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{"parentUuid":"e84454b1-02dc-4a6d-b557-1b8857004e2e","isSidechain":false,"type":"user","message":{"role":"user","content":"<command-message>ij</command-message>\n<command-name>/ij</command-name>\n<command-args>second meeting with Suranjit Adhikari from CapitalOne\n\nCapital One AI Strategy and Role Discussion\n\nDate/Time: March 16, 2026, 09:45 AM PDT\n\nOverall Summary\n\nThe discussion covers Capital One’s AI strategy focusing on three key areas: AI evaluations, multi-agent conversational platforms (MCP), and advanced transformer-based customer modeling. The conversation also details organizational structure, decision-making, success metrics, tooling, and the interview process for an AI role.\n\nKey Points\n    •    Capital One is developing a unified AI evaluation platform for both Gen AI and traditional models to standardize and improve evaluation processes across teams.\n    •    MCP governance is a priority to avoid tool overlap and confusion, with exploration of dynamic registry and code generation approaches.\n    •    The company is building internal transformer models using extensive data to replace legacy models like XGBoost in credit markets, aiming for high impact with rigorous governance and audit.\n    •    Capital One’s AI organization (IFX) operates horizontally, consolidating data science and engineering efforts with strong product team influence on budgeting and strategy.\n    •    Success metrics include making Gen AI evaluations self-serve, deciding MCP rollout strategy, and expanding advanced model use cases while maintaining lean headcount.\n    •    The interview process involves two technical rounds (system design focused on Gen AI and coding) plus behavioral and a unique case study assessing problem-solving and business acumen.\n\nAction Items\n    •    Candidate to prepare for interviews focusing on Gen AI system design, inference optimizations, and coding exercises.\n    •    Vivek to coordinate with recruiter to schedule interviews and provide candidate support.\n\nOpen Questions\n    •    Final decision pending on MCP strategy: whether to allow broad SDK use or limit to a few opinionated MCP servers.\n    •    How to best balance tool design and governance to optimize agent workflows across diverse business units.\n    •    Ongoing efforts to improve internal software delivery processes and reduce bottlenecks in deploying AI solutions.</command-args>"},"uuid":"e04ecb19-18ea-4599-92e6-ee249f78b2bd","timestamp":"2026-03-16T17:32:50.340Z","userType":"external","cwd":"/Users/haldar/repos/gh/agent-kitchen","sessionId":"f7686875-7ad8-4666-8a32-e7830cc2d20e","version":"2.1.76","gitBranch":"main"}
{"parentUuid":"e04ecb19-18ea-4599-92e6-ee249f78b2bd","isSidechain":false,"type":"user","message":{"role":"user","content":[{"type":"text","text":"# Interstitial Journal Entry\n\nAppend a timestamped entry to today's Obsidian daily note.\n\n## Usage\n- `/ij working on auth refactor` - Add entry with message\n- `/ij` - Prompt will ask what you're doing\n\n## Instructions\n\n### Step 1: Get Entry Text\n\nThe entry text comes from `second meeting with Suranjit Adhikari from CapitalOne\n\nCapital One AI Strategy and Role Discussion\n\nDate/Time: March 16, 2026, 09:45 AM PDT\n\nOverall Summary\n\nThe discussion covers Capital One’s AI strategy focusing on three key areas: AI evaluations, multi-agent conversational platforms (MCP), and advanced transformer-based customer modeling. The conversation also details organizational structure, decision-making, success metrics, tooling, and the interview process for an AI role.