"description":"In AI engineering, \"agents\" refer to autonomous systems or components that can perceive their environment, make decisions, and take actions to achieve specific goals. Agents often interact with external systems, users, or other agents to carry out complex tasks. They can vary in complexity, from simple rule-based bots to sophisticated AI-powered agents that leverage machine learning models, natural language processing, and reinforcement learning.\n\nVisit the following resources to learn more:\n\n\\-[@article@Building an AI Agent Tutorial - LangChain](https://python.langchain.com/docs/tutorials/agents/) -[@article@Ai agents and their types](https://play.ht/blog/ai-agents-use-cases/) -[@video@The Complete Guide to Building AI Agents for Beginners](https://youtu.be/MOyl58VF2ak?si=-QjRD_5y3iViprJX)",
"description":"In AI engineering, \"agents\" refer to autonomous systems or components that can perceive their environment, make decisions, and take actions to achieve specific goals. Agents often interact with external systems, users, or other agents to carry out complex tasks. They can vary in complexity, from simple rule-based bots to sophisticated AI-powered agents that leverage machine learning models, natural language processing, and reinforcement learning.\n\nVisit the following resources to learn more:",
"links":[]
"links":[
{
"title":"Building an AI Agent Tutorial - LangChain",
"description":"Sending end-user IDs in your requests can be a useful tool to help OpenAI monitor and detect abuse. This allows OpenAI to provide your team with more actionable feedback in the event that we detect any policy violations in your application.\n\nVisit the following resources to learn more:\n\n\\-[@official@Sending end-user IDs - OpenAi](https://platform.openai.com/docs/guides/safety-best-practices/end-user-ids)",
"description":"Sending end-user IDs in your requests can be a useful tool to help OpenAI monitor and detect abuse. This allows OpenAI to provide your team with more actionable feedback in the event that we detect any policy violations in your application.\n\nVisit the following resources to learn more:",
"description":"Chroma is an open-source vector database and AI-native embedding database designed to handle and store large-scale embeddings and semantic vectors. It is used in applications that require fast, efficient similarity searches, such as natural language processing (NLP), machine learning (ML), and AI systems dealing with text, images, and other high-dimensional data.\n\nVisit the following resources to learn more:\n\n\\-[@official@Chroma](https://www.trychroma.com/) -[@article@Chroma Tutorials](https://lablab.ai/tech/chroma) -[@video@Chroma - Chroma - Vector Database for LLM Applications](https://youtu.be/Qs_y0lTJAp0?si=Z2-eSmhf6PKrEKCW)",
"description":"Chroma is an open-source vector database and AI-native embedding database designed to handle and store large-scale embeddings and semantic vectors. It is used in applications that require fast, efficient similarity searches, such as natural language processing (NLP), machine learning (ML), and AI systems dealing with text, images, and other high-dimensional data.\n\nVisit the following resources to learn more:",
"links":[]
"links":[
{
"title":"Chroma",
"url":"https://www.trychroma.com/",
"type":"article"
},
{
"title":"Chroma Tutorials",
"url":"https://lablab.ai/tech/chroma",
"type":"article"
},
{
"title":"Chroma - Chroma - Vector Database for LLM Applications",
"description":"In AI engineering, \"agents\" refer to autonomous systems or components that can perceive their environment, make decisions, and take actions to achieve specific goals. Agents often interact with external systems, users, or other agents to carry out complex tasks. They can vary in complexity, from simple rule-based bots to sophisticated AI-powered agents that leverage machine learning models, natural language processing, and reinforcement learning.\n\nVisit the following resources to learn more:\n\n\\-[@article@Building an AI Agent Tutorial - LangChain](https://python.langchain.com/docs/tutorials/agents/) -[@article@Ai agents and their types](https://play.ht/blog/ai-agents-use-cases/) -[@video@The Complete Guide to Building AI Agents for Beginners](https://youtu.be/MOyl58VF2ak?si=-QjRD_5y3iViprJX)",
"description":"In AI engineering, \"agents\" refer to autonomous systems or components that can perceive their environment, make decisions, and take actions to achieve specific goals. Agents often interact with external systems, users, or other agents to carry out complex tasks. They can vary in complexity, from simple rule-based bots to sophisticated AI-powered agents that leverage machine learning models, natural language processing, and reinforcement learning.\n\nVisit the following resources to learn more:",
"links":[]
"links":[
{
"title":"Building an AI Agent Tutorial - LangChain",
"description":"AI Agents have a variety of usecases ranging from customer support, workflow automation, cybersecurity, finance, marketing and sales, and more.\n\nVisit the following resources to learn more:\n\n\\-[@article@Top 15 Use Cases Of AI Agents In Business](https://www.ampcome.com/post/15-use-cases-of-ai-agents-in-business) -[@article@A Brief Guide on AI Agents: Benefits and Use Cases](https://www.codica.com/blog/brief-guide-on-ai-agents/) -[@video@The Complete Guide to Building AI Agents for Beginners](https://youtu.be/MOyl58VF2ak?si=-QjRD_5y3iViprJX)",
