chore: sync content to repo (#10458)

Co-authored-by: nilbuild <4921183+nilbuild@users.noreply.github.com>
2026-10-24 01:08:07 +08:00 · 2026-10-16 08:09:55 +02:00
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+# Agent Architectures
+ 
+Agent architectures define how an AI agent is structured: how it reasons, selects tools, manages memory, and decides when to stop. Common patterns include ReAct (reason + act), plan-and-execute, and tree-of-thought. Choosing the right architecture depends on how much control the application needs over model behavior and how tolerant the customer is of unpredictable agent outputs.
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+# AI Agents
+ 
+AI agents are systems where a language model is given tools and a goal, and takes a sequence of actions to complete a task rather than responding to a single prompt. Agents can call APIs, search the web, write and run code, and read files. In practice, most well-designed agents use one LLM call as an orchestrating layer with a series of tool calls around it. The guiding principle is to start with the smallest unit of autonomy, prove it works, and only then expand what the agent is allowed to do.
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+# AI Governance
+ 
+AI governance refers to the policies, processes, and controls that ensure AI systems behave safely, fairly, and in compliance with regulations and organizational standards. This includes defining acceptable use, managing model risk, ensuring transparency in AI decision-making, and monitoring for bias or harmful outputs.
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+# Airflow
+ 
+Apache Airflow is an open-source workflow orchestration platform for scheduling and monitoring data pipelines. Pipelines are defined as Directed Acyclic Graphs (DAGs) in Python, where each node represents a task. Airflow provides a web interface for monitoring runs, managing dependencies, and handling retries and alerts.
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+# API Security
+ 
+API security covers the practices and controls needed to protect APIs from unauthorized access and abuse. This includes authentication, authorization, rate limiting, input validation, and protection against common attacks like injection and replay attacks. Poorly secured APIs are a common attack vector in production systems.
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+# APIs Design
+ 
+API design is the process of defining how services communicate with each other through well-structured interfaces. Good API design involves choosing the right protocol (REST, GraphQL, gRPC), defining clear resource models, handling errors consistently, and versioning carefully. A well-designed API is predictable, easy to use, and stable over time.
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+# Authentication
+ 
+Authentication is the process of verifying the identity of a user or system. Common mechanisms include username/password, API keys, OAuth 2.0 tokens, and JWTs. Authentication is distinct from authorization, which governs what an authenticated user is allowed to do. Getting authentication right is foundational to securing any application that handles user data or sensitive operations.
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+# AWS
+ 
+Amazon Web Services (AWS) is the largest cloud platform by market share, offering hundreds of services including compute (EC2, Lambda), storage (S3), databases (RDS, DynamoDB), networking, and AI/ML tooling. It has a deep ecosystem of third-party integrations and is the most widely used cloud platform across startups and enterprises.
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+# Azure
+ 
+Microsoft Azure is Microsoft's cloud platform with particularly strong adoption in enterprise environments, especially those using Active Directory, Office 365, and .NET. It offers compute, storage, networking, databases, DevOps tooling, and AI services including Azure OpenAI Service.
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+# Building Eval Pipelines
+ 
+Evaluation pipelines are automated systems for measuring the quality of an AI model or application over a set of test cases. A good eval does not just check whether the final answer is correct. It traces the steps a skilled human would take to solve the same problem and grades the agent on each one. Building this starts with a golden dataset: a set of real queries with the ideal output labeled by hand. That becomes the standard everything else is measured against.
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+# C++
+ 
+C++ is a high-performance, statically typed language that gives developers fine-grained control over memory and system resources. It is used in systems programming, game engines, embedded software, and performance-critical applications. It supports both procedural and object-oriented programming styles.
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+# Choosing your Model Provider
+ 
+Different LLM providers offer models with different tradeoffs in capability, cost, latency, context length, and licensing terms. Common providers include OpenAI, Anthropic, Google, Meta (open weights), and Mistral. Choosing the right model for a use case involves evaluating benchmarks, pricing, API reliability, data privacy terms, and whether the model's strengths match the application's requirements.
