diff --git a/data/_catalog/manifests/0efdef2d-52bc-4154-82d4-056bb3a1915d.json b/data/_catalog/manifests/0efdef2d-52bc-4154-82d4-056bb3a1915d.json
new file mode 100644
index 0000000..e75242f
--- /dev/null
+++ b/data/_catalog/manifests/0efdef2d-52bc-4154-82d4-056bb3a1915d.json
@@ -0,0 +1,159 @@
+{
+  "id": "0efdef2d-52bc-4154-82d4-056bb3a1915d",
+  "name": "ethereal_workers",
+  "schema_fingerprint": "9a1286ffada5390b459b217f56263e5ebffec1520ea29bab0be2efc6d5381adc",
+  "objects": [
+    {
+      "bucket": "primary",
+      "key": "datasets/ethereal_workers.parquet",
+      "size_bytes": 6991716,
+      "created_at": "2026-04-17T03:23:29.399966984Z"
+    }
+  ],
+  "created_at": "2026-04-17T03:23:29.399967772Z",
+  "updated_at": "2026-04-17T03:23:29.400227081Z",
+  "description": "",
+  "owner": "",
+  "sensitivity": "pii",
+  "columns": [
+    {
+      "name": "worker_id",
+      "data_type": "Int64",
+      "sensitivity": null,
+      "description": "",
+      "is_pii": false
+    },
+    {
+      "name": "name",
+      "data_type": "Utf8",
+      "sensitivity": "pii",
+      "description": "",
+      "is_pii": true
+    },
+    {
+      "name": "role",
+      "data_type": "Utf8",
+      "sensitivity": null,
+      "description": "",
+      "is_pii": false
+    },
+    {
+      "name": "email",
+      "data_type": "Utf8",
+      "sensitivity": "pii",
+      "description": "",
+      "is_pii": true
+    },
+    {
+      "name": "phone",
+      "data_type": "Int64",
+      "sensitivity": "pii",
+      "description": "",
+      "is_pii": true
+    },
+    {
+      "name": "city",
+      "data_type": "Utf8",
+      "sensitivity": null,
+      "description": "",
+      "is_pii": false
+    },
+    {
+      "name": "state",
+      "data_type": "Utf8",
+      "sensitivity": null,
+      "description": "",
+      "is_pii": false
+    },
+    {
+      "name": "zip",
+      "data_type": "Int64",
+      "sensitivity": "pii",
+      "description": "",
+      "is_pii": true
+    },
+    {
+      "name": "skills",
+      "data_type": "Utf8",
+      "sensitivity": null,
+      "description": "",
+      "is_pii": false
+    },
+    {
+      "name": "certifications",
+      "data_type": "Utf8",
+      "sensitivity": null,
+      "description": "",
+      "is_pii": false
+    },
+    {
+      "name": "archetype",
+      "data_type": "Utf8",
+      "sensitivity": null,
+      "description": "",
+      "is_pii": false
+    },
+    {
+      "name": "reliability",
+      "data_type": "Float64",
+      "sensitivity": null,
+      "description": "",
+      "is_pii": false
+    },
+    {
+      "name": "responsiveness",
+      "data_type": "Float64",
+      "sensitivity": null,
+      "description": "",
+      "is_pii": false
+    },
+    {
+      "name": "engagement",
+      "data_type": "Float64",
+      "sensitivity": null,
+      "description": "",
+      "is_pii": false
+    },
+    {
+      "name": "compliance",
+      "data_type": "Float64",
+      "sensitivity": null,
+      "description": "",
+      "is_pii": false
+    },
+    {
+      "name": "availability",
+      "data_type": "Float64",
+      "sensitivity": null,
+      "description": "",
+      "is_pii": false
+    },
+    {
+      "name": "communications",
+      "data_type": "Utf8",
+      "sensitivity": null,
+      "description": "",
+      "is_pii": false
+    },
+    {
+      "name": "resume_text",
+      "data_type": "Utf8",
+      "sensitivity": null,
+      "description": "",
+      "is_pii": false
+    }
+  ],
+  "lineage": {
+    "source_system": "csv",
+    "source_file": "ethereal_workers.csv",
+    "ingest_job": "ingest-1776396209399",
+    "ingest_timestamp": "2026-04-17T03:23:29.399966984Z",
+    "parent_datasets": []
+  },
+  "freshness": null,
+  "tags": [],
+  "row_count": 10000,
+  "last_embedded_at": null,
+  "embedding_stale_since": null,
+  "embedding_refresh_policy": null
+}
\ No newline at end of file
diff --git a/data/datasets/ethereal_workers.parquet b/data/datasets/ethereal_workers.parquet
new file mode 100644
index 0000000..0244a57
Binary files /dev/null and b/data/datasets/ethereal_workers.parquet differ
diff --git a/scripts/serve_imagegen.py b/scripts/serve_imagegen.py
new file mode 100644
index 0000000..fc18ba0
--- /dev/null
+++ b/scripts/serve_imagegen.py
@@ -0,0 +1,433 @@
+#!/usr/bin/env python3
+"""Image generation service — proxies to ComfyUI API on :8188.
+
+Serves on :3600. Submits workflow to ComfyUI, polls for completion, returns image.
+Falls back to direct diffusers if ComfyUI is unavailable.
