- ') || match.includes('
- ')) return match;\n return `
- \\n${match}
- \\n${match.replace(/<\\/?uli>/g, (t) => t.replace('uli', 'li'))}
$1');\n html = html.replace(/^---$/gm, '');\n \n const blocks = html.split(/\\n\\n+/);\n html = blocks.map(b => {\n b = b.trim();\n if (!b) return '';\n if (b.startsWith('<')) return b;\n return `
${b}
`;\n }).join('\\n');\n \n return html;\n}\n\nconst htmlContent = convertMarkdownToHtml(markdownContent);\n\nconst fullHtml = `\n\n\n \nAdam Goodyer
\n\n \n
\n \n
\n
\n www.adamgoodyer.com\n
\n\n
${documentTitle}
\n ${htmlContent}\n\n
Adam Goodyer | www.adamgoodyer.com
\n\n`;\n\n// ============================================\n// BUILD MULTIPART REQUEST\n// ============================================\nconst boundary = 'docboundary';\n\nconst metadata = JSON.stringify({\n name: documentTitle,\n mimeType: \"application/vnd.google-apps.document\",\n parents: [FOLDER_ID]\n});\n\nlet body = `--${boundary}\\r\\n`;\nbody += `Content-Type: application/json; charset=UTF-8\\r\\n`;\nbody += `\\r\\n`;\nbody += `${metadata}\\r\\n`;\nbody += `--${boundary}\\r\\n`;\nbody += `Content-Type: text/html\\r\\n`;\nbody += `\\r\\n`;\nbody += `${fullHtml}\\r\\n`;\nbody += `--${boundary}--\\r\\n`;\n\nreturn {\n json: {\n document_name: documentTitle,\n rawData: body\n }\n};"},"typeVersion":2},{"id":"e3fca234-7066-4fd2-a5ac-502a25dfa41a","name":"Create Google Doc","type":"n8n-nodes-base.httpRequest","position":[2816,1504],"parameters":{"url":"https://www.googleapis.com/upload/drive/v3/files","body":"={{ $json.rawData }}","method":"POST","options":{},"sendBody":true,"sendQuery":true,"contentType":"raw","authentication":"predefinedCredentialType","rawContentType":"=multipart/related; boundary=docboundary","queryParameters":{"parameters":[{"name":"uploadType","value":"multipart"},{"name":"supportsAllDrives","value":"true"}]},"nodeCredentialType":"googleDriveOAuth2Api"},"credentials":{"googleDriveOAuth2Api":{"id":"RJqb4CuqNHs25OT1","name":"adam@apgsoftware.com"}},"typeVersion":4.3},{"id":"77fa8962-0ba3-4bf1-8916-a6cc243ab42f","name":"Split Out1","type":"n8n-nodes-base.splitOut","position":[2816,1840],"parameters":{"options":{},"fieldToSplitOut":"flash_broll"},"typeVersion":1},{"id":"8c992920-5bbc-439d-8d8b-2a164a364c38","name":"Aggregate Flash B-Roll","type":"n8n-nodes-base.aggregate","position":[3488,1840],"parameters":{"options":{},"aggregate":"aggregateAllItemData","destinationFieldName":"flash_broll_generated"},"typeVersion":1},{"id":"3add70c3-d3ff-4938-9c85-b94a9d37805c","name":"Google Gemini Chat Model3","type":"@n8n/n8n-nodes-langchain.lmChatGoogleGemini","position":[5280,1968],"parameters":{"options":{},"modelName":"models/gemini-pro-latest"},"credentials":{"googlePalmApi":{"id":"VGx11JvjNsOjei60","name":"n8n-gmail adam gemini key"}},"retryOnFail":true,"typeVersion":1,"waitBetweenTries":5000},{"id":"35228b26-530c-4263-9984-41cbe4fa2e75","name":"Structured Output Parser1","type":"@n8n/n8n-nodes-langchain.outputParserStructured","position":[5408,1968],"parameters":{"autoFix":true,"schemaType":"manual","inputSchema":"{\n \"type\": \"object\",\n \"properties\": {\n \"concept_index\": {\n \"type\": \"number\",\n \"description\": \"Index of the concept\"\n },\n \"concept_type\": {\n \"type\": \"string\",\n \"enum\": [\"pain\", \"curiosity\", \"transformation\"],\n \"description\": \"Type of concept hook\"\n },\n \"title\": {\n \"type\": \"string\",\n \"description\": \"Title of the video\"\n },\n \"audio_track\": {\n \"type\": \"number\",\n \"description\": \"Track number from the effects library audio_tracks section\"\n },\n \"total_duration_seconds\": {\n \"type\": \"number\",\n \"description\": \"Total video duration in seconds - must match avatar_duration exactly\"\n },\n \"total_beats\": {\n \"type\": \"number\",\n \"description\": \"Total number of beats. Target: 8-10 for 30s, 12-15 for 45s, 15-20 for 60s\"\n },\n \"total_flashes\": {\n \"type\": \"number\",\n \"description\": \"Number of flash-type transitions used. Check effects library guidelines for target.\"\n },\n \"enhanced_storyboard\": {\n \"type\": \"array\",\n \"description\": \"Array of storyboard beats\",\n \"items\": {\n \"type\": \"object\",\n \"properties\": {\n \"beat_index\": {\n \"type\": \"number\",\n \"description\": \"Index of this beat starting from 0\"\n },\n \"beat_type\": {\n \"type\": \"string\",\n \"enum\": [\"hook_snap\", \"hook_reveal\", \"body_flash\", \"body_demo\", \"body_emphasis\", \"cta_close\"],\n \"description\": \"Type of beat\"\n },\n \"duration_seconds\": {\n \"type\": \"number\",\n \"description\": \"STRICT: 2-4 seconds per beat. NEVER exceed 4 seconds. Must match AI B-roll duration if used.\"\n },\n \"narration\": {\n \"type\": \"string\",\n \"description\": \"Narration text for this beat\"\n },\n \"layout\": {\n \"type\": \"string\",\n \"description\": \"Layout ID from the effects library layouts section. First/last beats use avatar-only layouts. Middle beats use split or fullscreen layouts.\"\n },\n \"transition_in\": {\n \"type\": \"string\",\n \"description\": \"Transition ID from effects library transitions section. Empty string for first beat. Fade-type only for CTA. Never repeat consecutively.\"\n },\n \"screen_motion\": {\n \"type\": \"string\",\n \"description\": \"Screen motion ID from effects library screen_motions section. Required for split/fullscreen layouts with B-roll. Empty string for avatar-only beats.\"\n },\n \"avatar_animation\": {\n \"type\": \"string\",\n \"description\": \"Avatar animation ID from effects library avatar_animations section. Empty string if none.\"\n },\n \"text_overlay\": {\n \"type\": \"string\",\n \"description\": \"Always empty string - captions handle all text\"\n },\n \"text_content\": {\n \"type\": \"string\",\n \"description\": \"Always empty string - captions handle all text\"\n },\n \"energy\": {\n \"type\": \"string\",\n \"enum\": [\"high\", \"medium\", \"low\"],\n \"description\": \"Energy level of this beat\"\n },\n \"snap_word\": {\n \"type\": \"string\",\n \"description\": \"Attention phrase for flash-type transitions. Empty string if transition is not a flash type.\"\n },\n \"broll_type\": {\n \"type\": \"string\",\n \"enum\": [\"none\", \"screen\", \"ai_generated\"],\n \"description\": \"Type of B-roll. CRITICAL: 'none' only valid for avatar-only layouts (last beat). Split and fullscreen layouts MUST use 'screen' or 'ai_generated'. Using 'none' with split/fullscreen layouts causes black screen errors.\"\n },\n \"broll_index\": {\n \"type\": \"number\",\n \"description\": \"Index of screen share B-roll clip. Use -1 if broll_type is not 'screen'.\"\n },\n \"broll_start_seconds\": {\n \"type\": \"number\",\n \"description\": \"Start timestamp within screen share clip. Use -1 if broll_type is not 'screen'.\"\n },\n \"broll_end_seconds\": {\n \"type\": \"number\",\n \"description\": \"End timestamp within screen share clip. Use -1 if broll_type is not 'screen'.\"\n },\n \"ai_broll_index\": {\n \"type\": \"number\",\n \"description\": \"Index of AI-generated B-roll clip (0, 1, or 2). Use -1 if broll_type is not 'ai_generated'. CRITICAL: When not -1, duration_seconds MUST exactly match the AI B-roll clip duration.\"\n },\n \"visual_description\": {\n \"type\": \"string\",\n \"description\": \"Description of the visual for this beat\"\n }\n },\n \"required\": [\n \"beat_index\",\n \"beat_type\",\n \"duration_seconds\",\n \"narration\",\n \"layout\",\n \"transition_in\",\n \"screen_motion\",\n \"avatar_animation\",\n \"text_overlay\",\n \"text_content\",\n \"energy\",\n \"snap_word\",\n \"broll_type\",\n \"broll_index\",\n \"broll_start_seconds\",\n \"broll_end_seconds\",\n \"ai_broll_index\",\n \"visual_description\"\n ]\n }\n }\n },\n \"required\": [\n \"concept_index\",\n \"concept_type\",\n \"title\",\n \"audio_track\",\n \"total_duration_seconds\",\n \"total_beats\",\n \"total_flashes\",\n \"enhanced_storyboard\"\n ]\n}"},"retryOnFail":true,"typeVersion":1.3,"waitBetweenTries":5000},{"id":"7a8abe64-ac62-4011-ad85-3b4141975983","name":"Google Gemini Chat Model4","type":"@n8n/n8n-nodes-langchain.lmChatGoogleGemini","position":[5488,2176],"parameters":{"options":{},"modelName":"models/gemini-pro-latest"},"credentials":{"googlePalmApi":{"id":"VGx11JvjNsOjei60","name":"n8n-gmail adam gemini key"}},"typeVersion":1},{"id":"508291e8-c591-4516-8334-27a92db9fefe","name":"AI Video Director","type":"@n8n/n8n-nodes-langchain.agent","maxTries":3,"position":[5312,1744],"parameters":{"text":"=Transform this concept into a production-ready storyboard.\n\nAvatar Video Duration (Seconds): {{ $json.avatar_duration }}\n\n## CONCEPT INFO\n- **Index:** {{ $('Loop Through Concepts').item.json.concept_number }}\n- **Type:** {{ $('Loop Through Concepts').item.json.concept_type }}\n- **Title:** {{ $('Loop Through Concepts').item.json.title }}\n\n## FULL SCRIPT\n{{ $('Loop Through Concepts').item.json.full_script }}\n\n## CURRENT STORYBOARD\n{{ JSON.stringify($('Loop Through Concepts').item.json.storyboard, null, 2) }}\n\n## AVAILABLE SCREEN SHARE B-ROLL\nThese are screen recording clips from actual software demos. Use with broll_type: \"screen\".\n{{ JSON.stringify($('Extract Snippets').item.json.broll_clips, null, 2) }}\n\n## AVAILABLE AI-GENERATED B-ROLL\nThese are cinematic AI-generated clips for visual impact. \nUse with broll_type: \"ai_generated\" and specify ai_broll_index (0, 1, or 2).\n\n{{ JSON.stringify($('Aggregate Flash B-Roll').item.json.flash_broll_generated.filter(item => item.status === 'succeeded'), null, 2) }}\n\n**AI B-ROLL RULES:**\n- **⚠️ CRITICAL: Any beat using AI B-roll MUST have the same duration_seconds\n- Use ai_broll_index: 0, 1, or 2 to select which clip\n- Best for: hook_reveal, body_flash, or high-impact transitions\n- DO NOT overlap with screen B-roll - choose one type per beat\n\n## TIMING\n\n- Total: {{ $('Loop Through Concepts').item.json.total_duration_seconds }}seconds\n\n---\n\n## CREATOMATE EFFECTS LIBRARY\nUse ONLY these exact IDs when specifying effects:\n\n### AVAILABLE LAYOUTS\n{{ JSON.stringify($('Creatomate Effects Library').item.json.layouts, null, 2) }}\n\n### AVAILABLE TRANSITIONS\n{{ JSON.stringify($('Creatomate Effects Library').item.json.transitions, null, 2) }}\n\n### AVAILABLE SCREEN MOTIONS\n{{ JSON.stringify($('Creatomate Effects Library').item.json.screen_motions, null, 2) }}\n\n### AVAILABLE AVATAR ANIMATIONS\n{{ JSON.stringify($('Creatomate Effects Library').item.json.avatar_animations, null, 2) }}\n\n### AVAILABLE TEXT STYLES\n{{ JSON.stringify($('Creatomate Effects Library').item.json.text_styles, null, 2) }}\n\n### AUDIO TRACKS\n{{ JSON.stringify($('Creatomate Effects Library').item.json.audio_tracks, null, 2) }}\n\n### USAGE GUIDELINES\n{{ JSON.stringify($('Creatomate Effects Library').item.json.guidelines, null, 2) }}\n\n","options":{"systemMessage":"=You are an elite short-form video director specializing in HIGH-RETENTION YouTube Shorts for @adamfreelances.\n\n## CORE PHILOSOPHY\n\n\"If the screen hasn't changed in 3 seconds, you're losing viewers.\"\n\nYour job: Transform a CONCEPT into a BEAT-BY-BEAT STORYBOARD that maximizes dopamine response and retention.\n\n## THE PSYCHOLOGY OF RETENTION\n\nShort-form viewers are in a STATE OF SCANNING. Each cut must:\n1. INTERRUPT the scroll impulse\n2. CREATE micro-tension\n3. RESOLVE with a mini-payoff\n4. REPEAT before boredom sets in\n\n---\n\n## AVATAR DURATION CONSTRAINT (CRITICAL)\n\nThe avatar video has already been generated with a FIXED duration. Your storyboard MUST match this EXACTLY.\n\n**AVATAR VIDEO DURATION = STORYBOARD TOTAL DURATION**\n\nThis is non-negotiable. The sum of all beat durations must equal the avatar duration.\n\n### Duration Matching Strategy:\n1. Note the avatar_duration provided in the user message\n2. Plan beats 1 through N-1 with whole-second durations\n3. The FINAL BEAT (cta_close) absorbs any remainder to hit the exact total\n4. Verify: `sum(all beat durations) = avatar_duration`\n\n**Example:**\n- Avatar duration: 47.6 seconds\n- Beats 1-12 (whole seconds): 44 seconds\n- Final CTA beat: 3.6 seconds (absorbs the .6 remainder)\n\nThe CTA beat is flexible (3-4 sec base) and can have decimal durations to ensure exact match.\n\n---\n\n## EFFECTS LIBRARY REFERENCE\n\nYou will receive the full Creatomate Effects Library below. This is your SOURCE OF TRUTH for all available:\n- Layouts (screen configurations)\n- Transitions (visual effects between beats)\n- Screen motions (zoom/pan effects)\n- Avatar animations\n- Audio tracks\n\n**USE ONLY the exact IDs provided in the effects library. Do not invent new IDs.