1/8 Explore typed-prompt, a collection of modular TypeScript packages for building composable, strongly-typed prompt engineering solutions in AI applications. Find it here: via @xcomposer_co ⬇️.

6/6 While the system is functional, some issues still need to be resolved. One notable quirk is its interaction with the car's immobilizer. The Pi must boot up before the car starts, otherwise, the engine shuts off and requires a key cycle to restart. This effectively creates a.

5/6 The Pi's 3.5mm jack is connected to the stereo's CVBS input using a 3.5 to RCA cable. He humorously notes that the red or white cable is used because his jack is faulty and only works when partially inserted. This unconventional setup ensures his crucial diagnostics remain.

4/6 His current setup involves a Raspberry Pi taped to the ECU, connected to the OBD port via a VAG KKL cable. Power is supplied from the stereo's USB port, though this often triggers undervoltage warnings. An Ethernet connection allows him to SSH into the Pi for tinkering and.

3/6 A breakthrough came when he discovered his car stereo had a 'CVBS IN' input for RCA composite video. This sparked the idea of connecting a display to it. To avoid embedded coding, he opted for a Raspberry Pi to run the Rust code, leveraging its comfortable Linux environment.

2/6 The motivation behind `suzui-rs` stemmed from a desire to view engine parameters in his car without needing a laptop. Initially, he considered embedded development with ESP32, but found it too complex for his needs. He then pondered if he could build it in Rust, realizing a.

1/6 Shehriyar Qureshi, known as @thatdevsherry, has developed an innovative Suzuki Serial Data Line (SDL) viewer using Rust. This project, named `suzui-rs`, offers an oxidized version of his original prototype. You can explore the project further at via.

17/17 Unlike many other frameworks that define LLM tools and interoperable tools separately, Spring AI natively bridges them. This eliminates duplication and extra wiring, streamlining development. Muthukumaran Navaneethakrishnan's blog post is based on Chapter 5 of his book,.

16/17 A bonus feature of Spring AI is its ability to make tools work beyond chat, such as within other agents or frontend clients, with no extra code. By adding the MCP server starter, every `@Tool` method becomes an MCP-compliant endpoint. This provides instant interoperability

15/17 Spring AI also offers compatibility with various LLM providers, including OpenAI, Mistral, and Gemini. It integrates well with Spring Boot features like dependency injection, validation, and observability. This comprehensive support makes it a robust choice for tool calling.

14/17 With Spring AI, developers can define tools using simple annotations, such as `@Tool`. This approach automatically generates tool schemas and handles argument binding. The framework manages message state and orchestrates parallel and sequential tool calls seamlessly. ⬇️

13/17 Spring AI simplifies the tool calling process by handling much of the underlying complexity. It speaks the same REST protocol but abstracts away the manual work, allowing developers to focus on business logic. This significantly reduces the boilerplate code required. ⬇️.

12/17 Manually implementing tool calling can be challenging due to the need to write JSON schemas, track tool call IDs, parse arguments, and handle multi-tool orchestration. It also involves maintaining conversation history and injecting system prompts. This complexity highlights.

11/17 LLMs can also engage in sequential reasoning, performing actions step-by-step to fulfill a request. An example is dynamic SQL generation, where the model might first list tables, then get the schema, and finally run the SQL query. This demonstrates a more intricate

10/17 When a user's query requires multiple pieces of information, the model can return multiple tool calls in parallel. This multi-tool capability allows for more complex queries, such as comparing different products. The diagram visually represents this parallel execution. ⬇️.

9/17 A single tool call involves the LLM selecting one tool and making a function call with specific parameters. This direct interaction allows the LLM to retrieve precise information needed to address a user's request. The diagram illustrates this straightforward flow. ⬇️

8/17 Finally, the LLM processes the tool result and generates a final answer for the user. This completes the tool calling loop, demonstrating how LLMs can leverage external functions to provide more informed and actionable responses. This seamless integration enhances the LLM's

7/17 The serialized tool result is then sent back to the LLM. This provides the model with the necessary information to formulate a complete and accurate response to the user's initial query. The conversation history, including the tool call and its result, is maintained for

6/17 Upon receiving the tool call, you execute the specified function within your system. The result of this execution, such as product details and stock levels, is then serialized into a JSON format. This structured output is crucial for the next step in the tool calling

5/17 Next, the model responds with a tool call, indicating the specific function it wishes to execute and the arguments required. For instance, if a user asks about product stock, the model might request a 'findProductByName' function with 'AirPods Pro' as the argument. This

4/17 The process begins by sending a user prompt along with tool definitions to the LLM provider's chat completions endpoint. This initial request informs the LLM about the available functions it can utilize. The model then analyzes the user's query and the provided tools to