+
+Features:
+  - Disk cache — same prompt returns cached image instantly
+  - Negative prompt for quality (no faces, hands, text)
+  - DreamShaper XL Turbo for high-quality editorial illustrations
+"""
+
+import base64
+import hashlib
+import io
+import json
+import os
+import random
+import time
+import urllib.request
+import urllib.error
+from http.server import HTTPServer, BaseHTTPRequestHandler
+from pathlib import Path
+
+PORT = int(os.environ.get("IMAGEGEN_PORT", "3600"))
+COMFYUI_URL = os.environ.get("COMFYUI_URL", "http://localhost:8188")
+CACHE_DIR = Path(os.environ.get("IMAGEGEN_CACHE", "./data/_imagecache"))
+CACHE_DIR.mkdir(parents=True, exist_ok=True)
+WORKFLOW_PATH = "/opt/ComfyUI/workflows/editorial_hero.json"
+
+
+def _cache_key(prompt, width, height, steps):
+    return hashlib.sha256(f"{prompt}|{width}|{height}|{steps}".encode()).hexdigest()[:24]
+
+def _cache_get(key):
+    fp = CACHE_DIR / f"{key}.webp"
+    return base64.b64encode(fp.read_bytes()).decode() if fp.exists() else None
+
+def _cache_put(key, img_bytes):
+    (CACHE_DIR / f"{key}.webp").write_bytes(img_bytes)
+
+
+def _comfyui_generate(prompt, width=1024, height=512, steps=8, seed=None):
+    """Submit workflow to ComfyUI and wait for result."""
+    # Load workflow template
+    with open(WORKFLOW_PATH) as f:
+        workflow = json.load(f)
+
+    # Customize
+    if seed is None:
+        seed = random.randint(0, 2**32)
+    workflow["3"]["inputs"]["seed"] = seed
+    workflow["3"]["inputs"]["steps"] = steps
+    workflow["5"]["inputs"]["width"] = width
+    workflow["5"]["inputs"]["height"] = height
+    workflow["6"]["inputs"]["text"] = prompt
+
+    # Submit to ComfyUI
+    payload = json.dumps({"prompt": workflow}).encode()
+    req = urllib.request.Request(
+        f"{COMFYUI_URL}/prompt",
+        data=payload,
+        headers={"Content-Type": "application/json"}
+    )
+    resp = urllib.request.urlopen(req, timeout=10)
+    result = json.loads(resp.read())
+    prompt_id = result["prompt_id"]
+
+    # Poll for completion
+    for _ in range(120):  # up to 2 minutes
+        time.sleep(0.5)
+        try:
+            status_req = urllib.request.Request(f"{COMFYUI_URL}/history/{prompt_id}")
+            status_resp = urllib.request.urlopen(status_req, timeout=5)
+            history = json.loads(status_resp.read())
+            if prompt_id in history:
+                outputs = history[prompt_id].get("outputs", {})
+                # Find the SaveImage node output
+                for node_id, node_out in outputs.items():
+                    images = node_out.get("images", [])
+                    if images:
+                        img_info = images[0]
+                        # Fetch the image
+                        img_url = f"{COMFYUI_URL}/view?filename={img_info['filename']}&subfolder={img_info.get('subfolder', '')}&type={img_info.get('type', 'output')}"
+                        img_resp = urllib.request.urlopen(img_url, timeout=10)
+                        img_data = img_resp.read()
+                        # Convert to webp
+                        from PIL import Image
+                        img = Image.open(io.BytesIO(img_data))
+                        buf = io.BytesIO()
+                        img.save(buf, format="WEBP", quality=90)
+                        return buf.getvalue(), seed
+                return None, seed  # completed but no images
+        except Exception:
+            continue
+    return None, seed  # timeout
+
+
+def _diffusers_fallback(prompt, width, height, steps, seed):
+    """Fallback: use raw diffusers SDXL Turbo if ComfyUI is down."""
+    import torch
+    from diffusers import AutoPipelineForText2Image
+
+    pipe = AutoPipelineForText2Image.from_pretrained(
+        "stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16"
+    ).to("cuda")
+    pipe.enable_attention_slicing()
+
+    if seed is None:
+        seed = random.randint(0, 2**32)
+    gen = torch.Generator("cuda").manual_seed(seed)
+    result = pipe(prompt=prompt, num_inference_steps=steps, guidance_scale=0.0,
+                  width=width, height=height, generator=gen)
+    buf = io.BytesIO()
+    result.images[0].save(buf, format="WEBP", quality=90)
+    del pipe
+    torch.cuda.empty_cache()
+    return buf.getvalue(), seed
+
+
+class ImageHandler(BaseHTTPRequestHandler):
+    def log_message(self, fmt, *args): pass
+
+    def _json(self, code, data):
+        self.send_response(code)
+        self.send_header("Content-Type", "application/json")
+        self.send_header("Access-Control-Allow-Origin", "*")
+        self.end_headers()
+        self.wfile.write(json.dumps(data).encode())
+
+    def do_OPTIONS(self):
+        self.send_response(200)
+        self.send_header("Access-Control-Allow-Origin", "*")
+        self.send_header("Access-Control-Allow-Methods", "POST, GET, OPTIONS")
+        self.send_header("Access-Control-Allow-Headers", "Content-Type")
+        self.end_headers()
+
+    def do_GET(self):
+        if self.path == "/health":
+            comfy_ok = False
+            try:
+                r = urllib.request.urlopen(f"{COMFYUI_URL}/system_stats", timeout=3)
+                comfy_ok = r.status == 200
+            except: pass
+            cached = len(list(CACHE_DIR.glob("*.webp")))
+            self._json(200, {"status": "ok", "comfyui": comfy_ok, "cached_images": cached})
+        elif self.path == "/cache/stats":
+            files = list(CACHE_DIR.glob("*.webp"))
+            self._json(200, {"count": len(files), "total_mb": round(sum(f.stat().st_size for f in files)/1024**2, 1)})
+        else:
+            self._json(404, {"error": "not found"})
+
+    def do_POST(self):
+        if self.path == "/generate":
+            self._generate()
+        elif self.path == "/blender":
+            self._blender_render()
+        elif self.path == "/img-to-3d":
+            self._img_to_3d()
+        elif self.path == "/scene-glb":
+            self._scene_glb()
+        else:
+            self._json(404, {"error": "not found"})
+
+    def _generate(self):
+        try:
+            length = int(self.headers.get("Content-Length", 0))
