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昨天以前江边的旱鸭子

Unpacking the Data Structure of Manus Session

作者 John Chou
2025年3月21日 14:23

The data comes from these cases:

The history data of a session is provided by the API https://api.manus.im/api/chat/getSession?sessionId=xxx.

Data

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{
"id": "xFgpHb15vKqfRPWIs3JJPJ",
"title": "Comprehensive Tesla Stock Analysis and Investment Insights",
"iconInfo": {
"$typeName": "session.v1.IconInfo",
"url": "https://files.manuscdn.com/assets/icon/session/apple-stocks-data.svg",
"bgColorLight": "#E43573",
"bgColorDark": "#3F3F3F"
},
"agentTaskMode": 1,
"events": []
}

Q: how many kinds of agentTaskMode?

Events

chat

  • sender: “user”, “assistant”
  • content: Markdown format text
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{
"id": "vG1n3GhvODVIjGoJ12J2GS",
"type": "chat",
"timestamp": 1741060890011,
"sender": "user",
"messageType": "text",
"content": "I'd like a thorough analysis of Tesla stock, including:\n\nSummary: Company overview, key metrics, performance data and investment recommendations\nFinancial Data: Revenue trends, profit margins, balance sheet and cash flow analysis\nMarket Sentiment: Analyst ratings, sentiment indicators and news impact\nTechnical Analysis: Price trends, technical indicators and support/resistance levels\nCompare Assets: Market share and financial metrics vs. key competitors\nValue Investor: Intrinsic value, growth potential and risk factors\nInvestment Thesis: SWOT analysis and recommendations for different investor types",
"attachments": []
}

{
"type": "chat",
"messageType": "text",
"attachments": [],
"content": "I'll help you create a comprehensive analysis of Tesla stock. I'll gather the latest financial data, market sentiment, technical analysis, competitive comparisons, and develop investment recommendations. This will take some time to research thoroughly, but I'll work on it right away and provide you with a detailed report.",
"id": "4hJnLZZjels3kckkeY8WFQ",
"sender": "assistant",
"timestamp": 1741060892861
}

With attachments created before.

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{
"id": "JhkoA2vSJq7PUdIAVBpFzM",
"type": "chat",
"timestamp": 1740979547968,
"sender": "user",
"messageType": "text",
"content": "Here is the survey for available interview times from April 13 to April 15. Please create an interview schedule for me with two interview sessions each day (one in the morning and one in the afternoon). The number of candidates in each session should be as evenly distributed as possible, and the schedule should accommodate each student's available time. Please provide the most reasonable interview schedule.",
"attachments": [
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"type": "file",
"filename": "interview_survey_final.xlsx",
"contentType": "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
"contentLength": 11353
}
]
}

{
"id": "KX9zvuTApeB3f9cYpVcKEl",
"type": "chat",
"timestamp": 1740986701272,
"sender": "user",
"messageType": "text",
"content": "Please analyze these travel policies and provide a comparative table with key dimensions to clearly highlight the differences between them.",
"attachments": [
{
"id": "oUO7cTCVFHVvQVOkXF0i5q",
"url": "https://private-us-east-1.manuscdn.com/users/309511696588767621/uploads/oUO7cTCVFHVvQVOkXF0i5q_na1fn_dHJhdmVsLXBvbGljeS1kb2N1bWVudA.pdf?Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9wcml2YXRlLXVzLWVhc3QtMS5tYW51c2Nkbi5jb20vdXNlcnMvMzA5NTExNjk2NTg4NzY3NjIxL3VwbG9hZHMvb1VPN2NUQ1ZGSFZ2UVZPa1hGMGk1cV9uYTFmbl9kSEpoZG1Wc0xYQnZiR2xqZVMxa2IyTjFiV1Z1ZEEucGRmIiwiQ29uZGl0aW9uIjp7IkRhdGVMZXNzVGhhbiI6eyJBV1M6RXBvY2hUaW1lIjoxNzQxNTkwNTAxfX19XX0_&Key-Pair-Id=K2HSFNDJXOU9YS&Signature=FQaTrhK0ZTzHWF8QW4Vneobb26Tuzv6CDgFmVUfU2dL375FrH2TaJXIzI68wgHx0NYYMRSiqaALsOdnI0r7Hmam4QBV-7IIrsMlFuIAkRsN7pAlR-POZ6kHBN55L5fVrzV00tyQSymwXW75vRyHGgxpPxcu8HsAJHZBYh9pnelxRKe6eKZcL5fjU2M8qDDKOP7a1CpckPioDwzHAIgQ5y8a67HJmkqw7enTf16J7b6cOoqJW0Ba3eiz~7A83J1QjKDuMj15teiKqjtWMR5XP-V8z~r3iXILCGKWC0kJDxH8QP6Gds~TopIU3MtRC7lT39e2D308gl5Aygi8NE1ypNw__",
"type": "file",
"filename": "travel-policy-document.pdf",
"contentType": "application/pdf",
"contentLength": 779625
}
]
}

{
"type": "chat",
"messageType": "text",
"attachments": [
{
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"path": "/home/ubuntu/tesla_analysis/charts/tesla_historical_price.png",
"type": "image",
"filename": "tesla_historical_price.png",
"timestamp": 1741061983802,
"contentType": "image/png",
"contentLength": 47609
},
{
"url": "https://private-us-east-1.manuscdn.com/sessionFile/xFgpHb15vKqfRPWIs3JJPJ/sandbox/CZjwux2HibnKjGKnS2xC3V_1741061983802_na1fn_L2hvbWUvdWJ1bnR1L3Rlc2xhX2FuYWx5c2lzL2NoYXJ0cy90ZXNsYV9jYXBpdGFsX3N0cnVjdHVyZQ.png?Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9wcml2YXRlLXVzLWVhc3QtMS5tYW51c2Nkbi5jb20vc2Vzc2lvbkZpbGUveEZncEhiMTV2S3FmUlBXSXMzSkpQSi9zYW5kYm94L0Naand1eDJIaWJuS2pHS25TMnhDM1ZfMTc0MTA2MTk4MzgwMl9uYTFmbl9MMmh2YldVdmRXSjFiblIxTDNSbGMyeGhYMkZ1WVd4NWMybHpMMk5vWVhKMGN5OTBaWE5zWVY5allYQnBkR0ZzWDNOMGNuVmpkSFZ5WlEucG5nIiwiQ29uZGl0aW9uIjp7IkRhdGVMZXNzVGhhbiI6eyJBV1M6RXBvY2hUaW1lIjoxNzQzMTI4MjA4fX19XX0_&Key-Pair-Id=K2HSFNDJXOU9YS&Signature=rOY3ZIfUl3nxTME0-OmihUdCCh~syAj3USviGwc1sPEF40XTwFIXpd-~oa1UBgfoNPB3pR6T-e6diYUdX96KlShuEOjAloeLALOgWkErX2J4mlW8dOtBOFNo0~YTk0R~GeLHDtwT7OwbhrqvW1OMHZIk7foUjLDoDitGY8sIlf4Fpt7uDk3auN0WlVxer3aE5FvgIhrZbSSbYHeh5OCAvDXQ4cxgvYxzzkg3HH0bBmUF3sdogn6ZW4gPv-sWy15tgjcjYKCakUUMZUTP4todL6HgLH~ZCKJ2DKJjNJDiRiN6ns8LuUed8HO60Ao7LrlRhsdyZtK3dXZxhgKBwiY2Ng__",
"path": "/home/ubuntu/tesla_analysis/charts/tesla_capital_structure.png",
"type": "image",
"filename": "tesla_capital_structure.png",
"timestamp": 1741061983802,
"contentType": "image/png",
"contentLength": 30300
},
{
"url": "https://private-us-east-1.manuscdn.com/sessionFile/xFgpHb15vKqfRPWIs3JJPJ/sandbox/CZjwux2HibnKjGKnS2xC3V_1741061983802_na1fn_L2hvbWUvdWJ1bnR1L3Rlc2xhX2FuYWx5c2lzL2NoYXJ0cy90ZXNsYV9yZXZlbnVlX2dyb3d0aA.png?Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9wcml2YXRlLXVzLWVhc3QtMS5tYW51c2Nkbi5jb20vc2Vzc2lvbkZpbGUveEZncEhiMTV2S3FmUlBXSXMzSkpQSi9zYW5kYm94L0Naand1eDJIaWJuS2pHS25TMnhDM1ZfMTc0MTA2MTk4MzgwMl9uYTFmbl9MMmh2YldVdmRXSjFiblIxTDNSbGMyeGhYMkZ1WVd4NWMybHpMMk5vWVhKMGN5OTBaWE5zWVY5eVpYWmxiblZsWDJkeWIzZDBhQS5wbmciLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE3NDMxMjgyMDh9fX1dfQ__&Key-Pair-Id=K2HSFNDJXOU9YS&Signature=TOhZSNCFinyvfUW3pT1RxOHa9v5KMyMbZ0Tjcy6OzzIoW8JTUa7S0KNZskgUtuoBnqimVaEyqAVVida9-ZBSGpbwTocJH8IygaUZSkpRhtKDtrlUPGUHV~bZyYkURRkc7zo57C1HO9u3g3gfc55jjBJLZzfU4m2ht92Hfe0vwAoPLMoyjXSR7wgs1vGjQ4SR97N6pOFh-CA3q4BRPEPyk5BiX0F6EJjoXeAD5gF0lNBhhtMwXSar0u6kEAMPcgxdGfKGIe5B-4YGYi2DEhe4v7o26fNW-2oeJpL8kLpbLYY-0qYGpex8JyQCpDNbxwujkC-mMd3qizXjUqTkahyb2g__",
"path": "/home/ubuntu/tesla_analysis/charts/tesla_revenue_growth.png",
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{
"url": "https://private-us-east-1.manuscdn.com/sessionFile/xFgpHb15vKqfRPWIs3JJPJ/sandbox/CZjwux2HibnKjGKnS2xC3V_1741061983802_na1fn_L2hvbWUvdWJ1bnR1L3Rlc2xhX2FuYWx5c2lzL2NoYXJ0cy90ZXNsYV9wcm9maXRhYmlsaXR5X21hcmdpbnM.png?Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9wcml2YXRlLXVzLWVhc3QtMS5tYW51c2Nkbi5jb20vc2Vzc2lvbkZpbGUveEZncEhiMTV2S3FmUlBXSXMzSkpQSi9zYW5kYm94L0Naand1eDJIaWJuS2pHS25TMnhDM1ZfMTc0MTA2MTk4MzgwMl9uYTFmbl9MMmh2YldVdmRXSjFiblIxTDNSbGMyeGhYMkZ1WVd4NWMybHpMMk5vWVhKMGN5OTBaWE5zWVY5d2NtOW1hWFJoWW1sc2FYUjVYMjFoY21kcGJuTS5wbmciLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE3NDMxMjgyMDh9fX1dfQ__&Key-Pair-Id=K2HSFNDJXOU9YS&Signature=R4ECYV9kqx2Ktfx-lxMTyd7mpF-6EWqY~rjd1nu7g417pHP2XiAlANwDhCXHowizuu276LWMY5sv79w4qqxYkIZMcH~awKcEov~HD8jirSDvjAX7hD4Q3uiDw-gsoTF4705Ic3liw655GSGb35XDUOEDN6ml15szhqi9xP4XaqFVeMfdqXelYHldYrLQyB0h~2TggYHpeEdOHFGX7ea96N9CdbV3X3tT37gD5iLUkAJjnnx~ny0oF26Eflj1L0YdzwSK~tpC-VZYZl9js1vTd4J2FGbyz9yp3CZyTvz0inUt-WnmcrVCS1M7KFGYrtf5U3S75p7sdf6dpncZWgrGzw__",
"path": "/home/ubuntu/tesla_analysis/charts/tesla_profitability_margins.png",
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"contentType": "image/png",
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},
{
"url": "https://private-us-east-1.manuscdn.com/sessionFile/xFgpHb15vKqfRPWIs3JJPJ/sandbox/CZjwux2HibnKjGKnS2xC3V_1741061983802_na1fn_L2hvbWUvdWJ1bnR1L3Rlc2xhX2FuYWx5c2lzL2NoYXJ0cy90ZXNsYV9jYXNoX2Zsb3dfYW5hbHlzaXM.png?Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9wcml2YXRlLXVzLWVhc3QtMS5tYW51c2Nkbi5jb20vc2Vzc2lvbkZpbGUveEZncEhiMTV2S3FmUlBXSXMzSkpQSi9zYW5kYm94L0Naand1eDJIaWJuS2pHS25TMnhDM1ZfMTc0MTA2MTk4MzgwMl9uYTFmbl9MMmh2YldVdmRXSjFiblIxTDNSbGMyeGhYMkZ1WVd4NWMybHpMMk5vWVhKMGN5OTBaWE5zWVY5allYTm9YMlpzYjNkZllXNWhiSGx6YVhNLnBuZyIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTc0MzEyODIwOH19fV19&Key-Pair-Id=K2HSFNDJXOU9YS&Signature=P0GyMLi38cIp0ssnWkQYaG1g1wuSfFOv1yg8jIrse7HGQlMaWFUi2pQKA9IDrZv-UXDqitqy-S1-lSfRECFdUXI0sxqfSg8rlMfl9afGySE~5lYlue4Ym9ZGJeyZ3eqVhDP2sy-P9iY~QWmE5qSdBbcCMnIhK6fv4xQVXNXMW9-swcaM0yYaUyfYuYXNUe8taMIUjBnYJZu~rJ57zAfh8VgAoKrcVl-W3xdBP~Qk-2zZUk6jmcf8uZArUjnZ1L45KjQhmzh15D7B9b8zXIlIXX0s4ygnMHdQ6twYjVG-ITHOO1XqSjD8fEVt0ufEAV6UxgyM3lZkf4A~Ce9ol6BPRg__",
"path": "/home/ubuntu/tesla_analysis/charts/tesla_cash_flow_analysis.png",
"type": "image",
"filename": "tesla_cash_flow_analysis.png",
"timestamp": 1741061983802,
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}
],
"content": "I've completed the financial data collection and analysis for Tesla. Here's what I've gathered:\n\n1. Historical Stock Data: I've retrieved Tesla's stock price history from Yahoo Finance, including key metrics like the current price ($284.65), 52-week high ($488.54), and 52-week low ($138.80). I've created visualizations of the price trends and calculated volatility metrics.\n\n2. Revenue Analysis: Tesla has shown remarkable growth, with revenue increasing from $24.6 billion in 2019 to $96.8 billion in 2023, representing a strong compound annual growth rate.\n\n3. Profitability Metrics: I've analyzed Tesla's gross margins, operating margins, and net margins over the past five years, showing their progression from negative operating income in 2019 to substantial profitability in recent years.\n\n4. Balance Sheet Strength: Tesla has significantly improved its financial position, reducing debt while building cash reserves to over $29 billion by 2023.\n\n5. Cash Flow Analysis: The company has demonstrated strong cash generation capabilities with positive and growing free cash flow despite significant capital expenditures.\n\n6. R&D and Capital Investments: Tesla continues to invest heavily in research and development and production capacity expansion.\n\nI'm now moving on to analyze market sentiment for Tesla, including analyst ratings, price targets, and news impact on the stock. This will give us insight into how the market perceives Tesla's future prospects.",
"id": "CZjwux2HibnKjGKnS2xC3V",
"sender": "assistant",
"timestamp": 1741061983997
}