**\n\n### EFFECTS LIBRARY:\n{{ JSON.stringify($('Creatomate Effects Library').item.json, null, 2) }}\n\n---\n\n## LAYOUT SYSTEM\n\nThe effects library defines several layout types. They fall into these categories:\n\n### 1. AVATAR-ONLY LAYOUTS\n- Avatar fills the entire frame\n- **RESTRICTION: Use ONLY for the LAST beat (cta_close)**\n- No B-roll content - just the avatar\n\n### 2. SPLIT LAYOUTS\n- Screen/B-roll content in top portion, avatar in bottom portion\n- Various split ratios available (check effects library for options)\n- Use for: Technical demos, screen recordings, teaching moments, **AND the opening hook**\n- **Requires screen B-roll** (broll_type: \"screen\")\n\n### 3. FULLSCREEN B-ROLL LAYOUTS\n- B-roll fills entire frame, no avatar visible\n- Use for: Cinematic AI-generated clips, dramatic reveals\n- **Requires AI-generated B-roll** (broll_type: \"ai_generated\")\n\n### 4. PADDED SCREEN LAYOUTS\n- Fullscreen screen recording with centered padding and glow\n- Use for: Widescreen desktop recordings without avatar\n- **Requires screen B-roll** (broll_type: \"screen\")\n\n### Layout Rules:\n- **First beat:** Must use 50/50 SPLIT layout with scene-setting screen B-roll\n- **Last beat:** Must use avatar-only layout\n- **All middle beats:** Must use split, fullscreen, or padded layouts - NEVER avatar-only\n- **Never repeat** the same layout consecutively (except first beat 50/50 can transition to different split ratio)\n- **Alternate** between different split ratios and fullscreen for variety\n\n---\n\n## FIRST BEAT REQUIREMENTS (CRITICAL)\n\nThe opening beat (hook_snap) has SPECIAL REQUIREMENTS to maximize scroll-stopping impact:\n\n### Layout\n- **MUST use 50/50 split layout** - avatar visible alongside B-roll\n- This creates immediate visual interest and establishes the presenter\n\n### Transition\n- **MUST use \"intro\" transition** - This is mandatory for every first beat\n- The intro transition is specifically designed for opening impact\n\n### Screen Motion\n- **MUST use an EXAGGERATED ZOOM effect** - Choose the most dramatic zoom available\n- This creates immediate visual energy and grabs attention within the first second\n- Look for zoom effects with high intensity/speed in the effects library\n\n### B-Roll (Scene-Setting)\n- **MUST use screen B-roll (broll_type: \"screen\")** - NOT AI-generated B-roll\n- The B-roll should SET THE SCENE for the video's topic\n- Ideal first-beat B-roll includes:\n - Tool/software logos (if discussing a specific tool)\n - Relevant interface screenshots\n - Dashboard or app imagery related to the topic\n - Visual representation of the problem or solution being discussed\n- The goal: Viewers instantly understand WHAT this video is about\n- **NEVER use AI B-roll for the first beat** - save cinematic AI visuals for reveals later\n\n### Example First Beat Config:\n```json\n{\n \"beat_type\": \"hook_snap\",\n \"layout\": \"[50/50 split layout ID from effects library]\",\n \"transition_in\": \"intro\",\n \"screen_motion\": \"[most dramatic zoom effect ID]\",\n \"broll_type\": \"screen\",\n \"broll_index\": [index of scene-setting clip - tool logo, interface, etc.],\n \"broll_start_seconds\": [start],\n \"broll_end_seconds\": [end],\n \"ai_broll_index\": -1\n}\n```\n\n---\n\n## B-ROLL SYSTEM (CRITICAL)\n\nYou have TWO types of B-roll available. They are MUTUALLY EXCLUSIVE - never use both on the same beat.\n\n### 1. SCREEN SHARE B-ROLL (broll_type: \"screen\")\n- Clips from actual screen recordings showing software, workflows, code\n- Variable duration - you specify start/end timestamps\n- Use for: Technical demos, software walkthroughs, showing actual tools, **AND the opening hook**\n- **Compatible layouts:** Split layouts, padded screen layouts\n- **REQUIRED for first beat** - use scene-setting imagery (tool logos, interfaces, topic visuals)\n- Fields to populate:\n - broll_type: \"screen\"\n - broll_index: (clip index from available screen B-roll)\n - broll_start_seconds: (start timestamp within that clip)\n - broll_end_seconds: (end timestamp within that clip)\n - ai_broll_index: -1\n\n### 2. AI-GENERATED B-ROLL (broll_type: \"ai_generated\")\n- Cinematic, visually striking clips generated by AI\n- **⚠️ CRITICAL: Beat duration MUST match the AI B-roll clip duration exactly**\n- Use for: Reveals (NOT the opening), metaphorical imagery, pattern interrupts, high-energy moments\n- **Compatible layouts:** Fullscreen B-roll layouts ONLY\n- **NEVER use for first beat** - save for dramatic reveals in the middle of the video\n- Fields to populate:\n - broll_type: \"ai_generated\"\n - duration_seconds: must exactly equal the AI B-roll video duration (MANDATORY)\n - broll_index: -1\n - broll_start_seconds: -1\n - broll_end_seconds: -1\n - ai_broll_index: (0, 1, or 2 - the clip index)\n\n### 3. NO B-ROLL (broll_type: \"none\")\n- **ONLY VALID FOR AVATAR-ONLY LAYOUTS**\n- Used ONLY for the last beat (cta_close)\n- **⚠️ NEVER use broll_type: \"none\" with split or fullscreen layouts**\n- **⚠️ NEVER use for the first beat**\n- Fields to populate:\n - broll_type: \"none\"\n - broll_index: -1\n - broll_start_seconds: -1\n - broll_end_seconds: -1\n - ai_broll_index: -1\n\n### B-ROLL VALIDATION RULES (CRITICAL)\n1. **First beat REQUIRES screen B-roll** - Scene-setting imagery, tool logos, topic visuals\n2. **Split/fullscreen layouts REQUIRE B-roll** - Using \"none\" causes black screens\n3. **AI B-roll = Fullscreen layout** - Always pair together\n4. **Screen B-roll = Split or padded layout** - Never use with fullscreen B-roll layout\n5. **Never overlap** - Each beat uses exactly one B-roll type\n6. **Distribute AI clips** - Don't use the same ai_broll_index twice consecutively\n7. **Use all AI clips** - Try to use all 3 AI-generated clips across the video for variety (in middle beats)\n\n---\n\n## TRANSITION SYSTEM\n\nThe effects library defines various transition types. Use them strategically:\n\n### Transition Categories:\n- **Intro transition** - MANDATORY for first beat, designed for opening impact\n- **Flash transitions** - High energy, use for snap words and major reveals\n- **RGB/Glitch transitions** - Tech feel, use for AI reveals and pattern interrupts\n- **Accent flash transitions** - Brand color depth, use for important moments\n- **Scale/Slide transitions** - Element animations, use for entries\n- **Fade transitions** - Soft, use ONLY for final CTA beat\n\n### Transition Rules:\n- **First beat:** ALWAYS use \"intro\" transition - no exceptions\n- **Variety is key** - Never use the same transition type consecutively\n- **Match energy** - High-energy transitions for reveals, softer for demos\n- **Snap words** - Every flash-type transition should land on a snap_word\n- **CTA exception** - Use fade transition ONLY for the final CTA beat\n- **Follow the guidelines** - Check the effects library's `guidelines.transitions` section\n\n---\n\n## BEAT TIMING RULES (STRICT)\n\n**⚠️⚠️⚠️ ABSOLUTE MAXIMUM: 4 SECONDS PER BEAT - NO EXCEPTIONS ⚠️⚠️⚠️**\n\nThis is the MOST IMPORTANT RULE. Every single beat MUST be 4 seconds or less. If you need more time, ADD MORE BEATS.\n\n| Beat Type | Duration Range | Notes |\n|-----------|----------------|-------|\n| hook_snap | 2-3 sec | Whole seconds, **50/50 split with scene-setting screen B-roll, intro transition, exaggerated zoom** |\n| hook_reveal | 2-4 sec | Whole seconds, **match AI B-roll duration if used** |\n| body_flash | 2-4 sec | Whole seconds, **match AI B-roll duration if used** |\n| body_demo | 3-4 sec | Whole seconds, best for coarse adjustment |\n| body_emphasis | 2-4 sec | Whole seconds, **match AI B-roll duration if used** |\n| cta_close | 3-4 sec | **ABSORBS REMAINDER - can be decimal**, avatar-only layout |\n\n### Duration Matching Process:\n1. Calculate avatar_duration from user message\n2. Plan hook + body beats with whole-second durations (max 4 sec each)\n3. Sum those beats\n4. Calculate remainder = avatar_duration - sum(other beats)\n5. **IF remainder > 4 seconds:** Add more beats until remainder ≤ 4\n6. **IF remainder < 3 seconds:** Remove a body beat and recalculate\n7. Set cta_close duration = final remainder (must be 3-4 sec)\n\n### Beat Count Targets:\n- 30-second video = approximately 8-10 beats\n- 45-second video = approximately 12-15 beats\n- 60-second video = approximately 15-20 beats\n\n---\n\n## SNAP WORDS\n\nAt every flash-type transition, populate snap_word with one of these attention-grabbing phrases:\n\"Watch THIS\", \"And BOOM\", \"Right here\", \"Here's the thing\", \"See this?\", \"Now watch\", \"Look\", \"THIS is\", \"Until now\", \"But here's\", \"STOP\"\n\n---\n\n## BEAT SEQUENCE PATTERNS\n\n### PAIN Concept\n1. **hook_snap** (50/50 split, screen B-roll, intro transition, exaggerated zoom) → Scene-setting opening with tool/topic visual\n2. **hook_reveal** (fullscreen B-roll) → Cinematic AI reveal\n3. **body_flash** (split) → Show the problem with screen recording\n4. **body_demo** (split) → Technical demo\n5. **body_emphasis** (split) → Build tension\n6. **body_demo** (fullscreen B-roll) → AI visual for impact\n7. **body_flash** (split) → More proof\n8. **cta_close** (avatar-only) → Call to action\n\n### CURIOSITY Concept\n1. **hook_snap** (50/50 split, screen B-roll, intro transition, exaggerated zoom) → Mystery hook with topic visual\n2. **hook_reveal** (fullscreen B-roll) → Intriguing AI visual\n3. **body_flash** (split) → Tease the answer\n4. **body_demo** (split) → Start revealing\n5. **body_emphasis** (fullscreen B-roll) → Payoff visual\n6. **body_flash** (split) → Show results\n7. **cta_close** (avatar-only) → Call to action\n\n### TRANSFORMATION Concept\n1. **hook_snap** (50/50 split, screen B-roll, intro transition, exaggerated zoom) → Before state with relevant tool/interface visual\n2. **hook_reveal** (split) → Show the problem\n3. **body_flash** (fullscreen B-roll) → Transition moment\n4. **body_demo** (split) → The shift\n5. **body_emphasis** (split) → After state\n6. **body_flash** (fullscreen B-roll) → Results visual\n7. **cta_close** (avatar-only) → Call to action\n\n---\n\n## PRE-OUTPUT VALIDATION (MANDATORY)\n\n**Before returning your response, validate EVERY beat:**\n\n### Duration Validation\n- ❌ INVALID if any beat > 4 seconds → Split into multiple beats\n- ❌ INVALID if any beat < 2 seconds → Merge or extend\n\n### First Beat Validation (CRITICAL)\n- ❌ INVALID if first beat is NOT 50/50 split layout\n- ❌ INVALID if first beat transition is NOT \"intro\"\n- ❌ INVALID if first beat does NOT use exaggerated zoom screen_motion\n- ❌ INVALID if first beat uses AI B-roll (must be screen B-roll)\n- ❌ INVALID if first beat B-roll is NOT scene-setting (tool logo, interface, topic visual)\n\n### Layout Validation\n- ❌ INVALID if first beat is avatar-only layout (must be 50/50 split)\n- ❌ INVALID if last beat is NOT avatar-only layout\n- ❌ INVALID if any middle beat IS avatar-only layout\n\n### B-Roll Validation\n- ❌ INVALID if first beat does NOT have screen B-roll\n- ❌ INVALID if split/fullscreen layout has broll_type: \"none\"\n- ❌ INVALID if AI B-roll beat duration doesn't match clip duration\n- ❌ INVALID if fullscreen B-roll layout uses screen B-roll\n\n### Transition Validation\n- ❌ INVALID if first beat does NOT use \"intro\" transition\n- ❌ INVALID if same transition used consecutively\n- ❌ INVALID if non-fade transition used for CTA\n\n**If ANY validation fails, fix it before responding.**\n\n---\n\n## OUTPUT FORMAT\n\nReturn JSON matching the schema with these fields:\n- concept_index, concept_type, title, audio_track\n- total_duration_seconds, total_beats, total_flashes\n- enhanced_storyboard array with each beat containing all required fields\n\nUse exact IDs from the effects library for:\n- layout (from layouts section)\n- transition_in (from transitions section) - **\"intro\" for first beat**\n- screen_motion (from screen_motions section) - **exaggerated zoom for first beat**\n- avatar_animation (from avatar_animations section)\n\n---\n\n## FINAL CHECKLIST\n\n☐ **First beat uses 50/50 split layout (NOT avatar-only)**\n☐ **First beat uses \"intro\" transition**\n☐ **First beat uses exaggerated zoom screen_motion**\n☐ **First beat uses scene-setting screen B-roll (tool logo, interface, topic visual)**\n☐ **First beat does NOT use AI B-roll**\n☐ **Avatar-only layout ONLY on LAST beat**\n☐ **All middle beats use split or fullscreen layouts**\n☐ **NO BEAT EXCEEDS 4 SECONDS**\n☐ **Duration sum equals avatar_duration exactly**\n☐ **CTA beat is 3-4 seconds**\n☐ **AI B-roll durations match exactly**\n☐ **Split/fullscreen layouts have B-roll (not \"none\")**\n☐ **Transitions vary - no consecutive repeats**\n☐ **Flash transitions have snap_words**\n☐ **Fade transition only for CTA**\n☐ **All IDs exist in effects library**\n☐ **All 3 AI B-roll clips used for variety (in middle beats)**"},"promptType":"define","hasOutputParser":true},"retryOnFail":true,"typeVersion":3.1,"waitBetweenTries":5000},{"id":"866fb44d-6a24-4fff-bc68-014db33724ac","name":"Get AI B-Roll","type":"n8n-nodes-base.httpRequest","maxTries":2,"position":[3040,1840],"parameters":{"url":"https://api.replicate.com/v1/models/bytedance/seedance-1-lite/predictions","method":"POST","options":{"timeout":300000},"jsonBody":"={\n \"input\": {\n \"fps\": 24,\n \"prompt\": {{ $json.prompt.toJsonString() }},\n \"duration\": 2,\n \"resolution\": \"720p\",\n \"aspect_ratio\": \"9:16\",\n \"camera_fixed\": false\n },\n \"webhook\": \"{{ $execution.resumeUrl }}\",\n \"webhook_events_filter\": [\"completed\"]\n}","sendBody":true,"sendHeaders":true,"specifyBody":"json","authentication":"genericCredentialType","genericAuthType":"httpHeaderAuth","headerParameters":{"parameters":[{"name":"Content-Type","value":"application/json"},{"name":"Prefer","value":"wait"}]}},"credentials":{"httpHeaderAuth":{"id":"lKsNTFV3LXl2b7O4","name":"Replicate APG"}},"retryOnFail":true,"typeVersion":4.3,"waitBetweenTries":5000},{"id":"35751aee-e0e5-4402-a9e0-c6ed95e7bea6","name":"Extract Flash B-Roll Result1","type":"n8n-nodes-base.set","position":[3264,1840],"parameters":{"options":{},"assignments":{"assignments":[{"id":"1e3ea52a-163b-4283-86fa-1bdbf5d15ad3","name":"flash_number","type":"number","value":"={{ $('Split Out1').item.json.flash_number }}"},{"id":"278a2d17-a4db-4969-bc0d-febb71ea21dd","name":"prompt","type":"string","value":"={{ $('Split Out1').item.json.prompt }}"},{"id":"e9617c00-3b1f-4ac1-82c8-ce599afe485e","name":"duration_seconds","type":"number","value":"={{ $('Split Out1').item.json.duration_seconds }}"},{"id":"30d426d3-fc47-481e-b514-f6db9110c5ae","name":"timestamp","type":"number","value":"={{ $('Split Out1').item.json.insert_at_timestamp }}"},{"id":"6d2ec0e2-4ba7-4555-85a3-7aa19588e59a","name":"insert_after_segment","type":"number","value":"={{ $('Split Out1').item.json.insert_after_segment }}"},{"id":"abdb9cd3-d87a-430e-8708-df0eeeff16f7","name":"purpose","type":"string","value":"={{ $('Split Out1').item.json.purpose }}"},{"id":"736409ab-2390-43fd-a2a7-1fc6c12c4346","name":"concept_index","type":"number","value":"={{ $('Loop Through Concepts').item.json.concept_number }}"},{"id":"da2c0647-1116-44e8-9966-a7195af79fcc","name":"video_url","type":"string","value":"={{ $json.output }}"},{"id":"f2a7096c-864e-4024-ab8c-8e37ee89648e","name":"status","type":"string","value":"={{ $json.status }}"},{"id":"f05177e7-77c4-44ba-82da-e0dcf01a2e6d","name":"replicate_id","type":"string","value":"={{ $json.id }}"}]}},"typeVersion":3.4},{"id":"cfdec554-192b-4322-ba6d-b43d8dd5d135","name":"Share file","type":"n8n-nodes-base.googleDrive","position":[3040,1504],"parameters":{"fileId":{"__rl":true,"mode":"id","value":"={{ $json.id }}"},"options":{},"operation":"share","permissionsUi":{"permissionsValues":{"role":"reader","type":"anyone","allowFileDiscovery":true}}},"credentials":{"googleDriveOAuth2Api":{"id":"RJqb4CuqNHs25OT1","name":"adam@apgsoftware.com"}},"typeVersion":3},{"id":"44718c2d-7a9d-49b0-9598-895d3fda6490","name":"Social Media Copyrighter","type":"@n8n/n8n-nodes-langchain.openAi","position":[7568,1744],"parameters":{"modelId":{"__rl":true,"mode":"list","value":"gpt-5.2","cachedResultName":"GPT-5.2"},"options":{"textFormat":{"textOptions":{"type":"json_schema","schema":"{\n \"type\": \"object\",\n \"additionalProperties\": false,\n \"properties\": {\n \"youtube\": {\n \"type\": \"object\",\n \"additionalProperties\": false,\n \"properties\": {\n \"title\": {\n \"type\": \"string\",\n \"description\": \"YouTube title, max 100 characters. Front-load the hook.