{ inputs, pkgs, lib, ... }:
let
  pkgsUnstable = inputs.nixpkgs-unstable.legacyPackages.${pkgs.stdenv.hostPlatform.system};
  # llama.cpp from nixpkgs-unstable (0.4.x as of Sept 2026) with the Vulkan backend for
  # gfx1151 (Strix Halo). Laguna S 2.1 and Gemma 4 need a build newer than mid-July 2026,
  # so keep the unstable input reasonably fresh (`nix flake update nixpkgs-unstable`).
  llama-cpp = pkgsUnstable.llama-cpp.override { vulkanSupport = true; };
  llamaModelsDir = "/var/lib/llama-server/models";
  # llama-server runs in router mode: one endpoint, models load on demand, at most two
  # resident at a time (see --models-max below). Section names are the model ids that
  # clients pass in the "model" field. Weights are fetched by
  # /var/lib/llama-server/models/download.sh, not by the service.
  llamaModelsIni = pkgs.writeText "llama-models.ini" ''
    version = 1

    [*]
    jinja = true
    flash-attn = on
    cache-type-k = q8_0
    cache-type-v = q8_0
    n-gpu-layers = 999
    no-mmap = true
    ; llama.cpp splits ctx-size across parallel slots: 262144 / 2 = 131072 per slot.
    ctx-size = 262144
    parallel = 2
    ; Unload a model's weights and KV cache after six idle hours; the next request reloads it.
    sleep-idle-seconds = 21600

    ; Qwen3.6-35B-A3B, refusal-ablated (HauhauCS "Aggressive"). Benchmark candidate A.
    [qwen3.6-35b-a3b-abliterated]
    model = ${llamaModelsDir}/qwen3.6-35b-a3b-abliterated/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-Q4_K_M.gguf

    ; Gemma 4 26B-A4B QAT, refusal-ablated (HauhauCS "Balanced"), with its MTP draft head.
    ; Benchmark candidate B (the Western model of the same MoE class).
    [gemma4-26b-a4b-abliterated]
    model = ${llamaModelsDir}/gemma4-26b-a4b-abliterated/Gemma4-26B-A4B-QAT-Uncensored-HauhauCS-Balanced-Q4_K_M.gguf
    model-draft = ${llamaModelsDir}/gemma4-26b-a4b-abliterated/mtp-gemma-4-26B-A4B-it.gguf
    spec-type = draft-mtp

    ; Poolside Laguna S 2.1 (118B total, 8B active): primary coding agent.
    ; ~69 GB of weights, so it only loads once the raised TTM/GTT limit is active (reboot).
    [laguna-s-2.1]
    model = ${llamaModelsDir}/laguna-s-2.1-UD-Q4_K_S/Laguna-S-2.1-UD-Q4_K_S-00001-of-00003.gguf
    parallel = 1
    ctx-size = 131072

    ; Previous default model (a Qwen 3.5 derivative), kept for comparison runs. It was
    ; fetched by the old -hf flag into the HF cache; dedup hides the cache's own entry.
    [ornith-1.0-35b]
    hf-repo = deepreinforce-ai/Ornith-1.0-35B-GGUF:Q4_K_M
    dedup-cache-models = true

    ; Ornith 1.5 9B, refusal-ablated (junafinity / mradermacher GGUF). Multimodal:
    ; llama-server pulls mmproj with --hf-repo. Q4_K_M is the recommended quant.
    [ornith-1.5-9b-uncensored]
    hf-repo = mradermacher/Ornith-1.5-9B-uncensored-GGUF:Q4_K_M
    dedup-cache-models = true

    ; Ornith 1.5 35B-A3B MoE (official GGUF). ~3B active, multimodal; mmproj
    ; comes with --hf-repo. Q4_K_M is ~22 GB.
    [ornith-1.5-35b-a3b]
    hf-repo = ornith-ai/Ornith-1.5-35B-A3B-GGUF:Q4_K_M
    dedup-cache-models = true

    ; Qwen 3.8 27B dense, refusal-ablated. Preferred uncensored coding agent.
    ; orcarouter/Qwen3.8-27B-Uncensored-GGUF is gated; huihui Q4_K_L keeps
    ; ablation-target tensors at Q8_0. llama-server fetches on first load.
    [qwen3.8-27b-uncensored]
    hf-repo = huihui-ai/Huihui-Qwen3.8-27B-abliterated-GGUF:Q4_K_L
    dedup-cache-models = true
  '';
  # `llama-models` shows what the router has resident; `llama-unload` frees every loaded
  # model (e.g. before a gaming session), or just the ones named on the command line.
  llamaCtl = pkgs.writeText "llama-ctl.py" ''
    import json, sys, urllib.request