Ask for approval by requestDeployActionId.

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{
"type": "chat",
"messageType": "text",
"attachments": [],
"requestDeployActionId": "toolu_01Xea7dTETZUtxWThJB9Ggsi",
"content": "Manus 正在尝试将位于 `/home/ubuntu/tesla_analysis/dashboard` 的服务部署到公网上。请通过点击按钮确认您是否同意。",
"id": "YCrGxLFvxLLJlGHKSfx8zg",
"sender": "assistant",
"timestamp": 1741065199191
}

Text output result.

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{
"type": "chat",
"messageType": "text",
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],
"content": "I've completed the comparative analysis of the four travel insurance policies you provided. The analysis includes a comprehensive breakdown of key dimensions such as medical coverage, trip protection, baggage coverage, personal accident and liability, special benefits, plan structure, and additional features.\n\nI've created a detailed report that highlights the strengths and unique offerings of each policy, along with key insights for each dimension and specific recommendations based on different traveler needs.\n\nHere's the full comparative analysis document and the supporting comparison table.",
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"timestamp": 1740987315791
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"path": "/home/ubuntu/interview_schedule.txt",
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"content": "I've completed creating the interview schedule based on the survey data. Here's what I've prepared:\n\n1. I've created a balanced interview schedule with two sessions per day (morning and afternoon) from April 13-15.\n2. All 45 candidates have been assigned to sessions according to their availability.\n3. The distribution is well-balanced with 8, 7, 6, 8, 7, and 7 candidates per session.\n4. The schedule has been validated to ensure all candidates are assigned to times they're available.\n\nI'm attaching the schedule in multiple formats:\n- A text version with the basic schedule\n- A markdown version with more detailed formatting\n- An HTML version with enhanced visual presentation\n- A visual representation showing the schedule graphically\n\nThe schedule successfully accommodates all candidates' availability constraints while maintaining an even distribution across all six sessions.",
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liveStatus

Always show the latest one at the bottom before the job is finished.

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{
"id": "k7QnLyNeOeCtPCzteTREAz",
"type": "liveStatus",
"timestamp": 1741060890082,
"text": "初始化沙箱"
}

sandboxUpdate

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{
"id": "Kgfk50K5yUzFGh8kBmD25y",
"type": "sandboxUpdate",
"timestamp": 1741060891120,
"sandboxId": "****",
"status": "running",
"codeServerUrl": "****",
"vncUrl": "****"
}

{
"id": "DPtpw7TlfGM0F3JBFOe5Vf",
"type": "sandboxUpdate",
"timestamp": 1741066408097,
"sandboxId": "****",
"status": "stopped",
"codeServerUrl": "****",
"vncUrl": "****"
}

resourceAccessed

A read-only list showed on web page.

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{
"id": "FTSYIESAA0lX1IKWQEMDcx",
"type": "resourceAccessed",
"timestamp": 1741060897834,
"brief": "已连接到数据源",
"resourceType": "data_api",
"resources": [
{
"id": "api_19",
"title": "Get stock chart",
"kind": "builtin"
},
{
"id": "api_20",
"title": "Get stock holders",
"kind": "builtin"
},
{
"id": "api_21",
"title": "Get stock insights",
"kind": "builtin"
},
{
"id": "api_16",
"title": "Get stock profile",
"kind": "builtin"
},
{
"id": "api_22",
"title": "Get stock SEC filing",
"kind": "builtin"
},
{
"id": "api_23",
"title": "Get what analysts are saying of a stock",
"kind": "builtin"
}
]
}

statusUpdate

Note that planStepId will be generated before stepId.

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{
"id": "l5ckmcJ92qsBeEcPWdd517",
"type": "statusUpdate",
"timestamp": 1741060902886,
"agentStatus": "running",
"brief": "Manus 正在工作",
"description": "Creating a plan for Tesla stock analysis",
"noRender": false,
"planStepId": "E7Q4GDL3FsNrzRuv3Cb5Xa"
}

{
"id": "JjrDxkPfwe54YWM2akBGEp",
"type": "statusUpdate",
"timestamp": 1741065081706,
"agentStatus": "running",
"brief": "Manus 正在运行",
"description": "Manus 正在处理您的请求",
"noRender": true,
"planStepId": "jxqdGJWP8ipvTYx6Y4fCEZ"
}

{
"id": "WLYbSvWSWXO94sQHJzLNlf",
"type": "statusUpdate",
"timestamp": 1741065742563,
"agentStatus": "stopped",
"brief": "Manus 已完成工作",
"description": "Manus 已完成当前任务,并准备好接收新请求或修改。",
"noRender": false,
"planStepId": "qUIIRtCmzKhU9rXxeGIfpb"
}

toolUsed

  • tool: “terminal”, “text_editor”, “search”, “browser”
  • detail data should be used to show runtime outputs in “Manus’ computer” card

terminal

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{
"id": "lxD5h6ztASqqdXXvJMoKcq",
"type": "toolUsed",
"timestamp": 1741060905794,
"actionId": "toolu_014zqVgHFTWGj543mEUAmYnx",
"tool": "terminal",
"status": "success",
"brief": "Manus 正在使用终端",
"description": "已执行命令 `mkdir -p tesla_analysis && cd tesla_analysis && touch todo.md`",
"message": {
"action": "正在执行命令",
"param": "mkdir -p tesla_analysis && cd tesla_analysis && touch todo.md"
},
"detail": {
"terminal": {
"action": "execute",
"finished": true,
"shellId": "shell1",
"command": "mkdir -p tesla_analysis && cd tesla_analysis && touch todo.md",
"outputType": "append",
"output": [
"\u001b[32mubuntu@sandbox:~ $\u001b[0m cd /home/ubuntu && mkdir -p tesla_analysis && cd tesla_analysis && touch todo.md\n\n\u001b[32mubuntu@sandbox:~/tesla_analysis $\u001b[0m"
]
}
},
"planStepId": "E7Q4GDL3FsNrzRuv3Cb5Xa"
}

