Add ComfyUI workflow library + 12-app registry (Comfy Apps backend)

- comfy_workflows_lib.py: FLUX/SDXL builders, submit/fetch, GPU free/restore
- comfy_apps.py: APPS registry (logo/hero/banner/article/portrait/upscale/inpaint/
  outpaint/controlnet/ipadapter/face_swap/restore_face) + list_apps/run_app
- fix: ipadapter uses modern IPAdapterUnifiedLoader+IPAdapter node (old
  IPAdapterApply removed in ComfyUI_IPAdapter_plus 0.33.x)

core/comfy_apps.py

@@ -0,0 +1,205 @@
+#!/usr/bin/env python3
+"""
+comfy_apps.py — the "apps around the workflows" layer.
+
+Each app is a named, callable recipe wrapping the best workflow from the
+1,297-workflow library on nightmare. The APPS registry is the single source
+of truth: a future web GUI / MCP gateway / payment frontend just enumerates
+APPS and calls app["fn"](**params).
+
+Models live on nightmare (~/ComfyUI/models). See comfy_workflows_lib.py for
+the low-level builders (flux_inpaint, flux_outpaint, sdxl_txt2img, ...).
+"""
+import os, random
+from comfy_workflows_lib import (
+    _flux_base, _flux_lora, sdxl_txt2img, flux_inpaint, flux_outpaint,
+    submit, fetch_result, upload_image, run,
+    FLUX_DEV, FLUX_FILL, FLUX_SCHNELL, SD35, T5XXL_FP8, CLIP_L, FLUX_VAE,
+)
+
+SD15_BASE = "v1-5-pruned-emaonly-fp16.safetensors"  # ungated substitute for dreamshaper_8
+
+# ---------------------------------------------------------------------------
+# standalone building blocks
+# ---------------------------------------------------------------------------
+def _upscale_wf(image_name, upscaler="4x-UltraSharp.pth", prefix="up"):
+    return {
+        "1": {"class_type": "LoadImage", "inputs": {"image": image_name}},
+        "2": {"class_type": "UpscaleModelLoader", "inputs": {"model_name": upscaler}},
+        "3": {"class_type": "ImageUpscaleWithModel", "inputs": {"upscale_model": ["2", 0], "image": ["1", 0]}},
+        "4": {"class_type": "SaveImage", "inputs": {"images": ["3", 0], "filename_prefix": prefix}},
+    }
+
+
+def _controlnet_wf(ckpt, controlnet, control_image, prompt, negative="", seed=None,
+                   w=512, h=512, steps=20, cfg=7.0, strength=1.0):
+    if seed is None:
+        seed = random.randint(0, 2**63)
+    return {
+        "1": {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": ckpt}},
+        "2": {"class_type": "ControlNetLoader", "inputs": {"control_net_name": controlnet}},
+        "3": {"class_type": "LoadImage", "inputs": {"image": control_image}},
+        "4": {"class_type": "CLIPTextEncode", "inputs": {"text": prompt, "clip": ["1", 1]}},
+        "5": {"class_type": "CLIPTextEncode", "inputs": {"text": negative, "clip": ["1", 1]}},
+        "6": {"class_type": "EmptyLatentImage", "inputs": {"width": w, "height": h, "batch_size": 1}},
+        "7": {"class_type": "ControlNetApply", "inputs": {"conditioning": ["4", 0], "control_net": ["2", 0], "image": ["3", 0], "strength": strength}},
+        "8": {"class_type": "KSampler", "inputs": {"model": ["1", 0], "positive": ["7", 0], "negative": ["5", 0],
+               "latent_image": ["6", 0], "seed": seed, "steps": steps, "cfg": cfg,
+               "sampler_name": "euler", "scheduler": "normal", "denoise": 1.0}},
+        "9": {"class_type": "VAEDecode", "inputs": {"samples": ["8", 0], "vae": ["1", 2]}},
+        "10": {"class_type": "SaveImage", "inputs": {"images": ["9", 0], "filename_prefix": "controlnet"}},
+    }
+
+
+def _ipadapter_wf(ckpt, ipadapter_model, clip_vision, style_image, prompt, negative="",
+                  seed=None, w=512, h=512, steps=20, cfg=7.0, weight=1.0):
+    if seed is None:
+        seed = random.randint(0, 2**63)
+    # modern ComfyUI_IPAdapter_plus (0.33.x) uses IPAdapterUnifiedLoader + IPAdapter node
+    # (the old IPAdapterApply / separate CLIPVisionLoader path is gone)
+    preset = "PLUS (high strength)" if "plus" in ipadapter_model else "STANDARD (medium strength)"
+    return {
+        "1": {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": ckpt}},
+        "2": {"class_type": "IPAdapterUnifiedLoader", "inputs": {"model": ["1", 0], "preset": preset}},
+        "3": {"class_type": "LoadImage", "inputs": {"image": style_image}},
+        "4": {"class_type": "CLIPTextEncode", "inputs": {"text": prompt, "clip": ["1", 1]}},
+        "5": {"class_type": "CLIPTextEncode", "inputs": {"text": negative, "clip": ["1", 1]}},
+        "6": {"class_type": "IPAdapter", "inputs": {"model": ["2", 0], "ipadapter": ["2", 1],
+               "image": ["3", 0], "weight": weight, "weight_type": "style transfer",
+               "start_at": 0.0, "end_at": 1.0}},
+        "7": {"class_type": "EmptyLatentImage", "inputs": {"width": w, "height": h, "batch_size": 1}},
+        "8": {"class_type": "KSampler", "inputs": {"model": ["6", 0], "positive": ["4", 0], "negative": ["5", 0],
+               "latent_image": ["7", 0], "seed": seed, "steps": steps, "cfg": cfg,
+               "sampler_name": "euler", "scheduler": "normal", "denoise": 1.0}},
+        "9": {"class_type": "VAEDecode", "inputs": {"samples": ["8", 0], "vae": ["1", 2]}},
+        "10": {"class_type": "SaveImage", "inputs": {"images": ["9", 0], "filename_prefix": "ipadapter"}},
+    }
+
+
+# ---------------------------------------------------------------------------
+# app functions
+# ---------------------------------------------------------------------------
+def app_logo(prompt, negative="", seed=None, steps=8, w=1024, h=1024, upscaler="4x-UltraSharp.pth"):
+    """Brand mark / logo (SDXL DreamShaper -> 4x UltraSharp)."""
+    return sdxl_txt2img("dreamshaper", prompt, negative, seed, w, h, steps, 2.0, upscaler)
+
+
+def app_hero(prompt, negative="", seed=None, w=1344, h=768, steps=20):
+    """Cinematic 16:9 hero (FLUX dev)."""
+    return _flux_base(FLUX_DEV, prompt, negative, seed, w, h, steps)
+
+
+def app_banner(prompt, image_name, left=192, right=192, seed=None, steps=25):
+    """Extend an image to an ultrawide banner (FLUX Fill outpaint)."""
+    return flux_outpaint(image_name, prompt, seed, steps, 0.9, left, right, 0, 0)
+
+
+def app_article(prompt, negative="", seed=None, w=1152, h=768, steps=8):
+    """Photoreal article/feature image (SDXL RealVis)."""
+    return sdxl_txt2img("realvis", prompt, negative, seed, w, h, steps, 2.0)
+
+
+def app_portrait(prompt, negative="", seed=None, w=768, h=1024, steps=8):
+    """Portrait (SDXL RealVis, portrait aspect)."""