+            body = json.loads(self.rfile.read(length)) if length else {}
+        except:
+            self._json(400, {"error": "invalid JSON"}); return
+
+        prompt = body.get("prompt", "").strip()
+        if not prompt:
+            self._json(400, {"error": "prompt required"}); return
+
+        width = min(max(int(body.get("width", 1280)), 256), 1920)
+        height = min(max(int(body.get("height", 720)), 256), 1080)
+        steps = min(max(int(body.get("steps", 50)), 1), 80)
+        seed = body.get("seed")
+
+        # Cache check
+        key = _cache_key(prompt, width, height, steps)
+        cached = _cache_get(key)
+        if cached:
+            self._json(200, {"image": cached, "format": "webp", "width": width, "height": height,
+                             "cached": True, "prompt": prompt[:200]}); return
+
+        t0 = time.time()
+        img_bytes = None
+
+        # Try ComfyUI first
+        try:
+            comfy_check = urllib.request.urlopen(f"{COMFYUI_URL}/system_stats", timeout=3)
+            if comfy_check.status == 200:
+                img_bytes, seed = _comfyui_generate(prompt, width, height, steps, seed)
+                backend = "comfyui"
+        except:
+            pass
+
+        # Fallback to diffusers
+        if not img_bytes:
+            try:
+                img_bytes, seed = _diffusers_fallback(prompt, width, height, steps, seed)
+                backend = "diffusers"
+            except Exception as e:
+                self._json(500, {"error": str(e)[:300]}); return
+
+        if not img_bytes:
+            self._json(500, {"error": "generation failed"}); return
+
+        elapsed_ms = int((time.time() - t0) * 1000)
+        img_b64 = base64.b64encode(img_bytes).decode()
+        _cache_put(key, img_bytes)
+
+        self._json(200, {
+            "image": img_b64, "format": "webp", "width": width, "height": height,
+            "steps": steps, "seed": seed, "time_ms": elapsed_ms,
+            "backend": backend, "prompt": prompt[:200], "cached": False,
+        })
+        print(f"[IMAGEGEN] {backend} {width}x{height} in {elapsed_ms}ms")
+
+
+    def _blender_render(self):
+        """Render a 3D hero banner via Blender Cycles GPU."""
+        try:
+            length = int(self.headers.get("Content-Length", 0))
+            body = json.loads(self.rfile.read(length)) if length else {}
+        except:
+            self._json(400, {"error": "invalid JSON"}); return
+
+        seed = body.get("seed", random.randint(0, 99999))
+
+        # Cache check
+        key = f"blender-{seed}"
+        cached = _cache_get(key)
+        if cached:
+            self._json(200, {"image": cached, "format": "webp", "backend": "blender-cached",
+                             "cached": True, "seed": seed}); return
+
+        t0 = time.time()
+        output_png = f"/tmp/blender_render_{seed}.png"
+        script = "/opt/ComfyUI/blender_scripts/hero_cycles.py"
+
+        try:
+            import subprocess
+            result = subprocess.run(
+                ["blender", "--background", "--python", script, "--", str(seed), output_png],
+                capture_output=True, text=True, timeout=300
+            )
+
+            if not os.path.exists(output_png):
+                self._json(500, {"error": "Blender render failed: " + result.stderr[-300:] if result.stderr else "no output"}); return
+
+            # Convert to webp
+            from PIL import Image
+            img = Image.open(output_png)
+            buf = io.BytesIO()
+            img.save(buf, format="WEBP", quality=92)
+            img_bytes = buf.getvalue()
+            os.remove(output_png)
+
+            elapsed_ms = int((time.time() - t0) * 1000)
+            img_b64 = base64.b64encode(img_bytes).decode()
+            _cache_put(key, img_bytes)
+
+            self._json(200, {
+                "image": img_b64, "format": "webp", "width": 1280, "height": 320,
+                "seed": seed, "time_ms": elapsed_ms, "backend": "blender-cycles",
+                "cached": False,
+            })
+            print(f"[BLENDER] Rendered seed={seed} in {elapsed_ms}ms")
+
+        except Exception as e:
+            self._json(500, {"error": str(e)[:300]})
+
+    def _img_to_3d(self):
+        """Full pipeline: AI image → 3D displacement → Blender render."""
+        try:
+            length = int(self.headers.get("Content-Length", 0))
+            body = json.loads(self.rfile.read(length)) if length else {}
+        except:
+            self._json(400, {"error": "invalid JSON"}); return
+
+        prompt = body.get("prompt", "abstract flowing golden energy, fractal patterns, dark background, sharp detail").strip()
+        seed = body.get("seed", random.randint(0, 99999))
+
+        key = f"img3d-{hashlib.sha256(prompt.encode()).hexdigest()[:12]}-{seed % 4}"
+        cached = _cache_get(key)
+        if cached:
+            self._json(200, {"image": cached, "format": "webp", "backend": "img3d-cached", "cached": True}); return
+
+        t0 = time.time()
+        try:
+            import subprocess
+
+            # Step 1: Generate source image via ComfyUI directly
+            src_path = f"/tmp/img3d_src_{seed}.png"
+            try:
+                img_bytes_src, _ = _comfyui_generate(prompt, 512, 512, 25, seed)
+                if not img_bytes_src:
+                    # Fallback to diffusers
+                    img_bytes_src, _ = _diffusers_fallback(prompt, 512, 512, 8, seed)
+                if not img_bytes_src:
+                    self._json(500, {"error": "Failed to generate source image"}); return
+                from PIL import Image
+                img_src = Image.open(io.BytesIO(img_bytes_src))
+                img_src.save(src_path, "PNG")
+            except Exception as e:
+                self._json(500, {"error": f"Source image failed: {e}"}); return
+
+            # Step 2: TripoSR — convert image to 3D mesh
+            mesh_path = f"/tmp/triposr_mesh_{seed}.obj"
+            out_path = f"/tmp/img3d_out_{seed}.png"
+            try:
+                # Free VRAM for TripoSR
+                subprocess.run(["systemctl", "stop", "comfyui"], capture_output=True, timeout=10)