text_editor

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{
"id": "g5UfojfdB2VhWIxwcvs6Uj",
"type": "toolUsed",
"timestamp": 1741060924172,
"actionId": "toolu_01HjcgTGFBtBfZpRCKZRzZ32",
"tool": "text_editor",
"status": "success",
"planStepId": "E7Q4GDL3FsNrzRuv3Cb5Xa",
"brief": "Manus 正在使用文本编辑器",
"description": "正在创建文件 `tesla_analysis/todo.md`",
"message": {
"action": "正在创建文件",
"param": "tesla_analysis/todo.md"
},
"detail": {
"textEditor": {
"action": "write",
"path": "/home/ubuntu/tesla_analysis/todo.md",
"content": "# Tesla Stock Analysis Todo List\n\n## 1. Create Todo List\n- [x] Create project directory structure\n- [x] Create todo.md file\n- [x] Outline all required analysis components\n\n## 2. Gather Tesla Company Overview\n- [ ] Collect basic company information (ticker, exchange, etc.)\n- [ ] Get company profile and business summary\n- [ ] Identify key executives and leadership team\n- [ ] Document company history and major milestones\n- [ ] Analyze business model and revenue streams\n\n## 3. Collect Tesla Financial Data\n- [ ] Retrieve historical stock price data\n- [ ] Analyze revenue trends over past 5 years\n- [ ] Calculate profit margins and profitability metrics\n- [ ] Examine balance sheet strength and liabilities\n- [ ] Analyze cash flow statements and free cash flow\n- [ ] Review capital expenditures and R&D investments\n\n## 4. Analyze Market Sentiment for Tesla\n- [ ] Gather recent analyst ratings and price targets\n- [ ] Collect analyst commentary and research reports\n- [ ] Analyze recent news impact on stock price\n- [ ] Review insider trading activity\n- [ ] Examine institutional ownership changes\n- [ ] Assess social media and retail investor sentiment\n\n## 5. Perform Technical Analysis on Tesla Stock\n- [ ] Create price charts for different time periods\n- [ ] Identify key support and resistance levels\n- [ ] Calculate and analyze moving averages\n- [ ] Evaluate momentum indicators (RSI, MACD, etc.)\n- [ ] Identify chart patterns and potential breakout points\n- [ ] Assess volume trends and distribution\n\n## 6. Compare Tesla with Competitors\n- [ ] Identify key competitors in EV and automotive space\n- [ ] Compare market share and growth rates\n- [ ] Analyze financial metrics relative to peers\n- [ ] Compare valuation multiples (P/E, P/S, EV/EBITDA)\n- [ ] Assess technological advantages and disadvantages\n- [ ] Evaluate manufacturing capacity and efficiency\n\n## 7. Calculate Intrinsic Value of Tesla\n- [ ] Apply discounted cash flow (DCF) model\n- [ ] Estimate future growth rates and margins\n- [ ] Calculate weighted average cost of capital (WACC)\n- [ ] Perform sensitivity analysis on key assumptions\n- [ ] Compare intrinsic value to current market price\n- [ ] Assess margin of safety for potential investors\n\n## 8. Develop Investment Thesis for Tesla\n- [ ] Conduct SWOT analysis (Strengths, Weaknesses, Opportunities, Threats)\n- [ ] Identify key growth drivers and catalysts\n- [ ] Assess major risk factors and potential headwinds\n- [ ] Develop bull case scenario and price target\n- [ ] Develop bear case scenario and price target\n- [ ] Create recommendations for different investor types\n\n## 9. Compile Comprehensive Tesla Stock Report\n- [ ] Write executive summary with key findings\n- [ ] Organize all analysis sections into cohesive report\n- [ ] Create visualizations for key metrics and trends\n- [ ] Format report with proper sections and references\n- [ ] Review and proofread final document\n\n## 10. Deliver Report to User\n- [ ] Finalize all report components\n- [ ] Ensure all analyses are complete and accurate\n- [ ] Package report with all supporting files\n- [ ] Deliver final report to user\n",
"oldContent": ""
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search

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{
"id": "AUsLw5l0nxo5Qwa4jZhZQM",
"type": "toolUsed",
"timestamp": 1741009272646,
"actionId": "toolu_014ymijbAfUVMC9RFfPht8oA",
"tool": "browser",
"status": "error",
"brief": "Manus 正在使用浏览器",
"description": "browser:Error executing action browser_navigate: Page.goto: net::ERR_EMPTY_RESPONSE at http://8000-i5hfldcpydtx82vqb7cu1-226a4af3.manus.computer/\nCall log:\n - navigating to \"http://8000-i5hfldcpydtx82vqb7cu1-226a4af3.manus.computer/\", waiting until \"commit\"\n",
"message": {
"action": "正在处理浏览器错误"
},
"planStepId": "iSOY2zdYOeyP0kAWNHrDLF"
}

planUpdate

  • status: “doing”, “todo”, “done”
  • endAt for “done” status task

It seems that this event will be emitted after a toolUsed or newPlanStep event.

tasks list in planUpdate event will be appended after user asked a new question.

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{
"id": "XrXQO9oRo34MYtstDRLZwE",
"type": "planUpdate",
"timestamp": 1741060908470,
"tasks": [
{
"id": "step_001",
"title": "Create comprehensive Tesla stock analysis task list",
"status": "doing",
"startedAt": 1741060908465
},
{
"id": "step_002",
"title": "Gather Tesla company overview and key information",
"status": "todo",
"startedAt": 1741060908465
},
{
"id": "step_003",
"title": "Collect and analyze Tesla's financial data",
"status": "todo",
"startedAt": 1741060908465
},
{
"id": "step_004",
"title": "Evaluate market sentiment for Tesla",
"status": "todo",
"startedAt": 1741060908465
},
{
"id": "step_005",
"title": "Perform technical analysis on Tesla stock",
"status": "todo",
"startedAt": 1741060908465
},
{
"id": "step_006",
"title": "Compare Tesla with key industry competitors",
"status": "todo",
"startedAt": 1741060908465
},
{
"id": "step_007",
"title": "Calculate intrinsic value of Tesla stock",
"status": "todo",
"startedAt": 1741060908465
},
{
"id": "step_008",
"title": "Develop comprehensive investment thesis",
"status": "todo",
"startedAt": 1741060908465
},
{
"id": "step_009",
"title": "Compile detailed Tesla stock analysis report",
"status": "todo",
"startedAt": 1741060908465
},
{
"id": "step_010",
"title": "Deliver comprehensive stock analysis to user",
"status": "todo",
"startedAt": 1741060908465
}
]
}

newPlanStep

stepId instead of planStepId in data, which will wrap the steps like this:

newPlanStep seems to be the flag to indicate next step is beginning. (Guess the step status is also controlled by this.)

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{
"id": "4UHAXLMlsuovhOWGm6WBzB",
"type": "newPlanStep",
"timestamp": 1741060902693,
"title": "Create comprehensive Tesla stock analysis task list",
"stepId": "E7Q4GDL3FsNrzRuv3Cb5Xa"
}

knowledgeSuggested

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{
"id": "Gg74U4LDSvOC4x1wGDW1IL",
"type": "knowledgeSuggested",
"timestamp": 1741064324520,
"brief": "建议的新知识",
"resources": [
{
"title": "Tesla stock analysis dashboard creation"
}
]
}

A steps collection of plan

statusUpdate and toolUsed events searched by a same planStepId .