\n\nKey Points\n    •    Capital One is developing a unified AI evaluation platform for both Gen AI and traditional models to standardize and improve evaluation processes across teams.\n    •    MCP governance is a priority to avoid tool overlap and confusion, with exploration of dynamic registry and code generation approaches.\n    •    The company is building internal transformer models using extensive data to replace legacy models like XGBoost in credit markets, aiming for high impact with rigorous governance and audit.\n    •    Capital One’s AI organization (IFX) operates horizontally, consolidating data science and engineering efforts with strong product team influence on budgeting and strategy.\n    •    Success metrics include making Gen AI evaluations self-serve, deciding MCP rollout strategy, and expanding advanced model use cases while maintaining lean headcount.\n    •    The interview process involves two technical rounds (system design focused on Gen AI and coding) plus behavioral and a unique case study assessing problem-solving and business acumen.\n\nAction Items\n    •    Candidate to prepare for interviews focusing on Gen AI system design, inference optimizations, and coding exercises.\n    •    Vivek to coordinate with recruiter to schedule interviews and provide candidate support.\n\nOpen Questions\n    •    Final decision pending on MCP strategy: whether to allow broad SDK use or limit to a few opinionated MCP servers.\n    •    How to best balance tool design and governance to optimize agent workflows across diverse business units.\n    •    Ongoing efforts to improve internal software delivery processes and reduce bottlenecks in deploying AI solutions.`. If empty, ask the user what they're doing.\n\n### Step 2: Append to Daily Note\n\n```bash\nDATE=$(date +%Y-%m-%d)\nTIME=$(date +%H:%M)\nDAILY_NOTE=~/Obsidian_iCloud/notes.md/${DATE}.md\nENTRY=\"- ${TIME} - second meeting with Suranjit Adhikari from CapitalOne\n\nCapital One AI Strategy and Role Discussion\n\nDate/Time: March 16, 2026, 09:45 AM PDT\n\nOverall Summary\n\nThe discussion covers Capital One’s AI strategy focusing on three key areas: AI evaluations, multi-agent conversational platforms (MCP), and advanced transformer-based customer modeling. The conversation also details organizational structure, decision-making, success metrics, tooling, and the interview process for an AI role.\n\nKey Points\n    •    Capital One is developing a unified AI evaluation platform for both Gen AI and traditional models to standardize and improve evaluation processes across teams.\n    •    MCP governance is a priority to avoid tool overlap and confusion, with exploration of dynamic registry and code generation approaches.\n    •    The company is building internal transformer models using extensive data to replace legacy models like XGBoost in credit markets, aiming for high impact with rigorous governance and audit.\n    •    Capital One’s AI organization (IFX) operates horizontally, consolidating data science and engineering efforts with strong product team influence on budgeting and strategy.\n    •    Success metrics include making Gen AI evaluations self-serve, deciding MCP rollout strategy, and expanding advanced model use cases while maintaining lean headcount.