"description":"AI Agents have a variety of usecases ranging from customer support, workflow automation, cybersecurity, finance, marketing and sales, and more.\n\nVisit the following resources to learn more:",
"links":[]
"links":[
{
"title":"Top 15 Use Cases Of AI Agents In Business",
"description":"Each Terraform resource is subject to the lifecycle: Create, Update or Recreate, Destroy. When executing `terraform apply`, each resource:\n\n* which exists in configuration but not in state is created\n* which exists in configuration and state and has changed is updated\n* which exists in configuration and state and has changed, but cannot updated due to API limitation is destroyed and recreated\n* which exists in state, but not (anymore) in configuration is destroyed\n\nThe lifecycle behaviour can be modified to some extend using the `lifecycle` meta argument.\n\nLearn more from the following resources:",
"description":"Terraform sensitive outputs are a feature used to protect sensitive information in Terraform configurations. When an output is marked as sensitive, Terraform obscures its value in the console output and state files, displaying it as \"\" instead of the actual value. This is crucial for protecting sensitive data like passwords or API keys.\n\nTo mark an output as sensitive, use the sensitive argument in the output block:\n\n output \"database_password\" {\n value = aws_db_instance.example.password\n sensitive = true\n }\n \n\nSensitive outputs are still accessible programmatically, but their values are hidden in logs and the console to prevent accidental exposure. This feature helps maintain security when sharing Terraform configurations or outputs with team members or in CI/CD pipelines.\n\nLearn more from the following resources:",
"description":"Terraform sensitive outputs are a feature used to protect sensitive information in Terraform configurations. When an output is marked as sensitive, Terraform obscures its value in the console output, displaying it as `<sensitive>` instead of the actual value. This is crucial for protecting sensitive data like passwords or API keys.\n\nTo mark an output as sensitive, use the sensitive argument in the output block:\n\n output \"database_password\" {\n value = aws_db_instance.example.password\n sensitive = true\n }\n \n\nSensitive outputs are still accessible programmatically and are written to the state in clear text, but their values are hidden in logs and the console to prevent accidental exposure. This feature helps maintain security when sharing Terraform configurations or outputs with team members or in CI/CD pipelines.\n\nLearn more from the following resources:",
"links":[
"links":[
{
{
"title":"How to output sensitive data in Terraform",
"title":"How to output sensitive data in Terraform",
"description":"Creating local modules in Terraform involves organizing a set of related resources into a reusable package within your project. To create a local module, you typically create a new directory within your project structure and place Terraform configuration files (`.tf`) inside it. These files define the resources, variables, and outputs for the module. The module can then be called from your root configuration using a module block, specifying the local path to the module directory. Local modules are useful for encapsulating and reusing common infrastructure patterns within a project, improving code organization and maintainability. They can accept input variables for customization and provide outputs for use in the calling configuration. Local modules are particularly beneficial for breaking down complex infrastructures into manageable, logical components and for standardizing resource configurations across a project.\n\nLearn more from the following resources:",
"description":"Creating local modules in Terraform involves organizing a set of related resources into a reusable package within your project. To create a local module, you typically create a new directory within your project structure and place Terraform configuration files (`.tf`) inside it. These files define the resources, variables, and outputs for the module. The module can then be called from your root configuration using a module block, specifying the local path to the module directory. Local modules are useful for encapsulating and reusing common infrastructure patterns within a project, improving code organization and maintainability. They can accept input variables for customization and provide outputs for use in the calling configuration. Local modules are particularly beneficial for breaking down complex infrastructures into manageable, logical components and for standardizing resource configurations across a project.\n\nLearn more from the following resources:",
"description":"Prettier is an opinionated code formatter with support for JavaScript, HTML, CSS, YAML, Markdown, GraphQL Schemas. By far the biggest reason for adopting Prettier is to stop all the on-going debates over styles.\n\nVisit the following resources to learn more:",
"description":"Prettier is an opinionated code formatter with support for JavaScript, HTML, CSS, YAML, Markdown, GraphQL Schemas. By far the biggest reason for adopting Prettier is to stop all the on-going debates over styles. Biome is a faster alternative to Prettier! (It also does linting!)\n\nVisit the following resources to learn more:",
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