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[
{
"id": "wQ0tsoHiqVskSwU9Ud93sA",
"type": "statusUpdate",
"timestamp": 1741061972527,
"agentStatus": "running",
"brief": "Manus 正在工作",
"description": "Moving to analyze market sentiment for Tesla",
"noRender": false,
"planStepId": "7galTGLGCzryoM1tSeM1YR"
},
{
"id": "XYu58JLESxdSecDmgKLeJi",
"type": "newPlanStep",
"timestamp": 1741061972511,
"title": "Evaluate market sentiment for Tesla",
"stepId": "7galTGLGCzryoM1tSeM1YR"
},
{
"id": "ooZqdunPTMjCQY1WZe75mY",
"type": "statusUpdate",
"timestamp": 1741061994707,
"agentStatus": "running",
"brief": "Manus 正在工作",
"description": "Analyzing market sentiment for Tesla",
"noRender": false,
"planStepId": "7galTGLGCzryoM1tSeM1YR"
},
{
"id": "dH95W9Qz9mwR9Xd9Vh029D",
"type": "toolUsed",
"timestamp": 1741062198875,
"actionId": "toolu_01PbZbqkKu517na1HvYTWsZu",
"tool": "terminal",
"status": "success",
"brief": "Manus 正在使用终端",
"description": "已执行命令 `cd /home/ubuntu/tesla_analysis/data && touch tesla_market_sentiment.py`",
"message": {
"action": "正在执行命令",
"param": "cd /home/ubuntu/tesla_analysis/data && touch tesla_market_sentiment.py"
},
"detail": {
"terminal": {
"action": "execute",
"finished": true,
"shellId": "shell2",
"command": "cd /home/ubuntu/tesla_analysis/data && touch tesla_market_sentiment.py",
"outputType": "append",
"output": [
"\u001b[32mubuntu@sandbox:~/tesla_analysis/data $\u001b[0m cd /home/ubuntu/tesla_analysis/data && cd /home/ubuntu/tesla_analysis/data && touch tesla_market_sentiment.py\n\n\u001b[32mubuntu@sandbox:~/tesla_analysis/data $\u001b[0m"
]
}
},
"planStepId": "7galTGLGCzryoM1tSeM1YR"
},
{
"id": "kp9yBKsz4TVgQn8oNtp8wD",
"type": "statusUpdate",
"timestamp": 1741062306008,
"agentStatus": "running",
"brief": "Manus 正在工作",
"description": "Analyzing market sentiment for Tesla",
"noRender": false,
"planStepId": "7galTGLGCzryoM1tSeM1YR"
},
{
"id": "5sP3g1brITVABMTqR6WwgC",
"type": "toolUsed",
"timestamp": 1741062396685,
"actionId": "toolu_01BRfXbq4SLzWftS4fjUBLCM",
"tool": "text_editor",
"status": "success",
"planStepId": "7galTGLGCzryoM1tSeM1YR",
"brief": "Manus 正在使用文本编辑器",
"description": "正在创建文件 `tesla_analysis/data/tesla_market_sentiment.py`",
"message": {
"action": "正在创建文件",
"param": "tesla_analysis/data/tesla_market_sentiment.py"
},
"detail": {
"textEditor": {
"action": "write",
"path": "/home/ubuntu/tesla_analysis/data/tesla_market_sentiment.py",
"content": "import sys\nsys.path.append('/opt/.manus/.sandbox-runtime')\nfrom data_api import ApiClient\nimport json\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom datetime import datetime\nimport os\nimport re\nfrom collections import Counter\n\n# Initialize API client\n<response clipped><NOTE>To save on context only part of this file has been shown to you. You should retry this tool after you have searched inside the file with `grep -n` in order to find the line numbers of what you are looking for.</NOTE>",
"oldContent": ""
}
}
},
{
"id": "Rsk8LFoezHv2npHjVCwlRo",
"type": "statusUpdate",
"timestamp": 1741062403749,
"agentStatus": "running",
"brief": "Manus 正在工作",
"description": "Analyzing market sentiment for Tesla",
"noRender": false,
"planStepId": "7galTGLGCzryoM1tSeM1YR"
},
{
"id": "IoPrLfFwxxO3VuwnBLogJO",
"type": "toolUsed",
"timestamp": 1741062438389,
"actionId": "toolu_0115Z943k9mQYDZgPT2vp46Q",
"tool": "terminal",
"planStepId": "7galTGLGCzryoM1tSeM1YR",
"status": "success",
"brief": "Manus 正在使用终端",
"message": {
"action": "正在执行命令",
"param": "cd /home/ubuntu/tesla_analysis/data && python3 tesla_market_sentiment.py"
},
"description": "执行命令 `cd /home/ubuntu/tesla_analysis/data && python3 tesla_market_sentiment.py` 并等待了 `30` 秒",
"detail": {
"terminal": {
"action": "execute",
"finished": false,
"shellId": "shell2",
"command": "cd /home/ubuntu/tesla_analysis/data && python3 tesla_market_sentiment.py",
"outputType": "append",
"output": ["(No output from terminal)"]
}
}
},
{
"id": "dg1mAnR7Mpxs7R2oVY4czy",
"type": "statusUpdate",
"timestamp": 1741062544145,
"agentStatus": "running",
"brief": "Manus 正在工作",
"description": "Analyzing market sentiment for Tesla",
"noRender": false,
"planStepId": "7galTGLGCzryoM1tSeM1YR"
},
{
"id": "aRArJ9xWzOlLzzUUh4cmFM",
"type": "toolUsed",
"timestamp": 1741062548731,
"actionId": "toolu_01Q8eFq4SuY8icHma6NNc9B4",
"tool": "terminal",
"status": "success",
"brief": "Manus 正在使用终端",
"description": "已执行命令 `cd /home/ubuntu/tesla_analysis/data && ls -la`",
"message": {
"action": "正在执行命令",
"param": "cd /home/ubuntu/tesla_analysis/data && ls -la"
},
"detail": {
"terminal": {
"action": "execute",
"finished": true,
"shellId": "shell3",
"command": "cd /home/ubuntu/tesla_analysis/data && ls -la",
"outputType": "append",
"output": [
"\u001b[32mubuntu@sandbox:~ $\u001b[0m cd /home/ubuntu/tesla_analysis/data && cd /home/ubuntu/tesla_analysis/data && ls -la\ntotal 660\ndrwxrwxr-x 2 ubuntu ubuntu 4096 Mar 3 14:38 \u001b[0m\u001b[01;34m.\u001b[0m\ndrwxrwxr-x 4 ubuntu ubuntu 4096 Mar 3 14:25 \u001b[01;34m..\u001b[0m\n-rw-rw-r-- 1 ubuntu ubuntu 174 Mar 3 14:38 tesla_analyst_reports_raw.json\n-rw-rw-r-- 1 ubuntu ubuntu 1675 Mar 3 14:20 tesla_business_model.json\n-rw-rw-r-- 1 ubuntu ubuntu 1166 Mar 3 14:20 tesla_business_model.txt\n-rw-rw-r-- 1 ubuntu ubuntu 2404 Mar 3 14:18 tesla_company_profile.py\n-rw-rw-r-- 1 ubuntu ubuntu 1848 Mar 3 14:18 tesla_company_profile.txt\n-rw-rw-r-- 1 ubuntu ubuntu 12466 Mar 3 14:29 tesla_financial_analysis.py\n-rw-rw-r-- 1 ubuntu ubuntu 3744 Mar 3 14:30 tesla_financial_analysis.txt\n-rw-rw-r-- 1 ubuntu ubuntu 232 Mar 3 14:25 tesla_financial_metrics.json\n-rw-rw-r-- 1 ubuntu ubuntu 1548 Mar 3 14:29 tesla_financial_metrics_detailed.json\n-rw-rw-r-- 1 ubuntu ubuntu 287 Mar 3 14:25 tesla_financial_summary.txt\n-rw-rw-r-- 1 ubuntu ubuntu 21753 Mar 3 14:25 tesla_historical_prices.csv\n-rw-rw-r-- 1 ubuntu ubuntu 1741 Mar 3 14:20 tesla_history.json\n-rw-rw-r-- 1 ubuntu ubuntu 945 Mar 3 14:20 tesla_history.txt\n-rw-rw-r-- 1 ubuntu ubuntu 8602 Mar 3 14:38 tesla_holders_raw.json\n-rw-rw-r-- 1 ubuntu ubuntu 216917 Mar 3 14:38 tesla_insights_raw.json\n-rw-rw-r-- 1 ubuntu ubuntu 891 Mar 3 14:20 tesla_leadership.json\n-rw-rw-r-- 1 ubuntu ubuntu 8480 Mar 3 14:20 tesla_leadership.py\n-rw-rw-r-- 1 ubuntu ubuntu 443 Mar 3 14:20 tesla_leadership.txt\n-rw-rw-r-- 1 ubuntu ubuntu 21364 Mar 3 14:38 tesla_market_sentiment.py\n-rw-rw-r-- 1 ubuntu ubuntu 3150 Mar 3 14:38 tesla_market_sentiment.txt\n-rw-rw-r-- 1 ubuntu ubuntu 2579 Mar 3 14:18 tesla_profile_raw.json\n-rw-rw-r-- 1 ubuntu ubuntu 126156 Mar 3 14:38 tesla_sec_filings_raw.json\n-rw-rw-r-- 1 ubuntu ubuntu 6083 Mar 3 14:38 tesla_sentiment_data.json\n-rw-rw-r-- 1 ubuntu ubuntu 6926 Mar 3 14:23 tesla_stock_data.py\n-rw-rw-r-- 1 ubuntu ubuntu 9321 Mar 3 14:25 tesla_stock_data_raw_1mo.json\n-rw-rw-r-- 1 ubuntu ubuntu 20505 Mar 3 14:25 tesla_stock_data_raw_1y.json\n-rw-rw-r-- 1 ubuntu ubuntu 60 Mar 3 14:25 tesla_stock_data_raw_5y.json\n-rw-rw-r-- 1 ubuntu ubuntu 43112 Mar 3 14:25 tesla_stock_data_raw_6mo.json\n-rw-rw-r-- 1 ubuntu ubuntu 62875 Mar 3 14:25 tesla_stock_data_raw_max.json\n-rw-rw-r-- 1 ubuntu ubuntu 182 Mar 3 14:38 tesla_twitter_raw.json\n\u001b[32mubuntu@sandbox:~/tesla_analysis/data $\u001b[0m"
]
}
},
"planStepId": "7galTGLGCzryoM1tSeM1YR"
},
{
"id": "FWT7evw7q3d78b0ordJp58",
"type": "statusUpdate",
"timestamp": 1741062614593,
"agentStatus": "running",
"brief": "Manus 正在工作",
"description": "Completing market sentiment analysis for Tesla",
"noRender": false,
"planStepId": "7galTGLGCzryoM1tSeM1YR"
},
{
"id": "oU4CahZLkVztLlRx78Mho6",
"type": "toolUsed",
"timestamp": 1741062614950,
"actionId": "toolu_01QsGKEkNeUoZFWoyiFaiUX1",
"tool": "terminal",
"status": "success",
"brief": "Manus 正在使用终端",
"description": "已执行命令 `cd /home/ubuntu/tesla_analysis/charts && ls -la`",
"message": {
"action": "正在执行命令",
"param": "cd /home/ubuntu/tesla_analysis/charts && ls -la"
},
"detail": {
"terminal": {
"action": "execute",
"finished": true,
"shellId": "shell3",
"command": "cd /home/ubuntu/tesla_analysis/charts && ls -la",
"outputType": "append",
"output": [
"\u001b[32mubuntu@sandbox:~/tesla_analysis/data $\u001b[0m cd /home/ubuntu/tesla_analysis && cd /home/ubuntu/tesla_analysis/charts && ls -la\ntotal 252\ndrwxrwxr-x 2 ubuntu ubuntu 4096 Mar 3 14:38 \u001b[0m\u001b[01;34m.\u001b[0m\ndrwxrwxr-x 4 ubuntu ubuntu 4096 Mar 3 14:25 \u001b[01;34m..\u001b[0m\n-rw-rw-r-- 1 ubuntu ubuntu 30300 Mar 3 14:30 tesla_capital_structure.png\n-rw-rw-r-- 1 ubuntu ubuntu 44464 Mar 3 14:30 tesla_cash_flow_analysis.png\n-rw-rw-r-- 1 ubuntu ubuntu 47609 Mar 3 14:25 tesla_historical_price.png\n-rw-rw-r-- 1 ubuntu ubuntu 61140 Mar 3 14:30 tesla_profitability_margins.png\n-rw-rw-r-- 1 ubuntu ubuntu 32821 Mar 3 14:30 tesla_revenue_growth.png\n-rw-rw-r-- 1 ubuntu ubuntu 23416 Mar 3 14:38 tesla_technical_outlook.png\n\u001b[32mubuntu@sandbox:~/tesla_analysis/charts $\u001b[0m"
]
}
},
"planStepId": "7galTGLGCzryoM1tSeM1YR"
},
{
"id": "sPndWS5ngDLS0XuGHYep6N",
"type": "statusUpdate",
"timestamp": 1741062620453,
"agentStatus": "running",
"brief": "Manus 正在工作",
"description": "Completing market sentiment analysis for Tesla",
"noRender": false,
"planStepId": "7galTGLGCzryoM1tSeM1YR"
},
{
"id": "FpA1ynrLVk70PPIjM3RGKA",
"type": "toolUsed",
"timestamp": 1741062626134,
"actionId": "toolu_01F7XnUyYY9SXZYnLS4xmMPV",
"tool": "text_editor",
"status": "success",
"planStepId": "7galTGLGCzryoM1tSeM1YR",
"brief": "Manus 正在使用文本编辑器",
"description": "编辑文件 `tesla_analysis/todo.md`",
"message": {
"action": "正在编辑文件",
"param": "tesla_analysis/todo.md"
},
"detail": {
"textEditor": {
"action": "update",
"path": "/home/ubuntu/tesla_analysis/todo.md",
"content": "# Tesla Stock Analysis Todo List\n\n## 1. Create Todo List\n- [x] Create project directory structure\n- [x] Create todo.md file\n- [x] Outline all required analysis components\n\n## 2. Gather Tesla Company Overview\n- [x] Collect basic company information (ticker, exchange, etc.)\n- [x] Get company profile and business summary\n- [x] Identify key executives and leadership team\n- [x] Document company history and major milestones\n- [x] Analyze business model and revenue streams\n\n## 3. Collect Tesla Financial Data\n- [x] Retrieve historical stock price data\n- [x] Analyze revenue trends over past 5 years\n- [x] Calculate profit margins and profitability metrics\n- [x] Examine balance sheet strength and liabilities\n- [x] Analyze cash flow statements and free cash flow\n- [x] Review capital expenditures and R&D investments\n\n## 4. Analyze Market Sentiment for Tesla\n- [x] Gather recent analyst ratings and price targets\n- [x] Collect analyst commentary and research reports\n- [x] Analyze recent news impact on stock price\n- [x] Review insider trading activity\n- [x] Examine institutional ownership changes\n- [x] Assess social media and retail investor sentiment\n\n## 5. Perform Technical Analysis on Tesla Stock\n- [ ] Create price charts for different time periods\n- [ ] Identify key support and resistance levels\n- [ ] Calculate and analyze moving averages\n- [ ] Evaluate momentum indicators (RSI, MACD, etc.)\n- [ ] Identify chart patterns and potential breakout points\n- [ ] Assess volume trends and distribution\n\n## 6. Compare Tesla with Competitors\n- [ ] Identify key competitors in EV and automotive space\n- [ ] Compare market share and growth rates\n- [ ] Analyze financial metrics relative to peers\n- [ ] Compare valuation multiples (P/E, P/S, EV/EBITDA)\n- [ ] Assess technological advantages and disadvantages\n- [ ] Evaluate manufacturing capacity and efficiency\n\n## 7. Calculate Intrinsic Value of Tesla\n- [ ] Apply discounted cash flow (DCF) model\n- [ ] Estimate future growth rates and margins\n- [ ] Calculate weighted average cost of capital (WACC)\n- [ ] Perform sensitivity analysis on key assumptions\n- [ ] Compare intrinsic value to current market price\n- [ ] Assess margin of safety for potential investors\n\n## 8. Develop Investment Thesis for Tesla\n- [ ] Conduct SWOT analysis (Strengths, Weaknesses, Opportunities, Threats)\n- [ ] Identify key growth drivers and catalysts\n- [ ] Assess major risk factors and potential headwinds\n- [ ] Develop bull case scenario and price target\n- [ ] Develop bear case scenario and price target\n- [ ] Create recommendations for different investor types\n\n## 9. Compile Comprehensive Tesla Stock Report\n- [ ] Write executive summary with key findings\n- [ ] Organize all analysis sections into cohesive report\n- [ ] Create visualizations for key metrics and trends\n- [ ] Format report with proper sections and references\n- [ ] Review and proofread final document\n\n## 10. Deliver Report to User\n- [ ] Finalize all report components\n- [ ] Ensure all analyses are complete and accurate\n- [ ] Package report with all supporting files\n- [ ] Deliver final report to user\n",
"oldContent": "# Tesla Stock Analysis Todo List\n\n## 1. Create Todo List\n- [x] Create project directory structure\n- [x] Create todo.md file\n- [x] Outline all required analysis components\n\n## 2. Gather Tesla Company Overview\n- [x] Collect basic company information (ticker, exchange, etc.)\n- [x] Get company profile and business summary\n- [x] Identify key executives and leadership team\n- [x] Document company history and major milestones\n- [x] Analyze business model and revenue streams\n\n## 3. Collect Tesla Financial Data\n- [x] Retrieve historical stock price data\n- [x] Analyze revenue trends over past 5 years\n- [x] Calculate profit margins and profitability metrics\n- [x] Examine balance sheet strength and liabilities\n- [x] Analyze cash flow statements and free cash flow\n- [x] Review capital expenditures and R&D investments\n\n## 4. Analyze Market Sentiment for Tesla\n- [ ] Gather recent analyst ratings and price targets\n- [ ] Collect analyst commentary and research reports\n- [ ] Analyze recent news impact on stock price\n- [ ] Review insider trading activity\n- [ ] Examine institutional ownership changes\n- [ ] Assess social media and retail investor sentiment\n\n## 5. Perform Technical Analysis on Tesla Stock\n- [ ] Create price charts for different time periods\n- [ ] Identify key support and resistance levels\n- [ ] Calculate and analyze moving averages\n- [ ] Evaluate momentum indicators (RSI, MACD, etc.)\n- [ ] Identify chart patterns and potential breakout points\n- [ ] Assess volume trends and distribution\n\n## 6. Compare Tesla with Competitors\n- [ ] Identify key competitors in EV and automotive space\n- [ ] Compare market share and growth rates\n- [ ] Analyze financial metrics relative to peers\n- [ ] Compare valuation multiples (P/E, P/S, EV/EBITDA)\n- [ ] Assess technological advantages and disadvantages\n- [ ] Evaluate manufacturing capacity and efficiency\n\n## 7. Calculate Intrinsic Value of Tesla\n- [ ] Apply discounted cash flow (DCF) model\n- [ ] Estimate future growth rates and margins\n- [ ] Calculate weighted average cost of capital (WACC)\n- [ ] Perform sensitivity analysis on key assumptions\n- [ ] Compare intrinsic value to current market price\n- [ ] Assess margin of safety for potential investors\n\n## 8. Develop Investment Thesis for Tesla\n- [ ] Conduct SWOT analysis (Strengths, Weaknesses, Opportunities, Threats)\n- [ ] Identify key growth drivers and catalysts\n- [ ] Assess major risk factors and potential headwinds\n- [ ] Develop bull case scenario and price target\n- [ ] Develop bear case scenario and price target\n- [ ] Create recommendations for different investor types\n\n## 9. Compile Comprehensive Tesla Stock Report\n- [ ] Write executive summary with key findings\n- [ ] Organize all analysis sections into cohesive report\n- [ ] Create visualizations for key metrics and trends\n- [ ] Format report with proper sections and references\n- [ ] Review and proofread final document\n\n## 10. Deliver Report to User\n- [ ] Finalize all report components\n- [ ] Ensure all analyses are complete and accurate\n- [ ] Package report with all supporting files\n- [ ] Deliver final report to user\n"
}
}
}
]