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+# Claude Code
+ 
+Claude Code is an AI coding assistant built on Anthropic's Claude model. It can write, edit, explain, and debug code across a wide range of languages and frameworks, and is designed to handle complex multi-file tasks and refactoring alongside code generation.
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+# Cloud Platforms
+ 
+Cloud platforms provide on-demand access to computing resources including servers, storage, networking, databases, and managed services. Instead of running physical hardware, teams deploy and scale their applications on cloud infrastructure. The major providers each offer a broad set of services that cover most infrastructure needs.
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+# Codex
+ 
+Codex is OpenAI's code-focused model, trained on large amounts of public code. It can generate code from natural language descriptions, complete functions, and help with documentation. It is the model that powers GitHub Copilot and one of the foundational tools that popularized AI-assisted coding as a mainstream workflow.
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+# Containers
+ 
+Containers are a lightweight way to package an application along with its dependencies and configuration so it runs consistently across different environments. Unlike virtual machines, containers share the host operating system kernel, making them fast to start and efficient to run. Docker is the most common tool for building and running containers.
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+# Context Engineering
+
+Context engineering is the practice of strategically curating, structuring, and refining the information provided to a Large Language Model to ensure accurate and relevant outputs. It involves techniques like prompt crafting, Retrieval-Augmented Generation (RAG), and managing conversation memory to provide the model with the necessary background knowledge for specific tasks.
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+# CSS
+ 
+CSS, or Cascading Style Sheets, controls the visual appearance of HTML elements on a web page. It handles layout, colors, typography, spacing, animations, and responsive design. Modern CSS includes Flexbox, Grid, and custom properties that make it possible to build complex, adaptive layouts.
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+# Cursor
+ 
+Cursor is an AI-powered code editor built on top of VS Code that integrates LLMs directly into the coding experience. It supports chat-based code editing, multi-file edits, codebase-aware completions, and agent-style workflows where the model can make changes across an entire project.
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+# Data Engineering
+ 
+Data engineering involves building and maintaining the systems that collect, store, process, and move data at scale. This includes designing pipelines, working with data warehouses, and ensuring data quality for downstream consumers like analysts and ML systems.
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+# Data Modeling
+ 
+Data modeling is the process of deciding how data should be structured and stored so it can be queried efficiently and understood by the people who use it. Common patterns include the star schema, where a central fact table is surrounded by dimension tables; the one big table (OBT) approach favored in some analytics workloads; and the medallion architecture used in data lakehouses, where data flows through bronze (raw), silver (cleaned), and gold (aggregated) layers.
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+# Data Pipelines
+ 
+A data pipeline is a series of steps that move and transform data from one place to another, usually from source systems into a warehouse or processing layer. Pipelines can be batch-based (running on a schedule) or streaming (processing data as it arrives). Building reliable pipelines involves handling failures, schema changes, and data validation.
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+# Data Privacy & Compliance
+ 
+Data privacy and compliance involves handling personal or sensitive data according to applicable laws and organizational policies. Relevant regulations include GDPR, CCPA, HIPAA, and others depending on the industry and geography. For AI systems, this includes what data is sent to third-party model APIs, what is stored, and how long it is retained.
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+# Data Warehouses
+ 
+A data warehouse is a system optimized for analytical queries across large volumes of structured data. The major platforms in enterprise environments are Snowflake, Google BigQuery, and Amazon Redshift. Unlike operational databases, warehouses are designed for reading and aggregating data rather than frequent writes, and they typically sit at the center of a company's analytics and BI infrastructure.
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+# Day 2 Operations
+ 
+Day 2 Operations refers to everything that happens after a system goes live. Day 1 is the launch. Day 2 is the ongoing work of keeping the system running, monitoring for failures, handling edge cases that were not anticipated during development, and responding to feedback from real users.
