superbrain-server - npm Package Compare versions

Comparing version 1.0.60 to 2.0.0

package.json

 {
   "name": "superbrain-server",
-  "version": "1.0.60",
+  "version": "2.0.0",
   "description": "1-Line Auto-Installer and Server Execution wrapper for SuperBrain",
@@ -5,0 +5,0 @@ "main": "index.js",

payload/config/model_rankings.json

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@@ -651,2 +651,12 @@ "last_error": "No module named 'ollama'",
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@@ -660,3 +670,3 @@ "key": "dyn_nvidia_nemotron-nano-9b-v2_free",
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+    "base_priority": 29
   },
@@ -671,3 +681,3 @@ "dyn_minimax_minimax-m2_5_free": {
     "last_error": null,
-    "base_priority": 29
+    "base_priority": 30
   },
@@ -682,3 +692,3 @@ "dyn_qwen_qwen3-coder_free": {
     "last_error": null,
-    "base_priority": 30
+    "base_priority": 31
   },
@@ -693,3 +703,3 @@ "dyn_liquid_lfm-2_5-1_2b-thinking_free": {
     "last_error": null,
-    "base_priority": 31
+    "base_priority": 32
   },
@@ -704,3 +714,3 @@ "dyn_liquid_lfm-2_5-1_2b-instruct_free": {
     "last_error": null,
-    "base_priority": 32
+    "base_priority": 33
   },
@@ -715,3 +725,3 @@ "dyn_arcee-ai_trinity-large-preview_free": {
     "last_error": null,
-    "base_priority": 34
+    "base_priority": 35
   },
@@ -726,3 +736,3 @@ "dyn_meta-llama_llama-3_2-3b-instruct_free": {
     "last_error": null,
-    "base_priority": 38
+    "base_priority": 39
   },
@@ -737,3 +747,3 @@ "dyn_google_gemma-3n-e2b-it_free": {
     "last_error": null,
-    "base_priority": 42
+    "base_priority": 43
   },
@@ -748,3 +758,3 @@ "dyn_google_gemma-3-4b-it_free": {
     "last_error": null,
-    "base_priority": 43
+    "base_priority": 44
   },
@@ -759,4 +769,24 @@ "dyn_v_google_gemma-3-4b-it_free": {
     "last_error": null,
-    "base_priority": 43
+    "base_priority": 44
   },
+  "dyn_meta-llama_llama-guard-4-12b_free": {
+    "key": "dyn_meta-llama_llama-guard-4-12b_free",
+    "avg_response_s": null,
+    "success_count": 0,
+    "fail_count": 0,
+    "down_until": null,
+    "last_used": null,
+    "last_error": null,
+    "base_priority": 45
+  },
+  "dyn_v_meta-llama_llama-guard-4-12b_free": {
+    "key": "dyn_v_meta-llama_llama-guard-4-12b_free",
+    "avg_response_s": null,
+    "success_count": 0,
+    "fail_count": 0,
+    "down_until": null,
+    "last_used": null,
+    "last_error": null,
+    "base_priority": 45
+  },
   "dyn_google_gemma-3n-e4b-it_free": {
@@ -770,3 +800,3 @@ "key": "dyn_google_gemma-3n-e4b-it_free",
     "last_error": null,
-    "base_priority": 44
+    "base_priority": 46
   },
@@ -781,3 +811,3 @@ "dyn_google_gemma-3-12b-it_free": {
     "last_error": null,
-    "base_priority": 45
+    "base_priority": 47
   },
@@ -792,4 +822,4 @@ "dyn_v_google_gemma-3-12b-it_free": {
     "last_error": null,
-    "base_priority": 45
+    "base_priority": 47
   }
 }