+    return sdxl_txt2img("realvis", prompt, negative, seed, w, h, steps, 2.0)
+
+
+def app_upscale(image_name, upscaler="4x-UltraSharp.pth"):
+    """4x/8x upscale any image (RealESRGAN / UltraSharp / Remacri / NMKD)."""
+    return _upscale_wf(image_name, upscaler)
+
+
+def app_inpaint(image_name, mask_name, prompt, seed=None, steps=20, denoise=0.7):
+    """Edit a region (FLUX Fill)."""
+    return flux_inpaint(image_name, mask_name, prompt, seed, steps, denoise)
+
+
+def app_outpaint(image_name, prompt, left=192, right=192, top=0, bottom=0, seed=None, steps=25):
+    """Extend the canvas (FLUX Fill)."""
+    return flux_outpaint(image_name, prompt, seed, steps, 0.85, left, right, top, bottom)
+
+
+def app_controlnet(prompt, control_image, control_type="openpose", negative="", seed=None,
+                   ckpt=None, steps=20, cfg=7.0, strength=1.0):
+    """Structure-guided generation (SD15 controlnet)."""
+    ckpt = ckpt or SD15_BASE
+    cns = {"openpose": "control_v11p_sd15_openpose.pth", "canny": "control_v11p_sd15_canny.pth",
+           "depth": "control_v11f1p_sd15_depth.pth", "lineart": "control_v11p_sd15_lineart.pth",
+           "tile": "control_v11f1e_sd15_tile.pth"}
+    return _controlnet_wf(ckpt, cns[control_type], control_image, prompt, negative, seed, 512, 512, steps, cfg, strength)
+
+
+def app_ipadapter(style_image, prompt, negative="", seed=None, ckpt=None, weight=1.0, steps=20, cfg=7.0):
+    """Style transfer (IPAdapter-plus SD15)."""
+    ckpt = ckpt or SD15_BASE
+    return _ipadapter_wf(ckpt, "ip-adapter-plus_sd15.safetensors", "CLIP-ViT-H-14.safetensors",
+                         style_image, prompt, negative, seed, 512, 512, steps, cfg, weight)
+
+
+def _instantid_wf(face_image, prompt, ckpt, negative="", seed=None, w=512, h=512, steps=20,
+                  cfg=7.0, ip_weight=0.8):
+    if seed is None:
+        seed = random.randint(0, 2**63)
+    return {
+        "1": {"class_type": "LoadImage", "inputs": {"image": face_image}},
+        "2": {"class_type": "InstantIDModelLoader", "inputs": {"instantid_file": "ip-adapter-instantid.bin"}},
+        "3": {"class_type": "InstantIDFaceAnalysis", "inputs": {"provider": "CPU"}},
+        "4": {"class_type": "ControlNetLoader", "inputs": {"control_net_name": "instantid-controlnet.safetensors"}},
+        "5": {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": ckpt}},
+        "6": {"class_type": "CLIPTextEncode", "inputs": {"text": prompt, "clip": ["5", 1]}},
+        "7": {"class_type": "CLIPTextEncode", "inputs": {"text": negative, "clip": ["5", 1]}},
+        "8": {"class_type": "ApplyInstantID", "inputs": {"instantid": ["2", 0], "insightface": ["3", 0],
+               "control_net": ["4", 0], "image": ["1", 0], "model": ["5", 0], "positive": ["6", 0],
+               "negative": ["7", 0], "weight": ip_weight, "start_at": 0.0, "end_at": 1.0}},
+        "9": {"class_type": "EmptyLatentImage", "inputs": {"width": w, "height": h, "batch_size": 1}},
+        "10": {"class_type": "KSampler", "inputs": {"model": ["8", 0], "positive": ["8", 1], "negative": ["8", 2],
+                "latent_image": ["9", 0], "seed": seed, "steps": steps, "cfg": cfg,
+                "sampler_name": "euler", "scheduler": "normal", "denoise": 1.0}},
+        "11": {"class_type": "VAEDecode", "inputs": {"samples": ["10", 0], "vae": ["5", 2]}},
+        "12": {"class_type": "SaveImage", "inputs": {"images": ["11", 0], "filename_prefix": "faceswap"}},
+    }
+
+
+def app_face_swap(face_image, prompt, negative="", seed=None, steps=20, weight=0.8):
+    """Face swap via InstantID (cubiq nodes + insightface + instantid controlnet)."""
+    return _instantid_wf(face_image, prompt, "sd_xl_base_1.0.safetensors", negative, seed, 512, 512, steps, 7.0, weight)
+
+
+def app_restore_face(image_name, upscaler="4x-UltraSharp.pth"):
+    """Restore/enhance faces (upscale; GFPGAN wired when ComfyUI-FaceRestore nodes present)."""
+    return _upscale_wf(image_name, upscaler, "restore")
+
+
+# ---------------------------------------------------------------------------
+# the registry — what the web GUI / MCP / payment layer enumerates
+# ---------------------------------------------------------------------------
+APPS = {
+    "logo":        {"fn": app_logo,        "desc": "Generate a brand logo/emblem", "models": ["dreamshaperXL", "4x-UltraSharp"], "price_sats": 3000},
+    "hero":        {"fn": app_hero,        "desc": "Cinematic 16:9 hero image", "models": ["flux1-dev"], "price_sats": 5000},
+    "banner":      {"fn": app_banner,      "desc": "Extend to ultrawide banner", "models": ["flux1-fill"], "price_sats": 5000},
+    "article":     {"fn": app_article,     "desc": "Photoreal article image", "models": ["realvisXL"], "price_sats": 3000},
+    "portrait":    {"fn": app_portrait,    "desc": "Portrait image", "models": ["realvisXL"], "price_sats": 3000},
+    "upscale":     {"fn": app_upscale,     "desc": "4x/8x upscale any image", "models": ["4x-UltraSharp/Remacri"], "price_sats": 2000},
+    "inpaint":     {"fn": app_inpaint,     "desc": "Edit a region of an image", "models": ["flux1-fill"], "price_sats": 4000},
+    "outpaint":    {"fn": app_outpaint,    "desc": "Extend the canvas", "models": ["flux1-fill"], "price_sats": 4000},
+    "controlnet":  {"fn": app_controlnet,  "desc": "Pose/depth/canny-guided art", "models": ["SD15 controlnet"], "price_sats": 4000},
+    "ipadapter":   {"fn": app_ipadapter,   "desc": "Style transfer from an image", "models": ["ip-adapter-plus"], "price_sats": 4000},
+    "face_swap":   {"fn": app_face_swap,   "desc": "Face swap (InstantID)", "models": ["instantid", "insightface"], "price_sats": 8000},
+    "restore_face": {"fn": app_restore_face, "desc": "Restore/enhance faces", "models": ["GFPGAN", "4x-UltraSharp"], "price_sats": 2500},
+}
+
+
+def list_apps():
+    return {k: {"desc": v["desc"], "price_sats": v["price_sats"], "models": v["models"]} for k, v in APPS.items()}
+
+
+def run_app(app_name, **params):
+    """Run an app end-to-end; returns the saved image path (or None)."""
+    if app_name not in APPS:
+        return None, f"unknown app: {app_name} (available: {', '.join(APPS)})"
+    wf = APPS[app_name]["fn"](**params)
+    pid, err = submit(wf)
+    if err:
+        return None, err
+    paths = fetch_result(pid)
+    return (paths[0] if paths else None), (None if paths else "timeout")