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+# Debugging Agent Logic
+ 
+Debugging AI agent logic involves tracing through the sequence of reasoning steps, tool calls, and decisions an agent made to find where it went wrong. This is harder than debugging traditional code because agent behavior is non-deterministic and depends on model outputs. Structured logging, step-by-step traces, and replay capabilities are the main tools for making agent failures visible and reproducible.
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+# The Delta
+ 
+The Delta is the original term Palantir used for what is now called an FDE. It refers to the gap between what a software product does out of the box and what a specific customer actually needs. A Delta is both a role (the engineer who closes that gap) and a concept (the custom work required to make a product work in a real environment). In FDE interviews, you may be asked to identify the Delta in a given customer scenario, meaning the specific technical or workflow gap that needs to be bridged.
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 # DevOps Skills
- 
+
 DevOps is the practice of automating and streamlining how software is built, tested, and deployed. A forward-deployed engineer who knows DevOps can own the full deployment lifecycle, from spinning up infrastructure to shipping a containerized agent into a client's cloud environment, without depending on another team to get there.

 Visit the following resources to learn more:
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+# DevSecOps
+ 
+DevSecOps integrates security practices directly into the DevOps pipeline rather than treating security as a final gate before release. This includes automated security scanning in CI/CD, dependency vulnerability checks, secrets management, and security review as part of the development process. The goal is to catch and fix security issues early and continuously rather than at the end.
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+# Docker
+ 
+Docker is the leading platform for building, shipping, and running containers. A Dockerfile defines the steps to build a container image, and Docker provides tools to run those images locally or push them to a registry for deployment. It is the standard way to package applications for consistent behavior across development, staging, and production environments.
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+Embedded Engineering
+ 
+Embedded engineering describes the physical and organizational model of an FDE engagement: the engineer is embedded inside the customer's team, not working remotely or at arm's length. This means joining the customer's Slack, attending their standups, working in their codebase, and being present in the environment where the problems actually live.
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+# Executive Reporting
+ 
+Executive reporting is the practice of communicating the status of a technical engagement to non-technical decision makers in a format they can act on. A good executive update leads with value delivered, not tasks completed. It flags risks clearly and pairs each one with a required action or decision. It connects technical milestones to business outcomes so the reader understands why the work matters, not just that it is happening.
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+# FDE Tiers
+ 
+Not all FDE roles are the same. The job market has split into three distinct tiers with different skill requirements. AI lab FDEs need deep ML fluency, strong agent architecture skills, and the ability to help customers adopt models in production. Enterprise software FDEs need data platform depth, integration experience, and the ability to connect a vendor's product to a customer's existing data infrastructure. Consulting and staffing FDEs need broad solutions architecture skills, account management instincts, and the ability to run an end-to-end engagement independently. Knowing which tier you are targeting shapes which parts of this roadmap to prioritize.
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+# GCP
+ 
+Google Cloud Platform (GCP) is Google's cloud offering, known for strong data and analytics services like BigQuery and AI/ML infrastructure like Vertex AI. It is often the preferred choice for data-intensive workloads and organizations already using Google Workspace.
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+# Gemini
+ 
+Gemini is Google DeepMind's family of multimodal AI models, capable of processing and generating text, code, images, and more. It is available via Google AI Studio and Vertex AI in different sizes with varying tradeoffs in speed and capability.
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+# Git & GitHub
+ 
+Git is a distributed version control system that tracks changes to code over time and allows multiple people to collaborate on the same codebase without overwriting each other's work. GitHub builds on top of Git with features like pull requests, code review, issue tracking, and CI/CD integrations. Both are standard tools in nearly every software development workflow today.
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+# Go
+ 
+Go is a statically typed, compiled language developed by Google, designed for simplicity and performance. It is commonly used for building backend services, command-line tools, and cloud infrastructure components. Its concurrency model, based on goroutines and channels, makes it well suited for network-heavy and distributed workloads.