payload/config/openrouter_free_models.json

 {
-  "cached_at": "2026-04-10T06:49:01.068927",
+  "cached_at": "2026-04-16T05:18:30.220245",
   "models": [
@@ -431,2 +431,54 @@ {
     {
+      "id": "openrouter/elephant-alpha",
+      "canonical_slug": "openrouter/elephant-alpha",
+      "hugging_face_id": null,
+      "name": "Elephant",
+      "created": 1776052598,
+      "description": "Elephant Alpha is a 100B-parameter text model focused on intelligence efficiency, delivering strong performance while minimizing token usage. It supports a 256K context window with up to 32K output tokens,...",
+      "context_length": 262144,
+      "architecture": {
+        "modality": "text->text",
+        "input_modalities": [
+          "text"
+        ],
+        "output_modalities": [
+          "text"
+        ],
+        "tokenizer": "Other",
+        "instruct_type": null
+      },
+      "pricing": {
+        "prompt": "0",
+        "completion": "0"
+      },
+      "top_provider": {
+        "context_length": 262144,
+        "max_completion_tokens": 32768,
+        "is_moderated": false
+      },
+      "per_request_limits": null,
+      "supported_parameters": [
+        "max_tokens",
+        "response_format",
+        "structured_outputs",
+        "temperature",
+        "tool_choice",
+        "tools",
+        "top_p"
+      ],
+      "default_parameters": {
+        "temperature": null,
+        "top_p": null,
+        "top_k": null,
+        "frequency_penalty": null,
+        "presence_penalty": null,
+        "repetition_penalty": null
+      },
+      "knowledge_cutoff": null,
+      "expiration_date": null,
+      "links": {
+        "details": "/api/v1/models/openrouter/elephant-alpha/endpoints"
+      }
+    },
+    {
       "id": "nvidia/nemotron-nano-9b-v2:free",
@@ -508,3 +560,3 @@ "canonical_slug": "nvidia/nemotron-nano-9b-v2",
         "context_length": 196608,
-        "max_completion_tokens": 196608,
+        "max_completion_tokens": 8192,
         "is_moderated": true
@@ -916,3 +968,3 @@ },
         "context_length": 131072,
-        "max_completion_tokens": 131072,
+        "max_completion_tokens": 8192,
         "is_moderated": true
@@ -988,13 +1040,14 @@ },
     {
-      "id": "cognitivecomputations/dolphin-mistral-24b-venice-edition:free",
-      "canonical_slug": "venice/uncensored",
-      "hugging_face_id": "cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition",
-      "name": "Venice: Uncensored (free)",
-      "created": 1752094966,
-      "description": "Venice Uncensored Dolphin Mistral 24B Venice Edition is a fine-tuned variant of Mistral-Small-24B-Instruct-2501, developed by dphn.ai in collaboration with Venice.ai. This model is designed as an \u201cuncensored\u201d instruct-tuned LLM, preserving...",
-      "context_length": 32768,
+      "id": "google/gemma-3-27b-it:free",
+      "canonical_slug": "google/gemma-3-27b-it",
+      "hugging_face_id": "google/gemma-3-27b-it",
+      "name": "Google: Gemma 3 27B (free)",
+      "created": 1741756359,
+      "description": "Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities,...",
+      "context_length": 131072,
       "architecture": {
-        "modality": "text->text",
+        "modality": "text+image->text",
         "input_modalities": [
-          "text"
+          "text",
+          "image"
         ],
@@ -1004,4 +1057,4 @@ "output_modalities": [
         ],
-        "tokenizer": "Other",
-        "instruct_type": null
+        "tokenizer": "Gemini",
+        "instruct_type": "gemma"
       },
@@ -1013,4 +1066,4 @@ "pricing": {
       "top_provider": {
-        "context_length": 32768,
-        "max_completion_tokens": null,
+        "context_length": 131072,
+        "max_completion_tokens": 8192,
         "is_moderated": false
@@ -1020,32 +1073,32 @@ },
       "supported_parameters": [
-        "frequency_penalty",
         "max_tokens",
-        "presence_penalty",
         "response_format",
+        "seed",
         "stop",
-        "structured_outputs",
         "temperature",
-        "top_k",
         "top_p"
       ],
-      "default_parameters": {},