\n    •    The interview process involves two technical rounds (system design focused on Gen AI and coding) plus behavioral and a unique case study assessing problem-solving and business acumen.\n\nAction Items\n    •    Candidate to prepare for interviews focusing on Gen AI system design, inference optimizations, and coding exercises.\n    •    Vivek to coordinate with recruiter to schedule interviews and provide candidate support.\n\nOpen Questions\n    •    Final decision pending on MCP strategy: whether to allow broad SDK use or limit to a few opinionated MCP servers.\n    •    How to best balance tool design and governance to optimize agent workflows across diverse business units.\n    •    Ongoing efforts to improve internal software delivery processes and reduce bottlenecks in deploying AI solutions.\"\n\nif [ ! -f \"$DAILY_NOTE\" ]; then\n  # Create new file with Interstitial section\n  printf \"## Interstitial\\n\\n%s\\n\" \"$ENTRY\" > \"$DAILY_NOTE\"\n  echo \"Created $DAILY_NOTE with first entry\"\nelif grep -q \"^## Interstitial\" \"$DAILY_NOTE\"; then\n  # Section exists - find last entry line (last line starting with \"- \") in section\n  SECTION_LINE=$(grep -n \"^## Interstitial\" \"$DAILY_NOTE\" | cut -d: -f1)\n  NEXT_SECTION_REL=$(tail -n +$((SECTION_LINE + 1)) \"$DAILY_NOTE\" | grep -n \"^## \" | head -1 | cut -d: -f1)\n\n  if [ -z \"$NEXT_SECTION_REL\" ]; then\n    # No next section - just append to file\n    echo \"$ENTRY\" >> \"$DAILY_NOTE\"\n  else\n    # Find last \"- \" line before next section\n    NEXT_SECTION_ABS=$((SECTION_LINE + NEXT_SECTION_REL))\n    LAST_ENTRY=$(head -n $((NEXT_SECTION_ABS - 1)) \"$DAILY_NOTE\" | grep -n \"^- \" | tail -1 | cut -d: -f1)\n\n    if [ -z \"$LAST_ENTRY\" ]; then\n      LAST_ENTRY=$((SECTION_LINE + 1))  # Insert after header + blank line\n    fi\n\n    # Insert after last entry\n    { head -n $LAST_ENTRY \"$DAILY_NOTE\"; echo \"$ENTRY\"; tail -n +$((LAST_ENTRY + 1)) \"$DAILY_NOTE\"; } > /tmp/daily_note_tmp.md\n    mv /tmp/daily_note_tmp.md \"$DAILY_NOTE\"\n  fi\n  echo \"Added entry to $DAILY_NOTE\"\nelse\n  # File exists but no Interstitial section - add it at the top\n  { printf \"## Interstitial\\n\\n%s\\n\\n\" \"$ENTRY\"; cat \"$DAILY_NOTE\"; } > /tmp/daily_note_tmp.md\n  mv /tmp/daily_note_tmp.md \"$DAILY_NOTE\"\n  echo \"Added Interstitial section to $DAILY_NOTE\"\nfi\n```\n\n### Step 3: Confirm\n\nReport what was logged:\n- Show the timestamp and entry\n- Mention the file path\n- Keep it brief - this should be fast\n"}]},"isMeta":true,"uuid":"b78ed6fe-c415-476c-b9a0-e22df01dacca","timestamp":"2026-03-16T17:32:50.340Z","userType":"external","cwd":"/Users/haldar/repos/gh/agent-kitchen","sessionId":"f7686875-7ad8-4666-8a32-e7830cc2d20e","version":"2.1.76","gitBranch":"main"}
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{"parentUuid":"d8c14888-6eb5-4a5a-83f2-b66bc95ccd0a","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_01Q2N7smEt49DYnY766tymDQ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XC3m9tvvyXcvfSfSCuoVTy","name":"Bash","input":{"command":"DATE=$(date +%Y-%m-%d)\nTIME=$(date +%H:%M)\nDAILY_NOTE=~/Obsidian_iCloud/notes.md/${DATE}.md\nENTRY=\"- ${TIME} - second meeting with Suranjit Adhikari from CapitalOne\n\nCapital One AI Strategy and Role Discussion\n\nDate/Time: March 16, 2026, 09:45 AM PDT\n\nOverall Summary\n\nThe discussion covers Capital One's AI strategy focusing on three key areas: AI evaluations, multi-agent conversational platforms (MCP), and advanced transformer-based customer modeling. The conversation also details organizational structure, decision-making, success metrics, tooling, and the interview process for an AI role.