邂逅爵士乐——记在台湾的一段美好经历

作者 John Chou
2021年2月22日 18:06

各位新春快乐!

很早就想创建一个音乐分类,不如就在新年伊始开个头,碎碎念几句我是如何喜欢上爵士乐的。

大家可能在咖啡厅、欧美流行音乐中都或多或少听过一点爵士乐,但要说第一次正儿八经接触这种音乐形态,并让我深深爱上它,源自一场在我研究生入学没多久的爵士乐社招新演出。

那是2014年秋,成大爵士乐社在校园社团博览会上的表演。 露天舞台下所有人享受着美妙的音乐和社员分发的小杯鸡尾酒,炎炎夏日感受到无限清凉。 虽然这只是LG G2手持录制的片段,但每次看到这段回忆仍会被深深感动。说起来G2是最早搭载OIS光学防抖的手机,也是当时最窄边框的手机,我从那个时候开始就对手机拍照有较高的要求,淘了这么个韩国水货“洋垃圾”,现在回看当年拍的照片和视频也算能入眼,深感欣慰!

没有任何迟疑,我立即报名参加了爵士乐社,在此期间参加了几次社内的培训活动,了解了爵士乐的一些基本概念,甚至那会就装上了iReal Pro,可惜荒废了这么多年!现在还能回想起,有一次大伙去一个社员家的天台烧烤,酒足饭饱,自然是jam时间,那种氛围真的是超级好。

另一件非常值得分享和回忆的活动,便是在加入爵士乐社后没多久,地处花莲的东华大学音乐学院爵士组举办的第二届爵士研习营。报名费用非常便宜,三天时间人民币800块(新台币4000)包吃住,师资阵容也不错,不得不夸一句台湾的这些活动真的很良心。在收到成大爵士乐社的报名通知并跟研究生导师请好假后,我也毫不犹豫地报了名。不过去了后才知道,几乎没有我这种毫无基础的营员😥,除了少量玩得不错的爱好者外,大多数还是职业乐手和音乐系的学生。

社团成员都是穷书生,大家为了省钱一大早坐六点多的台铁出发前往花莲,抵达花莲站时已经中午十二点多。

一群人抵达东华大学后,立马前往音乐系报道并领取研习营的资料,包含时间表、一些简易的课件等。

当天晚上便开始研习营的课程,每节课后必会有jam,特别的嗨!

东华大学是台湾最美的大学之一,同时是台湾最早开始在音乐系设立爵士主修的学校,其音乐系也是台湾唯一大学部跟硕士班同时有爵士演奏主修的音乐系。

音乐系看起来很朴素,与大自然融为一体。

爵士萨克斯风演奏家张坤德老师上课中,他毕业于伯克利音乐学院。这节课应该是研习营的第一次乐队齐奏课程,当所有声乐一同奏响的那一刻,我感受到了音乐现场的美妙与冲击力,那是在家里听音乐永远都体会不到的乐趣。

那一届爵士研习营请来了美国的爵士电颤琴演奏家Tony Miceli,尽管我当时无法辨别他的技术有多牛,不过仍记得他的演奏音乐性很强,能打动我这么一个小白,简单说就是好听。在他的乐理课程上,他要求我们每个人根据和声弹点东西,轮到我时我说不会,他说不要紧,无论你弹什么,你至少要弹几个音符给我。更囧的是,我当时带的还是一把没拾音器的箱琴,和我的看客身份简直绝配😅。其实我是一个自尊心很强的人,在研习营第一天发现自己与环境格格不入后,就有过想走的念头。已经忘记当时是和哪位老师交流,他劝我先留下来跟着听听再说,真的感谢他的建议,我也做到了!

傍晚黄昏,美到心醉。现在只能惋惜当初只去了两次花莲。

午饭后的jam活动,研习营的一大好处就是为营员创造了大量演出机会,只要想表演就上。说起来虽然东华大学音乐系的爵士组一直没有爵士吉他教师,但研习营还是欢迎吉他手参加,并邀请来了曾给《中国好歌曲》《中国梦之声》、刘若英、蔡健雅、丁当等等节目和歌手巡演做过电吉他手的邱安炜老师,专门给我们上了一节分享课。课上的 Q&A 环节我厚着脸皮问老师,对于我这种零基础的人该怎么学琴,老师非常详细地介绍了他学琴、练琴过程中不断检视自己的几个方面,当下就有不少顿悟。小炜老师当时拿了一把James Tyler的琴,那也是我第一次认识这个品牌。

研习营非常有心,除了演奏家、职业乐手外,还请来了一位业内非常资深的音控师高敏福老师,给大家分享现场演出时的调音知识。

除了常规的教学课程外,爵士研习营的举办人之一、音乐系爵士组的领头人魏广皓教授还与大家探讨了职业发展规划的话题。别说爵士乐这种“没市场的音乐”,就算从事流行大众音乐领域想赚到钱也不容易,未来的路该怎么走值得思考。

短暂而美好的爵士研习营结束后,我便回归课业将重心放在学业和项目,一晃就是五年。

感谢这段难得的经历,让我与爵士结缘。我会一直记得这些日子。

From Research to Product: Customer Insights on Prompt flow

作者 John Chou
2024年5月27日 21:55

Time to navigate the frontier

In the dynamic landscape of Large Language Models (LLM), our team is once again at the cutting edge, pioneering a new venture called Prompt flow (PF). My role transcends the rapid and high-quality delivery of products. I need to contemplate the features that can deliver real values to our customers and the user experience that resonates with the essence of those features. This new challenge is substantial for a web front-end engineer and has been a focal point of my professional contemplation.

As 2023 drew to a close, a fortuitous invitation from a university peer led me to explore the synergy between LLMs and conventional ML models. This exploration transformed me into an amateur researcher, granting me the privilege to scrutinize the research process through our customers’ lens, with the aim of pinpointing their pain points to better inform our product design.

Recent academic work I learned from

This paper has been accepted by ACL 2024 Findings few days ago, a great encouragement for us. Please read the full paper if you’re interested in details, which will not be interpreted here.

Harnessing LLMs as post-hoc correctors

A fixed LLM is leveraged to propose corrections to an arbitrary ML model’s predictions without additional training or the need for additional datasets.

Figure 1: Harnessing LLMs as post-hoc correctors. A fixed LLM is leveraged to propose corrections to an arbitrary ML model's predictions without additional training or the need for additional datasets.

A high-level overview of LLMCORR

Harnessing Large Language Models (LLMs) as post-hoc correctors to refine predictions made by an arbitrary Machine Learning (ML) model.

Figure 2: A high-level overview of LLMCORR, harnessing Large Language Models (LLMs) as post-hoc correctors to refine predictions made by an arbitrary Machine Learning (ML) model.

LLMCORR prompt template

Multiple contextual knowledge from training and validation datasets can be included by expanding the template.

Figure 3: LLMCORR prompt template. Multiple contextual knowledge from training and validation datasets can be included by expanding the template.

Reflections on Prompt flow

I have been deeply involved in PF since the inception of this project. Naturally, I endeavored to integrate it into our research, yet reality diverged from my original intentions. Hence, let’s see these few reflections on PF throughout the research journey. Please note that most of our works were done before early February of 2024, so I don’t mean to be a monday morning quarterback for some points.

1. Support and optimization of local inference experience will gain more customer favor

Most researchers and engineers have certain computational resources, and from the perspective of cost control, they are likely to choose open-source large language models (LLMs) for local inference work, which PF does not support. We have similar story that opted for LLMs like Llama at the beginning of the experiment, which meant giving up on PF.

2. Flex mode is crucial for the use of flow

Despite transitioning to OpenAI’s GPT3.5/4 models, our repository was already rich with Python utilities and Jupyter notebooks, complemented by a wealth of projects from previous research endeavors. The core competitive edge of PF aside, the availability of flex mode at that juncture would have allowed for an exploratory integration with our established GNN workflow, potentially igniting a synergistic spark.

3. Is Prompt engineering really that important?

The value of our work lies in placing LLM to an interesting position with right work in the system to maximize its value. The prompts designed and used are relatively common today: simple structure, essential knowledge and few-shots. Therefore prompt engineering have not played the key role in this work.

It is worth mentioning that PF recently launched the Prompty feature, which provides quick access and focuses on the value of tuning prompts. This may be practical in large engineering applications, where the content of a single prompt can range from hundreds to thousands of lines. If the scenario holds, then support complex Jinja Template Designer features and preview the final prompt content will be of great help (just like Overleaf does).