-      "knowledge_cutoff": "2024-04-30",
+      "default_parameters": {
+        "temperature": null,
+        "top_p": null,
+        "frequency_penalty": null
+      },
+      "knowledge_cutoff": "2024-08-31",
       "expiration_date": null,
       "links": {
-        "details": "/api/v1/models/venice/uncensored/endpoints"
+        "details": "/api/v1/models/google/gemma-3-27b-it/endpoints"
       }
     },
     {
-      "id": "google/gemma-3-27b-it:free",
-      "canonical_slug": "google/gemma-3-27b-it",
-      "hugging_face_id": "google/gemma-3-27b-it",
-      "name": "Google: Gemma 3 27B (free)",
-      "created": 1741756359,
-      "description": "Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities,...",
-      "context_length": 131072,
+      "id": "cognitivecomputations/dolphin-mistral-24b-venice-edition:free",
+      "canonical_slug": "venice/uncensored",
+      "hugging_face_id": "cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition",
+      "name": "Venice: Uncensored (free)",
+      "created": 1752094966,
+      "description": "Venice Uncensored Dolphin Mistral 24B Venice Edition is a fine-tuned variant of Mistral-Small-24B-Instruct-2501, developed by dphn.ai in collaboration with Venice.ai. This model is designed as an \u201cuncensored\u201d instruct-tuned LLM, preserving...",
+      "context_length": 32768,
       "architecture": {
-        "modality": "text+image->text",
+        "modality": "text->text",
         "input_modalities": [
-          "text",
-          "image"
+          "text"
         ],
@@ -1055,4 +1108,4 @@ "output_modalities": [
         ],
-        "tokenizer": "Gemini",
-        "instruct_type": "gemma"
+        "tokenizer": "Other",
+        "instruct_type": null
       },
@@ -1064,4 +1117,4 @@ "pricing": {
       "top_provider": {
-        "context_length": 131072,
-        "max_completion_tokens": 8192,
+        "context_length": 32768,
+        "max_completion_tokens": null,
         "is_moderated": false
@@ -1071,18 +1124,17 @@ },
       "supported_parameters": [
+        "frequency_penalty",
         "max_tokens",
+        "presence_penalty",
         "response_format",
-        "seed",
         "stop",
+        "structured_outputs",
         "temperature",
+        "top_k",
         "top_p"
       ],
-      "default_parameters": {
-        "temperature": null,
-        "top_p": null,
-        "frequency_penalty": null
-      },
-      "knowledge_cutoff": "2024-08-31",
+      "default_parameters": {},
+      "knowledge_cutoff": "2024-04-30",
       "expiration_date": null,
       "links": {
-        "details": "/api/v1/models/google/gemma-3-27b-it/endpoints"
+        "details": "/api/v1/models/venice/uncensored/endpoints"
       }
@@ -1228,2 +1280,45 @@ },
     {
+      "id": "meta-llama/llama-guard-4-12b:free",
+      "canonical_slug": "meta-llama/llama-guard-4-12b",
+      "hugging_face_id": "meta-llama/Llama-Guard-4-12B",
+      "name": "Meta: Llama Guard 4 12B (free)",
+      "created": 1745975193,
+      "description": "Llama Guard 4 is a Llama 4 Scout-derived multimodal pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM...",
+      "context_length": 163840,
+      "architecture": {
+        "modality": "text+image->text",
+        "input_modalities": [
+          "image",
+          "text"
+        ],
+        "output_modalities": [
+          "text"
+        ],
+        "tokenizer": "Other",
+        "instruct_type": null
+      },
+      "pricing": {
+        "prompt": "0",
+        "completion": "0"
+      },
+      "top_provider": {
+        "context_length": 163840,
+        "max_completion_tokens": 65000,
+        "is_moderated": false
+      },
+      "per_request_limits": null,
+      "supported_parameters": [
+        "max_tokens",
+        "seed",
+        "temperature",
+        "top_p"
+      ],
+      "default_parameters": {},
+      "knowledge_cutoff": "2024-08-31",
+      "expiration_date": null,
+      "links": {
+        "details": "/api/v1/models/meta-llama/llama-guard-4-12b/endpoints"
+      }
+    },
+    {
       "id": "google/gemma-3n-e4b-it:free",
@@ -1230,0 +1325,0 @@ "canonical_slug": "google/gemma-3n-e4b-it",