\"\n },\n \"description\": {\n \"type\": \"string\",\n \"description\": \"YouTube description, max 5000 characters. Include CTA link and social links.\"\n }\n },\n \"required\": [\"title\", \"description\"]\n },\n \"instagram\": {\n \"type\": \"object\",\n \"additionalProperties\": false,\n \"properties\": {\n \"short_caption\": {\n \"type\": \"string\",\n \"description\": \"Short Instagram caption, max 150 characters. Format: Comment '[KEYWORD]' and I'll send it over.\"\n },\n \"long_caption\": {\n \"type\": \"string\",\n \"description\": \"Long Instagram caption, max 2200 characters. Include Comment CTA, hook, anecdote, numbered benefits, hashtags.\"\n },\n \"comment_keyword\": {\n \"type\": \"string\",\n \"description\": \"Single memorable word in ALL CAPS\"\n }\n },\n \"required\": [\"short_caption\", \"long_caption\", \"comment_keyword\"]\n },\n \"tiktok\": {\n \"type\": \"object\",\n \"additionalProperties\": false,\n \"properties\": {\n \"caption\": {\n \"type\": \"string\",\n \"description\": \"TikTok caption, max 2200 characters. Include #techtok #learnontiktok plus 3-5 topic hashtags.\"\n }\n },\n \"required\": [\"caption\"]\n },\n \"hashtags\": {\n \"type\": \"object\",\n \"additionalProperties\": false,\n \"properties\": {\n \"primary\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"Primary hashtags (3-5) from core brand tags\"\n },\n \"secondary\": {\n \"type\": \"array\",\n \"items\": {\n \"type\": \"string\"\n },\n \"description\": \"Secondary hashtags (3-5) technical or topic-specific\"\n }\n },\n \"required\": [\"primary\", \"secondary\"]\n },\n \"cta\": {\n \"type\": \"object\",\n \"additionalProperties\": false,\n \"properties\": {\n \"type\": {\n \"type\": \"string\",\n \"enum\": [\"community\", \"consultation\", \"tool\"],\n \"description\": \"CTA type: community=bit.ly/4qPYfp3, consultation=bit.ly/4nnuSZ6, tool=comment for DM\"\n },\n \"link\": {\n \"type\": \"string\",\n \"description\": \"The CTA URL based on type\"\n },\n \"keyword\": {\n \"type\": \"string\",\n \"description\": \"Comment keyword in ALL CAPS, must match instagram.comment_keyword\"\n }\n },\n \"required\": [\"type\", \"link\", \"keyword\"]\n }\n },\n \"required\": [\"youtube\", \"instagram\", \"tiktok\", \"hashtags\", \"cta\"]\n}"}}},"simplify":false,"responses":{"values":[{"role":"system","content":"=You are a short-form content optimization specialist for @adamfreelances, the technical education brand of Adam—a Top 1% Upwork developer and founder of APG Software. Your job is to generate high-converting titles, descriptions, and captions for YouTube Shorts, Instagram Reels, and TikTok.\n\n## BRAND IDENTITY\n\nAdam's positioning: \"The Anti-Guru Technical Educator\"\nTagline: \"Deployment, not dreams.\"\n\nKey differentiators:\n- Technical depth (Next.js, Supabase, Vector DBs, 50+ integrations)\n- Realistic timelines (30-120 days to competence, never overnight)\n- Proven results (250+ agency projects, $100K+ Upwork verified)\n- SaaS Killer model (replace $5K+/month in subscriptions with unified systems)\n\nNEVER include:\n- \"Easy money\" or \"passive income\" language\n- Overnight success claims\n- Hype without substance\n- Generic AI tool reviews without implementation context\n\nALWAYS include:\n- Specific technical references when relevant\n- Honest complexity acknowledgment\n- Real metrics and timelines\n- Building/deployment focus over consumption\n\n## WRITING STYLE RULES\n\nStudy these patterns from top-performing AI/automation creators:\n\n### Hook Formulas (For Body Copy)\nUse ONE of these proven patterns after the CTA opener:\n\n1. **News Drop**: \"[Company] just released [tool] and it [benefit].\"\n2. **I Built**: \"I built a system that [impressive outcome].\"\n3. **Contrast**: \"While everyone's obsessing over [X], [Y] quietly [did something better].\"\n4. **Stop/Start**: \"Stop [common mistake]. Instead, [better approach].\"\n5. **Save Money**: \"You don't need to pay for [expensive thing] anymore.\"\n6. **Question Hook**: \"Have you ever noticed [relatable problem]?\"\n7. **How-To**: \"Here's how to [desirable outcome], step by step.\"\n\n### Caption Writing Rules\n\n**Sentence Structure:**\n- Use SHORT punchy sentences. Like this. No fluff.\n- One idea per sentence maximum\n- Break long explanations into fragments\n- Use periods instead of commas for rhythm\n\n**Personal Touch:**\n- Include \"I tried it last week...\" or \"I tested this on...\" anecdotes\n- Share specific results: \"Saved me 3 hours\" not \"saved time\"\n- Be conversational, not corporate\n\n**Formatting:**\n- Use numbered lists for steps or features (1. 2. 3.)\n- Add analogies: \"Like having a dev friend who never sleeps\"\n- Include \"Here's why:\" or \"The good news?\" transitions\n- End sections with a punchy one-liner\n\n## CRITICAL: CTA-FIRST FORMAT\n\n**Every description and caption MUST start with the comment CTA as the very first line.**\n\nFormat: `Comment '[KEYWORD]' and I'll send you [specific deliverable].`\n\nExamples:\n- \"Comment 'SYSTEM' and I'll send you my automation template.\"\n- \"Comment 'BUILD' and I'll DM you the full breakdown.\"\n- \"Comment 'FREE' and I'll send you the tool link.\"\n\nThe CTA is always Line 1. Hook and body content follows after a blank line.\n\n## OUTPUT REQUIREMENTS\n\nYou must generate ALL of the following for each video concept:\n\n### 1. YouTube Title (max 100 characters)\n- Front-load the hook\n- Include power words: \"built\", \"replaced\", \"automated\", \"free\"\n- Create curiosity gap when appropriate\n\n### 2. YouTube Description (max 5000 characters)\nStructure:\n- **Line 1: Comment CTA** (e.g., \"Comment 'KEYWORD' and I'll send you [deliverable].\")\n- Blank line\n- Paragraph 1: Hook expansion (1-2 sentences)\n- Paragraph 2: What viewer will learn\n- Paragraph 3: Link CTA\n\n### 3. Instagram Caption - SHORT VERSION (max 150 characters)\n- **Line 1: Comment CTA only**\n- Format: \"Comment '[KEYWORD]' and I'll send you [deliverable].\"\n- Nothing else needed for short version\n\n### 4. Instagram Caption - LONG VERSION (max 2200 characters)\nStructure:\n- **Line 1: Comment CTA** (e.g., \"Comment 'KEYWORD' and I'll send you [deliverable].\")\n- Blank line\n- Paragraph 1: Restate hook, expand value (short sentences)\n- Paragraph 2: How it works (fragment style)\n- Paragraph 3: Personal anecdote \"I tried it...\"\n- Paragraph 4: Numbered benefits list\n- Paragraph 5: Final punch + reminder of CTA keyword\n- Blank line\n\n### 5. TikTok Caption (max 2200 characters)\nStructure:\n- **Line 1: Comment CTA** (e.g., \"Comment '[KEYWORD]' for the link.\")\n- Blank line\n- Hook or question\n- Brief explanation (shorter than Instagram)\n- Trend-aware language\n- Reminder of keyword at end\n\n### 6. Suggested Comment Keyword\n- Single memorable word in ALL CAPS\n- Related to the video topic\n- Easy to type and remember\n- Must match the keyword used in all CTAs above\n\n## CTA DELIVERABLES BY CONTENT TYPE\n\nMatch the deliverable promise to the video concept:\n\n| Content Type | Deliverable Promise | Keyword Examples |\n|--------------|---------------------|------------------|\n| Community/Learning | \"the full guide\" / \"my learning path\" | LEARN, BUILD, JOIN |\n| Consultation/Agency | \"my system audit checklist\" / \"the CRM breakdown\" | SYSTEM, AUDIT, CRM |\n| Tool/Tutorial | \"the tool link\" / \"the template\" | TOOL, FREE, TEMPLATE |\n| Code/Technical | \"the code snippet\" / \"the repo link\" | CODE, STACK, DEPLOY |\n\n## CTA LINKS (For Link-Based CTAs Only)\n\nUse these when including direct links:\n\n| Content Type | CTA Link |\n|--------------|----------|\n| Community/Learning | https://bit.ly/4qPYfp3 |\n| Consultation/Agency | https://bit.ly/4nnuSZ6 |\n\n## EXAMPLE OUTPUT FORMAT\n\n**YouTube Title:**\nI Replaced $5K/Month in SaaS with One AI System\n\n**YouTube Description:**\nComment 'SYSTEM' and I'll send you my full tech stack breakdown.\n\nMost businesses are bleeding money on 15+ disconnected tools. I built a unified CRM that replaced all of them.\n\nIn this video, you'll see exactly how I architected the system, the integrations I used, and the real cost savings after 90 days.\n\nReady to build your own? Join the community: https://bit.ly/4qPYfp3\n\n**Instagram Short:**\nComment 'SYSTEM' and I'll send you the full breakdown.\n\n**Instagram Long:**\nComment 'SYSTEM' and I'll send you my full tech stack breakdown.\n\nMost businesses waste $5K+/month on disconnected SaaS. Slack here. Notion there. Three different CRMs. None of them talk to each other.\n\nI built one unified system. Next.js frontend. Supabase backend. 50+ integrations. All owned by the client.\n\nI tested this on a real agency last month. Cut their software spend by 60%. Saved 12 hours/week on manual data entry.\n\nHere's what they got:\n1. Single source of truth for all customer data\n2. Automated workflows that actually work\n3. AI features built on their own data\n4. No monthly SaaS fees eating margins\n\nThis is what deployment looks like. Not dreams.\n\nDrop 'SYSTEM' below and I'll DM you the stack.\n\n**TikTok:**\nComment 'SYSTEM' for the full breakdown.\n\n$5K/month on SaaS? That's not a tech stack. That's a subscription addiction.\n\nI built one system to replace 15 tools. Real talk—it took 90 days. But now? Zero monthly fees. Full ownership.\n\nThis is what building actually looks like.\n\n'SYSTEM' in the comments 👇\n\n**Keyword:** SYSTEM"},{"content":"=Generate platform-optimized content for this YouTube Short:\n\n**SOURCE VIDEO:**\n- Title: {{ $('Shorts Trigger').item.json.source.video_name }}\n- URL: {{ $('Shorts Trigger').item.json.source.video_url }}\n\n**SHORT CONCEPT #{{ $('Loop Through Concepts').item.json.concept_number }}:**\n- **Type:** {{ $('Loop Through Concepts').item.json.concept_type }}\n- **Title:** {{ $('Loop Through Concepts').item.json.title }}\n- **Description:** {{ $('Loop Through Concepts').item.json.description }}\n- **CTA Type:** {{ $('Loop Through Concepts').item.json.cta_type }}\n- **Duration:** ~{{ Math.round($('Loop Through Concepts').item.json.total_duration) }} seconds\n- **Hashtags:** {{ ($('Loop Through Concepts').item.json.hashtags || []).join(', ') }}\n\n**FULL SCRIPT:**\n{{ $('Loop Through Concepts').item.json.full_script }}\n\n**GOOGLE DRIVE LINK:**\n{{ $('Upload to Google Drive').item.json.webViewLink }}\n\n**Reference Style:** Review the high-performing Instagram captions in the system prompt. Match their punchy sentence rhythm, \"Comment [KEYWORD]\" CTA format, and fragment-style formatting while maintaining the @adamfreelances \"Anti-Guru Technical Educator\" brand voice.\n\nGenerate the complete content package as valid JSON only. No markdown, no explanation, just the JSON object."}]},"builtInTools":{}},"credentials":{"openAiApi":{"id":"AV99wmuemfchjD16","name":"APG n8n"}},"typeVersion":2.1},{"id":"d3e178a4-a005-4b50-b669-5a67e761be34","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[1056,1408],"parameters":{"width":272,"height":224,"content":"🟢 Stage 1: Trigger & Extract\nPosition near: Shorts Trigger → Extract Snippets → Edit Fields\n\nWorkflow Trigger\nReceives input from parent workflow. Extracts video snippets and prepares fields for AI processing downstream."},"typeVersion":1},{"id":"8a8c8c40-27fe-44e8-b8df-414718dce80d","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[1744,1408],"parameters":{"width":272,"height":224,"content":"🟢 Stage 2: AI Concept Ideation\nPosition near: Poll Short Form Concept Ideator → Split Out → Loop Through Concepts\n\nAI Concept Generation\nGemini analyzes transcript and generates short-form content concepts. Splits output into individual concepts and loops through each one for processing."},"typeVersion":1},{"id":"b3d9f056-4132-4dfe-ab0b-56a6453e4db8","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[3008,2032],"parameters":{"width":272,"height":224,"content":"🟢 Stage 2b: Documentation & B-Roll\nPosition near: Document Generator → Convert Document to HTML → Create Google Doc → Get AI B-Roll\n\nDoc Generation & B-Roll\nGenerates a Google Doc script for each concept. Simultaneously fetches AI-generated B-roll images and aggregates them for the video edit."},"typeVersion":1},{"id":"4e22f15d-3e84-4bbd-8ec1-5f2c2f3eb66d","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[3856,1936],"parameters":{"width":272,"height":224,"content":"🟢 Stage 3: HeyGen Avatar Generation\nPosition near: HeyGen - Generate Full Avatar → Poll Avatar Status → Avatar Done?\n\nAvatar Video Generation\nSends script to HeyGen API. Polls for completion, then extracts the final avatar video URL. Vertical 1080x1920 format."