    URL = "http://127.0.0.1:11434"

    def models():
        with urllib.request.urlopen(URL + "/models", timeout=10) as r:
            d = json.load(r)
        return d.get("data", d if isinstance(d, list) else [])

    def name(m):
        return m.get("id") or m.get("model") or m.get("name")

    cmd = sys.argv[1] if len(sys.argv) > 1 else "list"
    if cmd == "list":
        for m in models():
            print("%-10s %s" % ((m.get("status") or {}).get("value", "?"), name(m)))
    elif cmd == "unload":
        loaded = [name(m) for m in models()
                  if (m.get("status") or {}).get("value") in ("loaded", "loading", "sleeping")]
        targets = sys.argv[2:] or loaded
        if not targets:
            print("nothing loaded")
        for t in targets:
            req = urllib.request.Request(URL + "/models/unload", data=json.dumps({"model": t}).encode(),
                                         headers={"Content-Type": "application/json"}, method="POST")
            with urllib.request.urlopen(req, timeout=30) as r:
                print("unload %s: %s" % (t, r.read().decode().strip()))
    else:
        sys.exit("usage: llama-ctl.py [list|unload [model ...]]")
  '';
  llamaModelsCmd = pkgs.writeShellScriptBin "llama-models" "exec ${pkgs.python3}/bin/python3 ${llamaCtl} list \"$@\"";
  llamaUnloadCmd = pkgs.writeShellScriptBin "llama-unload" "exec ${pkgs.python3}/bin/python3 ${llamaCtl} unload \"$@\"";
in
{
  imports = [
    ./hardware-configuration.nix
    ../../configs/desktop.nix
    ../../configs/qemu.nix
    ../../configs/mcpkg.nix
    ../../configs/mcp.nix # MCP agent + mcp user (straylight is becoming the core host)
  ];

  config = {
    # Let the GPU address up to 104 GiB of the 128 GiB of unified memory (the TTM default
    # caps GTT at 50% of RAM, ~62 GiB). Units are 4 KiB pages; takes effect after a reboot.
    # amdgpu.gttsize is deprecated on recent kernels; ttm.* is the supported knob.
    boot.kernelParams = [ "ttm.pages_limit=27262976" "ttm.page_pool_size=27262976" ];

    # llama.cpp server (router mode) with the Vulkan backend for gfx1151 (Strix Halo).
    # Models and per-model settings live in llamaModelsIni above.
    # NOTE: BIOS "UMA Frame Buffer Size" must be ≥32GB for a 23GB model to fit on-GPU.
    users.users.llama-server = {
      isSystemUser = true;
      group = "llama-server";
      home = "/var/lib/llama-server";
    };
    users.groups.llama-server = {};

    systemd.services.llama-server = {
      description = "llama.cpp inference server";
      wantedBy = [ "multi-user.target" ];
      after = [ "network-online.target" ];
      wants = [ "network-online.target" ];
      environment = {
        HOME = "/var/lib/llama-server";
        HF_HOME = "/var/lib/llama-server/huggingface";
      };
      serviceConfig = {
        # Bind all interfaces so localhost and the tailnet can both reach the
        # OpenAI-compatible API. LAN access is still blocked: 11434 is opened
        # only on tailscale0, not in the global allowedTCPPorts list.
        ExecStart = ''
          ${llama-cpp}/bin/llama-server \
            --host 0.0.0.0 \
            --port 11434 \
            --models-preset ${llamaModelsIni} \
            --models-max 2
        '';
        User = "llama-server";
        Group = "llama-server";
        StateDirectory = "llama-server";
        WorkingDirectory = "/var/lib/llama-server";
        SupplementaryGroups = [ "render" "video" ];
        Restart = "on-failure";
        RestartSec = "10s";
        TimeoutStartSec = "600";
      };
    };
    # straylight is the unikernel host. The shared mcp.nix locks the agent
    # down with PrivateDevices=true, which hides /dev/kvm and /dev/net/tun.
    # Relax that here (only on straylight) so the agent can boot Nanos
    # unikernel VMs under QEMU/KVM and (Phase 2) manage TAP devices.
    systemd.services.mcp-agent.serviceConfig = {
      PrivateDevices = lib.mkForce false;
      DeviceAllow = [ "/dev/kvm rw" "/dev/net/tun rw" ];
      SupplementaryGroups = [ "kvm" ];
      AmbientCapabilities = [ "CAP_NET_ADMIN" ];
      # The agent launches each unikernel as a daemonized QEMU process in its
      # own cgroup. With the default KillMode=control-group, restarting the
      # agent would SIGKILL every running VM. KillMode=process kills only the
      # agent's main process on stop/restart, so VMs survive an agent upgrade.
      # (If a VM does die, the agent's startup Recover restarts it.)
      KillMode = lib.mkForce "process";
    };
    # Let the mcp user reach /dev/kvm directly as well.
    users.users.mcp.extraGroups = [ "kvm" ];