4. What PF did right?

When we began to learn and try to implement RAG App, we naturally looked at some LangChain samples first, then… went from beginner to giving up. My teammate chose the OpenAI Playground to use the GPT4 Assistants feature, meanwhile Azure OpenAI had not supported Assistants yet, so I chose to build a RAG flow following the PF sample. In this scenario, there are a few advantages to note:

  • Low-code is always easy to build PoC.
  • Orchestration to do batch run.
  • Tracing (Not implemented yet at that time, but definitely a keeper feature).

Of course, there are also points worth discussing, such as whether you still need to write some code that will affect the ease of use assessment if it is not clear in the sample that embeddings are generated using the same model set and stored in the vector database; or the data input and output in the batch run scenario, which also involves a lot of manual work.

Another aside, the performance of File retrieval of OpenAI (OAI) Assistants was not satisfactory at that time. I wonder if there has been a significant improvement after it was renamed to File Search now.

5. What should PF focus on if it conducts Experiments?

Firstly, there are some old topics, such as experiment status display and refresh, CRUD operations and viewing logs at each step, which are essential features of various products.

When the amount of experimental data is huge, limitations on metrics like RPM and TPM will start to trouble users. Thus, how to estimate the number of tokens and requests for experiments under these constraints by services like OAI and Azure OpenAI (AOAI) to achieve automated high-concurrency scheduling, and even support multi-endpoints concurrency, will be a great value to customer. In previous experiments, we implemented very basic token calculation and request interval logic, and I believe we are not the only ones with such needs.

Last few words

It’s not commonly encouraged for engineers to delve into academic pursuits, since not everyone possesses the passion, foundation, or even time. However, in the era of Generative AI, immersing oneself in scholarly articles is always a wise move!

Whether in practical application or academic experimentation, only through in-depth engagement can one truly understand and unearth the pain points of users. I believe this embodies the spirit of our current discussion.

What I learned from a part-time research experience?

作者 John Chou
2024年5月27日 21:55

Time to navigate the frontier

In the dynamic landscape of Large Language Models (LLM), our team is once again at the cutting edge, pioneering a new venture called Prompt flow (PF). My role transcends the rapid and high-quality delivery of products. I need to contemplate the features that can deliver real values to our customers and the user experience that resonates with the essence of those features. This new challenge is substantial for a web front-end engineer and has been a focal point of my professional contemplation.

As 2023 drew to a close, a fortuitous invitation from a university peer led me to explore the synergy between LLMs and conventional ML models. This exploration transformed me into an amateur researcher, granting me the privilege to scrutinize the research process through our customers’ lens, with the aim of pinpointing their pain points to better inform our product design.

Brief introduction of recent work

This paper has been accepted by ACL 2024 few days ago, a great encouragement for us. Let’s go through the proposed framework quickly by primary figures in the paper. Please read the paper if you have any detailed question.

Figure 1: Harnessing LLMs as post-hoc correctors

A fixed LLM is leveraged to propose corrections to an arbitrary ML model’s predictions without additional training or the need for additional datasets.

Figure 1: Harnessing LLMs as post-hoc correctors. A fixed LLM is leveraged to propose corrections to an arbitrary ML model's predictions without additional training or the need for additional datasets.

Figure 2: A high-level overview of LLMCORR

Harnessing Large Language Models (LLMs) as post-hoc correctors to refine predictions made by an arbitrary Machine Learning (ML) model.

Figure 2: A high-level overview of LLMCORR, harnessing Large Language Models (LLMs) as post-hoc correctors to refine predictions made by an arbitrary Machine Learning (ML) model.

Figure 3: LLMCORR prompt template

Multiple contextual knowledge from training and validation datasets can be included by expanding the template.

Figure 3: LLMCORR prompt template. Multiple contextual knowledge from training and validation datasets can be included by expanding the template.

Reflections on Prompt flow

I have been deeply involved in PF since the inception of this project. Naturally, I endeavored to integrate it into our research, yet reality diverged from my original intentions. Hence, let’s see these few reflections on PF throughout the research journey. Please note that most of our works were done before early February of 2024, so I don’t mean to be a monday morning quarterback for some points.

1. Support and optimization of local inference experience will gain more customer favor

Most researchers and engineers have certain computational resources, and from the perspective of cost control, they are likely to choose open-source large language models (LLMs) for local inference work, which PF does not support. We have similar story that opted for LLMs like Llama at the beginning of the experiment, which meant giving up on PF.

2. Flex mode is crucial for the use of flow

Despite transitioning to OpenAI’s GPT3.5/4 models, our repository was already rich with Python utilities and Jupyter notebooks, complemented by a wealth of projects from previous research endeavors. The core competitive edge of PF aside, the availability of flex mode at that juncture would have allowed for an exploratory integration with our established GNN workflow, potentially igniting a synergistic spark.

3. Is Prompt engineering really that important?

The value of our work lies in placing LLM to an interesting position with right work in the system to maximize its value. The prompts designed and used are relatively common today: simple structure, essential knowledge and few-shots. Therefore prompt engineering have not played the key role in this work.

It is worth mentioning that PF recently launched the Prompty feature, which provides quick access and focuses on the value of tuning prompts. This may be practical in large engineering applications, where the content of a single prompt can range from hundreds to thousands of lines. If the scenario holds, then support complex Jinja Template Designer features and preview the final prompt content will be of great help (just like Overleaf does).

4. Where does PF excel over LangChain? What’s the value?

When we began to learn and try to implement RAG App, we naturally looked at some LangChain samples first, then… went from beginner to giving up. My teammate chose the OpenAI Playground to use the GPT4 Assistants feature, meanwhile Azure OpenAI had not supported Assistants yet, so I chose to build a RAG flow following the PF sample. In this scenario, there are a few advantages to note:

  • Low-code is always easy to build PoC.
  • Orchestration to do batch run.
  • Tracing (Not implemented yet at that time, but definitely a keeper feature).

Of course, there are also points worth discussing, such as whether you still need to write some code that will affect the ease of use assessment if it is not clear in the sample that embeddings are generated using the same model set and stored in the vector database; or the data input and output in the batch run scenario, which also involves a lot of manual work.

Another aside, the performance of File retrieval of OAI Assistants was not satisfactory at that time. I wonder if there has been a significant improvement after it was renamed to File Search now.

5. What should PF focus on if it conducts Experiments?

Firstly, there are some old topics, such as experiment status display and refresh, CRUD operations and viewing logs at each step, which are essential features of various products.

When the amount of experimental data is huge, limitations on metrics like RPM and TPM will start to trouble users. Thus, how to estimate the number of tokens and requests for experiments under these constraints by services like OAI and AOAI to achieve automated high-concurrency scheduling, and even support multi-endpoints scheduling, will be a great value to customer. In previous experiments, we implemented very basic token calculation and request interval logic, and I believe we are not the only ones with such needs.

Last few words

It’s not commonly encouraged for engineers to delve into academic pursuits, since not everyone possesses the passion, foundation, or even time. However, in the era of Generative AI, immersing oneself in scholarly articles is always a wise move!

Whether in practical application or academic experimentation, only through in-depth engagement can one truly understand and unearth the pain points of users. I believe this embodies the spirit of our current discussion.

What I’ve learned from a part-time research experience?

作者 John Chou
2024年5月27日 21:55

Time to navigate the frontier

In the dynamic landscape of Large Language Models (LLM), our team is once again at the cutting edge, pioneering a new venture called Prompt flow (PF). My role transcends the rapid and high-quality delivery of products. I need to contemplate the features that can deliver real values to our customers and the user experience that resonates with the essence of those features. This challenge is substantial for a web front-end engineer and has been a focal point of my professional contemplation.

As 2023 drew to a close, a fortuitous invitation from a university peer led me to explore the synergy between LLMs and conventional ML models. This exploration transformed me into an amateur researcher, granting me the privilege to scrutinize the research process through our customers’ lens, with the aim of pinpointing their pain points to better inform our product design.

Introduction of recent work

This paper has been accepted by ACL 2024 few days ago, a great encouragement for us.

Reflections on Prompt flow

I have been deeply involved in PF since the inception of this project. Naturally, I endeavored to integrate it into our research, yet reality diverged from my original intentions. Hence, let’s see these few reflections on PF throughout the research journey. Please note that most of our works were done before early February of 2024, so I don’t mean to be a monday morning quarterback for some points.

1. Support and optimization of local inference experience will gain more customer favor

Most researchers and engineers have certain computational resources, and from the perspective of cost control, they are likely to choose open-source large language models (LLMs) for local inference work, which PF does not support. We have similar story that opted for LLMs like Llama at the beginning of the experiment, which meant giving up on PF.

2. Flex mode is crucial for the use of flow

Despite transitioning to OpenAI’s GPT3.5/4 models, our repository was already rich with Python utilities and Jupyter notebooks, complemented by a wealth of projects from previous research endeavors. The core competitive edge of PF aside, the availability of flex mode at that juncture would have allowed for an exploratory integration with our established GNN workflow, potentially igniting a synergistic spark.

3. Is Prompt engineering really that important?

The value of our work lies in placing LLM to an interesting position with right work in the system to maximize its value. The prompts designed and used are relatively common today: simple structure, essential knowledge and few-shots. Therefore prompt engineering have not played the key role in this work.

It is worth mentioning that PF recently launched the Prompty feature, which provides quick access and focuses on the value of tuning prompts. This may be practical in large engineering applications, where the content of a single prompt can range from hundreds to thousands of lines. If the scenario holds, then support complex Jinja Template Designer features and preview the final prompt content will be of great help (just like Overleaf does).

4. Where does PF excel over LangChain? What’s the value?

When we began to learn and try to implement RAG App, we naturally looked at some LangChain samples first, then… went from beginner to giving up. My teammate chose the OpenAI Playground to use the GPT4 Assistants feature, meanwhile Azure OpenAI had not supported Assistants yet, so I chose to build a RAG flow following the PF sample. In this scenario, there are a few advantages to note:

  • Low-code is always easy to build PoC.
  • Orchestration to do batch run.
  • Tracing (Not implemented yet at that time, but definitely a keeper feature).

Of course, there are also points worth discussing, such as whether you still need to write some code that will affect the ease of use assessment if it is not clear in the sample that embeddings are generated using the same model set and stored in the vector database; or the data input and output in the batch run scenario, which also involves a lot of manual work.

Another aside, the performance of File retrieval of OAI Assistants was not satisfactory at that time. I wonder if there has been a significant improvement after it was renamed to File Search now.

5. What should PF focus on if it conducts Experiments?

Firstly, there are some old topics, such as experiment status display and refresh, CRUD operations and viewing logs at each step, which are essential features of various products.

When the amount of experimental data is huge, limitations on metrics like RPM and TPM will start to trouble users. Thus, how to estimate the number of tokens and requests for experiments under these constraints by services like OAI and AOAI to achieve automated high-concurrency scheduling, and even support multi-endpoints scheduling, will be a great value to customer. In previous experiments, we implemented very basic token calculation and request interval logic, and I believe we are not the only ones with such needs.

Last few words

It’s not commonly encouraged for engineers to delve into academic pursuits, since not everyone possesses the passion, foundation, or even time. However, in the era of Generative AI, immersing oneself in scholarly articles is always a wise move!

Whether in practical application or academic experimentation, only through in-depth engagement can one truly understand and unearth the pain points of users. I believe this embodies the spirit of our current discussion.