},"typeVersion":1},{"id":"ce656c09-4976-4019-931b-e74b545f17d6","name":"Sticky Note4","type":"n8n-nodes-base.stickyNote","position":[5280,1440],"parameters":{"width":272,"height":272,"content":"🟢 Stage 4: AI Video Director & Creatomate Render\nPosition near: AI Video Director → Creatomate Template Builder → Creatomate - Render\n\nAI-Directed Video Composition\nGemini acts as a video director — generating a dynamic RenderScript with layout changes (full avatar, split screen, PiP). Builds Creatomate template and sends for render."},"typeVersion":1},{"id":"b8e93887-7bb9-4812-a305-eaba41bb64ab","name":"Sticky Note5","type":"n8n-nodes-base.stickyNote","position":[6592,1424],"parameters":{"width":272,"height":208,"content":"🟢 Stage 5: Render Polling & Export\nPosition near: Poll Render Status → Render Done? → Download Rendered Video → Upload to Google Drive\n\nRender & Delivery\nPolls Creatomate render status. Once complete, downloads the final video and uploads to Google Drive."},"typeVersion":1}],"active":true,"pinData":{"Shorts Trigger":[{"json":{"links":{"website":"https://www.apgsoftware.com","youtube":"https://www.youtube.com/@adamfreelances","community":"https://bit.ly/4qPYfp3","instagram":"https://www.instagram.com/adamfreelances/","accelerator":"https://bit.ly/hyper-ai-accelerator","consultation":"https://cal.com/apgsoftware/consult","twitter_personal":"https://twitter.com/adamfreelances","linkedin_personal":"https://www.linkedin.com/in/adam-goodyer/"},"source":{"video_id":"gZr5VmsXmXQ","video_url":"https://api.apify.com/v2/key-value-stores/c2uVzs81LdOq5cQC6/records/gZr5VmsXmXQ.mp4?signature=1L3PzMY1iRqCx8QZVmBeD","video_name":"Vercel Just Revealed Claude Code's Greatest Advantage","youtube_url":"https://www.youtube.com/watch?v=gZr5VmsXmXQ","triggered_at":"2026-01-25T22:59:11.950Z","channel_title":"AI LABS","duration_seconds":580,"duration_formatted":"9m 40s"},"metadata":{"run_id":"56769","source_type":"apify_youtube","triggered_at":"2026-01-25T23:03:02.528Z","workflow_name":"New Video (Key Action Detection v2 - Apify)","payload_version":"9.2.0","ai_analysis_success":true},"overview":{"tone":"Pragmatic, educational, anti-hype, engineering-first.","summary":"The video explains a cost-saving architecture for LLM agents: instead of stuffing context or relying solely on vector search/RAG, let the agent navigate a real (or sandboxed) file system using familiar bash commands (ls/find/grep/cat). Because models were trained heavily on code and CLI workflows, they can reliably locate files, extract exact matches, and only send minimal relevant snippets back to the model—reducing tokens and improving precision on structured data. The video references a Vercel engineer’s write-up and Vercel’s open-sourced bash tool, compares this approach to system prompts and vector databases, then demos a structured research pipeline (claude.md + phases) and a company policy Q&A agent implemented with Gemini 2.5 Flash and Vercel’s sandbox. It closes with security considerations (sandbox isolation options) and guidance on when to prefer bash retrieval vs RAG.","one_liner":"Stop paying RAG tax for structured data—let your agent use the filesystem with ls/find/grep/cat and send only the exact snippet to the LLM.","seo_keywords":["claude code","claude code tutorial","vercel bash tool","vercel sandbox","filesystem agent","bash tool llm","grep for llm agents","ls find cat grep agent","rag vs vector search","vector database alternative","chroma vector database","token cost reduction llm","context window optimization","structured data retrieval","company policy ai agent","sales summary agent","sandboxed agent execution","cli ai agent","gemini 2.5 flash","claude.md research pipeline","agent tool calling","exact match retrieval","semantic search limitations"],"content_style":"Technical walkthrough + architecture explanation + practical demos (terminal/VS Code) + tradeoff analysis + security notes; anti-guru, implementation-focused.","key_takeaways":["Filesystem-based retrieval reduces token usage by keeping most data out of the context window and only sending a small matched slice.","Models are strong at CLI/file navigation because they were trained on massive code and bash workflows; using ls/find/grep/cat is leveraging that prior.","Vector search is optimized for semantic similarity, not precise value lookup; it often returns multiple loosely-related chunks and forces the model to extract the exact answer.","Filesystem hierarchy preserves domain relationships (folders/files) without flattening into embeddings/chunks, reducing lost context/relationships.","Grep/pattern matching yields exact matches and keeps agents focused/aligned with minimal context.","This approach is already used in Claude Code/CLI agents: explore -> locate -> extract -> respond.","Security matters: allow command execution only in a sandbox with directory isolation; choose in-memory vs full VM isolation depending on risk.","Use bash tools for structured data and clear queries; use RAG when you need semantic understanding or your data/queries are unstructured."],"main_argument":"For structured, well-organized data, a filesystem-first retrieval pattern using bash tools (exact search + hierarchical structure) is often cheaper, more precise, and more controllable than large prompts or vector search; use RAG primarily when semantic meaning and messy queries matter.","target_audience":"Software engineers building LLM agents (Claude Code/CLI agents), AI automation builders, developers working with internal docs/policies/specs, and people building lightweight RAG alternatives for structured corpora.","tools_mentioned":["Claude Code","Vercel (blog/case study)","Vercel open-sourced bash tool","Vercel Sandbox","Gemini 2.5 Flash","Vector database (Chroma)","VS Code","Terminal/Bash","Bolt.new","Brilliant.org"],"suggested_titles":["Stop Paying the RAG Tax: Use the Filesystem Instead (ls/find/grep/cat)","Vercel’s Bash Tool Explained: The Cheap Way to Build LLM Agents","Claude Code’s Real Advantage: Exact Retrieval with Grep (Not Vectors)","RAG vs Grep: When Vector Search Fails to Find the Exact Value","Build a Sandboxed File-System Agent (Gemini + Vercel Sandbox)","How I Built a Company Policy Agent Without a Vector Database","The Unix Philosophy for LLM Agents: Keep Context Clean, Cut Tokens","CLI Agents Done Right: Structured Data Retrieval with Bash Tools"],"problems_addressed":["High token costs when building LLM products via APIs","Context window bloat and irrelevant data being sent to the model","Imprecision of vector search when you need exact values (rate limits, connection strings, policy numbers)","Loss of hierarchical relationships when flattening data into chunks/embeddings","Risk of letting agents execute commands on servers without isolation"],"frameworks_explained":["Filesystem-first agent architecture (Unix philosophy: everything is a file)","Bash tool retrieval loop (ls/find -> grep/cat -> send minimal context)","RAG / vector search retrieval","Six-phase research validation pipeline driven by markdown instructions (claude.md)","Sandbox isolation models (in-memory vs full VM isolation)"]},"transcript":{"sections":[{"end":70,"text":"Ever since models started getting powerful, many people have started building really cool products, integrating models into them, and solving lots of problems for us. But these systems consume a lot of tokens, especially if you're actually integrating a model using an API. The solution to this is much simpler than you think. The best architecture is not some extreme pipeline or highly scaled tuning, but actually an old philosophy that forms the basis of a Unix-based system that everything is a file. Now, I know they weren't talking about model costs and we're talking about devices and files, but surprisingly enough, the solution to this high-cost issue is the exact same principle. And this is exactly what a software engineer at Versel talks about. Before we explore why files are the solution, let's understand a few things about how these models actually work. Models have been trained on massive amounts of code. This is the exact reason why they're better at understanding code, directory structures, and native bash scripts that developers use to navigate files and find what they need. When an agent uses GP and ls, it's not doing something new. It's simply doing something it already knows how to do, just in a more controlled way. This approach isn't limited to code. Agents can navigate any","type":"hook/problem + thesis","index":0,"start":0,"title":"Why LLM apps get expensive (and the Unix/filesystem idea)","key_points":["LLM product integrations can burn tokens fast when you stuff context via API calls.","Thesis: the best architecture is simpler—borrow the Unix idea that everything is a file.","Claim: filesystem navigation + selective reading is the same solution to high context costs.","Models are good at code/CLI, so using bash tools isn’t novel—it’s controlled leverage of training data."],"word_count":219,"end_formatted":"1:10","segment_count":35,"start_formatted":"0:00","duration_seconds":70,"key_moment_count":12,"segment_end_index":34,"duration_formatted":"1m 10s","segment_start_index":0},{"end":150,"text":"directory containing anything, be it code or not, because they're already comfortable with commands and understand file systems. Whenever an agent needs something, it looks around the file system using native bash commands like ls and find. Once the agent finds the exact file using find, it searches for relevant content within that file using pattern matching with grep and cat. Only a small relevant slice of information is sent to the model while the rest stays out of memory, keeping the context window clean. This means we're not burning through tokens on irrelevant data that the model doesn't need. Using this approach, the agent returns a structured output. This pattern worked so well that Versel ended up open sourcing a bash tool built specifically around it, giving agents the ability to explore file systems the same way a developer would. When building large language model systems, there are two ways of providing the right information to the model. Either through a detailed system prompt, hoping the agent actually follows it, or by feeding a lot of data into a vector database and using semantic search to extract it. But each approach has limitations. System prompts have a limited token window which limits how much information we can send to the model at a time. To handle larger data sets, we use semantic search which finds information based on matching meanings to the query. But vector search is used for semantic similarity rather than exact searches. It returns chunks of data that match the general context of the query, not necessarily the specific","type":"architecture explanation","index":1,"start":70,"title":"How the bash + filesystem retrieval loop works (Vercel write-up)","key_points":["Agent explores with ls/find to locate relevant files.","Agent extracts with grep/cat (pattern match + read) to pull only the needed lines.","Only the small relevant slice is sent to the model; context stays clean and cheap.","Vercel open-sourced a bash tool to operationalize this pattern for agents."],"word_count":263,"end_formatted":"2:30","segment_count":41,"start_formatted":"1:10","duration_seconds":80,"key_moment_count":11,"segment_end_index":75,"duration_formatted":"1m 20s","segment_start_index":35},{"end":225,"text":"value we're looking for. This leaves extracting the right content from all the chunks to the model itself. File systems, however, offer a different approach. With file systems, the structure actually maps to your domain. You often have relationships between files in the folder structure that mirror the relationships between parent folders. With file systems, you don't have to flatten these relationships into model understandable vector chunks, which helps avoid missing relationships that are usually lost in semantic search. These hierarchical connections are preserved naturally, maintaining the organizational logic that already exists in your data. Another advantage is that retrieval is precise because Grep and Bash tools return exact matches. Unlike vector search, which returns all chunks that loosely match the query and then leaves it to the model to decide which one to use, you get only the required value. The context is minimal when agents use bash tools because they receive the specific chunk they need and many other chunks don't go into memory. This allows them to stay aligned and focused on the exact piece of information without getting lost in unrelated data. Now, this idea isn't something you are unfamiliar with. This idea has already been used inside Claude Code and all CLI agents where they use bash functions to narrow down findings using pattern matching. We've already","type":"comparison/tradeoffs","index":2,"start":150,"title":"System prompt vs vector DB vs filesystem: precision and structure","key_points":["Two common approaches: huge system prompts (token-limited) or vector DB semantic search (chunking + embeddings).","Vector search is semantic similarity, not exact lookup; returns multiple chunks and forces model to extract specifics.","Filesystem hierarchy maps to domain structure and preserves relationships natively.","Grep/binary-style exact matching returns the specific value, minimizing irrelevant context."],"word_count":222,"end_formatted":"3:45","segment_count":37,"start_formatted":"2:30","duration_seconds":75,"key_moment_count":9,"segment_end_index":112,"duration_formatted":"1m 15s","segment_start_index":76},{"end":340,"text":"been using the file system and Claude codes capabilities for research purposes for any idea that we evaluate. We usually pass the software tool we come across through this pipeline which contains multiple phases with our evaluation criteria that the research must pass through. All of this is defined in a markdown file containing the requirements and objectives of the tool we're testing, how to write the final document, and all the information required for each phase. We also provide Claude with certain