\n\nKey Points\n    •    Capital One is developing a unified AI evaluation platform for both Gen AI and traditional models to standardize and improve evaluation processes across teams.\n    •    MCP governance is a priority to avoid tool overlap and confusion, with exploration of dynamic registry and code generation approaches.\n    •    The company is building internal transformer models using extensive data to replace legacy models like XGBoost in credit markets, aiming for high impact with rigorous governance and audit.\n    •    Capital One's AI organization (IFX) operates horizontally, consolidating data science and engineering efforts with strong product team influence on budgeting and strategy.\n    •    Success metrics include making Gen AI evaluations self-serve, deciding MCP rollout strategy, and expanding advanced model use cases while maintaining lean headcount.\n    •    The interview process involves two technical rounds (system design focused on Gen AI and coding) plus behavioral and a unique case study assessing problem-solving and business acumen.\n\nAction Items\n    •    Candidate to prepare for interviews focusing on Gen AI system design, inference optimizations, and coding exercises.\n    •    Vivek to coordinate with recruiter to schedule interviews and provide candidate support.\n\nOpen Questions\n    •    Final decision pending on MCP strategy: whether to allow broad SDK use or limit to a few opinionated MCP servers.\n    •    How to best balance tool design and governance to optimize agent workflows across diverse business units.\n    •    Ongoing efforts to improve internal software delivery processes and reduce bottlenecks in deploying AI solutions.\"\n\nif [ ! -f \"$DAILY_NOTE\" ]; then\n  printf \"## Interstitial\\n\\n%s\\n\" \"$ENTRY\" > \"$DAILY_NOTE\"\n  echo \"Created $DAILY_NOTE with first entry\"\nelif grep -q \"^## Interstitial\" \"$DAILY_NOTE\"; then\n  SECTION_LINE=$(grep -n \"^## Interstitial\" \"$DAILY_NOTE\" | cut -d: -f1)\n  NEXT_SECTION_REL=$(tail -n +$((SECTION_LINE + 1)) \"$DAILY_NOTE\" | grep -n \"^## \" | head -1 | cut -d: -f1)\n\n  if [ -z \"$NEXT_SECTION_REL\" ]; then\n    echo \"$ENTRY\" >> \"$DAILY_NOTE\"\n  else\n    NEXT_SECTION_ABS=$((SECTION_LINE + NEXT_SECTION_REL))\n    LAST_ENTRY=$(head -n $((NEXT_SECTION_ABS - 1)) \"$DAILY_NOTE\" | grep -n \"^- \" | tail -1 | cut -d: -f1)\n\n    if [ -z \"$LAST_ENTRY\" ]; then\n      LAST_ENTRY=$((SECTION_LINE + 1))\n    fi\n\n    { head -n $LAST_ENTRY \"$DAILY_NOTE\"; echo \"$ENTRY\"; tail -n +$((LAST_ENTRY + 1)) \"$DAILY_NOTE\"; } > /tmp/daily_note_tmp.md\n    mv /tmp/daily_note_tmp.md \"$DAILY_NOTE\"\n  fi\n  echo \"Added entry to $DAILY_NOTE\"\nelse\n  { printf \"## Interstitial\\n\\n%s\\n\\n\" \"$ENTRY\"; cat \"$DAILY_NOTE\"; } > /tmp/daily_note_tmp.md\n  mv /tmp/daily_note_tmp.md \"$DAILY_NOTE\"\n  echo \"Added Interstitial section to $DAILY_NOTE\"\nfi","description":"Append Capital One meeting journal entry to today's daily note"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":20580,"cache_read_input_tokens":8756,"output_tokens":1127,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":20580,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZ7ACUsyD7Q6HXwcB7S94","type":"assistant","uuid":"729d887f-b541-495b-9131-8ccd94777b12","timestamp":"2026-03-16T17:33:03.367Z","userType":"external","cwd":"/Users/haldar/repos/gh/agent-kitchen","sessionId":"f7686875-7ad8-4666-8a32-e7830cc2d20e","version":"2.1.76","gitBranch":"main"}