payload/core/database.py

@@ -163,2 +163,13 @@ #!/usr/bin/env python3
 
+        # Deleted-log table — tracks when posts are soft-deleted so the
+        # mobile app can sync deletions via /sync/deleted.
+        self._conn.executescript("""
+            CREATE TABLE IF NOT EXISTS deleted_log (
+                shortcode   TEXT PRIMARY KEY,
+                deleted_at  TEXT
+            );
+            CREATE INDEX IF NOT EXISTS idx_deleted_log_at ON deleted_log (deleted_at);
+        """)
+        self._conn.commit()
+
     # ------------------------------------------------------------------
@@ -168,2 +179,8 @@ # Helpers
 
+    # Columns safe to send to the mobile app (excludes heavy analysis blobs)
+    LIGHT_COLUMNS = (
+        "shortcode, url, username, content_type, analyzed_at, updated_at, "
+        "post_date, likes, thumbnail, title, summary, tags, music, category, is_hidden"
+    )
+
     def _row_to_dict(self, row):
@@ -265,2 +282,64 @@ if row is None:
 
+    def get_recent_light(self, limit=50, offset=0):
+        """Return recent posts with only UI-essential fields (no analysis blobs)."""
+        if not self.is_connected():
+            return []
+        try:
+            cur = self._conn.cursor()
+            cur.execute(
+                f"SELECT {self.LIGHT_COLUMNS} FROM analyses "
+                "WHERE (is_hidden IS NULL OR is_hidden = 0) "
+                "ORDER BY analyzed_at DESC LIMIT ? OFFSET ?",
+                (limit, offset)
+            )
+            return [self._row_to_dict(r) for r in cur.fetchall()]
+        except Exception as e:
+            print(f"[WARNING]  Error retrieving recent (light): {e}")
+            return []
+
+    def get_posts_since(self, updated_after: str, limit=1000):
+        """Return posts updated after the given ISO timestamp (delta sync).
+        Includes soft-deleted posts so the app knows to hide them."""
+        if not self.is_connected():
+            return []
+        try:
+            cur = self._conn.cursor()
+            cur.execute(
+                f"SELECT {self.LIGHT_COLUMNS} FROM analyses "
+                "WHERE updated_at > ? "
+                "ORDER BY updated_at ASC LIMIT ?",
+                (updated_after, limit)
+            )
+            return [self._row_to_dict(r) for r in cur.fetchall()]
+        except Exception as e:
+            print(f"[WARNING]  Error getting posts since {updated_after}: {e}")
+            return []
+
+    def get_deleted_since(self, since: str):
+        """Return shortcodes of posts deleted after the given ISO timestamp."""
+        if not self.is_connected():
+            return []
+        try:
+            cur = self._conn.cursor()
+            cur.execute(
+                "SELECT shortcode, deleted_at FROM deleted_log WHERE deleted_at > ? ORDER BY deleted_at ASC",
+                (since,)
+            )
+            return [{"shortcode": r["shortcode"], "deleted_at": r["deleted_at"]} for r in cur.fetchall()]
+        except Exception as e:
+            print(f"[WARNING]  Error getting deleted since {since}: {e}")
+            return []
+
+    def get_total_count(self):
+        """Return total number of visible (non-hidden) posts."""
+        if not self.is_connected():
+            return 0
+        try:
+            cur = self._conn.cursor()
+            cur.execute("SELECT COUNT(*) FROM analyses WHERE (is_hidden IS NULL OR is_hidden = 0)")
+            return cur.fetchone()[0]
+        except Exception as e:
+            print(f"[WARNING]  Error getting total count: {e}")
+            return 0
+
     def get_by_category(self, category, limit=20):
@@ -640,6 +719,13 @@ """Return all analyses for a given category (excludes soft-deleted)."""
         try:
+            now = datetime.utcnow().isoformat()
             cur = self._conn.execute(
                 "UPDATE analyses SET is_hidden = 1, updated_at = ? WHERE shortcode = ?",
-                (datetime.utcnow().isoformat(), shortcode)
+                (now, shortcode)
             )
+            if cur.rowcount > 0:
+                # Record in deleted_log so mobile app can sync deletions
+                self._conn.execute(
+                    "INSERT OR REPLACE INTO deleted_log (shortcode, deleted_at) VALUES (?, ?)",
+                    (shortcode, now)
+                )
             self._conn.commit()
@@ -646,0 +732,0 @@ return cur.rowcount > 0

New alerts

AI-detected potential code anomaly

Supply chain risk

AI has identified unusual behaviors that may pose a security risk.

Fixed alerts

AI-detected potential code anomaly

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AI has identified unusual behaviors that may pose a security risk.

Improved metrics

Total package byte prevSize

523056

2.3%

Lines of code

11293

2.31%

No dependency changes