documents as samples, which act as a guide for style matching, and the final document is saved in a research results folder. To guide the research, we have a claude.md file explaining how to pass the idea through each phase one by one, ultimately giving us a research document that meets all our checks. Whenever we have anything to research, I just go to Claude and tell it the idea or the tool to research. It then runs it through the six-phase validation process first by understanding the tool or idea and then passing it through each phase one by one. Once the idea has gone through all the phases, Claude generates a final report which we can read to verify whether the idea has potential or not. This file system approach saves us a lot of time by automating a research process that we would otherwise have to do step by step. If you want to try out this pipeline for yourself for your own use case, you can get a readytouse template for creating your own research pipeline similar to ours in our recently launched community called AI Labs Pro. For this and for all the previous videos, you get readytouse templates, prompts, all the commands and skills that you can plug directly into your projects. If you found value in what we do and want to support the channel, this is the best way to do it. Links in the description. I was going through their case study in which they explained how to build a sales summary. agent using this architecture. They've also open- sourced it, but it gave me a really interesting idea that I wanted to try out on my own. I was actually building a company policy project where I had a lot of company data in the form of JSON, markdown, and txt files, all separated by department.","type":"workflow demo","index":3,"start":225,"title":"Real workflow: Claude Code research pipeline driven by files","key_points":["This pattern is already used in Claude Code/CLI agents: use bash to narrow results via pattern matching.","Example internal workflow: a multi-phase research pipeline defined in markdown (requirements, objectives, output format).","claude.md guides a six-phase validation process; samples are provided for style matching.","Agent runs phases automatically and saves final report to a results folder; reduces manual step-by-step research work.","Community CTA: templates/commands/prompts provided in AI Labs Pro."],"word_count":399,"end_formatted":"5:40","segment_count":58,"start_formatted":"3:45","duration_seconds":115,"key_moment_count":11,"segment_end_index":170,"duration_formatted":"1m 55s","segment_start_index":113},{"end":430,"text":"Normally, I would have implemented this system using a vector database like Chroma, but I decided to give this tool a shot. I went ahead and implemented this architecture. On the back end, I included the path to the document folder containing the company's data and gave the agent access to the ls, cat, gp, and find commands along with a guide on how to use the tool and when to use each command. I used the Gemini 2.5 flash model, provided it with Versel's bash tool, and gave it the path to the documents inside the tool. And so when I tested the agent by asking it any question related to the data, it basically answered based on the exact content from the company's policies, including the handbook and leave policy documents. To verify how it was working, I logged its tool usage on the terminal. The agent first used the ls command to see what documents were available and then used grep with pattern matching to look for off days or any similar terms. This set of commands handled our query and gave us results with the same level of accuracy as a rag system would. If you want the source code for this project, you can find it on our community from where you can download and try out for yourself. Now, the first question that came to my mind while going through this tool was, is it really safe to equip agents to execute commands on the server? We literally saw a vulnerability in React server components this past December, which scored a 10.0, the highest on the scale and it involved code being executed on the server. So this is a really powerful and potentially dangerous capability to give agents. So why did I actually trust this tool? It's because it runs in a sandbox and has isolation. It only","type":"implementation + demo","index":4,"start":340,"title":"Build demo: company policy agent (structured docs, no vector DB)","key_points":["Inspired by Vercel sales summary case study; applied idea to a company policy project (JSON/MD/TXT by department).","Would normally use Chroma, but implemented filesystem approach instead.","Backend: provide doc folder path + allow ls/cat/grep/find with usage guidance.","Model: Gemini 2.5 Flash + Vercel’s bash tool; agent answers questions from exact policy text.","Verification via tool-call logs: ls to enumerate, grep with patterns (e.g., off days) to retrieve exact lines; achieved RAG-level accuracy for this data."],"word_count":315,"end_formatted":"7:10","segment_count":45,"start_formatted":"5:40","duration_seconds":90,"key_moment_count":5,"segment_end_index":215,"duration_formatted":"1m 30s","segment_start_index":171},{"end":500,"text":"accesses the specific directory we provide. It doesn't modify anything else. In the article, they also mentioned that the agent explores the files without access to the production system. So your production code remains safe even if the agent tries to run harmful commands on the server. It provides two types of isolation. The first is an in-memory environment. In this setup, it uses just bash tool which runs bash scripts only on the files it has access to just like we did when creating our agent. The second type is a fully compatible sandbox environment offering full virtual machine isolation using the versal sandbox. We can choose either based on our needs. The in-memory approach is lighter and faster for simple use cases while the full VM isolation is better when you need stronger security guarantees. Even though this approach is really good for saving costs per model call, it's not the right approach for all kinds of problems. It's definitely not ideal if you need to match the meaning of words because bash tools are for exact matching. As we saw when we called our agent, it used specific keywords to locate the required data. It's also not suitable for unorganized file structures where the agent would have to struggle with multiple tool calls. A structure that the agent can easily navigate is","type":"security considerations","index":5,"start":430,"title":"Security + isolation: is it safe to let agents run commands?","key_points":["Concern: command execution is powerful; server-side execution vulnerabilities exist (example mentioned: React server components CVE scored 10.0).","Reason to trust: sandboxed execution with directory isolation; no production access.","Two isolation modes: in-memory bash environment (lighter/faster) vs full VM isolation via Vercel Sandbox (stronger guarantees).","Pick isolation level based on threat model and required guarantees."],"word_count":225,"end_formatted":"8:20","segment_count":35,"start_formatted":"7:10","duration_seconds":70,"key_moment_count":3,"segment_end_index":250,"duration_formatted":"1m 10s","segment_start_index":216},{"end":580,"text":"much better. My personal suggestion is to use the bash tool when you have highly structured data and your requests are mostly clear in terms of what you want. Use rag when you care more about the meaning of what's written in the files or when your queries are likely to be messy. Before we wrap up, here's a word from our sponsor, Brilliant. The best engineers don't just know syntax. They break down problems from first principles. That is why we've partnered with Brilliant. Their philosophy is that you learn best by doing. They prioritize active problem solving. So you get hands-on with concepts instead of just memorizing. For example, in their course named how AI works. You don't just read, you manipulate the actual logic. You'll get hands-on with technicalities like calculating loss in the loss space and visualizing interpolation, building a deep intuition you just can't get from a video lecture. Through their interactive technical courses, you get the most effective way to truly master the concepts we talk about. You'll also get 20% off on annual premium subscription, unlocking their entire catalog of math, data, and CS courses, giving you a complete road map to upskill. Click the link in the description or scan the QR code on your screen to claim your free 30-day trial. That brings us to the end of this video. If you'd like to support the channel and help us keep making videos like this, you can do so by using the super thanks button below. As always, thank you for watching and I'll see you in the next one.","type":"guidance + wrap-up","index":6,"start":500,"title":"When to use bash retrieval vs RAG + sponsor/close","key_points":["Not a universal solution: bash tools are for exact matching, not semantic meaning.","Avoid if file structure is messy/unorganized; too many tool calls and navigation overhead.","Recommendation: bash tool for highly structured data + clear requests; RAG for semantic understanding or messy queries.","Sponsor segment (Brilliant) and closing CTAs (community resources, support via Super Thanks)."],"word_count":269,"end_formatted":"9:40","segment_count":40,"start_formatted":"8:20","duration_seconds":80,"key_moment_count":6,"segment_end_index":291,"duration_formatted":"1m 20s","segment_start_index":251}],"segments":[{"end":0.16,"text":"Ever since models started getting","index":0,"start":0.16,"end_formatted":"0:00","start_formatted":"0:00"},{"end":1.76,"text":"powerful, many people have started","index":1,"start":1.76,"end_formatted":"0:01","start_formatted":"0:01"},{"end":3.6,"text":"building really cool products,","index":2,"start":3.6,"end_formatted":"0:03","start_formatted":"0:03"},{"end":5.12,"text":"integrating models into them, and","index":3,"start":5.12,"end_formatted":"0:05","start_formatted":"0:05"},{"end":6.879,"text":"solving lots of problems for us. But","index":4,"start":6.879,"end_formatted":"0:06","start_formatted":"0:06"},{"end":8.72,"text":"these systems consume a lot of tokens,","index":5,"start":8.72,"end_formatted":"0:08","start_formatted":"0:08"},{"end":10.8,"text":"especially if you're actually","index":6,"start":10.8,"end_formatted":"0:10","start_formatted":"0:10"},{"end":12,"text":"integrating a model using an API. The","index":7,"start":12,"end_formatted":"0:12","start_formatted":"0:12"},{"end":14.48,"text":"solution to this is much simpler than","index":8,"start":14.48,"end_formatted":"0:14","start_formatted":"0:14"},{"end":16.08,"text":"you think. The best architecture is not","index":9,"start":16.08,"end_formatted":"0:16","start_formatted":"0:16"},{"end":18,"text":"some extreme pipeline or highly scaled","index":10,"start":18,"end_formatted":"0:18","start_formatted":"0:18"},{"end":20.16,"text":"tuning, but actually an old philosophy","index":11,"start":20.16,"end_formatted":"0:20","start_formatted":"0:20"},{"end":22.24,"text":"that forms the basis of a Unix-based","index":12,"start":22.24,"end_formatted":"0:22","start_formatted":"0:22"},{"end":24.24,"text":"system that everything is a file. Now, I","index":13,"start":24.24,"end_formatted":"0:24","start_formatted":"0:24"},{"end":26.64,"text":"know they weren't talking about model","index":14,"start":26.64,"end_formatted":"0:26","start_formatted":"0:26"},{"end":28.24,"text":"costs and we're talking about devices","index":15,"start":28.24,"end_formatted":"0:28","start_formatted":"0:28"},{"end":30,"text":"and files, but surprisingly enough, the","index":16,"start":30,"end_formatted":"0:30","start_formatted":"0:30"},{"end":32.16,"text":"solution to this high-cost issue is the","index":17,"start":32.16,"end_formatted":"0:32","start_formatted":"0:32"},{"end":34.16,"text":"exact same principle. And this is","index":18,"start":34.16,"end_formatted":"0:34","start_formatted":"0:34"},{"end":35.84,"text":"exactly what a software engineer at","index":19,"start":35.84,"end_formatted":"0:35","start_formatted":"0:35"},{"end":37.6,"text":"Versel talks about. Before we explore","index":20,"start":37.6,"end_formatted":"0:37","start_formatted":"0:37"},{"end":39.84,"text":"why files are the solution, let's","index":21,"start":39.84,"end_formatted":"0:39","start_formatted":"0:39"},{"end":41.84,"text":"understand a few things about how these","index":22,"start":41.84,"end_formatted":"0:41","start_formatted":"0:41"},{"end":43.68,"text":"models actually work. Models have been","index":23,"start":43.68,"end_formatted":"0:43","start_formatted":"0:43"},{"end":45.76,"text":"trained on massive amounts of code. This","index":24,"start":45.76,"end_formatted":"0:45","start_formatted":"0:45"},{"end":48,"text":"is the exact reason why they're better","index":25,"start":48,"end_formatted":"0:48","start_formatted":"0:48"},{"end":49.68,"text":"at understanding code, directory","index":26,"start":49.68,"end_formatted":"0:49","start_formatted":"0:49"},{"end":51.6,"text":"structures, and native bash scripts that","index":27,"start":51.6,"end_formatted":"0:51","start_formatted":"0:51"},{"end":53.76,"text":"developers use to navigate files and","index":28,"start":53.76,"end_formatted":"0:53","start_formatted":"0:53"},{"end":55.84,"text":"find what they need. When an agent uses","index":29,"start":55.84,"end_formatted":"0:55","start_formatted":"0:55"},{"end":57.68,"text":"GP and ls, it's not doing something new.","index":30,"start":57.68,"end_formatted":"0:57","start_formatted":"0:57"},{"end":60.079,"text":"It's simply doing something it already","index":31,"start":60.079,"end_formatted":"1:00","start_formatted":"1:00"},{"end":61.84,"text":"knows how to do, just in a more","index":32,"start":61.84,"end_formatted":"1:01","start_formatted":"1:01"},{"end":63.6,"text":"controlled way. This approach