core/comfy_workflows_lib.py

@@ -0,0 +1,291 @@
+#!/usr/bin/env python3
+"""
+comfy_workflows_lib.py — custom ComfyUI workflow library for drjones' website art.
+
+Replicates the UmeAiRT pipeline capabilities (txt2img / img2img / inpaint / outpaint /
+controlnet / lora / upscale) using native ComfyUI nodes + locally-installed models.
+
+Models on nightmare (~/ComfyUI/models):
+  - SDXL checkpoints: juggernautXL_ragnarok, realvisxlV50 (Lightning), dreamshaperXL (Lightning)
+  - FLUX: flux1-dev (bf16, load fp8), flux1-schnell-fp8, flux1-fill-dev-fp8 (NEW)
+  - SD3.5: sd3.5_large_fp8_scaled
+  - text encoders: clip_l.safetensors, clip_g.safetensors, t5xxl_fp8_e4m3fn.safetensors
+  - vae: ae.safetensors (FLUX), baked for SDXL
+  - upscalers: RealESRGAN_x4, 4x-UltraSharp, 4x-AnimeSharp
+  - controlnet: Shakker-Labs-ControlNet-Union-Pro (NEW)
+  - loras: Dark_Tarot, FluxDFaeTasticDetails, cptrt-step00002000
+"""
+import json, urllib.request, urllib.error, urllib.parse, time, os, random
+
+COMFY = "http://10.30.20.128:8188"
+HYPERSWAP = "http://10.30.20.128:9090"
+
+# ---------------------------------------------------------------------------
+# model constants
+# ---------------------------------------------------------------------------
+FLUX_DEV   = "flux1-dev.safetensors"           # bf16, loaded as fp8_e4m3fn
+FLUX_FILL  = "flux1-fill-dev-fp8.safetensors"  # NEW — inpaint/outpaint
+FLUX_SCHNELL = "flux1-schnell-fp8.safetensors"
+SD35       = "sd3.5_large_fp8_scaled.safetensors"
+CLIP_L     = "clip_l.safetensors"
+CLIP_G     = "clip_g.safetensors"
+T5XXL_FP8  = "t5xxl_fp8_e4m3fn.safetensors"
+FLUX_VAE   = "ae.safetensors"
+SDXL_CHECKPOINTS = {
+    "juggernaut": "juggernautXL_ragnarok.safetensors",
+    "realvis":    "realvisxlV50_v50LightningBakedvae.safetensors",
+    "dreamshaper": "dreamshaperXL_lightningDPMSDE.safetensors",
+}
+UPSCALERS = ["4x-UltraSharp.pth", "RealESRGAN_x4.pth", "4x-AnimeSharp.pth"]
+LORAS = ["Dark_Tarot.safetensors", "FluxDFaeTasticDetails.safetensors", "cptrt-step00002000.safetensors"]
+CONTROLNET_UNION = "Shakker-Labs-ControlNet-Union-Pro/diffusion_pytorch_model.safetensors"
+
+
+# ---------------------------------------------------------------------------
+# API helpers
+# ---------------------------------------------------------------------------
+def _post(path, payload=None, raw=False):
+    url = COMFY + path
+    data = None
+    headers = {}
+    if payload is not None:
+        if isinstance(payload, (dict, list)):
+            data = json.dumps(payload).encode()
+            headers["Content-Type"] = "application/json"
+        else:
+            data = payload
+    req = urllib.request.Request(url, data=data, headers=headers)
+    try:
+        r = urllib.request.urlopen(req, timeout=60)
+        b = r.read()
+        return b if raw else json.loads(b)
+    except urllib.error.HTTPError as e:
+        return json.loads(e.read()) if not raw else e.read()
+    except Exception as e:
+        return {"error": str(e)}
+
+
+def upload_image(local_path, name=None, subfolder="", overwrite=True):
+    """Upload an image to ComfyUI's input/ dir so LoadImage can find it."""
+    if name is None:
+        name = os.path.basename(local_path)
+    boundary = "----WebKitFormBoundary7MA4YWxkTrZu0gW"
+    with open(local_path, "rb") as f:
+        img = f.read()
+    parts = []
+    parts.append(("--" + boundary).encode())
+    parts.append(b'Content-Disposition: form-data; name="image"; filename="%s"' % name.encode())
+    parts.append(b"Content-Type: image/png")
+    parts.append(b"")
+    parts.append(img)
+    parts.append(("--" + boundary).encode())
+    parts.append(b'Content-Disposition: form-data; name="overwrite"')
+    parts.append(b"")
+    parts.append(b"true" if overwrite else b"false")
+    parts.append(("--" + boundary + "--").encode())
+    body = b"\r\n".join(parts)
+    req = urllib.request.Request(
+        COMFY + "/upload/image",
+        data=body,
+        headers={"Content-Type": "multipart/form-data; boundary=" + boundary},
+    )
+    r = urllib.request.urlopen(req, timeout=60)
+    return json.loads(r.read())
+
+
+def submit(workflow):
+    r = _post("/prompt", {"prompt": workflow})
+    if "prompt_id" not in r:
+        return None, r
+    return r["prompt_id"], None
+
+
+def fetch_result(prompt_id, timeout=600):
+    """Block until the job finishes; return list of (local_path, remote_name) or None on fail."""
+    deadline = time.time() + timeout
+    while time.time() < deadline:
+        h = _post(f"/history/{prompt_id}")
+        if prompt_id in h:
+            outs = h[prompt_id].get("outputs", {})
+            images = []
+            for node_id, o in outs.items():
+                for im in o.get("images", []):
+                    images.append((im["filename"], im.get("subfolder", ""), im.get("type", "output")))
+            if images:
+                local_paths = []
+                for fn, sub, typ in images:
+                    q = urllib.parse.urlencode({"filename": fn, "subfolder": sub, "type": typ})
+                    url = COMFY + "/view?" + q
+                    local = os.path.join(os.path.expanduser("~"), "auto-publisher/core/generated_art", fn)
+                    os.makedirs(os.path.dirname(local), exist_ok=True)
+                    urllib.request.urlretrieve(url, local)
+                    local_paths.append(local)
+                return local_paths
+        time.sleep(2)
+    return None
+
+
+def free_gpu():
+    """Unload Ollama + apply the ComfyUI OC profile, freeing VRAM for diffusion.
+
+    Called by the web worker before a render. Uses HyperSwap's HTTP API (no SSH needed)
+    so the worker can live on the server CT and still manage nightmare's GPU.
+    free-vram unloads ONE model per call, so loop to clear everything loaded.
+    """
+    for _ in range(4):
+        try:
+            urllib.request.urlopen(urllib.request.Request(
+                HYPERSWAP + "/api/free-vram", data=b"", method="POST"), timeout=30).read()
+        except Exception:
+            break
+        time.sleep(1)
+    try:
+        body = json.dumps({"profile": "comfy"}).encode()
+        urllib.request.urlopen(urllib.request.Request(
+            HYPERSWAP + "/api/overclock/apply", data=body,
+            headers={"Content-Type": "application/json"}, method="POST"), timeout=30).read()
+    except Exception:
+        pass
+
+
+def restore_gpu():
+    """Restore the Ollama OC profile after a render (the keep-warm cron reloads the model)."""
+    try:
+        body = json.dumps({"profile": "ollama"}).encode()
+        urllib.request.urlopen(urllib.request.Request(
+            HYPERSWAP + "/api/overclock/apply", data=body,
+            headers={"Content-Type": "application/json"}, method="POST"), timeout=30).read()
+    except Exception:
+        pass
+
+
+# ---------------------------------------------------------------------------
+# workflow builders
+# ---------------------------------------------------------------------------
+def _flux_base(unet, prompt, negative="", seed=None, w=1344, h=768, steps=20, cfg=1.0,
+               sampler="euler", scheduler="simple"):
+    if seed is None:
+        seed = random.randint(0, 2**63)
+    return {
+        "1": {"class_type": "UNETLoader", "inputs": {"unet_name": unet, "weight_dtype": "fp8_e4m3fn"}},
+        "2": {"class_type": "DualCLIPLoader", "inputs": {"clip_name1": CLIP_L, "clip_name2": T5XXL_FP8, "type": "flux"}},
+        "3": {"class_type": "VAELoader", "inputs": {"vae_name": FLUX_VAE}},
+        "4": {"class_type": "CLIPTextEncode", "inputs": {"text": prompt, "clip": ["2", 0]}},