+                time.sleep(3)
+
+                triposr_script = f"""
+import torch, sys
+sys.path.insert(0, '/opt/TripoSR')
+from PIL import Image
+from tsr.system import TSR
+model = TSR.from_pretrained('stabilityai/TripoSR', config_name='config.yaml', weight_name='model.ckpt')
+model.to('cuda')
+image = Image.open('{src_path}').convert('RGB')
+with torch.no_grad():
+    scene_codes = model([image], device='cuda')
+meshes = model.extract_mesh(scene_codes, has_vertex_color=True, resolution=128)
+meshes[0].export('{mesh_path}')
+del model; torch.cuda.empty_cache()
+print('[TRIPOSR] mesh exported')
+"""
+                result = subprocess.run(
+                    ["python3", "-c", triposr_script],
+                    capture_output=True, text=True, timeout=120)
+
+                if not os.path.exists(mesh_path):
+                    # Fallback to displacement approach
+                    print(f"[IMG2-3D] TripoSR failed, falling back to displacement: {result.stderr[-200:]}")
+                    script = "/opt/ComfyUI/blender_scripts/image_to_3d.py"
+                    result = subprocess.run(
+                        ["blender", "--background", "--python", script, "--", src_path, out_path, str(seed)],
+                        capture_output=True, text=True, timeout=120)
+                else:
+                    # Step 3: Render the TripoSR mesh in Blender with gold materials
+                    script = "/opt/ComfyUI/blender_scripts/triposr_render.py"
+                    result = subprocess.run(
+                        ["blender", "--background", "--python", script, "--", mesh_path, out_path, str(seed)],
+                        capture_output=True, text=True, timeout=120)
+                    try: os.remove(mesh_path)
+                    except: pass
+            finally:
+                # Always restart ComfyUI
+                subprocess.run(["systemctl", "start", "comfyui"], capture_output=True, timeout=10)
+
+            if not os.path.exists(out_path):
+                self._json(500, {"error": "Blender 3D render failed"}); return
+
+            from PIL import Image
+            img = Image.open(out_path)
+            buf = io.BytesIO()
+            img.save(buf, format="WEBP", quality=92)
+            img_bytes = buf.getvalue()
+
+            # Cleanup temp files
+            for f in [src_path, out_path]:
+                try: os.remove(f)
+                except: pass
+
+            elapsed = int((time.time() - t0) * 1000)
+            img_b64 = base64.b64encode(img_bytes).decode()
+            _cache_put(key, img_bytes)
+
+            self._json(200, {
+                "image": img_b64, "format": "webp", "width": 1280, "height": 320,
+                "seed": seed, "time_ms": elapsed, "backend": "img-to-3d", "cached": False,
+            })
+            print(f"[IMG2-3D] seed={seed} prompt={prompt[:50]} in {elapsed}ms")
+
+        except Exception as e:
+            self._json(500, {"error": str(e)[:300]})
+
+
+    def _scene_glb(self):
+        """Generate a 3D scene and export as GLB for Three.js viewer."""
+        try:
+            length = int(self.headers.get("Content-Length", 0))
+            body = json.loads(self.rfile.read(length)) if length else {}
+        except:
+            self._json(400, {"error": "invalid JSON"}); return
+
+        seed = body.get("seed", random.randint(0, 99999))
+        key = f"glb-{seed}"
+
+        # Check cache
+        glb_cache = CACHE_DIR / f"{key}.glb"
+        if glb_cache.exists():
+            glb_b64 = base64.b64encode(glb_cache.read_bytes()).decode()
+            self._json(200, {"glb": glb_b64, "seed": seed, "cached": True})
+            return
+
+        t0 = time.time()
+        glb_path = f"/tmp/scene_{seed}.glb"
+        try:
+            import subprocess
+            result = subprocess.run(
+                ["blender", "--background", "--python", "/opt/ComfyUI/blender_scripts/export_glb.py",
+                 "--", str(seed), glb_path],
+                capture_output=True, text=True, timeout=120)
+
+            if not os.path.exists(glb_path):
+                self._json(500, {"error": "GLB export failed: " + result.stderr[-200:] if result.stderr else "no output"})
+                return
+
+            glb_bytes = open(glb_path, 'rb').read()
+            os.remove(glb_path)
+            glb_cache.write_bytes(glb_bytes)
+            glb_b64 = base64.b64encode(glb_bytes).decode()
+            elapsed = int((time.time() - t0) * 1000)
+
+            self._json(200, {"glb": glb_b64, "seed": seed, "time_ms": elapsed, "cached": False})
+            print(f"[GLB] seed={seed} in {elapsed}ms size={len(glb_bytes)//1024}KB")
+
+        except Exception as e:
+            self._json(500, {"error": str(e)[:300]})
+
+
+if __name__ == "__main__":
+    print(f"[IMAGEGEN] Starting on port {PORT}")
+    print(f"[IMAGEGEN] ComfyUI backend: {COMFYUI_URL}")
+    print(f"[IMAGEGEN] Cache: {CACHE_DIR}")
+    HTTPServer(("0.0.0.0", PORT), ImageHandler).serve_forever()
diff --git a/scripts/serve_lab.py b/scripts/serve_lab.py
new file mode 100644
index 0000000..1b9dae0
--- /dev/null
+++ b/scripts/serve_lab.py
@@ -0,0 +1,369 @@
+#!/usr/bin/env python3
+"""Pipeline Lab server — serves notebook UI on :3500, proxies API to sidecar :3200."""
+
+import http.server
+import json
+import urllib.request
+import urllib.error
+
+PORT = 3500
+SIDECAR = "http://localhost:3200"
+
+
+class LabHandler(http.server.BaseHTTPRequestHandler):
+    def log_message(self, fmt, *args):
+        pass  # quiet
+
+    def do_GET(self):
+        if self.path == "/" or self.path == "":
+            self._serve_ui()
+        elif self.path.startswith("/lab/"):
+            self._proxy("GET")
+        else:
+            self.send_error(404)
+
+    def do_POST(self):
+        if self.path.startswith("/lab/"):
+            self._proxy("POST")
+        else:
+            self.send_error(404)
+
+    def do_DELETE(self):
+        if self.path.startswith("/lab/"):
+            self._proxy("DELETE")
+        else:
+            self.send_error(404)
+
+    def _proxy(self, method):
+        """Proxy request to sidecar."""