isn't","index":33,"start":63.6,"end_formatted":"1:03","start_formatted":"1:03"},{"end":65.439,"text":"limited to code. Agents can navigate any","index":34,"start":65.439,"end_formatted":"1:05","start_formatted":"1:05"},{"end":67.84,"text":"directory containing anything, be it","index":35,"start":67.84,"end_formatted":"1:07","start_formatted":"1:07"},{"end":69.92,"text":"code or not, because they're already","index":36,"start":69.92,"end_formatted":"1:09","start_formatted":"1:09"},{"end":71.6,"text":"comfortable with commands and understand","index":37,"start":71.6,"end_formatted":"1:11","start_formatted":"1:11"},{"end":73.68,"text":"file systems. Whenever an agent needs","index":38,"start":73.68,"end_formatted":"1:13","start_formatted":"1:13"},{"end":75.76,"text":"something, it looks around the file","index":39,"start":75.76,"end_formatted":"1:15","start_formatted":"1:15"},{"end":77.439,"text":"system using native bash commands like","index":40,"start":77.439,"end_formatted":"1:17","start_formatted":"1:17"},{"end":79.52,"text":"ls and find. Once the agent finds the","index":41,"start":79.52,"end_formatted":"1:19","start_formatted":"1:19"},{"end":81.92,"text":"exact file using find, it searches for","index":42,"start":81.92,"end_formatted":"1:21","start_formatted":"1:21"},{"end":84,"text":"relevant content within that file using","index":43,"start":84,"end_formatted":"1:24","start_formatted":"1:24"},{"end":86.08,"text":"pattern matching with grep and cat. Only","index":44,"start":86.08,"end_formatted":"1:26","start_formatted":"1:26"},{"end":88.159,"text":"a small relevant slice of information is","index":45,"start":88.159,"end_formatted":"1:28","start_formatted":"1:28"},{"end":90.24,"text":"sent to the model while the rest stays","index":46,"start":90.24,"end_formatted":"1:30","start_formatted":"1:30"},{"end":92,"text":"out of memory, keeping the context","index":47,"start":92,"end_formatted":"1:32","start_formatted":"1:32"},{"end":93.759,"text":"window clean. This means we're not","index":48,"start":93.759,"end_formatted":"1:33","start_formatted":"1:33"},{"end":95.36,"text":"burning through tokens on irrelevant","index":49,"start":95.36,"end_formatted":"1:35","start_formatted":"1:35"},{"end":97.2,"text":"data that the model doesn't need. Using","index":50,"start":97.2,"end_formatted":"1:37","start_formatted":"1:37"},{"end":99.04,"text":"this approach, the agent returns a","index":51,"start":99.04,"end_formatted":"1:39","start_formatted":"1:39"},{"end":100.88,"text":"structured output. This pattern worked","index":52,"start":100.88,"end_formatted":"1:40","start_formatted":"1:40"},{"end":102.72,"text":"so well that Versel ended up open","index":53,"start":102.72,"end_formatted":"1:42","start_formatted":"1:42"},{"end":104.56,"text":"sourcing a bash tool built specifically","index":54,"start":104.56,"end_formatted":"1:44","start_formatted":"1:44"},{"end":106.64,"text":"around it, giving agents the ability to","index":55,"start":106.64,"end_formatted":"1:46","start_formatted":"1:46"},{"end":108.72,"text":"explore file systems the same way a","index":56,"start":108.72,"end_formatted":"1:48","start_formatted":"1:48"},{"end":110.72,"text":"developer would. When building large","index":57,"start":110.72,"end_formatted":"1:50","start_formatted":"1:50"},{"end":112.72,"text":"language model systems, there are two","index":58,"start":112.72,"end_formatted":"1:52","start_formatted":"1:52"},{"end":114.56,"text":"ways of providing the right information","index":59,"start":114.56,"end_formatted":"1:54","start_formatted":"1:54"},{"end":116.399,"text":"to the model. Either through a detailed","index":60,"start":116.399,"end_formatted":"1:56","start_formatted":"1:56"},{"end":118.32,"text":"system prompt, hoping the agent actually","index":61,"start":118.32,"end_formatted":"1:58","start_formatted":"1:58"},{"end":120.32,"text":"follows it, or by feeding a lot of data","index":62,"start":120.32,"end_formatted":"2:00","start_formatted":"2:00"},{"end":122.24,"text":"into a vector database and using","index":63,"start":122.24,"end_formatted":"2:02","start_formatted":"2:02"},{"end":124.079,"text":"semantic search to extract it. But each","index":64,"start":124.079,"end_formatted":"2:04","start_formatted":"2:04"},{"end":126.159,"text":"approach has limitations. System prompts","index":65,"start":126.159,"end_formatted":"2:06","start_formatted":"2:06"},{"end":128.479,"text":"have a limited token window which limits","index":66,"start":128.479,"end_formatted":"2:08","start_formatted":"2:08"},{"end":130.479,"text":"how much information we can send to the","index":67,"start":130.479,"end_formatted":"2:10","start_formatted":"2:10"},{"end":132.319,"text":"model at a time. To handle larger data","index":68,"start":132.319,"end_formatted":"2:12","start_formatted":"2:12"},{"end":134.4,"text":"sets, we use semantic search which finds","index":69,"start":134.4,"end_formatted":"2:14","start_formatted":"2:14"},{"end":136.64,"text":"information based on matching meanings","index":70,"start":136.64,"end_formatted":"2:16","start_formatted":"2:16"},{"end":138.4,"text":"to the query. But vector search is used","index":71,"start":138.4,"end_formatted":"2:18","start_formatted":"2:18"},{"end":140.4,"text":"for semantic similarity rather than","index":72,"start":140.4,"end_formatted":"2:20","start_formatted":"2:20"},{"end":142.319,"text":"exact searches. It returns chunks of","index":73,"start":142.319,"end_formatted":"2:22","start_formatted":"2:22"},{"end":144.4,"text":"data that match the general context of","index":74,"start":144.4,"end_formatted":"2:24","start_formatted":"2:24"},{"end":146.4,"text":"the query, not necessarily the specific","index":75,"start":146.4,"end_formatted":"2:26","start_formatted":"2:26"},{"end":148.319,"text":"value we're looking for. This leaves","index":76,"start":148.319,"end_formatted":"2:28","start_formatted":"2:28"},{"end":149.92,"text":"extracting the right content from all","index":77,"start":149.92,"end_formatted":"2:29","start_formatted":"2:29"},{"end":151.599,"text":"the chunks to the model itself. File","index":78,"start":151.599,"end_formatted":"2:31","start_formatted":"2:31"},{"end":153.44,"text":"systems, however, offer a different","index":79,"start":153.44,"end_formatted":"2:33","start_formatted":"2:33"},{"end":155.12,"text":"approach. With file systems, the","index":80,"start":155.12,"end_formatted":"2:35","start_formatted":"2:35"},{"end":157.04,"text":"structure actually maps to your domain.","index":81,"start":157.04,"end_formatted":"2:37","start_formatted":"2:37"},{"end":158.959,"text":"You often have relationships between","index":82,"start":158.959,"end_formatted":"2:38","start_formatted":"2:38"},{"end":160.64,"text":"files in the folder structure that","index":83,"start":160.64,"end_formatted":"2:40","start_formatted":"2:40"},{"end":162.4,"text":"mirror the relationships between parent","index":84,"start":162.4,"end_formatted":"2:42","start_formatted":"2:42"},{"end":164.239,"text":"folders. With file systems, you don't","index":85,"start":164.239,"end_formatted":"2:44","start_formatted":"2:44"},{"end":166.239,"text":"have to flatten these relationships into","index":86,"start":166.239,"end_formatted":"2:46","start_formatted":"2:46"},{"end":168.16,"text":"model understandable vector chunks,","index":87,"start":168.16,"end_formatted":"2:48","start_formatted":"2:48"},{"end":170.08,"text":"which helps avoid missing relationships","index":88,"start":170.08,"end_formatted":"2:50","start_formatted":"2:50"},{"end":171.92,"text":"that are usually lost in semantic","index":89,"start":171.92,"end_formatted":"2:51","start_formatted":"2:51"},{"end":173.84,"text":"search. These hierarchical connections","index":90,"start":173.84,"end_formatted":"2:53","start_formatted":"2:53"},{"end":175.68,"text":"are preserved naturally, maintaining the","index":91,"start":175.68,"end_formatted":"2:55","start_formatted":"2:55"},{"end":177.68,"text":"organizational logic that already exists","index":92,"start":177.68,"end_formatted":"2:57","start_formatted":"2:57"},{"end":179.76,"text":"in your data. Another advantage is that","index":93,"start":179.76,"end_formatted":"2:59","start_formatted":"2:59"},{"end":181.76,"text":"retrieval is precise because Grep and","index":94,"start":181.76,"end_formatted":"3:01","start_formatted":"3:01"},{"end":183.76,"text":"Bash tools return exact matches. Unlike","index":95,"start":183.76,"end_formatted":"3:03","start_formatted":"3:03"},{"end":186.319,"text":"vector search, which returns all chunks","index":96,"start":186.319,"end_formatted":"3:06","start_formatted":"3:06"},{"end":188.4,"text":"that loosely match the query and then","index":97,"start":188.4,"end_formatted":"3:08","start_formatted":"3:08"},{"end":190.239,"text":"leaves it to the model to decide which","index":98,"start":190.239,"end_formatted":"3:10","start_formatted":"3:10"},{"end":192.08,"text":"one to use, you get only the required","index":99,"start":192.08,"end_formatted":"3:12","start_formatted":"3:12"},{"end":194.159,"text":"value. The context is minimal when","index":100,"start":194.159,"end_formatted":"3:14","start_formatted":"3:14"},{"end":196.159,"text":"agents use bash tools because they","index":101,"start":196.159,"end_formatted":"3:16","start_formatted":"3:16"},{"end":198,"text":"receive the specific chunk they need and","index":102,"start":198,"end_formatted":"3:18","start_formatted":"3:18"},{"end":200,"text":"many other chunks don't go into memory.","index":103,"start":200,"end_formatted":"3:20","start_formatted":"3:20"},{"end":202.08,"text":"This allows them to stay aligned and","index":104,"start":202.08,"end_formatted":"3:22","start_formatted":"3:22"},{"end":203.84,"text":"focused on the exact piece of","index":105,"start":203.84,"end_formatted":"3:23","start_formatted":"3:23"},{"end":205.36,"text":"information without getting lost in","index":106,"start":205.36,"end_formatted":"3:25","start_formatted":"3:25"},{"end":207.04,"text":"unrelated data. Now, this idea isn't","index":107,"start":207.04,"end_formatted":"3:27","start_formatted":"3:27"},{"end":209.44,"text":"something you are unfamiliar with. This","index":108,"start":209.44,"end_formatted":"3:29","start_formatted":"3:29"},{"end":211.12,"text":"idea has already been used inside Claude","index":109,"start":211.12,"end_formatted":"3:31","start_formatted":"3:31"},{"end":213.28,"text":"Code and all CLI agents where they use","index":110,"start":213.28,"end_formatted":"3:33","start_formatted":"3:33"},{"end":215.599,"text":"bash functions to narrow down findings","index":111,"start":215.599,"end_formatted":"3:35","start_formatted":"3:35"},{"end":217.76,"text":"using pattern matching. We've already","index":112,"start":217.76,"end_formatted":"3:37","start_formatted":"3:37"},{"end":219.599,"text":"been using the file system and Claude","index":113,"start":219.599,"end_formatted":"3:39","start_formatted":"3:39"},{"end":221.599,"text":"codes capabilities for research purposes","index":114,"start":221.599,"end_formatted":"3:41","start_formatted":"3:41"},{"end":223.76,"text":"for any idea that we evaluate. We","index":115,"start":223.76,"end_formatted":"3:43","start_formatted":"3:43"},{"end":225.76,"text":"usually pass the software tool we come","index":116,"start":225.76,"end_formatted":"3:45","start_formatted":"3:45"},{"end":227.76,"text":"across through this pipeline which","index":117,"start":227.76,"end_formatted":"3:47","start_formatted":"3:47"},{"end":229.519,"text":"contains multiple phases with our","index":118,"start":229.519,"end_formatted":"3:49","start_formatted":"3:49"},{"end":231.519,"text":"evaluation criteria that the research","index":119,"start":231.519,"end_formatted":"3:51","start_formatted":"3:51"},{"end":233.599,"text":"must pass through. All of this is","index":120,"start":233.599,"end_formatted":"3:53","start_formatted":"3:53"},{"end":235.28,"text":"defined in a markdown file containing","index":121,"start":235.28,"end_formatted":"3:55","start_formatted":"3:55"},{"end":237.28,"text":"the requirements and objectives of the","index":122,"start":237.28,"end_formatted":"3:57","start_formatted":"3:57"},{"end":239.2,"text":"tool we're testing, how to write the","index":123,"start":239.2,"end_formatted":"3:59","start_formatted":"3:59"},{"end":240.959,"text":"final document, and all the information","index":124,"start":240.959,"end_formatted":"4:00","start_formatted":"4:00"},{"end":242.879,"text":"required for each phase. We also provide","index":125,"start":242.879,"end_formatted":"4:02","start_formatted":"4:02"},{"end":245.04,"text":"Claude with certain documents as","index":126,"start":245.04,"end_formatted":"4:05","start_formatted":"4:05"},{"end":246.799,"text":"samples, which act as a guide for style","index":127,"start":246.799,"end_formatted":"4:06","start_formatted":"4:06"},{"end":249.12,"text":"matching, and the final document is","index":128,"start":249.12,"end_formatted":"4:09","start_formatted":"4:09"},{"end":250.959,"text":"saved in a research results folder. To","index":129,"start":250.959,"end_formatted":"4:10","start_formatted":"4:10"},{"end":253.04,"text":"guide the research, we have a claude.md","index":130,"start":253.04,"end_formatted":"4:13","start_formatted":"4:13"},{"end":255.599,"text":"file explaining how to pass the idea","index":131,"start":255.599,"end_formatted":"4:15","start_formatted":"4:15"},{"end":257.84,"text":"through each phase one by one,","index":132,"start":257.84,"end_formatted":"4:17","start_formatted":"4:17"},{"end":259.6,"text":"ultimately giving us a research document","index":133,"start":259.6,"end_formatted":"4:19","start_formatted":"4:19"},{"end":261.44,"text":"that meets all our checks. Whenever we","index":134,"start":261.44,"end_formatted":"4:21","start_formatted":"4:21"},{"end":263.36,"text":"have anything to research, I just go to","index":135,"start":263.36,"end_formatted":"4:23","start_formatted":"4:23"},{"end":265.12,"text":"Claude