Click **Start coding job** in the workspace sidebar.
In the setup screen, select the connected computer and repository folder for the job.
Under **Agent**, choose **Wafer**. The card is labeled **Open models**.
The setup screen shows your available Linzumi Wafer/free-token balance before you send the job.
Linzumi's Wafer model picker currently includes:
* `GLM-5.2`
* `glm5.2-fast`
* `GLM-5.1`
* `Kimi K2.7 Code`
* `Kimi K2.6`
* `Qwen 3.5 397B`

Use `GLM-5.2` for the default coding-agent path. The **Thinking** picker supports `default`, `Low`, `Medium`, `High`, and `XHigh`.
Pick approval and filesystem permissions before sending:
* **Approval**: `On request` or `On failure`
* **Permissions**: `Read only`, `Workspace write`, or `Full access`

Use **Read only** for exploration, **Workspace write** for normal repo edits, and reserve **Full access** for jobs that truly need to operate outside the selected workspace.
Quick smoke test: start a read-only Linzumi job with Wafer selected and ask, "Please do not edit files. Confirm which agent provider/model this Linzumi job is using, then stop." If it responds in the Linzumi thread, the Wafer integration is working.

Set Up Claude Code

Wafer exposes an Anthropic-compatible Messages endpoint at https://pass.wafer.ai/v1/messages, so Claude Code can connect directly — no proxy needed.

For Claude Code, set ANTHROPIC_BASE_URL to https://pass.wafer.ai, not https://pass.wafer.ai/v1.

```bash theme={null} npm install -g @anthropic-ai/claude-code ``` Set these environment variables in your shell profile (`~/.zshrc`, `~/.bashrc`, etc.):
```bash theme={null}
export ANTHROPIC_BASE_URL=https://pass.wafer.ai
export ANTHROPIC_API_KEY=YOUR_WAFER_API_KEY
```

Or add them to `~/.claude/settings.json` for a persistent, per-user config:

```json theme={null}
{
  "env": {
    "ANTHROPIC_BASE_URL": "https://pass.wafer.ai",
    "ANTHROPIC_API_KEY": "YOUR_WAFER_API_KEY"
  }
}
```

Replace `YOUR_WAFER_API_KEY` with your Wafer Serverless API key.

<Warning>
  Do not share your API key or commit it to version control.
</Warning>
Claude Code sends Anthropic model strings (`claude-opus-…`, `claude-sonnet-…`) by default — those don't match a Wafer model. Pin Claude Code to a Wafer model with these env vars:
For `Qwen3.5-397B-A17B`:

```bash theme={null}
export ANTHROPIC_DEFAULT_OPUS_MODEL="Qwen3.5-397B-A17B"
export ANTHROPIC_DEFAULT_SONNET_MODEL="Qwen3.5-397B-A17B"
export ANTHROPIC_DEFAULT_HAIKU_MODEL="Qwen3.5-397B-A17B"
export CLAUDE_CODE_SUBAGENT_MODEL="Qwen3.5-397B-A17B"
```

For `GLM-5.1`:

```bash theme={null}
export ANTHROPIC_DEFAULT_OPUS_MODEL="GLM-5.1"
export ANTHROPIC_DEFAULT_SONNET_MODEL="GLM-5.1"
export ANTHROPIC_DEFAULT_HAIKU_MODEL="GLM-5.1"
export CLAUDE_CODE_SUBAGENT_MODEL="GLM-5.1"
```

Or in `~/.claude/settings.json`:

```json theme={null}
{
  "env": {
    "ANTHROPIC_BASE_URL": "https://pass.wafer.ai",
    "ANTHROPIC_API_KEY": "YOUR_WAFER_API_KEY",
    "ANTHROPIC_DEFAULT_OPUS_MODEL": "GLM-5.1",
    "ANTHROPIC_DEFAULT_SONNET_MODEL": "GLM-5.1",
    "ANTHROPIC_DEFAULT_HAIKU_MODEL": "GLM-5.1",
    "CLAUDE_CODE_SUBAGENT_MODEL": "GLM-5.1"
  }
}
```

<Note>
  These env vars follow the same pattern as [OpenRouter's Claude Code integration](https://openrouter.ai/docs/guides/coding-agents/claude-code-integration). They override the model Claude Code sends for opus, sonnet, haiku, and subagent calls.
</Note>
```bash theme={null} claude ```
Claude Code now routes requests through the Wafer endpoint.

Set Up Conductor

Conductor runs a team of parallel Claude Code agents in isolated Git worktrees on macOS. Because Conductor uses Claude Code under the hood, it picks up Wafer the same way Claude Code does — pin the model to any Wafer model ID from Models.

