diff --git a/.obsidian/plugins/recent-files-obsidian/data.json b/.obsidian/plugins/recent-files-obsidian/data.json index b0dd99c9d..628820f69 100644 --- a/.obsidian/plugins/recent-files-obsidian/data.json +++ b/.obsidian/plugins/recent-files-obsidian/data.json @@ -1,5 +1,21 @@ { "recentFiles": [ + { + "basename": "a-gentleman-in-moscow", + "path": "book-review/a-gentleman-in-moscow.md" + }, + { + "basename": "microsoft-copilot-notes", + "path": "tech/microsoft-copilot-notes.md" + }, + { + "basename": "timegpt-and-lag-llama-two-foundation-models-for-time-series-forecasting", + "path": "tech/timegpt-and-lag-llama-two-foundation-models-for-time-series-forecasting.md" + }, + { + "basename": "new-post", + "path": "_templates/new-post.md" + }, { "basename": "jacob-1-viewing-christ", "path": "christianity/jacob-1-viewing-christ.md" @@ -116,10 +132,6 @@ "basename": "chronos-time-series-foundation-model-by-amazon", "path": "tech/chronos-time-series-foundation-model-by-amazon.md" }, - { - "basename": "timegpt-and-lag-llama-two-foundation-models-for-time-series-forecasting", - "path": "tech/timegpt-and-lag-llama-two-foundation-models-for-time-series-forecasting.md" - }, { "basename": "ever-shifting-tides", "path": "gratitude/ever-shifting-tides.md" @@ -187,18 +199,6 @@ { "basename": "infinite-suffering-of-everyone", "path": "sacrament-symbols/infinite-suffering-of-everyone.md" - }, - { - "basename": "2024-03-15", - "path": "goals/2024-03-15.md" - }, - { - "basename": "the-happiest-man-on-earth", - "path": "book-review/the-happiest-man-on-earth.md" - }, - { - "basename": "prayer-of-perspective", - "path": "creative/prayer-of-perspective.md" } ], "omittedPaths": [], diff --git a/.obsidian/workspace-mobile.json b/.obsidian/workspace-mobile.json index 1765df982..88f13ad7f 100644 --- a/.obsidian/workspace-mobile.json +++ b/.obsidian/workspace-mobile.json @@ -13,7 +13,7 @@ "state": { "type": "markdown", "state": { - "file": "christianity/jacob-1-viewing-christ.md", + "file": "book-review/a-gentleman-in-moscow.md", "mode": "source", "source": false } @@ -93,7 +93,7 @@ "state": { "type": "backlink", "state": { - "file": "christianity/jacob-1-viewing-christ.md", + "file": "book-review/a-gentleman-in-moscow.md", "collapseAll": false, "extraContext": false, "sortOrder": "alphabetical", @@ -110,7 +110,7 @@ "state": { "type": "outline", "state": { - "file": "christianity/jacob-1-viewing-christ.md" + "file": "book-review/a-gentleman-in-moscow.md" } } }, @@ -120,7 +120,7 @@ "state": { "type": "outgoing-link", "state": { - "file": "christianity/jacob-1-viewing-christ.md", + "file": "book-review/a-gentleman-in-moscow.md", "linksCollapsed": false, "unlinkedCollapsed": true } @@ -141,8 +141,13 @@ }, "active": "be767112276aa446", "lastOpenFiles": [ - "news/llms-need-to-call-functions.md", + "tech/microsoft-copilot-notes.md", + "img/screenshot-write-speech-from-doc.jpeg", + "tech/timegpt-and-lag-llama-two-foundation-models-for-time-series-forecasting.md", + "book-review/a-gentleman-in-moscow.md", + "_templates/new-post.md", "christianity/jacob-1-viewing-christ.md", + "news/llms-need-to-call-functions.md", "scriptures/jacob-1.8.md", "book-review/brave-new-world.md", "gratitude/mentors.md", @@ -169,14 +174,8 @@ "sacrament-symbols/wearing-a-tie-reminds-me-of-the-noose-around-his-neck.md", "scriptures/the-purifying-power-of-gethsemane.md", "sacrament-symbols/the-messages-remind-me-of-his-message-after-his-resurrection.md", - "christianity/easter-the-most-important-holiday.md", - "gratitude/green-oasis.md", "img/photo-nearby-green-hills.jpeg", - "goals/2024-03-30.md", - "book-review/heal-your-nervous-system.md", - "gratitude/bodyfulness.md", "img/dalle-meditating-bodyfulness.jpeg", - "img/photo-fairy-house.jpeg", "quotes", "code/2024-02-02-libby.py", "code", diff --git a/_templates/new-post.md b/_templates/new-post.md index 55da8f5c6..fb8ad6db8 100644 --- a/_templates/new-post.md +++ b/_templates/new-post.md @@ -34,7 +34,10 @@ let fileNameSlug = title if (folder === 'goals') { slug = tp.date.now() + "-goals"; } else { - slug = await tp.system.prompt("Slug/Filename:", fileNameSlug) + // ask the user if they agree with the slug + // slug = await tp.system.prompt("Slug/Filename:", fileNameSlug) + // skip asking + slug = fileNameSlug } } diff --git a/book-review/a-gentleman-in-moscow.md b/book-review/a-gentleman-in-moscow.md