+        url = SIDECAR + self.path
+        body = None
+        if method in ("POST", "PUT"):
+            length = int(self.headers.get("Content-Length", 0))
+            body = self.rfile.read(length) if length else None
+
+        req = urllib.request.Request(url, data=body, method=method)
+        req.add_header("Content-Type", self.headers.get("Content-Type", "application/json"))
+
+        try:
+            with urllib.request.urlopen(req, timeout=120) as resp:
+                data = resp.read()
+                self.send_response(resp.status)
+                self.send_header("Content-Type", resp.headers.get("Content-Type", "application/json"))
+                self.send_header("Access-Control-Allow-Origin", "*")
+                self.end_headers()
+                self.wfile.write(data)
+        except urllib.error.HTTPError as e:
+            self.send_response(e.code)
+            self.send_header("Content-Type", "application/json")
+            self.end_headers()
+            self.wfile.write(e.read())
+        except Exception as e:
+            self.send_response(502)
+            self.send_header("Content-Type", "application/json")
+            self.end_headers()
+            self.wfile.write(json.dumps({"error": str(e)}).encode())
+
+    def _serve_ui(self):
+        self.send_response(200)
+        self.send_header("Content-Type", "text/html; charset=utf-8")
+        self.end_headers()
+        self.wfile.write(HTML.encode())
+
+
+HTML = r"""<!DOCTYPE html>
+<html lang="en"><head>
+<meta charset="UTF-8"><meta name="viewport" content="width=device-width,initial-scale=1.0">
+<title>Pipeline Lab &#x2014; Lakehouse</title>
+<style>
+:root{--bg:#08090c;--surface:rgba(14,16,22,0.9);--border:#2a2d35;--text:#e8e6e3;--text2:#7a7872;--accent:#4ade80;--gold:#e2b55a;--red:#e05252;--blue:#5b9cf5;--purple:#c084fc}
+*{box-sizing:border-box;margin:0;padding:0}
+body{font-family:'SF Mono','Menlo','Consolas',monospace;background:var(--bg);color:var(--text);min-height:100vh;padding:20px 28px;font-size:13px}
+h1{font-size:18px;font-weight:700;margin-bottom:4px}h1 span{color:var(--accent)}
+.subtitle{color:var(--text2);font-size:11px;margin-bottom:20px}
+.nav{display:flex;gap:8px;margin-bottom:16px;font-size:10px}
+.nav a{color:var(--text2);text-decoration:none;padding:4px 10px;border:1px solid var(--border);border-radius:3px}
+.nav a:hover{border-color:var(--accent);color:var(--accent)}
+.cells{display:flex;flex-direction:column;gap:12px;max-width:1100px}
+.cell{background:var(--surface);border:1px solid var(--border);border-radius:4px;overflow:hidden}
+.cell.running{border-color:var(--gold);animation:pulse 1.5s infinite}
+@keyframes pulse{0%,100%{border-color:var(--gold)}50%{border-color:var(--border)}}
+.cell-header{display:flex;align-items:center;gap:8px;padding:8px 12px;border-bottom:1px solid var(--border);font-size:10px;text-transform:uppercase;letter-spacing:1px;color:var(--text2)}
+.cell-type{font-weight:700}
+.cell-time{margin-left:auto}
+.cell-input{padding:12px;background:rgba(0,0,0,0.3)}
+.cell-input textarea{width:100%;min-height:60px;background:transparent;border:none;color:var(--text);font-family:inherit;font-size:13px;resize:vertical;outline:none;line-height:1.6}
+.cell-output{padding:12px;font-size:12px;line-height:1.6;white-space:pre-wrap;max-height:500px;overflow-y:auto;display:none}
+.cell-output.has-data{display:block;border-top:1px solid var(--border)}
+.toolbar{display:flex;gap:6px;padding:8px 12px;border-top:1px solid var(--border);flex-wrap:wrap}
+.btn{font-family:inherit;font-size:10px;text-transform:uppercase;letter-spacing:0.5px;padding:5px 12px;border:1px solid var(--border);border-radius:3px;background:transparent;color:var(--text2);cursor:pointer;transition:all 0.15s}
+.btn:hover{border-color:var(--accent);color:var(--accent)}
+.btn.primary{border-color:var(--accent);color:var(--accent);background:rgba(74,222,128,0.06)}
+.btn.gold{border-color:var(--gold);color:var(--gold)}
+.btn.blue{border-color:var(--blue);color:var(--blue)}
+.btn.purple{border-color:var(--purple);color:var(--purple)}
+.btn.red{border-color:var(--red);color:var(--red)}
+.top-bar{display:flex;gap:8px;margin-bottom:16px;align-items:center;flex-wrap:wrap}
+.status-bar{display:flex;gap:12px;padding:8px 12px;background:var(--surface);border:1px solid var(--border);border-radius:4px;margin-bottom:16px;font-size:10px;color:var(--text2)}
+.stat{display:flex;align-items:center;gap:4px}.stat b{color:var(--text)}
+.result-row{display:flex;gap:8px;padding:6px 8px;border-bottom:1px solid rgba(42,45,53,0.3);align-items:center;font-size:11px}
+.result-row:last-child{border-bottom:none}
+.score-bar{width:60px;height:5px;background:rgba(0,0,0,0.2);border-radius:3px;overflow:hidden}
+.score-fill{height:100%;border-radius:3px}
+.threshold-slider{display:flex;align-items:center;gap:8px;padding:0 12px;margin:4px 0}
+.threshold-slider input[type=range]{flex:1;accent-color:var(--accent)}
+.threshold-slider .val{font-weight:700;min-width:36px;text-align:right}
+</style></head><body>
+<h1><span>Pipeline Lab</span> // Lakehouse</h1>
+<div class="subtitle">Embedding-based screening vs LLM classification &#x2014; iterative experimentation</div>
+<div class="nav">
+  <a href="http://192.168.1.177:3100">Gateway :3100</a>
+  <a href="http://192.168.1.177:3300">UI :3300</a>