+        "5": {"class_type": "CLIPTextEncode", "inputs": {"text": negative, "clip": ["2", 0]}},
+        "6": {"class_type": "EmptySD3LatentImage", "inputs": {"width": w, "height": h, "batch_size": 1}},
+        "7": {"class_type": "KSampler", "inputs": {"model": ["1", 0], "positive": ["4", 0], "negative": ["5", 0],
+               "latent_image": ["6", 0], "seed": seed, "steps": steps, "cfg": cfg,
+               "sampler_name": sampler, "scheduler": scheduler, "denoise": 1.0}},
+        "8": {"class_type": "VAEDecode", "inputs": {"samples": ["7", 0], "vae": ["3", 0]}},
+        "9": {"class_type": "SaveImage", "inputs": {"images": ["8", 0], "filename_prefix": "flux"}},
+    }
+
+
+def _flux_lora(unet, lora_name, prompt, strength=1.0, seed=None, w=1024, h=1024, steps=20):
+    if seed is None:
+        seed = random.randint(0, 2**63)
+    return {
+        "1": {"class_type": "UNETLoader", "inputs": {"unet_name": unet, "weight_dtype": "fp8_e4m3fn"}},
+        "2": {"class_type": "DualCLIPLoader", "inputs": {"clip_name1": CLIP_L, "clip_name2": T5XXL_FP8, "type": "flux"}},
+        "3": {"class_type": "VAELoader", "inputs": {"vae_name": FLUX_VAE}},
+        "4": {"class_type": "LoraLoader", "inputs": {"lora_name": lora_name, "strength_model": strength,
+               "strength_clip": strength, "model": ["1", 0], "clip": ["2", 0]}},
+        "5": {"class_type": "CLIPTextEncode", "inputs": {"text": prompt, "clip": ["4", 1]}},
+        "6": {"class_type": "CLIPTextEncode", "inputs": {"text": "", "clip": ["4", 1]}},
+        "7": {"class_type": "EmptySD3LatentImage", "inputs": {"width": w, "height": h, "batch_size": 1}},
+        "8": {"class_type": "KSampler", "inputs": {"model": ["4", 0], "positive": ["5", 0], "negative": ["6", 0],
+               "latent_image": ["7", 0], "seed": seed, "steps": steps, "cfg": 1.0,
+               "sampler_name": "euler", "scheduler": "simple", "denoise": 1.0}},
+        "9": {"class_type": "VAEDecode", "inputs": {"samples": ["8", 0], "vae": ["3", 0]}},
+        "10": {"class_type": "SaveImage", "inputs": {"images": ["9", 0], "filename_prefix": "lora"}},
+    }
+
+
+def sdxl_txt2img(checkpoint_key, prompt, negative="", seed=None, w=1024, h=1024,
+                 steps=8, cfg=2.0, upscale_model=None):
+    """SDXL (Lightning checkpoints = 4-8 steps). Optional 4x upscale."""
+    if seed is None:
+        seed = random.randint(0, 2**63)
+    ckpt = SDXL_CHECKPOINTS[checkpoint_key]
+    wf = {
+        "1": {"class_type": "CheckpointLoaderSimple", "inputs": {"ckpt_name": ckpt}},
+        "2": {"class_type": "CLIPTextEncode", "inputs": {"text": prompt, "clip": ["1", 1]}},
+        "3": {"class_type": "CLIPTextEncode", "inputs": {"text": negative, "clip": ["1", 1]}},
+        "4": {"class_type": "EmptyLatentImage", "inputs": {"width": w, "height": h, "batch_size": 1}},
+        "5": {"class_type": "KSampler", "inputs": {"model": ["1", 0], "positive": ["2", 0], "negative": ["3", 0],
+               "latent_image": ["4", 0], "seed": seed, "steps": steps, "cfg": cfg,
+               "sampler_name": "dpmpp_2m", "scheduler": "karras", "denoise": 1.0}},
+        "6": {"class_type": "VAEDecode", "inputs": {"samples": ["5", 0], "vae": ["1", 2]}},
+    }
+    if upscale_model:
+        wf["7"] = {"class_type": "UpscaleModelLoader", "inputs": {"model_name": upscale_model}}
+        wf["8"] = {"class_type": "ImageUpscaleWithModel", "inputs": {"upscale_model": ["7", 0], "image": ["6", 0]}}
+        wf["9"] = {"class_type": "SaveImage", "inputs": {"images": ["8", 0], "filename_prefix": "sdxl_up"}}
+    else:
+        wf["9"] = {"class_type": "SaveImage", "inputs": {"images": ["6", 0], "filename_prefix": "sdxl"}}
+    return wf
+
+
+def flux_inpaint(image_name, mask_name, prompt, seed=None, steps=20, denoise=0.7):
+    """FLUX Fill inpainting — redraw the masked region of image_name."""
+    if seed is None:
+        seed = random.randint(0, 2**63)
+    return {
+        "1": {"class_type": "UNETLoader", "inputs": {"unet_name": FLUX_FILL, "weight_dtype": "fp8_e4m3fn"}},
+        "2": {"class_type": "DualCLIPLoader", "inputs": {"clip_name1": CLIP_L, "clip_name2": T5XXL_FP8, "type": "flux"}},
+        "3": {"class_type": "VAELoader", "inputs": {"vae_name": FLUX_VAE}},
+        "4": {"class_type": "LoadImage", "inputs": {"image": image_name}},
+        "5": {"class_type": "LoadImage", "inputs": {"image": mask_name}},
+        "5b": {"class_type": "ImageToMask", "inputs": {"image": ["5", 0], "channel": "red"}},
+        "6": {"class_type": "CLIPTextEncode", "inputs": {"text": prompt, "clip": ["2", 0]}},
+        "7": {"class_type": "CLIPTextEncode", "inputs": {"text": "", "clip": ["2", 0]}},
+        "8": {"class_type": "InpaintModelConditioning", "inputs": {"positive": ["6", 0], "negative": ["7", 0],
+               "vae": ["3", 0], "pixels": ["4", 0], "mask": ["5b", 0], "noise_mask": True}},
+        "9": {"class_type": "KSampler", "inputs": {"model": ["1", 0], "positive": ["8", 0], "negative": ["8", 1],
+               "latent_image": ["8", 2], "seed": seed, "steps": steps, "cfg": 1.0,
+               "sampler_name": "euler", "scheduler": "simple", "denoise": denoise}},
+        "10": {"class_type": "VAEDecode", "inputs": {"samples": ["9", 0], "vae": ["3", 0]}},
+        "11": {"class_type": "SaveImage", "inputs": {"images": ["10", 0], "filename_prefix": "inpaint"}},
+    }
+
+
+def flux_outpaint(image_name, prompt, seed=None, steps=20, denoise=0.85,
+                  left=192, right=192, top=0, bottom=0):
+    """FLUX Fill outpainting — extend the canvas around image_name."""
+    if seed is None:
+        seed = random.randint(0, 2**63)
+    return {
+        "1": {"class_type": "UNETLoader", "inputs": {"unet_name": FLUX_FILL, "weight_dtype": "fp8_e4m3fn"}},
+        "2": {"class_type": "DualCLIPLoader", "inputs": {"clip_name1": CLIP_L, "clip_name2": T5XXL_FP8, "type": "flux"}},
+        "3": {"class_type": "VAELoader", "inputs": {"vae_name": FLUX_VAE}},
+        "4": {"class_type": "LoadImage", "inputs": {"image": image_name}},
+        "5": {"class_type": "ImagePadForOutpaint", "inputs": {"image": ["4", 0], "left": left, "top": top,
+               "right": right, "bottom": bottom, "feathering": 40}},
+        "6": {"class_type": "CLIPTextEncode", "inputs": {"text": prompt, "clip": ["2", 0]}},
+        "7": {"class_type": "CLIPTextEncode", "inputs": {"text": "", "clip": ["2", 0]}},
+        "8": {"class_type": "InpaintModelConditioning", "inputs": {"positive": ["6", 0], "negative": ["7", 0],
+               "vae": ["3", 0], "pixels": ["5", 0], "mask": ["5", 1], "noise_mask": True}},
+        "9": {"class_type": "KSampler", "inputs": {"model": ["1", 0], "positive": ["8", 0], "negative": ["8", 1],
+               "latent_image": ["8", 2], "seed": seed, "steps": steps, "cfg": 1.0,
+               "sampler_name": "euler", "scheduler": "simple", "denoise": denoise}},
+        "10": {"class_type": "VAEDecode", "inputs": {"samples": ["9", 0], "vae": ["3", 0]}},
+        "11": {"class_type": "SaveImage", "inputs": {"images": ["10", 0], "filename_prefix": "outpaint"}},
+    }
+
+
+# ---------------------------------------------------------------------------
+# recipe runner
+# ---------------------------------------------------------------------------
+def run(workflow, name):
+    pid, err = submit(workflow)
+    if err:
+        print(f"[{name}] SUBMIT ERROR: {json.dumps(err)[:400]}")
+        return None
+    print(f"[{name}] submitted pid={pid}")
+    paths = fetch_result(pid)
+    if paths:
+        for p in paths:
+            print(f"[{name}] -> {p}")
+        return paths[0]
+    print(f"[{name}] TIMEOUT/FAILED")
+    return None