new file mode 100644 index 000000000..85e1d7f2e --- /dev/null +++ b/book-review/a-gentleman-in-moscow.md @@ -0,0 +1,17 @@ +--- +title: A Gentleman in Moscow +date: 2024-04-05 20:19:37 +created: 2024-04-05 20:19:37 +categories: + - book-review +draft: false +author: Amor Towles +book-year: 2029 +book-time: 18 +date-start: 2024-04-05 20:19:37 +date-finished: 2024-04-05 20:19:37 +pct-complete: +--- + + +![Gentleman in Moscow ](../img/book-a-gentleman-in-moscow.jpeg){.preview-image} \ No newline at end of file diff --git a/img/book-a-gentleman-in-moscow.jpeg b/img/book-a-gentleman-in-moscow.jpeg new file mode 100644 index 000000000..ba6207802 Binary files /dev/null and b/img/book-a-gentleman-in-moscow.jpeg differ diff --git a/img/screenshot-write-speech-from-doc.jpeg b/img/screenshot-write-speech-from-doc.jpeg new file mode 100644 index 000000000..2db107059 Binary files /dev/null and b/img/screenshot-write-speech-from-doc.jpeg differ diff --git a/tech/microsoft-copilot-notes.md b/tech/microsoft-copilot-notes.md new file mode 100644 index 000000000..5f9e2060a --- /dev/null +++ b/tech/microsoft-copilot-notes.md @@ -0,0 +1,51 @@ +--- +title: "Microsoft Copilot notes" +date: "2024-04-05 21:57:30" +created: "2024-04-05 21:57:30" +categories: tech +draft: false +--- +Three copilots: + +- copilot.Microsoft.com + + +[YouTube MS ignite](https://youtu.be/3Nb0FEe3kKw?si=Q8A2T101zA9pxx1B) + +{{< video https://youtu.be/3Nb0FEe3kKw?si=Q8A2T101zA9pxx1B >}} + +- uses RAG on the Microsoft graph +- Copilot can make a PowerPoint from a Doc +- TeamsKit +- plugins and graph connectors. Maybe plugin is right? Maybe graph connector is right. +- Not very useful PowerPoints unless tied to data +- LLMs are only as accurate as the data. + + +[YouTube create awesome documents](https://youtu.be/7JRaFAYSOgI?si=9NrnsVCO0rGrlAHh) + +- Edit and revise + +[YouTube | Excel tutorial](https://youtu.be/_nf56aMPdZE?si=Z6UR5qKPOvKb4y4d) + +- 20-30 second + +[YouTube | MS Cop featurs](https://youtu.be/AhywEEHg6Es?si=PDLQGwIJgaBxivbz) + +![Write speech](../img/screenshot-write-speech-from-doc.jpeg){.preview-image} + +[YouTube | Microsoft Loop is cool](https://youtu.be/oYijejDXLZQ?si=J-08luV6mNJPEgIU) + +- loop is cool. Makes collaborating across applications on a single thing easy. + +[YouTube | Global Enterprise](https://youtu.be/Xv_TmtRLHJY?si=H1N_UkWSli2xLJsG) + +- you can have it "sound like me" in outlook +- 41:35 copilot studio +- Can use open AIs GPTs + + +Things to explore: + +- TeamsKit +- Azure OpenAI \ No newline at end of file diff --git a/tech/timegpt-and-lag-llama-two-foundation-models-for-time-series-forecasting.md b/tech/timegpt-and-lag-llama-two-foundation-models-for-time-series-forecasting.md index c1edd31b9..e6abd4bb7 100644 --- a/tech/timegpt-and-lag-llama-two-foundation-models-for-time-series-forecasting.md +++ b/tech/timegpt-and-lag-llama-two-foundation-models-for-time-series-forecasting.md @@ -16,8 +16,14 @@ Time series analysis is now open sourced by discerning patterns in billions of u Time GPT: - [TimeGPT - Nixtla](https://nixtlaverse.nixtla.io/nixtla/index.html) +- [Revolutionizing Time Series Forecasting: Interview with TimeGPT's creators](https://www.turingpost.com/p/timegpt) Lag Llama resources: - [Lag-Llama: Open-Source Foundation Model for Time Series Forecasting | by Marco Peixeiro | Feb, 2024 | Towards Data Science](https://towardsdatascience.com/lag-llama-open-source-foundation-model-for-time-series-forecasting-9afdfaf2bd7c) -- [GitHub - time-series-foundation-models/lag-llama](https://github.com/time-series-foundation-models/lag-llama) \ No newline at end of file +- [GitHub - time-series-foundation-models/lag-llama](https://github.com/time-series-foundation-models/lag-llama) + + +Notes: + +- The main breakthrough of TimeGPT is that it showed for the first time in the history of the field that the idea of a general pre-trained model was possible. In other words, TimeGPT is the first large-scale example of the transferability of time series models ready for production. We believe this marks a new chapter in the time series field, and we are extremely happy to see entities like Google (TimesFM), ServiceNow (LagLlama), Amazon (Chronos), Salesforce (Moirai), and CMU (Moment) following in our footsteps and contributing to this idea of pre-trained models for time series. \ No newline at end of file