2022年我想练的歌单

作者 John Chou
2022年3月18日 14:56

虽然2022年已经过去俩月半,但“想练”意味着不一定能啃下来,先把目标都列下来再看看到年底具体能有哪些斩获吧😅。清单数量限定在十首左右,因此选择的都是我个人非常喜欢同时也具备一定技术难度、需要花时间去琢磨的曲目。每一首都在我工作或深夜单曲循环过无数遍,一定好听且耐听。这个清单也符合我一直以来的“杂食”风格,流行歌、爵士以及一些现代融合的风格都有,曲目按首字母排序,偏好无先后顺序。

爱我还是他 - 李荣浩

Autumn leaves - Eric Clapton

Covered in rain - John Mayer

  • 原曲:YouTubeBilibili
  • 碎碎念:可以说是从小听到大的歌曲,脑海中基本可以“全文背诵”。通过这首歌,第一次感受到电吉他solo堆砌起来的充沛叙事感和情感。毫无疑问JM是我心中的吉他英雄,因为他喜欢上吉他。我并不反感市场化或商业化,作品如果全是技巧没有感情,或全是感情没有旋律性,都不是我的菜。我觉得JM就是把这几件事结合得出类拔萃的天才,至于一些商业化带来的八卦以及他的”stupid mouth”,我不关心。
  • 参考资料:应该非常多,暂未搜寻。

Europa - Carlos Santana

  • 原曲:YouTubeBilibili
  • 碎碎念:又一支从小听到大的曲子,“全文背诵”什么的后面就不提了。除了这首曲子外,像《Game of love》、《Smooth》等歌也是往后想扒和练得目标。
  • 参考资料:应该非常多,暂未搜寻。

Gravity - John Mayer

  • 原曲:YouTubeBilibili
  • 碎碎念:这首曲子没什么好说的了吧,从技巧到音色,有朝一日必须拿下。
  • 参考资料:应该非常多,暂未搜寻。

How insensitive - Wes Montgomery

  • 原曲:YouTubeBilibili
  • 碎碎念:对于爵士乐我比较喜欢标准曲和bossa nova风格,说白了也都是流行歌过来的,只不过有更多丰富的语言词汇去表达情感。这首曲子也算从小听到大,除了非常好听之外,也是标准曲中为数不多用吉他演奏的最广为流传的版本,必须冲一发。
  • 参考资料:应该非常多,暂未搜寻。

Kyoto - Tomo Fujita

Little Wing - Stevie Ray Vaughan

  • 原曲:YouTube
  • 碎碎念:在这首歌和JM的《Bold as love》之间犹豫了一会,不过两首都是Jimi Hendrix的歌,练会哪首另一首应该也能很快能掌握。说实话我对Jimi的作品到目前为止没多少感觉,但SRV和JM演绎的版本立马能抓住我的耳朵,也听了很多年,该好好研究一下。
  • 参考资料:应该非常多,暂未搜寻。

Plastic love - Mariya Takeuchi

  • 原曲:YouTubeBilibili
  • 碎碎念:非常喜欢city pop风格,旋律和编曲百听不厌,这种纸醉金迷的都会氛围感,令人沉迷… 这首歌用来练习funk吉他正好,此外应该也有一些表演机会,毕竟是更有市场的音乐。
  • 参考资料:应该非常多,暂未搜寻。

South of the river - Tom Misch

  • 原曲:YouTubeBilibili
  • 碎碎念:Tom Misch是我20年的最大发现,基本上每首歌都很好听,融合了funk和爵士的元素兼具音乐性和技术性,网上也有人称他为英国JM… 🤣。这首歌的节奏吉他和键盘solo都想扒一下,弹出那种音乐带给人最简单的快乐(如果看过MV就会秒懂)。
  • 参考资料:

There will never be another you - Arturo Sandoval

  • 原曲:YouTubeBilibili
  • 碎碎念:很犹豫是否要放一首真正意义上的爵士标准曲,《How insensitive》可以看做练一首有“标准答案的指弹曲目”,而到了爵士标准曲,除了扒大师的谱外,即兴是必不可少的一部分。这就没有了标准答案,于是变成很难花费一年半载便能达到大师们那种好听水平的一件事。完美主义多少害了我,对于特别喜欢的作品,不太敢迈出第一步(JM的歌我也一首没练过)。但这首歌对我有太多的回忆,该逼自己成长了。
  • 参考资料:

Jazz guitar foundations

作者 John Chou
2021年8月22日 21:55

Note of preparatory courses of Cai Jian (蔡剑) jazz guitar lessons by Qihao Chen (陈启豪).

Course 1

Four stages of practice:

  1. Learn the thing from unknown to known
  2. Get familiar with it on the instrument
  3. Apply it to your playing like melodic sequence
  4. Internalization

The first step of learning jazz guitar

Know every note of strings and frets on the standard tuning guitar.

  • CAGED system
    • Think about the positions on fretboard corresponding to the note invervals
    • Playing a scale can reflect the sequence of intervals
  • 3NPS (3 Notes Per String)

Chord

Two important factors:

  • Root note
  • Type of chord

Think of the C major chord by harmonizing C note in C major scale, so the other notes in the scale with third intervals are E and G. Same thing to the other notes or scales, we have these common chords:

  • Triads: major (1, 3, 5), minor (1, b3, 5), augmented (1, 3, #5), diminished (1, b3, b5)
  • 7th chords: major (1, 3, 5, 7), dominant (1, 3, 5, b7), minor (1, b3, 5, b7), half diminished (1, b3, b5, b7), diminished (1, b3, b5, bb7), minor major (1, b3, 5, 7), augmented major (1, 3, #5, 7)

Figure out the root position of chords

  1. Know the members of chord due to its type
  2. Find the root notes on 4th to 6th strings
  3. Find the position of other notes due to the intervals

In this way we can infer 5 common patterns (R, 5, 7, 3 and R, 7, 3, 5) by CAGED system of each type of chords.

Chord-scale system

The chord is equal to the scale and vice versa. Think that a heptatonic scale (like diatonic scale) is consist of 4 notes of the 7th chord which the root note is harmonized with other 3 extensions. In the other hand, 4 notes determine the type of chord, and 3 extensions determine the type of scale. Like playing a maj7 could be Ionion or Lydian mode scales, plus a #4 extension makes it a Lydian.

Rhythm

  • Beat: the unit of time for music, BPM (Beats Per Minute)
  • Metre: the regularly recurring patterns and accents such as bars and beats
  • Rhythm: the combination of beat and metre over time

Homeworks

  1. C major scale on CAGED system:
    • Cover all notes of 5 positions
    • Start and end from other notes instead of the root note
    • Various scale patterns
  2. Find the note position due to interval
    • Imagine you have two pairs of cards, one pair are ascending and descending order cards, another one are interval cards like minor 2nd, major 2nd, minor 3rd, …, etc
    • Pick the root note and each one card from two pairs like F on 6th string, ascending and perfect 5th
    • Find the the ascending perfect 5th note of F on 6th string

Tips

  • Try to think faster before playing.
  • Inversion of interval of two notes, 9 = perfect 4th and perfect 5th = major 3rd and minor 6th
  • Maybe you can try this mini app Fingerboard mate

Course 2

Modes

Older than natural major/minor scales.

Tonic relative to major scaleNameInterval sequenceTonic 7th chord
INatural major scale, Ionian mode1, 2, 3, 4, 5, 6, 7△7
IIDorian mode1, 2, b3, 4, 5, 6, b7-7
IIIPhrygian mode1, b2, b3, 4, 5, b6, b7-7
IVLydian mode1, 2, 3, #4, 5, 6, 7△7
VMixolydian mode1, 2, 3, 4, 5, 6, b7dom7
VINatural minor scale, Aeolian mode1, 2, b3, 4, 5, b6, b7-7
VIILocrian mode1, b2, b3, 4, b5, b6, b7-7b5

C major scale we practiced before has same notes to the D dorian, E phrygian, F lydian, G mixolydian, A aeolian and B locrian.

Practice hints with fifth circle

  • Start from dorian and mixolydian modes which are ofren used for minor and dominant chords
  • Change root note and practice with corresponding interval sequence on C major scale on CAGED system
  • Modulation: play each key along with fifth circle on one position area of CAGED system
  • Write down the notes of major scale, modes name and corresponding tonic chords to help remembering
  • Play tonic chord before and after scale/mode
  • Practice root position for one kind of chords along with fifth circle on one position area of CAGED system, like play major chords in 12 keys on C position then go to other positions, then change to minor chords, dominant chords…

Subdivision

Dividing the beat into smaller units. Swing 8th are perfoming by one 1/3 triplets and another 2/3 triplets, counting triplets beats in mind.

Comping

Providing background like chords, timing, etc to make improvised solo or meldoy lines a complete work. It also has lots of possibilities as same as solo to create or renew the song. Recommend the guitarist Freddie Green to know more.

Homeworks

  1. Know well about the name and interval sequence of 7 modes
    • Random root note with random mode to practice
    • Play swing 8th
  2. Proficient in playing major II-V-I chords in each key
    • Random root note
    • Play swing 4th (mute the third beat in triplets)

Course 3

Minor II-V-I

In the natural minor scale (1, 2, b3, 4, 5, b6, b7), the tension can’t be resolved due to both V and I are minor chords. Thus leverage the harmonic minor scale (1, 2, b3, 4, 5, b6, 7) to make V to be dominant chord, but also keep the II and I chords in natural minor scale to construct the minor II-V-I.

Comparing to the V in major scale (1, 2, 3, 4, 5, 6, b7), the V in harmonic minor scale (1, b2, 3, 4, 5, b6, 7) has b9 and b13 notes which is called altered. Typically, a dominant seventh chord is considered altered if either or both the 5th or 9th are chromatically raised or lowered (#9 or b9 and #11 or b13).

In result, the minor II-V-I is m7b5-dom7alt-min7. You can also change the I to minMaj7 if you prefer harmonic minor scale.

Phrygian dominant mode

There are also 7 modes based on harmonic minor scale, the most common one is the 5th mode phrygian dominant. When you see a G13 chord, it must comes from a major scale. And when you see a dom7b9, you can infer that it comes from the V in minor scale, then you can play phrygian dominant.

Moreover, the basic patterns to play minor II-V-I are locrian for minor II, phrygian dominant for minor V and aeolian or dorian for minor I.

Avoid note

Avoid note will change the functionality of chords.

  • For major and dominant chords, the avoid note is 4th diatonic scale step (11), the available extensions are 9, #11, 13.
  • For minors chords, the avoid note is 6th scale step (13), the available extensions are 9, 11. There is an exception, it can work if the minor chord acts as dominant function. For instance, replace V dominant by IV minor and the progression turns to be IV major, IV minor and I major, sounds great.

Avoid notes for modes of the C major scale:

Scale degreeChordModeAvoid noteAvailable tensions
1Cmaj7IonianFourth scale step, F9, 13
2Dm7DorianSixth scale step, B9, 11
3Em7PhrygianSecond and sixth scale steps, F and C11
4Fmaj7LydianNo avoid note9, ♯11, 13
5G7MixolydianFourth scale step, C9, 13
6Am7AeolianSixth scale step, F9, 11
7Bø7LocrianSecond scale step, C11, ♭13

Steps to learn a standard

  1. Listen multiple versions of the song with lead sheet, figure out the form of the song.
  2. Practice the melody and chords of the song.
  3. Practice scales and arpeggios through the chords.
  4. Improvisation. This a really comprehensive topic, try to find the 3rd notes of every chords at first.

Homeworks

  1. Harmonic minor scales on CAGED system with 5th dominant 7th chord, for example A harmonic minor scale with E7 on 5 positions.
  2. II-V-I progression of relative keys. For C major and A minor keys, the progression is Dm7-G7-C-Bm7b5-E7Alt-Am7. Try chord progression in other keys as well as arpeggios.