    # Isolated host-only bridge for unikernel VMs (Phase 2). Each unikernel
    # gets a TAP on this bridge and a 10.99.0.0/24 static IP. The bridge has
    # NO uplink and NO NAT, and the firewall drops any VM traffic leaving the
    # bridge, so a VM can reach only the host gateway (10.99.0.1) -- mediation
    # is enforced by network topology, not convention.
    networking.bridges.mcp-br0.interfaces = [ ];
    networking.interfaces.mcp-br0.ipv4.addresses = [
      { address = "10.99.0.1"; prefixLength = 24; }
    ];
    # The host accepts traffic from VMs (so mc-proxy on the gateway can serve
    # them); the FORWARD drop prevents VMs from routing anywhere off-bridge.
    networking.firewall.trustedInterfaces = [ "mcp-br0" ];
    networking.firewall.extraCommands = ''
      iptables -D FORWARD -i mcp-br0 ! -o mcp-br0 -j DROP 2>/dev/null || true
      iptables -A FORWARD -i mcp-br0 ! -o mcp-br0 -j DROP
    '';
    networking.firewall.extraStopCommands = ''
      iptables -D FORWARD -i mcp-br0 ! -o mcp-br0 -j DROP 2>/dev/null || true
    '';

    # Allow rootless containers (podman) to bind low ports (53 for MCNS,
    # 443/8443/9443 for mc-proxy) as straylight takes over the core role.
    boot.kernel.sysctl."net.ipv4.ip_unprivileged_port_start" = 53;

    hardware.bluetooth.enable = true;        # enables BlueZ + bluetooth.service

    programs.steam.enable = true;
    programs.steam.protontricks.enable = true;

    # herdr: terminal workspace manager for coding agents (https://herdr.dev).
    # Pinned to the upstream flake (v0.9.0); nixpkgs-unstable only has 0.7.1.
    environment.systemPackages = [
      inputs.herdr.packages.x86_64-linux.default
      llamaModelsCmd
      llamaUnloadCmd
    ];

    services.open-webui = {
      enable = true;
      host = "0.0.0.0";
      port = 8080;
      environment = {
        SCARF_NO_ANALYTICS = "True";
        DO_NOT_TRACK = "True";
        ANONYMIZED_TELEMETRY = "False";
        # Point at the local llama.cpp server (OpenAI-compatible API).
        OPENAI_API_BASE_URLS = "http://127.0.0.1:11434/v1";
        OPENAI_API_KEYS = "none";
        ENABLE_OLLAMA_API = "False";
      };
    };

    # Open ports: DNS (53), mc-proxy (443/8443/9443), agent (9444), master (9555), open-webui (8080).
    networking.firewall.allowedTCPPorts = [ 53 443 8080 8443 9443 9444 9555 ];
    networking.firewall.allowedUDPPorts = [ 53 ];
    # llama.cpp OpenAI-compatible API: tailnet only (localhost is always allowed).
    networking.firewall.interfaces.tailscale0.allowedTCPPorts = [ 11434 ];

    # DNS: MCNS for internal zones, public resolvers as fallback.
    networking.nameservers = [
      "192.168.88.181"
      "100.95.252.120"
      "1.1.1.1"
      "8.8.8.8"
    ];
    services.resolved.settings.Resolve.Domains = [
      "~mcp.metacircular.net"
    ];
  };
}