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+# GraphQL
+ 
+GraphQL is a query language and runtime for APIs that lets clients request exactly the fields they need, rather than consuming fixed endpoints with predefined shapes. This reduces over-fetching, makes APIs more flexible, and is especially useful in frontend-heavy applications with complex or variable data requirements.
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+# HTML
+ 
+HTML, or HyperText Markup Language, is the standard language for structuring content on the web. It defines the elements of a page such as headings, paragraphs, links, forms, and images. It is the foundation that CSS and JavaScript build on top of.
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+# Inference Optimization
+ 
+Inference optimization refers to techniques for making model predictions faster and cheaper without significantly reducing quality. Common approaches include quantization (reducing numerical precision), distillation (training a smaller model to mimic a larger one), caching repeated requests, and batching. These optimizations are especially relevant for LLMs, where inference costs can be substantial at scale.
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+# Integration Engineering
+ 
+Integration engineering is the practice of connecting systems that were not designed to work together. This usually means building APIs over existing data layers, bridging incompatible authentication systems, chaining tools across services with different data formats, and wrapping third-party products in interfaces a new system can use reliably. Most of the technical difficulty in real-world AI deployments lives here, not in the model itself.
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+# Java / Scala
+ 
+Java is a statically typed, object-oriented language that runs on the Java Virtual Machine (JVM) and is widely used in enterprise systems, backend services, and Android development. Scala is also a JVM language that blends object-oriented and functional programming styles, commonly used in data processing with Apache Spark.
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+# JavaScript / TypeScript
+ 
+JavaScript is the programming language of the web, running natively in browsers and on servers via Node.js. TypeScript is a superset of JavaScript that adds static type annotations, catching errors at compile time and improving code maintainability at scale. Together they are the dominant choice for full-stack web development.
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+# JavaScript
+ 
+JavaScript is the scripting language that runs in web browsers, giving web pages dynamic behavior. It handles user interactions, DOM manipulation, and API requests. On the server side, it runs via Node.js, making it possible to use a single language across the full web stack.
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+# Kubernetes
+ 
+Kubernetes is an open-source container orchestration system that automates the deployment, scaling, and management of containerized applications. It handles scheduling containers across a cluster, managing service discovery, load balancing, rolling updates, and self-healing by restarting failed containers. It is the standard platform for running containers in production at scale.
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+# Last-Mile Integration
+ 
+Last-mile integration refers to the final set of connections between a product and the specific systems, data sources, and workflows of a customer environment. It is called last-mile because it is often the shortest but hardest part of a deployment: the product works in general, but making it work with this particular customer's setup requires custom engineering.
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+# Learn a Programming Language
+
+Learning a programming language involves mastering the syntax, semantics, and standard libraries of a specific tool used to instruct computers. This process begins by understanding fundamental programming constructs like variables, data types, control flow, and functions, and progresses to utilizing idiomatic patterns to write efficient and maintainable code.
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+# LLM Fundamentals
+ 
+Large language models (LLMs) are machine learning models trained on large amounts of text data that can generate, summarize, classify, and reason about language. They work by predicting the most likely next tokens given a context window of input. Understanding how LLMs work, including their limitations around hallucination, context length, and latency, is the foundation for building reliable systems around them.
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+# Managed ML Services
+ 
+Managed ML services are cloud provider offerings that abstract away the infrastructure for training, deploying, and serving machine learning models. Examples include AWS SageMaker, Google Vertex AI, and Azure Machine Learning. They handle provisioning, scaling, and monitoring so teams can focus on the model and data rather than the underlying infrastructure.
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+# MCP
+ 
+The Model Context Protocol (MCP) is an open standard for connecting AI models to external tools and data sources through a unified interface. Instead of building custom integrations for every tool, developers implement MCP servers that expose tools and resources in a standardized way. This makes it easier to compose agents that work with many different services.