Course 4

Melodic minor scale

  • The ascending melodic minor scale, aka jazz minor scale: 1, 2, ♭3, 4, 5, 6, 7
  • The descending melodic minor scale: 1, 2, ♭3, 4, 5, ♭6, ♭7

Modes of the ascending melodic minor scale:

ModeNameAssociated chords
IAscending melodic minorC minor major 7 (9, 11, 13) or C minor 6 chords (functions as i minor)
IIPhrygian ♮6, Dorian ♭2, Assyrian, or PhrygidorianD7sus (♭9, ♯9, 13) chord, with ♭2 as a non-chord tone producing a minor ninth
IIILydian augmented or Lydian ♯5E♭ major 7♯5 (9, #11) chord (functions as a III+)
IVLydian dominant, Lydian ♭7, Acoustic scale, Mixolydian ♯4, Overtone, or LydomyxianF7 (9, ♯11, 13) chord (functions as a dominant, secondary, or substitute dominant)
VMixolydian ♭6, Melodic major, fifth mode of Melodic minor, Hindu, or MyxaeolianG7 (9, ♭13) chord (functions as a dominant with ♭13 as a non-chord tone or the fifth avoided in the chord voicing as they produce a minor ninth)
VILocrian ♮2, Half-diminished, or AeolocrianA minor 7♭5 (9, 11, ♭13) (functions as a ii chord in the fifth mode of melodic minor)
VIISuper Locrian, Altered dominant scale, or altered scaleB7 (♯ or ♭9, ♯11, ♭13) chord (functions as a dominant with the fifth of the chord replaced by ♯11 or ♭13, may also be used to harmonize a vii7♭5 chord in melodic minor)

Tritone substitution

If two chords share the same 3rd and 7th notes, they will have similar sound thus can replace each other. For example, we can play IIm7-bII7-I△7 instead of IIm7-V7-I△7 to make bassline changing smoothly.

Lydian dominant is the most important mode of melodic scale, the only difference with mixolydian mode is #4 note. One common scenario to apply this mode is with tritone substitution, an approach to help finding the positions on fretboard quickly. If you want to play the V dominant altered scale on V7 chord, it’s exactly the bII lydian dominant scale on bII7.

Homework

Melodic minor scales on CAGED system with related tonic chords.

References

吉他保养简记

作者 John Chou
2021年8月11日 20:27

电吉他指板与漆面

品丝

品丝如果有锈迹可使用 MusicNomad MN104 品丝抛光剂打磨,注意使用品丝护理片或在打磨前贴上胶带以保护指板。如果观察品丝缝隙有较多污渍,可用小勾刀轻轻刮掉。

指板

  • 玫瑰木:使用 Dunlop 6524 指板清洁剂和 Dunlop 6532 深层护理,预算有限也可使用 Dunlop 6554 柠檬油。
  • 枫木:
    • 上过油漆,用布擦拭。
    • 无漆,用沾水的布擦拭,不要使用任何护理品。
  • 石墨、碳纤维:用布擦拭即可。
  • TBD:烤枫木、乌木。

琴体

  • 聚酯漆:如墨芬和日芬,用沾水的布擦拭即可。
  • 聚氨酯漆:现代琴居多,使用 Dunlop 654 上光清洁剂和 Dunlop 6574 抛光巴西棕榈乳液。抛光先将乳液抹匀,然后使用电磨机配抛光棉抛光。
  • 硝基漆:复古琴居多,用布擦干后使用 Prefox AC201 蜂蜡抛光。
  • 哑光漆:如 James Tyler,使用 Zippo 油擦拭。

电吉他状态调节

检查琴颈

  • 方法一:变调夹夹住第一品,手指按住六弦最后一格,看 8 品的弦距是否在 0.25mm 左右,大于该数值说明琴颈过弯需调直,小于则说明过直需放松。
  • 方法二:使用琴颈尺观察。

调节琴颈

以复古琴为例,松琴弦后用变调夹夹住 1 品,拧螺丝拆下琴颈。使用一字螺丝刀调节琴颈内的钢筋,若需调直则顺时针方向旋转,一次不要超过 30°。上螺丝时对角顺序安装。

调节琴桥

根据指板弧度选择对应的弧度尺。先调节琴码使六弦 12 品弦距 2mm、1弦 12 品弦距 1.6mm,将弧度尺放至琴弦下,挨个调节其他琴码使所有琴弦贴合弧度尺,可以拨动琴弦看是否会被弧度尺闷住。若琴码有两颗螺丝,则要注意将琴码调平。

对于 Gibson 类型的一体琴桥,由于其弧度是固定的,只需使用扳手调节一弦和六弦的高度即可。拉弦板为了获得最大的张力可调至最低,注意不要角度过大使琴弦碰到琴桥。还可以将琴弦反向穿过拉弦板再压上琴桥。

调节拾音器的高度

如果不知道要怎么调可以参考这篇文档的数据,具体还是以琴的实际听感为准。此外更换品牌拾音器通常可以参考官方提供的数值,以Lace拾音器为例:

  • 按住高音E弦的21品,琴弦到拾音器的高度为:琴颈 1.5mm,中间 2.0mm,琴桥 1.0mm;
  • 按住低音E弦的21品,琴弦到拾音器的高度为:琴颈 2.5mm,中间 3.5mm,琴桥 2.0mm;

如果一把琴的两个或三个拾音器的功率不同,将它们调至同一高度后,需对功率较小的拾音器再次调节,使其音量与其他拾音器音量保持一致。如果同时有单拾音器和双拾音器,可以不追求音量平衡,发挥各自拾音器的特色。

冬季木吉他保养

湿度控制是关键(40%-55%)

  • 加湿器:冷蒸发式 > 超声波式
  • 吉他恒湿柜
  • 音孔加湿器
  • 塑封袋一面打几个小孔,里面放一块干净湿润的毛巾,将塑封袋放入木吉他音孔中

保养tips

  • 一段时间不弹的琴放琴盒,琴弦松一个全音
  • 擦指板
    • 均匀喷洒 Dunlop 6524 指板清洁剂后,如果特别脏可先用毛刷轻刷,然后折叠纸巾(使用琴布略微有点浪费)擦拭
    • 使用纸巾按压吸收 Dunlop 6532 的护理油,再用纸巾轻轻擦拭指板
  • 擦琴体
    • 选一块好的琴布:表面无较硬颗粒、有一定的韧性、化纤含量少擦拭久了发热量小
    • 对折琴布到可以握住,擦拭的面积为长条型,检查擦拭区域不脏、无杂质
    • 将 Dunlop 654 清洁剂喷在琴布擦拭区域,轻擦琴体(有污渍的地方),擦拭时朝一个方向

参考与推荐视频

音乐基础速查笔记

作者 John Chou
2021年7月18日 16:47

和弦

七和弦

和弦英文名音程关系标记(以C和弦为例)
大七和弦Major seventh chord1、3、5、7Cmajor7、Cmaj7、Cma7、C△7
增大七和弦Augmented major seventh chord1、3、#5、7Cmaj7#5、C△7#5
小七和弦Minor seventh chord1、b3、5、b7Cminor7、Cmin7、Cmi7、Cm7、C-7
属七和弦Dominant seventh chord1、3、5、b7Cdominat7、Cdom7、C7
半减七和弦Half-diminished seventh chord1、b3、b5、b7Cm7b5、C-7b5、Cφ7
减七和弦Diminished seventh chord1、b3、b5、bb7Cdimish7、Cdim7、Co7
小大七和弦Minor major seventh chord1、b3、5、7CminMaj7、CmMaj7、CmM7
变化属七和弦Alt chord包含变化延伸音的属七和弦

斜线与转位和弦

  • 斜线和弦(Slash chord):将和弦原有的根音换成其他音,如 Dm/G 表示将 Dm 和弦的根音换成 G
  • 转位和弦(Chord Inversions):当和弦最低音为根音时称为原位和弦,最低音为其他构成音时称为转位和弦

密集与开放排列

  • 密集排列和弦(Close position chord):和弦音的排列从最低音到最高音不超过一个八度
  • 开放排列和弦(Open position chord):和弦音的排列从最低音到最高音超过一个八度

Drop和弦

  • Drop2 chord:将密集排列七和弦第二高音降低一个八度
  • Drop3 chord:将密集排列七和弦第三高音降低一个八度
  • Drop2&3 chord:将密集排列七和弦第二、三高音降低一个八度
  • Drop2&4 chord:将密集排列七和弦第二、四高音降低一个八度

251进行

  • 大调 251 进行:大调顺阶和弦的二级、五级和一级的和弦进行,如 C 大调的 251 进行为 Dm7、G7、Cmaj7
  • 小调 251 进行:爵士乐小调 251 即兴中,5 级和弦选择属和弦而非顺阶的小和弦,如 C 小调的 251 进行为 Dm7b5、G7、Cm7

重属和弦与三全音替代

  • 重(次)属和弦(Secondary dominant chord):解决到主和弦以外的其他调内和弦(半减七、减七和弦除外)的属和弦
  • 三全音替代(Tritone substitution):使用距离原本属和弦根音三全音位置的属和弦替代

和弦琶音、延伸音

  • 和弦琶音(Arpeggio):由和弦内音组成的一串音,通常用于即兴中构建乐句
  • 和弦延伸音(Tension):非和弦基础部分构成的音,为和弦增添了额外的色彩

音阶

自然大小调

音阶英文名音程关系
自然大调(I级)Natural major scale, Ionian mode1、2、3、4、5、6、7
大调II级调式Dorian mode1、2、b3、4、5、6、b7
大调III级调式Phrygian mode1、b2、b3、4、5、b6、b7
大调IV级调式Lydian mode1、2、3、#4、5、6、7
大调V级调式Mixolydian mode1、2、3、4、5、6、b7
自然小调、自然大调VI级Natural minor scale, Aeolian mode1、2、b3、4、5、b6、b7
大调VII级调式Locrian mode1、b2、b3、4、b5、b6、b7

和声小调

音阶英文名音程关系适用和弦
和声小调(I级)Harmonic minor1、2、b3、4、5、b6、7minMaj7
小调II级调式Locrian 61、b2、b3、4、b5、6、b7min7b5
小调III级调式Ionian augmented/#51、2、3、4、#5、6、7maj7#5
小调IV级调式Dorian #41、2、b3、#4、5、6、b7min7
小调V级调式Phrygian dominant1、b2、3、4、5、b6、b7dom7, susb9
小调VI级调式Lydian #21、#2、3、#4、5、6、7maj7
小调VII级调式Super locrian b71、b2、b3、b4、b5、b6、bb7dim7

旋律小调

TBC…

其他常用音阶

音阶英文名音程关系
大调五声音阶Major pentatonic scale1、2、3、5、6
小调五声音阶Minor pentatonic scale1、b3、4、5、b7
大调布鲁斯音阶Major blues scale1、2、b3、3、5、6
小调布鲁斯音阶Minor blues scale1、b3、4、#4、5、b7
全半减音阶Whole half diminished scale1、2、b3、4、b5、#5、6、7
半全减音阶Half whole diminished scale1、b9、#9、3、#11、5、13、b7
全音阶Whole tone scale1、2、3、#4、#5、#6
增音阶Augmented scale1、b3、3、5、#5、7
Bebop dorian1、2、b3、3、4、5、6、b7
Bebop dominant1、2、3、4、5、6、b7、7
Bebop major1、2、3、4、5、#5、6、7
Bebop melodic minor1、2、b3、4、5、#5、6、7

音程推导

intervals-calculation-1

intervals-calculation-2

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