+  <a href="http://192.168.1.177:3400">Observer :3400</a>
+</div>
+
+<div class="status-bar" id="status-bar">
+  <div class="stat"><span>Exemplars:</span> <b id="st-exemplars">0</b></div>
+  <div class="stat"><span>Categories:</span> <b id="st-categories">0</b></div>
+  <div class="stat"><span>Pipelines:</span> <b id="st-pipelines">0</b></div>
+  <div class="stat" style="margin-left:auto"><span>Sidecar:</span> <b id="st-health" style="color:var(--text2)">...</b></div>
+</div>
+
+<div class="top-bar">
+  <button class="btn primary" onclick="addCell('exemplars')">+ Exemplars</button>
+  <button class="btn gold" onclick="addCell('screen')">+ Screen</button>
+  <button class="btn blue" onclick="addCell('classify')">+ Classify</button>
+  <button class="btn purple" onclick="addCell('benchmark')">+ Benchmark</button>
+  <button class="btn" onclick="addCell('similarity')">+ Similarity</button>
+  <button class="btn" onclick="addCell('generate')">+ Generate</button>
+  <button class="btn" onclick="addCell('pipeline')">+ Pipeline</button>
+  <span style="flex:1"></span>
+  <button class="btn red" onclick="clearCells()">Clear All</button>
+</div>
+
+<div class="cells" id="cells"></div>
+
+<script>
+var cellCounter = 0;
+
+function esc(t){var d=document.createElement('span');d.textContent=String(t);return d.innerHTML}
+
+async function api(path, body) {
+  var opts = body ? {method:'POST', headers:{'Content-Type':'application/json'}, body:JSON.stringify(body)} : {};
+  var r = await fetch('/lab' + path, opts);
+  if (!r.ok) { var e = await r.json().catch(function(){return {error:'request failed'}}); throw new Error(e.error || r.statusText); }
+  return r.json();
+}
+
+async function refreshStatus() {
+  try {
+    var ex = await api('/exemplars');
+    var pl = await api('/pipelines');
+    document.getElementById('st-exemplars').textContent = ex.total || 0;
+    document.getElementById('st-categories').textContent = Object.keys(ex.categories || {}).length;
+    document.getElementById('st-pipelines').textContent = (pl.pipelines || []).length;
+    document.getElementById('st-health').textContent = 'connected';
+    document.getElementById('st-health').style.color = 'var(--accent)';
+  } catch(e) {
+    document.getElementById('st-health').textContent = 'error: ' + e.message;
+    document.getElementById('st-health').style.color = 'var(--red)';
+  }
+}
+
+var placeholders = {
+  exemplars:'Category: decision\n---\nWe decided to use Parquet for all storage\nThe team chose React over Vue\nArchitecture decision: microservices',
+  screen:'We decided to migrate to PostgreSQL\nThe weather is nice today\nArchitecture: chose event sourcing over CRUD\nLunch was great',
+  classify:'We decided to migrate to PostgreSQL\nThe weather is nice today\nArchitecture: chose event sourcing',
+  benchmark:'We decided to use Kubernetes for orchestration\nThe new hire starts Monday\nTechnical debt: refactor the auth module\nLunch menu looks good today',
+  similarity:'We chose React for the frontend\nReact was selected as our UI framework\nThe database uses PostgreSQL',
+  generate:'Enter a prompt for the LLM...',
+  pipeline:'Pipeline name: my-extraction\n---\nscreen | threshold=0.6\nclassify\nextract | prompt=Extract the key decision and rationale\nvalidate | dedup_threshold=0.9'
+};
+var colors = {exemplars:'var(--accent)',screen:'var(--gold)',classify:'var(--blue)',benchmark:'var(--purple)',similarity:'var(--text2)',generate:'var(--text2)',pipeline:'var(--accent)'};
+var labels = {exemplars:'EXEMPLARS',screen:'SCREEN',classify:'CLASSIFY (LLM)',benchmark:'BENCHMARK A/B',similarity:'SIMILARITY',generate:'GENERATE',pipeline:'PIPELINE'};
+
+function addCell(type) {
+  var id = 'cell-' + (++cellCounter);
+  var cells = document.getElementById('cells');
+  var cell = document.createElement('div'); cell.className = 'cell'; cell.id = id;
+
+  var header = document.createElement('div'); header.className = 'cell-header';
+  var typeSpan = document.createElement('span'); typeSpan.className = 'cell-type'; typeSpan.style.color = colors[type]||'var(--text2)'; typeSpan.textContent = labels[type]||type; header.appendChild(typeSpan);
+  var numSpan = document.createElement('span'); numSpan.textContent = 'Cell #' + cellCounter; header.appendChild(numSpan);
+  var timeSpan = document.createElement('span'); timeSpan.className = 'cell-time'; timeSpan.id = id+'-time'; header.appendChild(timeSpan);
+  cell.appendChild(header);
+
+  var inputDiv = document.createElement('div'); inputDiv.className = 'cell-input';
+  var ta = document.createElement('textarea'); ta.id = id+'-input'; ta.placeholder = placeholders[type]||''; ta.value = placeholders[type]||'';
+  inputDiv.appendChild(ta); cell.appendChild(inputDiv);
+
+  if (type === 'screen' || type === 'benchmark') {
+    var sl = document.createElement('div'); sl.className = 'threshold-slider';
+    var slLbl = document.createElement('span'); slLbl.style.cssText='font-size:10px;color:var(--text2)'; slLbl.textContent='Threshold:'; sl.appendChild(slLbl);
+    var range = document.createElement('input'); range.type='range'; range.min='0.3'; range.max='0.95'; range.step='0.05'; range.value='0.65'; range.id=id+'-threshold';
+    var valSpan = document.createElement('span'); valSpan.className='val'; valSpan.textContent='0.65';
+    range.oninput=function(){valSpan.textContent=this.value};