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{"parentUuid":"bcd82382-af23-452f-b799-5ba54b270d62","isSidechain":false,"type":"user","message":{"role":"user","content":"<command-message>ij</command-message>\n<command-name>/ij</command-name>\n<command-args>on the above, here are more detailed notes on the interview process and what to expect and how to prepare</command-args>"},"uuid":"8aeb52ad-91d4-41eb-97b2-a705b63992a6","timestamp":"2026-03-16T17:48:45.023Z","userType":"external","cwd":"/Users/haldar/repos/gh/agent-kitchen","sessionId":"f7686875-7ad8-4666-8a32-e7830cc2d20e","version":"2.1.76","gitBranch":"main"}
{"parentUuid":"8aeb52ad-91d4-41eb-97b2-a705b63992a6","isSidechain":false,"type":"user","message":{"role":"user","content":[{"type":"image","source":{"type":"base64","media_type":"image/png","data":"[encoded data, 553968 characters]"}},{"type":"text","text":"# Interstitial Journal Entry\n\nAppend a timestamped entry to today's Obsidian daily note.\n\n## Usage\n- `/ij working on auth refactor` - Add entry with message\n- `/ij` - Prompt will ask what you're doing\n\n## Instructions\n\n### Step 1: Get Entry Text\n\nThe entry text comes from `on the above, here are more detailed notes on the interview process and what to expect and how to prepare`. If empty, ask the user what they're doing.\n\n### Step 2: Append to Daily Note\n\n```bash\nDATE=$(date +%Y-%m-%d)\nTIME=$(date +%H:%M)\nDAILY_NOTE=~/Obsidian_iCloud/notes.md/${DATE}.md\nENTRY=\"- ${TIME} - on the above, here are more detailed notes on the interview process and what to expect and how to prepare\"\n\nif [ ! -f \"$DAILY_NOTE\" ]; then\n  # Create new file with Interstitial section\n  printf \"## Interstitial\\n\\n%s\\n\" \"$ENTRY\" > \"$DAILY_NOTE\"\n  echo \"Created $DAILY_NOTE with first entry\"\nelif grep -q \"^## Interstitial\" \"$DAILY_NOTE\"; then\n  # Section exists - find last entry line (last line starting with \"- \") in section\n  SECTION_LINE=$(grep -n \"^## Interstitial\" \"$DAILY_NOTE\" | cut -d: -f1)\n  NEXT_SECTION_REL=$(tail -n +$((SECTION_LINE + 1)) \"$DAILY_NOTE\" | grep -n \"^## \" | head -1 | cut -d: -f1)\n\n  if [ -z \"$NEXT_SECTION_REL\" ]; then\n    # No next section - just append to file\n    echo \"$ENTRY\" >> \"$DAILY_NOTE\"\n  else\n    # Find last \"- \" line before next section\n    NEXT_SECTION_ABS=$((SECTION_LINE + NEXT_SECTION_REL))\n    LAST_ENTRY=$(head -n $((NEXT_SECTION_ABS - 1)) \"$DAILY_NOTE\" | grep -n \"^- \" | tail -1 | cut -d: -f1)\n\n    if [ -z \"$LAST_ENTRY\" ]; then\n      LAST_ENTRY=$((SECTION_LINE + 1))  # Insert after header + blank line\n    fi\n\n    # Insert after last entry\n    { head -n $LAST_ENTRY \"$DAILY_NOTE\"; echo \"$ENTRY\"; tail -n +$((LAST_ENTRY + 1)) \"$DAILY_NOTE\"; } > /tmp/daily_note_tmp.md\n    mv /tmp/daily_note_tmp.md \"$DAILY_NOTE\"\n  fi\n  echo \"Added entry to $DAILY_NOTE\"\nelse\n  # File exists but no Interstitial section - add it at the top\n  { printf \"## Interstitial\\n\\n%s\\n\\n\" \"$ENTRY\"; cat \"$DAILY_NOTE\"; } > /tmp/daily_note_tmp.md\n  mv /tmp/daily_note_tmp.md \"$DAILY_NOTE\"\n  echo \"Added Interstitial section to $DAILY_NOTE\"\nfi\n```\n\n### Step 3: Confirm\n\nReport what was logged:\n- Show the timestamp and entry\n- Mention the file path\n- Keep it brief - this should be fast\n"}]},"isMeta":true,"uuid":"ec030738-0c62-4075-91e3-3d7143ac82cb","timestamp":"2026-03-16T17:48:45.023Z","userType":"external","cwd":"/Users/haldar/repos/gh/agent-kitchen","sessionId":"f7686875-7ad8-4666-8a32-e7830cc2d20e","version":"2.1.76","gitBranch":"main"}