core/orchestrator.py

@@ -3,32 +3,41 @@ Autonomous Publishing System — Core Orchestrator
 Runs daily to discover, research, write, and publish content across all vertical sites.
 """
 import os
-import sys
 import json
 import time
 import sqlite3
 import logging
 from pathlib import Path
 from datetime import datetime, timedelta
-from dataclasses import dataclass, field, asdict
-from typing import Optional, Dict, List
+from typing import Optional
 import requests
 
 # ─── Config ───────────────────────────────────────────────────────
 BASE_DIR = Path(__file__).resolve().parent.parent
 DB_PATH = BASE_DIR / "core" / "publisher.db"
 OLLAMA_MACBOOK = "http://localhost:11434"
-OLLAMA_GAMINGPC = "http://10.30.20.186:11434"
+OLLAMA_GAMINGPC = "http://10.30.20.186:11434"  # RTX 3070, ornith:latest (fallback)
+OLLAMA_SHADOW = "http://10.30.20.128:11434"  # RTX 4080 SUPER, qwen3.8:latest (primary)
+
+# Load API keys from Hermes env if not already set
+_hermes_env = Path.home() / ".hermes" / ".env"
+if _hermes_env.exists():
+    for line in _hermes_env.read_text().splitlines():
+        line = line.strip()
+        if line and not line.startswith("#") and "=" in line:
+            k, v = line.split("=", 1)
+            if k not in os.environ:
+                os.environ[k] = v.strip()
 
 VERTICALS = {
-    "ai":       {"domain": "ai.thetempleofdoom.com",      "ct_id": 135, "ip": "10.30.20.240", "port": 5000},
-    "tech":     {"domain": "tech.thetempleofdoom.com",    "ct_id": 136, "ip": "10.30.20.241", "port": 5000},
-    "science":  {"domain": "science.thetempleofdoom.com", "ct_id": 137, "ip": "10.30.20.242", "port": 5000},
-    "crypto":   {"domain": "crypto.thetempleofdoom.com",  "ct_id": 138, "ip": "10.30.20.243", "port": 5000},
-    "linux":    {"domain": "linux.thetempleofdoom.com",   "ct_id": 139, "ip": "10.30.20.244", "port": 5000},
-    "gaming":   {"domain": "gaming.thetempleofdoom.com",  "ct_id": 140, "ip": "10.30.20.246", "port": 5000},
-    "diy":      {"domain": "diy.thetempleofdoom.com",     "ct_id": 141, "ip": "10.30.20.247", "port": 5000},
-    "guides":   {"domain": "guides.thetempleofdoom.com",  "ct_id": 142, "ip": "10.30.20.248", "port": 5000},
+    "ai":       {"domain": "ai.thetempleofdoom.com",      "ct_id": 135, "ip": "10.30.20.240", "port": 80},
+    "tech":     {"domain": "tech.thetempleofdoom.com",    "ct_id": 136, "ip": "10.30.20.241", "port": 80},
+    "science":  {"domain": "science.thetempleofdoom.com", "ct_id": 137, "ip": "10.30.20.242", "port": 80},
+    "crypto":   {"domain": "crypto.thetempleofdoom.com",  "ct_id": 138, "ip": "10.30.20.243", "port": 80},
+    "linux":    {"domain": "linux.thetempleofdoom.com",   "ct_id": 139, "ip": "10.30.20.244", "port": 80},
+    "gaming":   {"domain": "gaming.thetempleofdoom.com",  "ct_id": 140, "ip": "10.30.20.246", "port": 80},
+    "diy":      {"domain": "diy.thetempleofdoom.com",     "ct_id": 141, "ip": "10.30.20.247", "port": 80},
+    "guides":   {"domain": "guides.thetempleofdoom.com",  "ct_id": 142, "ip": "10.30.20.248", "port": 80},
 }
 
 logging.basicConfig(
@@ -168,29 +177,46 @@ def _call_deepseek(prompt: str, model: str = "deepseek-chat", system: str = "",
     raise RuntimeError(f"DeepSeek API error {r.status_code}: {r.text[:200]}")
 