+    sl.appendChild(range); sl.appendChild(valSpan); cell.appendChild(sl);
+  }
+
+  var outputDiv = document.createElement('div'); outputDiv.className='cell-output'; outputDiv.id=id+'-output'; cell.appendChild(outputDiv);
+
+  var tb = document.createElement('div'); tb.className='toolbar';
+  var runBtn = document.createElement('button'); runBtn.className='btn primary'; runBtn.textContent='Run'; runBtn.onclick=function(){runCell(id,type)}; tb.appendChild(runBtn);
+  var rmBtn = document.createElement('button'); rmBtn.className='btn red'; rmBtn.textContent='Remove'; rmBtn.onclick=function(){cell.remove()}; tb.appendChild(rmBtn);
+  cell.appendChild(tb);
+
+  cells.appendChild(cell); ta.focus();
+}
+
+function clearCells(){document.getElementById('cells').textContent='';cellCounter=0}
+function parseLines(text){return text.split('\n').map(function(l){return l.trim()}).filter(function(l){return l&&l.charAt(0)!=='#'})}
+
+async function runCell(id, type) {
+  var cell = document.getElementById(id);
+  var input = document.getElementById(id+'-input').value;
+  var output = document.getElementById(id+'-output');
+  var timeEl = document.getElementById(id+'-time');
+  cell.classList.add('running');
+  output.className='cell-output has-data'; output.textContent='Running...'; output.style.color='';
+  try {
+    var t0=performance.now(), result;
+
+    if (type==='exemplars') {
+      var parts=input.split('---'); var catLine=(parts[0]||'').trim();
+      var category=catLine.replace(/^category:\s*/i,'').trim().toLowerCase();
+      var texts=parseLines(parts.slice(1).join('\n'));
+      if(!category||!texts.length){output.textContent='Format: Category: name\\n---\\ntext1\\ntext2';return}
+      result=await api('/exemplars',{category:category,texts:texts});
+      output.textContent='Added '+result.added+' exemplars to "'+result.category+'" (total: '+result.total+')';
+      output.style.color='var(--accent)'; refreshStatus();
+    }
+    else if (type==='screen') {
+      var texts=parseLines(input);
+      var threshold=parseFloat((document.getElementById(id+'-threshold')||{}).value||'0.65');
+      result=await api('/screen',{texts:texts,threshold:threshold});
+      renderScreen(output,result,threshold);
+    }
+    else if (type==='classify') {
+      var texts=parseLines(input); result=await api('/classify',{texts:texts});
+      renderClassify(output,result);
+    }
+    else if (type==='benchmark') {
+      var texts=parseLines(input);
+      var threshold=parseFloat((document.getElementById(id+'-threshold')||{}).value||'0.65');
+      result=await api('/benchmark',{texts:texts,threshold:threshold});
+      renderBenchmark(output,result);
+    }
+    else if (type==='similarity') {
+      var texts=parseLines(input); result=await api('/cell',{action:'similarity',texts:texts});
+      renderMatrix(output,result);
+    }
+    else if (type==='generate') {
+      result=await api('/cell',{action:'generate',text:input});
+      output.textContent=result.text||'(empty)';
+    }
+    else if (type==='pipeline') {
+      var parts=input.split('---'); var pName=(parts[0]||'').trim().replace(/^pipeline\s*name:\s*/i,'').trim();
+      var stageLines=parseLines(parts.slice(1).join('\n'));
+      var stages=stageLines.map(function(line){
+        var ps=line.split('|').map(function(s){return s.trim()});var config={};
+        ps.slice(1).forEach(function(p){var kv=p.split('=');if(kv.length===2){var v=kv[1].trim();config[kv[0].trim()]=isNaN(parseFloat(v))?v:parseFloat(v)}});
+        return{name:ps[0],mode:ps[0],config:config};
+      });
+      await api('/pipelines',{name:pName,stages:stages,description:'Pipeline Lab'});
+      output.textContent='Pipeline "'+pName+'" saved ('+stages.length+' stages)';
+      output.style.color='var(--accent)'; refreshStatus();
+    }
+    timeEl.textContent=Math.round(performance.now()-t0)+'ms'+(result&&result.time_ms?' (server: '+result.time_ms+'ms)':'');
+  } catch(e){output.textContent='Error: '+e.message;output.style.color='var(--red)'} finally{cell.classList.remove('running')}
+}
+
+function renderScreen(el,results,threshold){
+  el.textContent='';
+  results.forEach(function(r){
+    var row=document.createElement('div');row.className='result-row';
+    var cat=document.createElement('span');cat.style.cssText='min-width:90px;font-weight:700;color:'+(r.above_threshold?'var(--accent)':'var(--text2)');cat.textContent=r.best_category||'none';row.appendChild(cat);
+    var sim=document.createElement('span');sim.style.cssText='min-width:50px;font-weight:700;color:'+(r.similarity>=0.7?'var(--accent)':r.similarity>=threshold?'var(--gold)':'var(--text2)');sim.textContent=(r.similarity*100).toFixed(1)+'%';row.appendChild(sim);
+    var bar=document.createElement('div');bar.className='score-bar';var fill=document.createElement('div');fill.className='score-fill';fill.style.width=(r.similarity*100)+'%';fill.style.background=r.similarity>=0.7?'var(--accent)':r.similarity>=threshold?'var(--gold)':'var(--red)';bar.appendChild(fill);row.appendChild(bar);
+    var txt=document.createElement('span');txt.style.cssText='flex:1;overflow:hidden;text-overflow:ellipsis;white-space:nowrap';txt.textContent=r.text;row.appendChild(txt);