Download the macOS app from [conductor.build](https://www.conductor.build/) and launch it. In Conductor's **Settings → Environment** (or your shell profile, e.g. `~/.zshrc`), set:
```bash theme={null}
export ANTHROPIC_BASE_URL=https://pass.wafer.ai
export ANTHROPIC_API_KEY=YOUR_WAFER_API_KEY
```

Replace `YOUR_WAFER_API_KEY` with your Wafer Serverless API key.

<Warning>
  Do not share your API key or commit it to version control.
</Warning>
Conductor spawns Claude Code, which sends Anthropic model strings by default — those don't match a Wafer model. Pin Claude Code (and therefore Conductor) to a Wafer model with these env vars in the same Environment section:
For `Qwen3.5-397B-A17B`:

```bash theme={null}
export ANTHROPIC_DEFAULT_OPUS_MODEL="Qwen3.5-397B-A17B"
export ANTHROPIC_DEFAULT_SONNET_MODEL="Qwen3.5-397B-A17B"
export ANTHROPIC_DEFAULT_HAIKU_MODEL="Qwen3.5-397B-A17B"
export CLAUDE_CODE_SUBAGENT_MODEL="Qwen3.5-397B-A17B"
```

For `GLM-5.1`:

```bash theme={null}
export ANTHROPIC_DEFAULT_OPUS_MODEL="GLM-5.1"
export ANTHROPIC_DEFAULT_SONNET_MODEL="GLM-5.1"
export ANTHROPIC_DEFAULT_HAIKU_MODEL="GLM-5.1"
export CLAUDE_CODE_SUBAGENT_MODEL="GLM-5.1"
```

<Note>
  This follows the pattern OpenRouter documents in its [Claude Code integration guide](https://openrouter.ai/docs/guides/coding-agents/claude-code-integration). Because Conductor spawns Claude Code subprocesses, the same env vars override opus, sonnet, haiku, and subagent calls.
</Note>
Create a workspace in Conductor, pick a repo, and kick off an agent. Requests now route through Wafer.

Set Up Codex

Codex speaks the OpenAI Responses API (/v1/responses). Wafer Serverless exposes a native Responses-compatible endpoint at https://pass.wafer.ai/v1/responses, so current Codex clients can point directly at Wafer without a local proxy.

```bash theme={null} npm install -g @openai/codex ```
Verify Codex picks up the Responses API (`wire_api = "responses"` is the only supported value as of `0.92.0+`):

```bash theme={null}
codex --version
```
Export your Wafer Serverless key and make a direct Responses API request:
```bash theme={null}
export WAFER_API_KEY=YOUR_WAFER_API_KEY

curl -sS https://pass.wafer.ai/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAFER_API_KEY" \
  -d '{
    "model": "GLM-5.2",
    "input": "Reply with the single word: ready."
  }'
```

A `200` response with an `output` array means the Wafer Responses endpoint is healthy.
Add a Wafer model provider to `~/.codex/config.toml`:
```toml theme={null}
model = "GLM-5.2"
model_provider = "wafer"

[model_providers.wafer]
name = "Wafer"
base_url = "https://pass.wafer.ai/v1"
env_key = "WAFER_API_KEY"
wire_api = "responses"
```

`env_key` tells Codex which env var to read for the bearer token it sends to Wafer.

<Note>
  Switch models by changing the top-level `model = "..."` to any model ID from [**Models**](#models), such as `GLM-5.2`, `glm5.2-fast`, or `GLM-5.1`.
</Note>
```bash theme={null} export WAFER_API_KEY=YOUR_WAFER_API_KEY codex ```
Codex now sends Responses API traffic directly to Wafer. The footer in the TUI should show the Wafer model ID, for example `GLM-5.2`.
Older local proxies or private Wafer deployments that do not expose `/v1/responses` still need a translation layer such as LiteLLM. The public Serverless endpoint at `pass.wafer.ai` does not.

Set Up OpenClaw

**Model string:** the examples below use `GLM-5.1`. Swap in any [**Models**](#models) ID (`Qwen3.5-397B-A17B` or `GLM-5.1`). Applies to every OpenAI-compatible setup section that follows (OpenClaw, Hermes Agent, Cline, Roo Code, Kilo Code, OpenHands, and the generic section at the bottom). ```bash theme={null} curl -fsSL https://openclaw.ai/install.sh | bash ```
  <Tab title="Windows (PowerShell)">
    ```powershell theme={null}
    iwr -useb https://openclaw.ai/install.ps1 | iex
    ```
  </Tab>
</Tabs>
```bash theme={null} openclaw setup ``` Replace `YOUR_WAFER_API_KEY` with your Wafer Serverless API key:
```bash theme={null}
openclaw config set models.providers.wafer "$(cat <<'EOF'
{
  "baseUrl": "https://pass.wafer.ai/v1",
  "api": "openai-completions",
  "auth": "api-key",
  "apiKey": "YOUR_WAFER_API_KEY",
  "models": [
    { "id": "Qwen3.5-397B-A17B", "name": "Qwen 3.5 397B" },
    { "id": "GLM-5.1", "name": "GLM 5.1" }
  ]
}
EOF
)"
openclaw models set wafer/GLM-5.1
```

<Warning>
  Do not share your API key or commit it to version control.
</Warning>
```bash theme={null} openclaw agent --local --session-id wafer-test --message "Hello" ```