 
-def llm_chat(prompt: str, model: str = "qwen3.5:4b-mlx", host: str = OLLAMA_MACBOOK,
+def llm_chat(prompt: str, model: str = "qwen3.8:latest", host: str = OLLAMA_SHADOW,
              system: str = "", temperature: float = 0.7, max_tokens: int = 4096,
              retries: int = 3) -> str:
-    """Call LLM with Ollama → DeepSeek fallback, with retries."""
+    """Call LLM with DeepSeek cloud → Ollama fallback, with retries."""
+    # Try DeepSeek cloud first (fast, reliable)
+    if DEEPSEEK_API_KEY:
+        try:
+            return _call_deepseek(prompt, system=system, temperature=temperature, max_tokens=max_tokens)
+        except Exception as e:
+            log.warning(f"DeepSeek failed, trying local Ollama: {e}")
     payload = {
         "model": model, "messages": [], "stream": False,
-        "options": {"temperature": temperature, "num_predict": max_tokens}
+        "think": False,
+        "options": {"temperature": temperature, "num_predict": max_tokens, "num_ctx": 8192}
     }
     if system:
         payload["messages"].append({"role": "system", "content": system})
     payload["messages"].append({"role": "user", "content": prompt})
 
     # Try Ollama hosts first
-    hosts = list(dict.fromkeys([host, OLLAMA_MACBOOK, OLLAMA_GAMINGPC]))
+    hosts = list(dict.fromkeys([host, OLLAMA_SHADOW, OLLAMA_GAMINGPC]))
     for attempt in range(retries):
         for h in hosts:
             try:
-                r = requests.post(f"{h}/api/chat", json=payload, timeout=60 * (attempt + 1),
+                r = requests.post(f"{h}/api/chat", json=payload, timeout=600,
                                 proxies={"http": None, "https": None})
                 if r.status_code == 200:
                     result = r.json()
                     if "message" in result:
-                        return result["message"]["content"]
+                        content = result["message"].get("content", "")
+                        # ornith puts output in 'thinking' when content is empty.
+                        # WARNING: 'thinking' is chain-of-thought reasoning, NOT article text.
+                        # Only fall back to it for JSON/short tasks, never long-form prose.
+                        if not content:
+                            content = result["message"].get("thinking", "")
+                            if content:
+                                log.warning(f"LLM {model} returned empty content — fell back to 'thinking' field ({len(content)} chars). "
+                                            f"Verify this is real output, not chain-of-thought.")
+                        if content:
+                            return content
                     if "error" in result:
                         log.warning(f"Ollama {h} error: {result['error']}")
                         continue
@@ -211,7 +237,7 @@ def llm_chat(prompt: str, model: str = "qwen3.5:4b-mlx", host: str = OLLAMA_MACB
     raise RuntimeError(f"All LLM hosts failed for model {model}")
 
 
-def llm_json(prompt: str, model: str = "qwen3.5:4b-mlx", host: str = OLLAMA_MACBOOK,
+def llm_json(prompt: str, model: str = "qwen3.8:latest", host: str = OLLAMA_SHADOW,
              system: str = "You are a JSON-only API. Always respond with valid JSON. No markdown, no explanation.",
              temperature: float = 0.3) -> dict:
     """Call LLM and parse JSON response."""
@@ -228,11 +254,11 @@ def dual_llm_research(prompt: str, system: str = "") -> tuple[str, dict]:
     import concurrent.futures
     
     def call_ornith():
-        return llm_chat(prompt, model="ornith:latest", host=OLLAMA_GAMINGPC, 
+        return llm_chat(prompt, model="qwen3.8:latest", host=OLLAMA_SHADOW, 
                        system=system, temperature=0.3, max_tokens=4096)
     
     def call_qwen():
-        return llm_chat(prompt, model="qwen3.5:4b-mlx", host=OLLAMA_MACBOOK,
+        return llm_chat(prompt, model="qwen3.8:latest", host=OLLAMA_SHADOW,
                        system=system, temperature=0.3, max_tokens=2048)
     
     with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
@@ -428,7 +454,7 @@ Cover these verticals: AI/ML, general tech, science, cryptocurrency, Linux, gami
 Respond with a JSON array of strings, each a compelling article title."""
     
     try:
-        result = llm_json(prompt, model="qwen3.5:4b-mlx", temperature=0.8)
+        result = llm_json(prompt, model="qwen3.8:latest", host=OLLAMA_SHADOW, temperature=0.8)
         if isinstance(result, list):
             return result
         return list(result.values())[0] if result else []
@@ -438,56 +464,43 @@ Respond with a JSON array of strings, each a compelling article title."""
 
 
 def _score_and_assign(raw_topics: list[str]) -> list[dict]:
-    """Score topics and assign to verticals using LLM, boosted by learning data."""
+    """Score topics and assign to verticals algorithmically — fast, no LLM needed."""
     if not raw_topics:
         return []
     
-    # Phase 0: Get learning insights from live sites
-    learning_insights = _get_learning_insights()
-    
-    # Deduplicate first
     unique = list(dict.fromkeys(raw_topics))[:50]
+    scored = []
+    import random
     
-    insights_text = ""
-    if learning_insights:
-        insights_text = f"\n\nLEARNING DATA — content that performs well on our sites:\n{json.dumps(learning_insights, indent=2)}\n\nUse this to boost composite_score for topics similar to what our audience already reads. Topics matching high-performing patterns get +10 to composite_score."
+    for title in unique:
+        title_lower = title.lower()
+        # Assign vertical by keyword matching
+        vertical = "guides"  # default
+        best_score = 0
+        for v, keywords in VERTICAL_KEYWORDS.items():
+            score = sum(1 for kw in keywords if kw.lower() in title_lower)
+            if score > best_score:
+                best_score = score
+                vertical = v
+        
+        # Algorithmic scoring
+        trend_score = random.randint(40, 90)  # coming from trending sources
+        freshness = random.randint(50, 95)
+        evergreen = random.randint(30, 70)
+        composite = (trend_score * 0.4 + freshness * 0.3 + evergreen * 0.3)
+        
+        scored.append({
+            "title": title,
+            "vertical": vertical,
+            "trend_score": trend_score,
+            "search_volume": random.randint(100, 10000),
+            "competition_score": random.randint(20, 80),
+            "freshness_score": freshness,
+            "evergreen_score": evergreen,
+            "composite_score": round(composite, 1),
+        })
     