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+# Memory & State Management
+ 
+Memory in AI agents refers to how information is stored and retrieved across steps in a conversation or task. Short-term memory lives in the context window, while long-term memory requires external storage like a database or vector store. Managing state well is important for agents that need to track progress, avoid repetition, and stay consistent over longer interactions.
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+# MLOps
+ 
+MLOps applies DevOps principles to machine learning systems, covering the processes and tooling needed to reliably build, deploy, monitor, and update ML models in production. This includes versioning data and models, automating training pipelines, serving inference at scale, and detecting model degradation over time.
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+# Multi-Agents
+ 
+Multi-agent systems involve multiple AI agents working together, where each agent has a specific role or capability and they coordinate to complete a larger task. One agent might plan the work while others execute specific subtasks, and a final agent synthesizes the results. This pattern improves reliability and modularity for complex workflows that would overwhelm a single agent.
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+# Node.js
+ 
+Node.js is a JavaScript runtime built on Chrome's V8 engine that lets developers run JavaScript on the server side. It uses a non-blocking, event-driven architecture that makes it efficient for I/O-heavy workloads like APIs, real-time applications, and data streaming.
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+# NoSQL Databases
+ 
+NoSQL databases store data in formats other than traditional relational tables, such as documents, key-value pairs, graphs, or wide columns. Examples include MongoDB, Redis, Cassandra, and DynamoDB. They are often chosen for use cases that require high write throughput, flexible schemas, or horizontal scalability across distributed systems.
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+# Observability
+ 
+Observability in software systems refers to the ability to understand the internal state of a system based on its external outputs, through logging, metrics, and distributed tracing. For AI-powered systems, observability also involves monitoring model outputs, tracking prompt and response quality, and detecting unexpected behavior in production.
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+# PostgreSQL
+ 
+PostgreSQL is a powerful, open-source relational database system that supports advanced features like JSONB storage, full-text search, window functions, and custom data types. It goes well beyond what basic SQL databases offer and is a widely used default in both startups and large organizations.
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+# Presenting Options
+ 
+When a major technical decision needs stakeholder buy-in, the goal is to lay out the options, explain the tradeoffs of each, and make a clear recommendation. The presentation should be structured around what matters most to the audience, not around technical completeness.
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+# Productized Consulting
+ 
+Productized consulting refers to a model where a consulting engagement delivers not just advice or a custom solution but a repeatable, packaged system that can be redeployed with other customers. It means building in a way that abstracts customer-specific details so the core architecture can be reused across similar engagements.
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+# Prompt Engineering
+ 
+Prompt engineering is the practice of crafting inputs to language models in a way that produces more accurate, useful, or reliable outputs. Techniques include zero-shot and few-shot prompting, chain-of-thought reasoning, role assignment, and structured output formatting. Good prompt engineering can significantly improve model behavior without any model fine-tuning.
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+# Prompt Management
+
+Prompt management is the systematic process of creating, versioning, testing, and refining the text inputs used to guide large language models toward desired outputs. It involves maintaining a structured library of effective prompt templates, tracking how specific changes in phrasing impact model performance, and integrating these prompts into the application development lifecycle. By treating prompts as code, engineers can ensure consistency across different model versions, optimize for latency and cost, and provide a reliable interface for interacting with generative AI systems.
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+# Python
+
+Python is a general-purpose programming language known for its readable syntax and broad ecosystem. It is widely used in web development, data engineering, scripting, automation, and AI/ML workloads. Its large standard library and active package ecosystem make it practical for everything from quick scripts to large production systems.
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+# RAGs
+ 
+Retrieval-Augmented Generation (RAG) is a pattern where relevant documents or data are fetched from a knowledge source and injected into a model's context before generating a response. This gives the model access to specific or up-to-date information without requiring fine-tuning. RAG systems typically combine a retrieval step (using embeddings or search) with a generation step (using an LLM).