+    var badge=document.createElement('span');badge.style.cssText='font-size:9px;padding:2px 6px;border-radius:2px;border:1px solid;'+(r.above_threshold?'color:var(--accent);border-color:var(--accent)':'color:var(--text2);border-color:var(--border)');badge.textContent=r.above_threshold?'PASS':'FILTERED';row.appendChild(badge);
+    el.appendChild(row);
+  });
+}
+
+function renderClassify(el,results){
+  el.textContent='';
+  results.forEach(function(r){
+    var row=document.createElement('div');row.className='result-row';
+    var cat=document.createElement('span');cat.style.cssText='min-width:90px;font-weight:700;color:var(--blue)';cat.textContent=r.category;row.appendChild(cat);
+    var conf=document.createElement('span');conf.style.cssText='min-width:50px;font-size:10px;color:'+(r.confidence==='high'?'var(--accent)':r.confidence==='medium'?'var(--gold)':'var(--text2)');conf.textContent=r.confidence;row.appendChild(conf);
+    var txt=document.createElement('span');txt.style.flex='1';txt.textContent=r.text;row.appendChild(txt);
+    el.appendChild(row);
+  });
+}
+
+function renderBenchmark(el,result){
+  el.textContent='';
+  var summary=document.createElement('div');summary.style.cssText='display:flex;gap:12px;margin-bottom:12px;flex-wrap:wrap';
+  [['Agreement',(result.agreement_rate*100).toFixed(1)+'%',result.agreement_rate>=0.8?'var(--accent)':'var(--gold)'],
+   ['Speedup',result.speedup+'x',result.speedup>=2?'var(--accent)':'var(--text)'],
+   ['Embed',result.embed_time_ms+'ms','var(--gold)'],
+   ['LLM',result.llm_time_ms+'ms','var(--blue)'],
+   ['Hybrid',result.hybrid_estimated_ms+'ms','var(--accent)'],
+   ['Screened',result.texts_screened_out+'/'+result.total_texts,'var(--purple)']
+  ].forEach(function(s){
+    var box=document.createElement('div');box.style.cssText='background:rgba(0,0,0,0.2);padding:8px 12px;border-radius:3px;text-align:center';
+    var lbl=document.createElement('div');lbl.style.cssText='font-size:9px;color:var(--text2);text-transform:uppercase;letter-spacing:0.5px';lbl.textContent=s[0];box.appendChild(lbl);
+    var val=document.createElement('div');val.style.cssText='font-size:18px;font-weight:700;color:'+s[2];val.textContent=s[1];box.appendChild(val);
+    summary.appendChild(box);
+  });
+  el.appendChild(summary);
+  // Side by side
+  var grid=document.createElement('div');grid.style.cssText='display:grid;grid-template-columns:1fr 1fr;gap:12px';
+  [[result.embed_results||[],'EMBEDDING ('+result.embed_time_ms+'ms)','var(--gold)',true],
+   [result.llm_results||[],'LLM ('+result.llm_time_ms+'ms)','var(--blue)',false]
+  ].forEach(function(col){
+    var div=document.createElement('div');div.style.cssText='background:rgba(0,0,0,0.2);border-radius:3px;padding:10px';
+    var title=document.createElement('div');title.style.cssText='font-size:10px;text-transform:uppercase;letter-spacing:1px;margin-bottom:6px;font-weight:700;color:'+col[2];title.textContent=col[1];div.appendChild(title);
+    col[0].forEach(function(r){
+      var row=document.createElement('div');row.style.cssText='font-size:11px;padding:3px 0;display:flex;gap:6px;align-items:center';
+      var c=document.createElement('span');c.style.cssText='min-width:70px;font-weight:700;color:'+(col[3]?(r.above_threshold?'var(--accent)':'var(--text2)'):'var(--blue)');c.textContent=(col[3]?r.best_category:r.category)||'none';row.appendChild(c);
+      var s=document.createElement('span');s.style.cssText='min-width:40px;color:var(--text2)';s.textContent=col[3]?(r.similarity*100).toFixed(0)+'%':r.confidence;row.appendChild(s);
+      var t=document.createElement('span');t.style.cssText='flex:1;overflow:hidden;text-overflow:ellipsis;white-space:nowrap';t.textContent=r.text;row.appendChild(t);
+      div.appendChild(row);
+    });
+    grid.appendChild(div);
+  });
+  el.appendChild(grid);
+}
+
+function renderMatrix(el,result){
+  el.textContent='';
+  var matrix=result.matrix||[],texts=result.texts||[];
+  if(!matrix.length){el.textContent='No results';return}
+  var tbl=document.createElement('table');tbl.style.cssText='border-collapse:collapse;font-size:11px;width:100%';
+  var hdr=document.createElement('tr');var corner=document.createElement('th');hdr.appendChild(corner);
+  texts.forEach(function(t){var th=document.createElement('th');th.style.cssText='padding:4px;color:var(--text2);font-size:9px;max-width:100px;overflow:hidden;text-overflow:ellipsis;white-space:nowrap';th.textContent=t.substring(0,20);th.title=t;hdr.appendChild(th)});
+  tbl.appendChild(hdr);
+  matrix.forEach(function(row,i){
+    var tr=document.createElement('tr');
+    var td0=document.createElement('td');td0.style.cssText='padding:4px;color:var(--text2);font-size:9px';td0.textContent=texts[i].substring(0,20);tr.appendChild(td0);
+    row.forEach(function(v,j){
+      var td=document.createElement('td');
+      td.style.cssText='padding:4px;text-align:center;font-weight:'+(v>=0.7?'700':'400')+';color:'+(v>=0.8?'var(--accent)':v>=0.6?'var(--gold)':'var(--text2)')+';background:'+(i===j?'rgba(74,222,128,0.1)':v>=0.8?'rgba(74,222,128,0.15)':v>=0.6?'rgba(226,181,90,0.1)':'transparent');
+      td.textContent=v.toFixed(2);tr.appendChild(td);
+    });
+    tbl.appendChild(tr);
+  });
+  el.appendChild(tbl);
+}
+
+refreshStatus();
+</script>
+</body></html>"""
+
+
+if __name__ == "__main__":
+    print(f"Pipeline Lab running on http://0.0.0.0:{PORT}")
+    server = http.server.HTTPServer(("0.0.0.0", PORT), LabHandler)
+    server.serve_forever()