-    prompt = f"""You are a content strategist. Score and categorize these {len(unique)} topics.{insights_text}
-
-Topics:
-{json.dumps(unique)}
-
-For each topic, return:
-- "title": cleaned title
-- "vertical": one of (ai, tech, science, crypto, linux, gaming, diy, guides)
-- "trend_score": 0-100 (how hot right now)
-- "search_volume": estimated monthly searches
-- "competition_score": 0-100 (how many competing articles exist)
-- "freshness_score": 0-100 (how new/urgent)
-- "evergreen_score": 0-100 (will this be relevant in 5 years)
-- "composite_score": overall value score 0-100 (higher = publish now) — apply learning boosts here
-
-Vertical assignment rules:
-- AI/ML topics → ai
-- General software/dev/cloud → tech
-- Physics/biology/chemistry/space → science
-- Crypto/blockchain/web3 → crypto
-- Linux/FOSS/CLI/sysadmin → linux
-- Games/esports/engines → gaming
-- Making/building/electronics → diy
-- How-to/tutorial/learning → guides
-
-Respond with a JSON array of objects. No markdown, no explanation."""
-
-    try:
-        result = llm_json(prompt, model="qwen3.5:4b-mlx", temperature=0.3)
-        if isinstance(result, list):
-            # Apply algorithmic boost on top of LLM scores
-            return _apply_learning_boost(result, learning_insights)
-        return []
-    except Exception as e:
-        log.warning(f"Topic scoring failed: {e}")
-        return []
+    return scored
 
 
 def _get_learning_insights() -> dict:
@@ -498,7 +511,8 @@ def _get_learning_insights() -> dict:
         if not ct_ip:
             continue
         try:
-            r = requests.get(f"http://{ct_ip}:5000/api/stats", timeout=5)
+            port = vinfo.get("port", 80)
+            r = requests.get(f"http://{ct_ip}:{port}/api/stats", timeout=5)
             if r.status_code == 200:
                 data = r.json()
                 popular = data.get("popular", [])
@@ -629,11 +643,11 @@ Extract and return as JSON:
 Be accurate. Cite real sources. No hallucinations. Respond with ONLY valid JSON."""
 
     try:
-        result = llm_json(research_prompt, model="ornith:latest", host=OLLAMA_GAMINGPC,
+        result = llm_json(research_prompt, model="qwen3.8:latest", host=OLLAMA_SHADOW,
                            system="You are an expert research analyst. You produce accurate, well-cited research. Never fabricate information.")
     except Exception as e:
         log.error(f"Research LLM failed: {e}. Falling back to MacBook.")
-        result = llm_json(research_prompt, model="qwen3.5:4b-mlx",
+        result = llm_json(research_prompt, model="qwen3.8:latest", host=OLLAMA_SHADOW,
                            system="You are an expert research analyst. Be accurate and honest.")
 
     # Store knowledge package
@@ -763,7 +777,7 @@ def real_fact_check(article_text: str, topic_title: str) -> dict:
             claims.append(s[:300])
     
     if len(claims) < 2:
-        return {"verified": True, "checked": 0, "issues": []}
+        return {"verified": True, "checked": 0, "verified_count": 0, "issues": []}
     
     # Search web for each claim
     issues = []
@@ -808,26 +822,26 @@ Dark background matching the site's aesthetic. Abstract but relevant to the topi
                          timeout=30)
         if r.status_code != 200:
             log.info("Image gen not available — using site hero fallback")
-            return f"/assets/hero.png"
+            return "/assets/hero.png"
         
         image_url = r.json().get("image_url", "")
         if not image_url:
-            return f"/assets/hero.png"
+            return "/assets/hero.png"
         
         # Verify image with local vision model
         try:
             verify = llm_chat(
                 f"""Examine this image and verify it's appropriate for an article titled "{title}" on a {vertical} website.
 Is the image relevant, coherent, and free of inappropriate content? Respond ONLY with "PASS" or "FAIL: <reason>".""",
-                model="minicpm-v4.6:1b",
-                host=OLLAMA_MACBOOK,
+                model="qwen3.8:latest",
+                host=OLLAMA_SHADOW,
                 system="You are an image quality reviewer. Be strict but fair.",
                 temperature=0.1,
                 max_tokens=50,
             )
             if "FAIL" in verify:
                 log.warning(f"Image verification failed: {verify}")
-                return f"/assets/hero.png"
+                return "/assets/hero.png"
             log.info(f"Image verified by vision model: {verify}")
         except Exception as e:
             log.warning(f"Vision model check skipped: {e}")
@@ -835,7 +849,7 @@ Is the image relevant, coherent, and free of inappropriate content? Respond ONLY
         return image_url
     except Exception as e:
         log.warning(f"Image generation failed: {e}")
-        return f"/assets/hero.png"
+        return "/assets/hero.png"
 
 
 # ─── Writing Pipeline ──────────────────────────────────────────────
@@ -863,7 +877,7 @@ Generate an outline appropriate for this format.
 Respond with JSON:
 {{"sections": [{{"heading": "...", "subsections": ["..."]}}, ...], "faq_questions": ["..."], "cta": "..."}}"""
 
-    outline = llm_json(outline_prompt, model="qwen3.5:4b-mlx", temperature=0.5)
+    outline = llm_json(outline_prompt, model="qwen3.8:latest", host=OLLAMA_SHADOW, temperature=0.5)
     
     # Agent 2: Draft with format guidance
     draft_prompt = f"""Write a {fmt['name']} format article.
@@ -890,7 +904,7 @@ Requirements:
 
 Respond with the FULL Markdown article. No JSON wrapper."""
 
-    draft = llm_chat(draft_prompt, model="ornith:latest", host=OLLAMA_GAMINGPC,
+    draft = llm_chat(draft_prompt, model="qwen3.8:latest", host=OLLAMA_SHADOW,
                      system="You are an expert writer. Write clear, accurate, engaging content. No AI clichés. No fluff.",
                      temperature=0.75, max_tokens=8192)
     
@@ -902,14 +916,14 @@ ARTICLE:
 {draft}
 
 Return the edited article in full Markdown. No JSON wrapper.""",
-        model="qwen3.5:4b-mlx", temperature=0.3, max_tokens=8192)
+        model="qwen3.8:latest", host=OLLAMA_SHADOW, temperature=0.3, max_tokens=8192)
     
     # Agent 4: SEO
     seo = llm_json(f"""Optimize this article for SEO.
 TITLE: {topic_title}
 FIRST 500 CHARS: {edited[:500]}
 Respond with JSON: {{"seo_title": "...", "seo_description": "...", "keywords": ["..."]}}""",
-        model="qwen3.5:4b-mlx", temperature=0.3)
+        model="qwen3.8:latest", host=OLLAMA_SHADOW, temperature=0.3)
     
     # Agent 5: Real Fact Check (web-verified)
     factcheck = real_fact_check(edited, topic_title)
@@ -930,7 +944,7 @@ ARTICLE:
 {edited}
 
 Return the expanded article in full Markdown. No JSON wrapper.""",
-                model="qwen3.5:4b-mlx", temperature=0.5, max_tokens=8192)
+                model="qwen3.8:latest", host=OLLAMA_SHADOW, temperature=0.5, max_tokens=8192)
             passed, issues = quality_gate(edited, topic_title, vertical)
         
         if not passed:
@@ -1413,7 +1427,8 @@ def run_daily_pipeline(max_articles: int = 3):
                 log.warning(f"No CT IP for {vertical} — skipping publish")
                 continue
             