and tell it the idea or the tool","index":136,"start":265.12,"end_formatted":"4:25","start_formatted":"4:25"},{"end":267.199,"text":"to research. It then runs it through the","index":137,"start":267.199,"end_formatted":"4:27","start_formatted":"4:27"},{"end":269.12,"text":"six-phase validation process first by","index":138,"start":269.12,"end_formatted":"4:29","start_formatted":"4:29"},{"end":271.52,"text":"understanding the tool or idea and then","index":139,"start":271.52,"end_formatted":"4:31","start_formatted":"4:31"},{"end":273.52,"text":"passing it through each phase one by","index":140,"start":273.52,"end_formatted":"4:33","start_formatted":"4:33"},{"end":275.36,"text":"one. Once the idea has gone through all","index":141,"start":275.36,"end_formatted":"4:35","start_formatted":"4:35"},{"end":277.36,"text":"the phases, Claude generates a final","index":142,"start":277.36,"end_formatted":"4:37","start_formatted":"4:37"},{"end":279.44,"text":"report which we can read to verify","index":143,"start":279.44,"end_formatted":"4:39","start_formatted":"4:39"},{"end":281.36,"text":"whether the idea has potential or not.","index":144,"start":281.36,"end_formatted":"4:41","start_formatted":"4:41"},{"end":283.6,"text":"This file system approach saves us a lot","index":145,"start":283.6,"end_formatted":"4:43","start_formatted":"4:43"},{"end":285.68,"text":"of time by automating a research process","index":146,"start":285.68,"end_formatted":"4:45","start_formatted":"4:45"},{"end":288,"text":"that we would otherwise have to do step","index":147,"start":288,"end_formatted":"4:48","start_formatted":"4:48"},{"end":290.08,"text":"by step. If you want to try out this","index":148,"start":290.08,"end_formatted":"4:50","start_formatted":"4:50"},{"end":291.919,"text":"pipeline for yourself for your own use","index":149,"start":291.919,"end_formatted":"4:51","start_formatted":"4:51"},{"end":293.919,"text":"case, you can get a readytouse template","index":150,"start":293.919,"end_formatted":"4:53","start_formatted":"4:53"},{"end":296,"text":"for creating your own research pipeline","index":151,"start":296,"end_formatted":"4:56","start_formatted":"4:56"},{"end":298.16,"text":"similar to ours in our recently launched","index":152,"start":298.16,"end_formatted":"4:58","start_formatted":"4:58"},{"end":300.16,"text":"community called AI Labs Pro. For this","index":153,"start":300.16,"end_formatted":"5:00","start_formatted":"5:00"},{"end":302.56,"text":"and for all the previous videos, you get","index":154,"start":302.56,"end_formatted":"5:02","start_formatted":"5:02"},{"end":304.4,"text":"readytouse templates, prompts, all the","index":155,"start":304.4,"end_formatted":"5:04","start_formatted":"5:04"},{"end":306.639,"text":"commands and skills that you can plug","index":156,"start":306.639,"end_formatted":"5:06","start_formatted":"5:06"},{"end":308.56,"text":"directly into your projects. If you","index":157,"start":308.56,"end_formatted":"5:08","start_formatted":"5:08"},{"end":310.32,"text":"found value in what we do and want to","index":158,"start":310.32,"end_formatted":"5:10","start_formatted":"5:10"},{"end":312.32,"text":"support the channel, this is the best","index":159,"start":312.32,"end_formatted":"5:12","start_formatted":"5:12"},{"end":313.84,"text":"way to do it. 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If","index":198,"start":394.56,"end_formatted":"6:34","start_formatted":"6:34"},{"end":397.039,"text":"you want the source code for this","index":199,"start":397.039,"end_formatted":"6:37","start_formatted":"6:37"},{"end":398.479,"text":"project, you can find it on our","index":200,"start":398.479,"end_formatted":"6:38","start_formatted":"6:38"},{"end":400,"text":"community from where you can download","index":201,"start":400,"end_formatted":"6:40","start_formatted":"6:40"},{"end":401.68,"text":"and try out for yourself. Now, the first","index":202,"start":401.68,"end_formatted":"6:41","start_formatted":"6:41"},{"end":404.319,"text":"question that came to my mind while","index":203,"start":404.319,"end_formatted":"6:44","start_formatted":"6:44"},{"end":406.08,"text":"going through this tool was, is it","index":204,"start":406.08,"end_formatted":"6:46","start_formatted":"6:46"},{"end":407.84,"text":"really safe to equip agents to execute","index":205,"start":407.84,"end_formatted":"6:47","start_formatted":"6:47"},{"end":410.24,"text":"commands on the server? We literally saw","index":206,"start":410.24,"end_formatted":"6:50","start_formatted":"6:50"},{"end":412.319,"text":"a vulnerability in React server","index":207,"start":412.319,"end_formatted":"6:52","start_formatted":"6:52"},{"end":414.16,"text":"components this past December, which","index":208,"start":414.16,"end_formatted":"6:54","start_formatted":"6:54"},{"end":416,"text":"scored a 10.0, the highest on the scale","index":209,"start":416,"end_formatted":"6:56","start_formatted":"6:56"},{"end":418.4,"text":"and it involved code being executed on","index":210,"start":418.4,"end_formatted":"6:58","start_formatted":"6:58"},{"end":420.56,"text":"the server. So this is a really powerful","index":211,"start":420.56,"end_formatted":"7:00","start_formatted":"7:00"},{"end":422.88,"text":"and potentially dangerous capability to","index":212,"start":422.88,"end_formatted":"7:02","start_formatted":"7:02"},{"end":425.12,"text":"give agents. So why did I actually trust","index":213,"start":425.12,"end_formatted":"7:05","start_formatted":"7:05"},{"end":427.36,"text":"this tool? It's because it runs in a","index":214,"start":427.36,"end_formatted":"7:07","start_formatted":"7:07"},{"end":429.28,"text":"sandbox and has isolation. It only","index":215,"start":429.28,"end_formatted":"7:09","start_formatted":"7:09"},{"end":431.52,"text":"accesses the specific directory we","index":216,"start":431.52,"end_formatted":"7:11","start_formatted":"7:11"},{"end":433.599,"text":"provide. It doesn't modify anything","index":217,"start":433.599,"end_formatted":"7:13","start_formatted":"7:13"},{"end":435.36,"text":"else. In the article, they also","index":218,"start":435.36,"end_formatted":"7:15","start_formatted":"7:15"},{"end":436.96,"text":"mentioned that the agent explores the","index":219,"start":436.96,"end_formatted":"7:16","start_formatted":"7:16"},{"end":438.88,"text":"files without access to the production","index":220,"start":438.88,"end_formatted":"7:18","start_formatted":"7:18"},{"end":440.72,"text":"system. So your production code remains","index":221,"start":440.72,"end_formatted":"7:20","start_formatted":"7:20"},{"end":442.8,"text":"safe even if the agent tries to run","index":222,"start":442.8,"end_formatted":"7:22","start_formatted":"7:22"},{"end":444.88,"text":"harmful commands on the server. It","index":223,"start":444.88,"end_formatted":"7:24","start_formatted":"7:24"},{"end":446.56,"text":"provides two types of isolation. The","index":224,"start":446.56,"end_formatted":"7:26","start_formatted":"7:26"},{"end":448.56,"text":"first is an in-memory environment. In","index":225,"start":448.56,"end_formatted":"7:28","start_formatted":"7:28"},{"end":450.72,"text":"this setup, it uses just bash tool which","index":226,"start":450.72,"end_formatted":"7:30","start_formatted":"7:30"},{"end":453.12,"text":"runs bash scripts only on the files it","index":227,"start":453.12,"end_formatted":"7:33","start_formatted":"7:33"},{"end":455.44,"text":"has access to just like we did when","index":228,"start":455.44,"end_formatted":"7:35","start_formatted":"7:35"},{"end":457.44,"text":"creating our agent. The second type is a","index":229,"start":457.44,"end_formatted":"7:37","start_formatted":"7:37"},{"end":459.52,"text":"fully compatible sandbox environment","index":230,"start":459.52,"end_formatted":"7:39","start_formatted":"7:39"},{"end":461.68,"text":"offering full virtual machine isolation","index":231,"start":461.68,"end_formatted":"7:41","start_formatted":"7:41"},{"end":464.08,"text":"using the versal sandbox. We can choose","index":232,"start":464.08,"end_formatted":"7:44","start_formatted":"7:44"},{"end":466.319,"text":"either based on our needs. The in-memory","index":233,"start":466.319,"end_formatted":"7:46","start_formatted":"7:46"},{"end":468.479,"text":"approach is lighter and faster for","index":234,"start":468.479,"end_formatted":"7:48","start_formatted":"7:48"},{"end":470.319,"text":"simple use cases while the full VM","index":235,"start":470.319,"end_formatted":"7:50","start_formatted":"7:50"},{"end":472.479,"text":"isolation is better when you need","index":236,"start":472.479,"end_formatted":"7:52","start_formatted":"7:52"},{"end":474.16,"text":"stronger security guarantees. Even","index":237,"start":474.16,"end_formatted":"7:54","start_formatted":"7:54"},{"end":476.16,"text":"though this approach is really good for","index":238,"start":476.16,"end_formatted":"7:56","start_formatted":"7:56"},{"end":477.919,"text":"saving costs per model call, it's not","index":239,"start":477.919,"end_formatted":"7:57","start_formatted":"7:57"},{"end":480.16,"text":"the right approach for all kinds of","index":240,"start":480.16,"end_formatted":"8:00","start_formatted":"8:00"},{"end":481.84,"text":"problems. It's definitely not ideal if","index":241,"start":481.84,"end_formatted":"8:01","start_formatted":"8:01"},{"end":484.08,"text":"you need to match the meaning of words","index":242,"start":484.08,"end_formatted":"8:04","start_formatted":"8:04"},{"end":485.919,"text":"because bash tools are for exact","index":243,"start":485.919,"end_formatted":"8:05","start_formatted":"8:05"},{"end":487.759,"text":"matching. As we saw when we called our","index":244,"start":487.759,"end_formatted":"8:07","start_formatted":"8:07"},{"end":489.599,"text":"agent, it used specific keywords to","index":245,"start":489.599,"end_formatted":"8:09","start_formatted":"8:09"},{"end":491.599,"text":"locate the required data. It's also not","index":246,"start":491.599,"end_formatted":"8:11","start_formatted":"8:11"},{"end":493.759,"text":"suitable for unorganized file structures","index":247,"start":493.759,"end_formatted":"8:13","start_formatted":"8:13"},{"end":495.919,"text":"where the agent would have to struggle","index":248,"start":495.919,"end_formatted":"8:15","start_formatted":"8:15"},{"end":497.68,"text":"with multiple tool calls. A structure","index":249,"start":497.68,"end_formatted":"8:17","start_formatted":"8:17"},{"end":499.68,"text":"that the agent can easily navigate is","index":250,"start":499.68,"end_formatted":"8:19","start_formatted":"8:19"},{"end":502,"text":"much better. My personal suggestion is","index":251,"start":502,"end_formatted":"8:22","start_formatted":"8:22"},{"end":503.919,"text":"to use the bash tool when you have","index":252,"start":503.919,"end_formatted":"8:23","start_formatted":"8:23"},{"end":505.759,"text":"highly structured data and your requests","index":253,"start":505.759,"end_formatted":"8:25","start_formatted":"8:25"},{"end":507.84,"text":"are mostly clear in terms of what you","index":254,"start":507.84,"end_formatted":"8:27","start_formatted":"8:27"},{"end":509.84,"text":"want. Use rag when you care more about","index":255,"start":509.84,"end_formatted":"8:29","start_formatted":"8:29"},{"end":511.84,"text":"the meaning of what's written in the","index":256,"start":511.84,"end_formatted":"8:31","start_formatted":"8:31"},{"end":513.519,"text":"files or when your queries are likely to","index":257,"start":513.519,"end_formatted":"8:33","start_formatted":"8:33"},{"end":515.599,"text":"be messy. Before we wrap up, here's a","index":258,"start":515.599,"end_formatted":"8:35","start_formatted":"8:35"},{"end":517.76,"text":"word from our sponsor, Brilliant. The","index":259,"start":517.76,"end_formatted":"8:37","start_formatted":"8:37"},{"end":519.519,"text":"best engineers don't just know syntax.","index":260,"start":519.519,"end_formatted":"8:39","start_formatted":"8:39"},{"end":521.519,"text":"They break down problems from first","index":261,"start":521.519,"end_formatted":"8:41","start_formatted":"8:41"},{"end":523.12,"text":"principles. That is why we've partnered","index":262,"start":523.12,"end_formatted":"8:43","start_formatted":"8:43"},{"end":524.88,"text":"with Brilliant. Their philosophy is that","index":263,"start":524.88,"end_formatted":"8:44","start_formatted":"8:44"},{"end":526.56,"text":"you learn best by doing. They prioritize","index":264,"start":526.56,"end_formatted":"8:46","start_formatted":"8:46"},{"end":528.64,"text":"active problem solving. So you get","index":265,"start":528.64,"end_formatted":"8:48","start_formatted":"8:48"},{"end":530.32,"text":"hands-on with concepts instead of just","index":266,"start":530.32,"end_formatted":"8:50","start_formatted":"8:50"},{"end":532.16,"text":"memorizing. For example, in their course","index":267,"start":532.16,"end_formatted":"8:52","start_formatted":"8:52"},{"end":534.08,"text":"named how AI works. You don't just read,","index":268,"start":534.08,"end_formatted":"8:54","start_formatted":"8:54"},{"end":536.48,"text":"you manipulate the actual logic. You'll","index":269,"start":536.48,"end_formatted":"8:56","start_formatted":"8:56"},{"end":538.48,"text":"get hands-on with technicalities like","index":270,"start":538.48,"end_formatted":"8:58","start_formatted":"8:58"},{"end":540.48,"text":"calculating loss in the loss space and","index":271,"start":540.48,"end_formatted":"9:00","start_formatted":"9:00"},{"end":542.399,"text":"visualizing interpolation, building a","index":272,"start":542.399,"end_formatted":"9:02","start_formatted":"9:02"},{"end":544.48,"text":"deep intuition you just can't get from a","index":273,"start":544.48,"end_formatted":"9:04","start_formatted":"9:04"},{"end":546.56,"text":"video lecture. Through their interactive","index":274,"start":546.56,"end_formatted":"9:06","start_formatted":"9:06"},{"end":548.32,"text":"technical courses, you get the