Build Your Own AI Personal Assistant in TypeScript

Name: Build Your Own AI Personal Assistant in TypeScript
Price: 9 USD
Availability: InStock

3h 38m 48s

English

Paid

March 20, 2026

Unlock the true potential of large language models (LLMs) by building your own AI personal assistant in TypeScript. While universal chatbots provide some functionality, they often fall short by not utilizing personal data, failing to remember user preferences, and not adapting to specific workflows. This course will guide you in creating an AI system that's fully customized and under your control.

Course Overview

Develop a comprehensive understanding of system architecture in AI, which is more significant than the model itself. You'll learn data retrieval, memory management, context management, scripting agents, validation, and security.

What You Will Learn

During the 5-day intensive course, you'll gain hands-on experience to:

Implement intelligent search on your data using techniques like BM25, semantic search, RRF, and query rewriting.
Develop an advanced memory system that incorporates both semantic and episodic memory.
Enhance response quality and reliability through automatic evaluation mechanisms.
Ensure safe interaction with tools and APIs, including human-in-the-loop confirmations for added security.

Outcomes

By the end of this course, you'll have a fully functional and expandable personal AI assistant that:

Efficiently processes large volumes of data.
Adapts to your individual preferences and workflows.
Performs tasks safely and reliably.
Is model-independent, offering greater flexibility and control.

Skills You Will Gain

Beyond creating a personal assistant, this course will equip you with fundamental skills in modern AI engineering. These skills can be applied to developing custom LLM systems for a variety of applications, including professional work, business optimization, and personal projects.

About the Author: Matt Pocock

Before creating Total TypeScript, I was a member of the XState core team and worked as a developer advocate at Vercel. I've worked with TypeScript as a lead fullstack dev, a library maintainer, and now as a full-time educator. I built Total TypeScript to make the secrets of open-source maintainers and industry experts available to everyone.

Watch Online 113 lessons

0:00

#1: 001 Course Overview

All Course Lessons (113)