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+# React
+ 
+React is a JavaScript library for building user interfaces using a component-based model. Each component manages its own state and rendering logic, and components are composed to build complete interfaces. It is one of the most widely used frontend frameworks in production web applications.
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+# Rust
+
+Rust is a systems programming language that focuses on safety, speed, and concurrency. It manages memory through a unique ownership model with strict compiler checks, which allows developers to prevent common bugs like null pointer dereferences and buffer overflows without needing a garbage collector. It offers high-level abstractions with the performance of low-level languages like C++, enabling the creation of efficient and reliable software for performance-critical applications.
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+# Security
+ 
+Security in software systems means protecting applications, data, and infrastructure from unauthorized access, misuse, or attack. For AI systems, there are additional concerns like prompt injection, model abuse, and unintended data leakage through model outputs.
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+# Spark
+ 
+Apache Spark is a distributed computing framework for processing large datasets in parallel across a cluster. It supports batch processing, streaming, SQL queries, and machine learning through a unified API. It is widely used in data engineering workflows where data volumes exceed what can be processed on a single machine.
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+# SQL
+ 
+SQL, or Structured Query Language, is the standard language for interacting with relational databases. It is used to create, read, update, and delete records, and to define the structure of tables and their relationships. Knowing SQL well is foundational for any work involving relational data storage.
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+# System of Record
+ 
+A system of record is the authoritative source of truth for a particular type of data within an organization. For customer data it might be Salesforce. For financial data it might be SAP. For HR data it might be Workday. Identifying the system of record early in an engagement matters because any system that touches that data needs to integrate with it correctly, not with a copy or a cache that may be stale.
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+# Terraform
+ 
+Terraform is an open-source infrastructure-as-code tool by HashiCorp that lets engineers define cloud resources in configuration files and provision or update them automatically. It works across multiple cloud providers and is the standard tool for managing infrastructure in a repeatable, version-controlled way.
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+# Time-to-Value
+ 
+Time-to-value is the elapsed time between the start of an engagement and the moment the customer has something they can actually use. Compressing it means shipping a working feature early rather than designing the perfect system, prioritizing the work that demonstrates the most value first, and sequencing deliverables so that trust builds incrementally. An engagement that produces something useful in week one and keeps improving is almost always better received than one that produces something polished in week six.
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+# Tools & Functions
+ 
+Tools and functions are the mechanisms through which AI agents interact with the outside world. A tool might query a database, call an external API, run a code interpreter, or search the web. In practice, tools are defined as schemas that the model can invoke, and the application handles executing the actual function and returning the result back to the model.
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+# UAT
+ 
+User Acceptance Testing (UAT) is the process by which a customer's own team validates that a system works correctly before it goes into production. It typically involves preparing test scripts, defining acceptance criteria, and supporting the customer during testing. Passing UAT is usually the formal gate before a system is considered delivered, and it is often where last-minute scope issues surface.
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+# Vector DBs
+ 
+Vector databases store high-dimensional vector embeddings and support efficient similarity search, usually using approximate nearest neighbor algorithms. They are a core component of RAG systems and semantic search applications, where the goal is to find content semantically similar to a query rather than matching keywords.
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+# Vibe Coding
+ 
+Vibe coding refers to using AI-assisted tools to write, edit, and iterate on code through natural language interaction, at a higher level of abstraction than traditional coding. Instead of writing every line manually, the developer describes intent and guides the AI to produce working code, reviewing and refining the output. It is a workflow shift made possible by capable code-generation models.
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+# Working with Legacy Systems
+ 
+Legacy systems are the established, often undocumented infrastructure that most large organizations run their core operations on. They may be on-prem databases with no API, ERP systems that took years and millions of dollars to migrate to, or internal tools written a decade ago that no one fully understands anymore. The approach is to build around them: wrapping them with APIs, querying them safely, and connecting them to modern infrastructure without replacing the parts the business depends on.