Set Up Hermes Agent

```bash theme={null} curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash source ~/.bashrc # or source ~/.zshrc ``` Replace `YOUR_WAFER_API_KEY` with your Wafer Serverless API key:
```bash theme={null}
hermes config set WAFER_API_KEY YOUR_WAFER_API_KEY
hermes config set model.default glm5.2-fast
hermes config set model.provider custom
hermes config set model.base_url https://pass.wafer.ai/v1
hermes config set model.api_mode chat_completions
```
```bash theme={null} hermes ```
Hermes now uses `glm5.2-fast` through the Wafer endpoint by default.

Set Up Cline

Install the [Cline extension](https://marketplace.visualstudio.com/items?itemName=saoudrizwan.claude-dev) from the VS Code marketplace, or search "Cline" in VS Code Extensions. 1. Open VS Code and click the **Cline** icon in the sidebar 2. Click the **settings gear** icon in the Cline panel 3. In the **API Provider** dropdown, select **OpenAI Compatible** 4. Fill in these fields:
* **Base URL**: `https://pass.wafer.ai/v1`
* **API Key**: your Wafer API key
* **Model ID**: `Qwen3.5-397B-A17B`

<Warning>
  Do not include `/chat/completions` in the Base URL — Cline appends that automatically.
</Warning>
Expand **Model Configuration** and set:
* **Context Window Size**: `262144`
* **Max Output Tokens**: `32768`
* **Supports Images**: unchecked
Send a message in the Cline panel. If Cline responds, you're connected.

Set Up Roo Code

Install the [Roo Code extension](https://marketplace.visualstudio.com/items?itemName=RooVetGit.Roo-Code) from the VS Code marketplace, or search "Roo Code" in VS Code Extensions. 1. Open VS Code and click the **Roo Code** icon in the sidebar 2. Click the **settings gear** icon in the Roo Code panel 3. In the **API Provider** dropdown, select **OpenAI Compatible** 4. Fill in these fields:
* **Base URL**: `https://pass.wafer.ai/v1`
* **API Key**: your Wafer API key
* **Model ID**: `Qwen3.5-397B-A17B`
Optionally configure:
* **Context Window Size**: `262144`
* **Max Output Tokens**: `32768`
Send a message in the Roo Code panel to confirm the connection.

Set Up Kilo Code

Install the [Kilo Code extension](https://marketplace.visualstudio.com/items?itemName=kilocode.Kilo-Code) from the VS Code marketplace, or search "Kilo Code" in VS Code Extensions. 1. Open Kilo Code and click the **settings gear** icon 2. Go to the **Providers** tab 3. Click **Custom provider** at the bottom 4. Fill in the dialog:
* **Provider ID**: `wafer`
* **Display Name**: `Wafer`
* **Base URL**: `https://pass.wafer.ai/v1`
* **API Key**: your Wafer API key
* **Model**: `Qwen3.5-397B-A17B`

5. Click **Save**

If you're on an older version of Kilo Code without the Providers tab, select **OpenAI Compatible** from the API Provider dropdown and enter the same Base URL, API key, and Model ID.
Send a message in the Kilo Code panel to confirm the connection.

Set Up OpenHands

Follow the [OpenHands installation guide](https://docs.all-hands.dev/). The quickest way:
```bash theme={null}
docker pull docker.all-hands.dev/all-hands-ai/runtime:0.44-nikolaik
docker run -it --rm \
  -p 3000:3000 \
  -v /var/run/docker.sock:/var/run/docker.sock \
  docker.all-hands.dev/all-hands-ai/openhands:0.44
```
1. Open the OpenHands UI (usually at `http://localhost:3000`) 2. Click the **settings gear** icon 3. Click **Advanced** to expand advanced options 4. Set these fields:
* **Custom Model**: `openai/GLM-5.1`
* **Base URL**: `https://pass.wafer.ai/v1`
* **API Key**: your Wafer API key

<Warning>
  The `openai/` prefix is required. OpenHands uses litellm under the hood, and this prefix tells it to use the OpenAI-compatible completion path.
</Warning>
If you prefer file-based config, create or edit `config.toml` in the project root:
```toml theme={null}
[llm]
model = "openai/GLM-5.1"
api_key = "YOUR_WAFER_API_KEY"
base_url = "https://pass.wafer.ai/v1"
```
Open a conversation in the OpenHands UI to confirm the connection.