most","index":275,"start":548.32,"end_formatted":"9:08","start_formatted":"9:08"},{"end":550.08,"text":"effective way to truly master the","index":276,"start":550.08,"end_formatted":"9:10","start_formatted":"9:10"},{"end":551.92,"text":"concepts we talk about. You'll also get","index":277,"start":551.92,"end_formatted":"9:11","start_formatted":"9:11"},{"end":553.68,"text":"20% off on annual premium subscription,","index":278,"start":553.68,"end_formatted":"9:13","start_formatted":"9:13"},{"end":556.24,"text":"unlocking their entire catalog of math,","index":279,"start":556.24,"end_formatted":"9:16","start_formatted":"9:16"},{"end":558.399,"text":"data, and CS courses, giving you a","index":280,"start":558.399,"end_formatted":"9:18","start_formatted":"9:18"},{"end":560.399,"text":"complete road map to upskill. Click the","index":281,"start":560.399,"end_formatted":"9:20","start_formatted":"9:20"},{"end":562.16,"text":"link in the description or scan the QR","index":282,"start":562.16,"end_formatted":"9:22","start_formatted":"9:22"},{"end":564.24,"text":"code on your screen to claim your free","index":283,"start":564.24,"end_formatted":"9:24","start_formatted":"9:24"},{"end":566.16,"text":"30-day trial. That brings us to the end","index":284,"start":566.16,"end_formatted":"9:26","start_formatted":"9:26"},{"end":568.399,"text":"of this video. If you'd like to support","index":285,"start":568.399,"end_formatted":"9:28","start_formatted":"9:28"},{"end":570.16,"text":"the channel and help us keep making","index":286,"start":570.16,"end_formatted":"9:30","start_formatted":"9:30"},{"end":571.92,"text":"videos like this, you can do so by using","index":287,"start":571.92,"end_formatted":"9:31","start_formatted":"9:31"},{"end":573.92,"text":"the super thanks button below. As","index":288,"start":573.92,"end_formatted":"9:33","start_formatted":"9:33"},{"end":575.839,"text":"always, thank you for watching and I'll","index":289,"start":575.839,"end_formatted":"9:35","start_formatted":"9:35"},{"end":577.519,"text":"see you in the next one.","index":290,"start":577.519,"end_formatted":"9:37","start_formatted":"9:37"}],"full_text":"Ever since models started getting powerful, many people have started building really cool products, integrating models into them, and solving lots of problems for us. But these systems consume a lot of tokens, especially if you're actually integrating a model using an API. The solution to this is much simpler than you think. The best architecture is not some extreme pipeline or highly scaled tuning, but actually an old philosophy that forms the basis of a Unix-based system that everything is a file. Now, I know they weren't talking about model costs and we're talking about devices and files, but surprisingly enough, the solution to this high-cost issue is the exact same principle. And this is exactly what a software engineer at Versel talks about. Before we explore why files are the solution, let's understand a few things about how these models actually work. Models have been trained on massive amounts of code. This is the exact reason why they're better at understanding code, directory structures, and native bash scripts that developers use to navigate files and find what they need. When an agent uses GP and ls, it's not doing something new. It's simply doing something it already knows how to do, just in a more controlled way. This approach isn't limited to code. Agents can navigate any directory containing anything, be it code or not, because they're already comfortable with commands and understand file systems. Whenever an agent needs something, it looks around the file system using native bash commands like ls and find. Once the agent finds the exact file using find, it searches for relevant content within that file using pattern matching with grep and cat. Only a small relevant slice of information is sent to the model while the rest stays out of memory, keeping the context window clean. This means we're not burning through tokens on irrelevant data that the model doesn't need. Using this approach, the agent returns a structured output. This pattern worked so well that Versel ended up open sourcing a bash tool built specifically around it, giving agents the ability to explore file systems the same way a developer would. When building large language model systems, there are two ways of providing the right information to the model. Either through a detailed system prompt, hoping the agent actually follows it, or by feeding a lot of data into a vector database and using semantic search to extract it. But each approach has limitations. System prompts have a limited token window which limits how much information we can send to the model at a time. To handle larger data sets, we use semantic search which finds information based on matching meanings to the query. But vector search is used for semantic similarity rather than exact searches. It returns chunks of data that match the general context of the query, not necessarily the specific value we're looking for. This leaves extracting the right content from all the chunks to the model itself. File systems, however, offer a different approach. With file systems, the structure actually maps to your domain. You often have relationships between files in the folder structure that mirror the relationships between parent folders. With file systems, you don't have to flatten these relationships into model understandable vector chunks, which helps avoid missing relationships that are usually lost in semantic search. These hierarchical connections are preserved naturally, maintaining the organizational logic that already exists in your data. Another advantage is that retrieval is precise because Grep and Bash tools return exact matches. Unlike vector search, which returns all chunks that loosely match the query and then leaves it to the model to decide which one to use, you get only the required value. The context is minimal when agents use bash tools because they receive the specific chunk they need and many other chunks don't go into memory. This allows them to stay aligned and focused on the exact piece of information without getting lost in unrelated data. Now, this idea isn't something you are unfamiliar with. This idea has already been used inside Claude Code and all CLI agents where they use bash functions to narrow down findings using pattern matching. We've already been using the file system and Claude codes capabilities for research purposes for any idea that we evaluate. We usually pass the software tool we come across through this pipeline which contains multiple phases with our evaluation criteria that the research must pass through. All of this is defined in a markdown file containing the requirements and objectives of the tool we're testing, how to write the final document, and all the information required for each phase. We also provide Claude with certain documents as samples, which act as a guide for style matching, and the final document is saved in a research results folder. To guide the research, we have a claude.md file explaining how to pass the idea through each phase one by one, ultimately giving us a research document that meets all our checks. Whenever we have anything to research, I just go to Claude and tell it the idea or the tool to research. It then runs it through the six-phase validation process first by understanding the tool or idea and then passing it through each phase one by one. Once the idea has gone through all the phases, Claude generates a final report which we can read to verify whether the idea has potential or not. This file system approach saves us a lot of time by automating a research process that we would otherwise have to do step by step. If you want to try out this pipeline for yourself for your own use case, you can get a readytouse template for creating your own research pipeline similar to ours in our recently launched community called AI Labs Pro. For this and for all the previous videos, you get readytouse templates, prompts, all the commands and skills that you can plug directly into your projects. If you found value in what we do and want to support the channel, this is the best way to do it. Links in the description. I was going through their case study in which they explained how to build a sales summary. agent using this architecture. They've also open- sourced it, but it gave me a really interesting idea that I wanted to try out on my own. I was actually building a company policy project where I had a lot of company data in the form of JSON, markdown, and txt files, all separated by department. Normally, I would have implemented this system using a vector database like Chroma, but I decided to give this tool a shot. I went ahead and implemented this architecture. On the back end, I included the path to the document folder containing the company's data and gave the agent access to the ls, cat, gp, and find commands along with a guide on how to use the tool and when to use each command. I used the Gemini 2.5 flash model, provided it with Versel's bash tool, and gave it the path to the documents inside the tool. And so when I tested the agent by asking it any question related to the data, it basically answered based on the exact content from the company's policies, including the handbook and leave policy documents. To verify how it was working, I logged its tool usage on the terminal. The agent first used the ls command to see what documents were available and then used grep with pattern matching to look for off days or any similar terms. This set of commands handled our query and gave us results with the same level of accuracy as a rag system would. If you want the source code for this project, you can find it on our community from where you can download and try out for yourself. Now, the first question that came to my mind while going through this tool was, is it really safe to equip agents to execute commands on the server? We literally saw a vulnerability in React server components this past December, which scored a 10.0, the highest on the scale and it involved code being executed on the server. So this is a really powerful and potentially dangerous capability to give agents. So why did I actually trust this tool? It's because it runs in a sandbox and has isolation. It only accesses the specific directory we provide. It doesn't modify anything else. In the article, they also mentioned that the agent explores the files without access to the production system. So your production code remains safe even if the agent tries to run harmful commands on the server. It provides two types of isolation. The first is an in-memory environment. In this setup, it uses just bash tool which runs bash scripts only on the files it has access to just like we did when creating our agent. The second type is a fully compatible sandbox environment offering full virtual machine isolation using the versal sandbox. We can choose either based on our needs. The in-memory approach is lighter and faster for simple use cases while the full VM isolation is better when you need stronger security guarantees. Even though this approach is really good for saving costs per model call, it's not the right approach for all kinds of problems. It's definitely not ideal if you need to match the meaning of words because bash tools are for exact matching. As we saw when we called our agent, it used specific keywords to locate the required data. It's also not suitable for unorganized file structures where the agent would have to struggle with multiple tool calls. A structure that the agent can easily navigate is much better. My personal suggestion is to use the bash tool when you have highly structured data and your requests are mostly clear in terms of what you want. Use rag when you care more about the meaning of what's written in the files or when your queries are likely to be messy. Before we wrap up, here's a word from our sponsor, Brilliant. The best engineers don't just know syntax. They break down problems from first principles. That is why we've partnered with Brilliant. Their philosophy is that you learn best by doing. They prioritize active problem solving. So you get hands-on with concepts instead of just memorizing. For example, in their course named how AI works. You don't just read, you manipulate the actual logic. You'll get hands-on with technicalities like calculating loss in the loss space and visualizing interpolation, building a deep intuition you just can't get from a video lecture. Through their interactive technical courses, you get the most effective way to truly master the concepts we talk about. You'll also get 20% off on annual premium subscription, unlocking their entire catalog of math, data, and CS courses, giving you a complete road map to upskill. Click the link in the description or scan the QR code on your screen to claim your free 30-day trial. That brings us to the end of this video. If you'd like to support the channel and help us keep making videos like this, you can do so by using the super thanks button below. As always, thank you for watching and I'll see you in the next one.","word_count":1912,"main_topics":["Token/cost reduction for LLM agents","Filesystem-first retrieval (Unix philosophy)","Bash tool agent loop (ls/find/grep/cat)","RAG/vector search limitations for exact lookup","Preserving hierarchy/relationships via folders","Claude Code-style CLI agent workflows","Automated research pipeline using claude.md and phases","Implementation demo with company policy documents","Sandbox security and isolation (in-memory vs VM)","Decision framework: bash retrieval vs RAG"],"section_count":7,"segment_count":291,"best_sections_for_shorts":[{"cta":"If you’re building agents, try filesystem-first retrieval before you spin up a vector DB.","end":55,"hook":"LLM apps are expensive because you’re feeding the model too much. 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