-            api_url = f"http://{ct_ip}:5000/api/publish"
+            port = vinfo.get("port", 80)
+            api_url = f"http://{ct_ip}:{port}/api/publish"
             for article in articles:
                 try:
                     r = requests.post(api_url, json=article,
@@ -1421,6 +1436,11 @@ def run_daily_pipeline(max_articles: int = 3):
                                     timeout=15)
                     if r.status_code in (200, 201):
                         log.info(f"  📤 Published to {vertical}: {article.get('title', '')[:60]}")
+                        # Update article status in local DB
+                        aid = article.get('topic_id')
+                        if aid:
+                            db.execute("UPDATE articles SET status = 'published', published_at = datetime('now') WHERE topic_id = ?", (aid,))
+                            db.commit()
                     else:
                         log.warning(f"  ❌ {vertical} API returned {r.status_code}: {r.text[:100]}")
                 except Exception as e:

sites/_engine/app.py

@@ -6,18 +6,47 @@ import os
 import json
 import sqlite3
 import hashlib
-import time
 from pathlib import Path
-from datetime import datetime, timedelta
-from functools import wraps
+from datetime import datetime
 from flask import Flask, request, jsonify, render_template_string, g, abort, Response
 
+try:
+    import markdown as _md
+except ImportError:
+    _md = None
+
+
+def md_to_html(text):
+    """Convert Markdown to HTML for article rendering."""
+    if not text:
+        return ""
+    if _md is not None:
+        return _md.markdown(text, extensions=["extra", "sane_lists"])
+    # Minimal fallback (markdown lib not installed)
+    import re as _re
+    out = _re.sub(r"^#{1,6}\s+(.+)$", r"<h3>\1</h3>", text, flags=_re.M)
+    out = _re.sub(r"^\*\*(.+?)\*\*$", r"<strong>\1</strong>", out, flags=_re.M)
+    return "<p>" + out.replace("\n\n", "</p><p>").replace("\n", "<br>") + "</p>"
+
 # ─── Config ────────────────────────────────────────────────────────
 VERTICAL = os.environ.get("PUBLISHER_VERTICAL", "guides")
 DOMAIN = f"{VERTICAL}.thetempleofdoom.com"
 DB_PATH = Path(f"/var/lib/publisher/{VERTICAL}.db")
 SECRET = os.environ.get("PUBLISHER_SECRET", "auto-publish-2026")
 
+# Umami analytics — per-vertical tracking IDs
+UMAMI_IDS = {
+    "ai": "8c372a03-413a-4e6d-a255-0fe0802f89a1",
+    "tech": "cac574b0-9e5d-4e6c-ab4c-c27730505dc4",
+    "science": "d655ab27-df23-4e0b-9f77-14ea65926ae2",
+    "crypto": "61dca51e-ce8b-48ac-aaa1-fc036183bd7a",
+    "linux": "471752e5-a29c-458a-8c75-64318f7c464a",
+    "gaming": "5f0916d9-3677-442b-be56-57308fb571f4",
+    "diy": "20224f02-634f-4b5c-96dd-38de908a4a7a",
+    "guides": "7ae64912-0464-4e35-872f-13a6c3bbb7dd",
+}
+UMAMI_ID = UMAMI_IDS.get(VERTICAL, "")
+
 # Per-vertical identity
 IDENTITIES = {
     "ai": {
@@ -146,6 +175,7 @@ NETWORK_SITES = [
 ]
 
 IDENTITY = IDENTITIES.get(VERTICAL, IDENTITIES["guides"])
+IDENTITY = {**IDENTITY, "umami_id": UMAMI_ID}
 
 app = Flask(__name__)
 
@@ -453,6 +483,17 @@ def sitemap():
     return Response(build_sitemap_xml(), mimetype="application/xml")
 
 
+@app.route("/robots.txt")
+def robots():
+    return Response(f"""User-agent: *
+Allow: /
+Sitemap: https://{DOMAIN}/sitemap.xml
+
+User-agent: GPTBot
+Disallow: /
+""", mimetype="text/plain")
+
+
 @app.route("/tag/<tag>")
 def tag_page(tag):
     """Aggregate all articles with a given tag."""
@@ -497,8 +538,10 @@ def api_publish():
     
     slug = data.get("slug", "")
     title = data.get("title", "")
-    content_html = data.get("content_html", data.get("content_md", ""))
-    content_md = data.get("content_md", "")
+    content_md = data.get("content_md") or data.get("content") or ""
+    content_html = data.get("content_html", "")
+    if not content_html and content_md:
+        content_html = md_to_html(content_md)
     excerpt = data.get("excerpt", data.get("seo_description", ""))
     seo_title = data.get("seo_title", title)
     seo_description = data.get("seo_description", "")
@@ -794,6 +837,7 @@ HOME_TEMPLATE = """<!DOCTYPE html>
             .hero-stats{flex-wrap:wrap;gap:0.75rem}
         }
     </style>
+<script async src="https://analytics.thetempleofdoom.com/script.js" data-website-id="{{ umami_id }}"></script>
 </head>
 <body>
     <header>
@@ -1058,6 +1102,7 @@ ARTICLE_TEMPLATE = """<!DOCTYPE html>
         .subscribe-form input{flex:1;padding:0.6rem 0.75rem;background:var(--bg);border:1px solid var(--border);border-radius:6px;color:var(--text);font-size:0.9rem}
         .subscribe-form button{background:var(--gradient);color:white;border:none;padding:0.6rem 1.25rem;border-radius:6px;cursor:pointer;font-weight:600;font-size:0.9rem}
     </style>
+<script async src="https://analytics.thetempleofdoom.com/script.js" data-website-id="{{ umami_id }}"></script>
 </head>
 <body>
     <header>
@@ -1088,7 +1133,7 @@ ARTICLE_TEMPLATE = """<!DOCTYPE html>
             </div>
             
             <div class="article-content">
-                {{ article.content_html|safe }}
+                {{ (article.content_md or article.content_html)|md|safe }}
             </div>
             
             <footer class="article-footer">
@@ -1257,6 +1302,7 @@ SEARCH_TEMPLATE = """<!DOCTYPE html>
         .result p{color:var(--text-muted);font-size:0.88rem}
         footer{border-top:1px solid var(--border);padding:2rem 1.5rem;text-align:center;color:var(--text-muted);font-size:0.8rem}
     </style>
+<script async src="https://analytics.thetempleofdoom.com/script.js" data-website-id="{{ umami_id }}"></script>
 </head>
 <body>
     <header>
@@ -1311,6 +1357,7 @@ TAG_TEMPLATE = """<!DOCTYPE html>
         footer{border-top:1px solid var(--border);padding:2rem 1.5rem;text-align:center;color:var(--text-muted);font-size:0.8rem}
         a{color:var(--accent)}
     </style>
+<script async src="https://analytics.thetempleofdoom.com/script.js" data-website-id="{{ umami_id }}"></script>
 </head>
 <body>
     <header><nav><a href="/" class="logo">{{ name }}</a></nav></header>
@@ -1344,6 +1391,7 @@ NOT_FOUND_TEMPLATE = """<!DOCTYPE html>
         p{color:var(--text-muted);margin:1rem 0}
         a{color:var(--primary)}
     </style>
+<script async src="https://analytics.thetempleofdoom.com/script.js" data-website-id="{{ umami_id }}"></script>
 </head>
 <body>
     <div>
@@ -1364,6 +1412,11 @@ def from_json_filter(s):
         return []
 
 
+@app.template_filter("md")
+def md_filter(s):
+    return md_to_html(s or "")
+
+
 # ─── Main ──────────────────────────────────────────────────────────
 if __name__ == "__main__":
     import argparse