#	Lesson Title	Duration
1	001 Course Overview Demo	02:08
2	002 Skill Building Vs. Project	00:47
3	003 How to Do Skill Building Exercises	02:42
4	004 Project Repo Intro	03:56
5	005 How to Do The Project Exercises	04:43
6	006 Getting Model Keys	01:32
7	007 Introduction to Discord	01:47
8	008 What to Do Now	01:10
9	009 Retrieval Introduction	03:01
10	010 Overview of the BM25 Algorithm	03:48
11	011 Retrieval with BM25	01:20
12	012 Retrieval with BM25 solution	01:53
13	013 Replacing BM25 with Embeddings	04:13
14	014 Replacing BM25 with Embeddings solution	01:32
15	015 Rank Fusion	03:06
16	016 Query Rewriting	01:45
17	017 Query Rewriting solution	01:48
18	018 Building Your Own Dataset	01:42
19	019 Intro to Building a Search Algorithm	00:53
20	020 Adding BM25 Search	01:52
21	021 Caching Embeddings	02:23
22	022 Searching with Embeddings	01:08
23	023 Combining Results with Reciprocal Rank Fusion	01:29
24	024 Intro to Adding Search Tool to Agent	00:21
25	025 Search Tool	02:23
26	026 Context Engineering Search Tool	01:14
27	027 Chunking Intro	01:33
28	028 Fixed Size Chunks	02:53
29	029 Structural Chunking	02:06
30	030 Structural Chunking Solution	02:37
31	031 Retrieval with Chunks	01:43
32	032 Reranking	02:14
33	033 Reranking Solution	02:17
34	034 Chunking Emails Playground Intro	00:22
35	035 Abstracted Embeddings Cache	01:30
36	036 Adding the Text Splitter	01:21
37	037 Applying Chunking to Search Algorithms	02:09
38	038 Reranking and Chunking in Search Tool Intro	00:25
39	039 Adding the Reranker	01:44
40	040 Giving the Reranker More Context	02:21
41	041 Viewing Tool Calls in the Frontend	02:22
42	042 Custom Filter Tools Intro	00:45
43	043 Added Filter Emails Tool	03:29
44	044 Displayed Filter Tools in Frontend	00:41
45	045 Metadata First Retrieval Pattern Intro	00:48
46	046 Make Search Only Provide Metadata	04:13
47	047 Including Whole Threads	02:29
48	048 Basic Memory Setup	02:51
49	049 Basic Memory Setup Solution	02:10
50	050 Updating Previous Memories	01:52
51	051 Updating Previous Memories Solution	02:49
52	052 Memory as Tool Call	01:34
53	053 Memory as Tool Call Solution	03:05
54	054 Semantic Recall on Memories	02:26
55	055 Intro to Semantic Recall on Memories	00:42
56	056 Made Embedding Functions Generic	01:48
57	057 Added Memory Search Function	02:50
58	058 Added Memories to the Agent	01:25
59	059 Added Memory Extraction	02:05
60	060 Fixed System Prompt	01:29
61	061 Intro to Working Memory via Message Embeddings	01:01
62	062 Make Chat API Receive One Message	02:03
63	063 Added Search Messages Function	01:01
64	064 Implemented Working Memory	01:54
65	065 Intro to Episodic Memory from Conversations	01:11
66	066 Storing Chat Summaries	01:21
67	067 Added the reflectOnThat Function	01:58
68	068 Searching for Related Chats	00:49
69	069 Working Episodic Memory Setup	02:47
70	070 Evaluating Tool Call Agents	02:33
71	071 Evaluating Tool Call Agents Solution	01:37
72	072 Implementing a Deterministic Tool Call Scorer	01:31
73	073 Implementing a Deterministic Tool Call Scorer Solution	01:05
74	074 Using Evalite Each for A_B Testing	01:07
75	075 Using Evalite Each for A_B Testing Solution	01:52
76	076 Adversarial Inputs	01:09
77	077 Adversarial Inputs Solution	01:48
78	078 Creating a Clarifying Questions Tool	01:36
79	079 Creating a Clarifying Questions Tool Solution	01:45
80	080 Intro to Evaluating Memory Extraction	00:24
81	081 Make extractMemories Testable	01:02
82	082 Added createUIMessageFixture	01:24
83	083 Scaffold Eval	02:13
84	084 Added Dataset	01:59
85	085 Added answerCorrectness Scorer	04:20
86	086 Moved to answerSimilarity	01:56
87	087 Intro to Evaluating End to End Agent	01:04
88	088 Moved Agent Into Its Own File	01:33
89	089 Scaffolding the Eval	02:08
90	090 Customized Evalite's UI	01:13
91	091 Added Answer Correctness Scorer	01:43
92	092 Adding Multi-Hop and Filtering Queries	02:34
93	093 Human in the Loop Intro	01:11
94	094 Initiating Human in the Loop Requests	02:12
95	095 Initiating Human in the Loop Requests Solution	00:51
96	096 Approving Human in the Loop Requests	03:33
97	097 Converting Custom Data Parts For The LLM	01:27
98	098 Converting Custom Data Parts For The LLM Solution	01:01
99	099 Processing Human in the Loop Requests	02:53
100	100 Processing Human in the Loop Requests Solution	01:29
101	101 Executing the Human in the Loop Requests	01:57
102	102 Executing the Human in the Loop Requests Solution	02:28
103	103 Intro to Building the Destructive Tools	01:45
104	104 Hooked Up Zapier MCP	02:21
105	105 Removed Unwanted Tools	02:04
106	106 Added Local Definitions	02:16
107	107 Intro to Build the Human in the Loop Harness	00:15
108	108 HITL on the Backend	04:21
109	109 HITL on the Frontend	02:53
110	110 Intro to Filtering Tool Groups on the Frontend	02:15
111	111 Added Types and Helper Functions	02:19
112	112 Implemented App Tagging on Frontend	01:52
113	113 Implemented App Tagging on Backend	02:00

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