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{"parentUuid":"52406337-551b-4b1e-84aa-41011e627744","isSidechain":false,"message":{"model":"claude-opus-4-6","id":"msg_019Qntx7JSmwTR6tyQ2BJnfy","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MKRe5vmoCzCEUCzAfGjYsa","name":"Bash","input":{"command":"DATE=$(date +%Y-%m-%d)\nTIME=$(date +%H:%M)\nDAILY_NOTE=~/Obsidian_iCloud/notes.md/${DATE}.md\nENTRY=\"- ${TIME} - Capital One interview prep notes from Suranjit meeting. Four rounds: (1) Technical system design - GenAI/RAG app design, chunking, retrieval, eval metrics, inference optimization; (2) Technical coding - Python, LeetCode easy-medium, explain tradeoffs; (3) Behavioral - STAR format, senior leadership examples; (4) Case study - open-ended business problem-solving, structured reasoning. Prep: review end-to-end GenAI app design, evals/flywheels/LLM-as-judge, inference optimization (speculative decoding, FlashAttention, vLLM, TensorRT, KV-cache), practice explaining technical work clearly, light coding refresh, structured behavioral examples, open-ended business reasoning.\"\n\nif [ ! -f \"$DAILY_NOTE\" ]; then\n  printf \"## Interstitial\\n\\n%s\\n\" \"$ENTRY\" > \"$DAILY_NOTE\"\n  echo \"Created $DAILY_NOTE with first entry\"\nelif grep -q \"^## Interstitial\" \"$DAILY_NOTE\"; then\n  SECTION_LINE=$(grep -n \"^## Interstitial\" \"$DAILY_NOTE\" | cut -d: -f1)\n  NEXT_SECTION_REL=$(tail -n +$((SECTION_LINE + 1)) \"$DAILY_NOTE\" | grep -n \"^## \" | head -1 | cut -d: -f1)\n\n  if [ -z \"$NEXT_SECTION_REL\" ]; then\n    echo \"$ENTRY\" >> \"$DAILY_NOTE\"\n  else\n    NEXT_SECTION_ABS=$((SECTION_LINE + NEXT_SECTION_REL))\n    LAST_ENTRY=$(head -n $((NEXT_SECTION_ABS - 1)) \"$DAILY_NOTE\" | grep -n \"^- \" | tail -1 | cut -d: -f1)\n\n    if [ -z \"$LAST_ENTRY\" ]; then\n      LAST_ENTRY=$((SECTION_LINE + 1))\n    fi\n\n    { head -n $LAST_ENTRY \"$DAILY_NOTE\"; echo \"$ENTRY\"; tail -n +$((LAST_ENTRY + 1)) \"$DAILY_NOTE\"; } > /tmp/daily_note_tmp.md\n    mv /tmp/daily_note_tmp.md \"$DAILY_NOTE\"\n  fi\n  echo \"Added entry to $DAILY_NOTE\"\nelse\n  { printf \"## Interstitial\\n\\n%s\\n\\n\" \"$ENTRY\"; cat \"$DAILY_NOTE\"; } > /tmp/daily_note_tmp.md\n  mv /tmp/daily_note_tmp.md \"$DAILY_NOTE\"\n  echo \"Added Interstitial section to $DAILY_NOTE\"\nfi","description":"Append Capital One interview prep notes to daily note"},"caller":{"type":"direct"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2481,"cache_read_input_tokens":30505,"output_tokens":851,"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":2481,"ephemeral_5m_input_tokens":0},"inference_geo":"","iterations":[],"speed":"standard"}},"requestId":"req_011CZ7BQsVDRAf14aRzRt45y","type":"assistant","uuid":"4bc7d271-e129-4fb5-a368-a3a537fecc91","timestamp":"2026-03-16T17:49:04.756Z","userType":"external","cwd":"/Users/haldar/repos/gh/agent-kitchen","sessionId":"f7686875-7ad8-4666-8a32-e7830cc2d20e","version":"2.1.76","gitBranch":"main"}
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