Set Up LibreChat

Follow the [LibreChat installation guide](https://www.librechat.ai/docs/installation). The quickest way is Docker:
```bash theme={null}
git clone https://github.com/danny-avila/LibreChat.git
cd LibreChat
cp .env.example .env
docker compose up -d
```
Edit `librechat.yaml` in the project root and add a Wafer endpoint:
```yaml theme={null}
endpoints:
  custom:
    - name: "Wafer"
      baseURL: "https://pass.wafer.ai/v1"
      apiKey: "${WAFER_API_KEY}"
      iconURL: "https://avatars.githubusercontent.com/u/213847495?s=200&v=4"
      models:
        default:
          - "Qwen3.5-397B-A17B"
          - "GLM-5.1"
        fetch: false
      titleConvo: true
      modelDisplayLabel: "Wafer"
```

Set your API key in your `.env` file:

```bash theme={null}
WAFER_API_KEY=YOUR_WAFER_API_KEY
```

Replace `YOUR_WAFER_API_KEY` with your Wafer Serverless API key.

<Warning>
  Do not share your API key or commit it to version control.
</Warning>

<Note>
  The `default` list above exposes all Wafer models in LibreChat's model picker. Drop any IDs you don't want to surface.
</Note>
```bash theme={null} docker compose restart ```
Open LibreChat in your browser, select **Wafer** from the endpoint dropdown, and send a message.

Use Wafer with Other Harnesses

Most agent harnesses only need these settings:

OpenAI-compatible harnesses (Cline, Roo Code, Kilo Code, OpenClaw, OpenHands, etc.):

  • Base URL: https://pass.wafer.ai/v1
  • Model: any ID from Models, e.g. GLM-5.1 or Qwen3.5-397B-A17B
  • Authentication: your Wafer Serverless API key (same token in Authorization: Bearer … headers or in the client's API-key field — keys look like wfr_…)
  • Compatibility mode: OpenAI-compatible / OpenAI API
{
  "baseUrl": "https://pass.wafer.ai/v1",
  "apiKey": "YOUR_WAFER_API_KEY",
  "model": "GLM-5.1"
}

Pick any model string from Models.

Anthropic-compatible harnesses (Claude Code, Conductor, or any tool using the Anthropic Messages API):

  • Base URL: https://pass.wafer.ai (the tool appends /v1/messages automatically)
  • Authentication: your Wafer API key via ANTHROPIC_API_KEY
  • Model: pin a Wafer model via ANTHROPIC_DEFAULT_*_MODEL (the harness's default Anthropic model strings don't match a Wafer model)
export ANTHROPIC_BASE_URL=https://pass.wafer.ai
export ANTHROPIC_API_KEY=YOUR_WAFER_API_KEY
export ANTHROPIC_DEFAULT_OPUS_MODEL="GLM-5.1"
export ANTHROPIC_DEFAULT_SONNET_MODEL="GLM-5.1"
export ANTHROPIC_DEFAULT_HAIKU_MODEL="GLM-5.1"
export CLAUDE_CODE_SUBAGENT_MODEL="GLM-5.1"

If the harness asks for a provider name, you can label it Wafer. Pass your Wafer Serverless API key wherever the harness expects an Anthropic/API key (ANTHROPIC_API_KEY).

FAQ

See [**Models**](#models) for the current public catalog. Call `GET https://pass.wafer.ai/v1/models` for live availability and per-model pricing. Use the [**Models**](#models) IDs with `https://pass.wafer.ai/v1`. Sign up at [app.wafer.ai/signup](https://app.wafer.ai/signup), then mint a Serverless key from the app. For OpenAI-compatible harnesses, use a `model` string from [**Models**](#models) with `https://pass.wafer.ai/v1`. Model names are **case-insensitive** for the IDs above. For Claude Code (Anthropic-compatible), pin a Wafer model via `ANTHROPIC_DEFAULT_OPUS_MODEL` / `ANTHROPIC_DEFAULT_SONNET_MODEL` / `ANTHROPIC_DEFAULT_HAIKU_MODEL` / `CLAUDE_CODE_SUBAGENT_MODEL`. No. Linzumi exposes Wafer directly in the **Start coding job** flow. Choose **Wafer** under **Agent**, pick a model, and send the job. Use `https://pass.wafer.ai/v1` and a Wafer API key only for the other OpenAI-compatible harnesses on this page. It doesn't for the public Serverless endpoint. Codex requires the OpenAI Responses API (`/v1/responses`), and Wafer exposes that endpoint natively at `https://pass.wafer.ai/v1/responses`. A LiteLLM proxy is only needed for older private deployments or custom gateways that do not expose `/v1/responses`. Yes. We're optimizing the best coding models and adding them to the Serverless catalog.