chore: sync content to repo (#10305)

Co-authored-by: nilbuild <4921183+nilbuild@users.noreply.github.com>
2026-09-19 09:32:03 +08:00 · 2026-09-10 13:12:52 +02:00
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 # Atlan
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 Atlan is a data catalog and governance platform that helps organizations discover, document, and manage their data assets across warehouses, pipelines, and BI tools. It provides a searchable inventory of datasets along with lineage, ownership, and quality information, so teams know where data comes from and whether it can be trusted. For AI systems, this kind of catalog can act as a source of vetted context, helping ensure that agents pull from data that is current and properly governed rather than from unreliable or duplicate sources. Atlan is offered as a managed, cloud based product rather than a self hosted tool.

 Visit the following resources to learn more:
@@ -6,5 +6,5 @@ Visit the following resources to learn more:

 - [@roadmap@Visit the Dedicated Claude Code Roadmap](https://roadmap.sh/claude-code)
 - [@course@Claude Code in Action](https://anthropic.skilljar.com/claude-code-in-action)
- [@official@Claude Code Overview](https://code.claude.com/docs/en/overview)
- [@video@Introducing Claude Code](https://www.youtube.com/watch?v=AJpK3YTTKZ4)
+- [@article@Claude Cowork vs. Code vs. Chat: When to use each](https://roadmap.sh/claude-code/vs-cowork-vs-chat)
+- [@article@Vibe coding tutorial: Build your first app with Claude Code](https://roadmap.sh/vibe-coding/tutorial)
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 # Context Evaluation
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 Context evaluation measures whether the information delivered to a model actually helps it perform a task well. This includes checking whether retrieved documents are relevant, whether important details are missing, and whether irrelevant or outdated content is crowding out what matters. Teams use a mix of automated metrics, such as retrieval precision and recall, and manual review of model outputs to catch problems. Without this evaluation, a context pipeline can look complete while still giving the model poor material to work with.

 Visit the following resources to learn more:
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 # Context Failure Modes
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 Context failure modes are the common ways a context pipeline can go wrong and degrade an agent's performance. These include context poisoning, where incorrect information gets included and treated as fact, context distraction, where too much irrelevant content pulls the model's attention away from what matters, and context rot, where accuracy drops as the amount of content grows even within the model's stated limits. Other failures include stale data that no longer reflects reality and conflicting information from different sources that the model cannot reconcile.

 Visit the following resources to learn more:
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 # Context Security
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 Context security covers the risks that come from feeding external or untrusted content into an AI system. A malicious document, email, or web page can contain hidden instructions designed to manipulate the model, a technique known as prompt injection. Poor access controls can also let a model surface data to a user who should not see it, or let sensitive information leak through tool calls and logs. Building context pipelines securely means validating sources, applying permission checks before data reaches the model, and treating retrieved content as data rather than as trusted instructions.

 Visit the following resources to learn more:
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 # Context Sources
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 Context sources are the places an AI system pulls information from to build what a model sees at inference time. Common sources include documents and knowledge bases, databases and data warehouses, code repositories, chat and support tools, and the outputs of other tool calls or agents. Each source has its own update frequency, access rules, and format, so combining them well requires normalizing and filtering before the data reaches the model. Choosing the right sources for a task matters as much as how much data is included.

 Visit the following resources to learn more:
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 # Context vs Prompt Eng.
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 Prompt engineering focuses on how a single instruction is worded to get a better response from a model. Context engineering is broader: it deals with what information reaches the model at all, from which sources, in what order, and in what format, across an entire application or agent workflow. A well written prompt can still fail if the model lacks the right data, tools, or history to act on it. Context engineering treats the prompt as just one part of a larger system that also includes retrieval, memory, and tool outputs.

 Visit the following resources to learn more:
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 # Context Window
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 The context window is the amount of text an LLM can process in a single request, measured in tokens. It includes the system prompt, conversation history, retrieved documents, and the model's own output as it generates a response. Once the total content exceeds this limit, older or lower priority information has to be dropped, summarized, or moved out of the active context. Model providers have expanded context windows significantly over the past few years, but a larger window does not guarantee the model uses all of it well.

 Visit the following resources to learn more:
@@ -6,4 +6,5 @@ Visit the following resources to learn more:

 - [@official@Cursor Docs](https://cursor.com/docs)
 - [@official@Cursor Learn](https://cursor.com/learn)
+- [@article@Claude Code vs Cursor: Which AI Coding Tool To Choose](https://roadmap.sh/claude-code/vs-cursor)
 - [@video@Cursor AI Tutorial for Beginners](https://www.youtube.com/watch?v=3289vhOUdKA)
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 # DataHub
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 DataHub is an open source metadata platform originally built at LinkedIn for cataloging and managing data across an organization. It tracks where data lives, how it flows between systems, who owns it, and how it is used, giving teams a searchable map of their data assets. In the context of AI systems, this kind of metadata helps determine which datasets are trustworthy and relevant enough to feed into a model. Because it is open source, teams can self host and extend it rather than relying on a managed vendor.

 Visit the following resources to learn more:
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 # Long-Context Processing
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 Long-context processing deals with feeding an AI model large amounts of text, such as entire codebases, long documents, or extended conversation histories, in a way that keeps the model accurate. Even when a model's context window is technically large enough, performance can degrade as the amount of content grows, a problem often called context rot. Techniques to manage this include chunking content, retrieving only the most relevant sections, and summarizing older material instead of passing it in full.

 Visit the following resources to learn more:
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 # MCP
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 MCP, or Model Context Protocol, is an open standard that lets AI applications connect to external tools, data sources, and services in a consistent way. Instead of writing a custom integration for every tool a model might need, developers expose that tool through an MCP server, and any MCP compatible client can then call it. This reduces the number of one-off integrations needed as agents connect to more systems. Anthropic introduced MCP, and it has since been adopted by other model providers and tool builders.

 Visit the following resources to learn more:
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 # Memory Systems
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 Memory systems let an AI agent retain information across interactions instead of starting fresh every time. Short-term memory usually covers the current session, while long-term memory stores facts, preferences, or past decisions that persist across sessions. Common implementations combine a vector store for semantic recall with structured storage for facts that need to be retrieved exactly. Designing memory well involves deciding what to keep, how long to keep it, and how to summarize or discard information that is no longer useful.

 Visit the following resources to learn more:
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 # modus
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 modus is a Context Warehouse, an infrastructure layer that sits between a company's internal data stack and its AI agents. Instead of asking teams to hand-model semantics, it mines context from how the organization already uses its data (query logs, dbt models, dashboards, pipelines, code, docs) and keeps that understanding current as the business changes. At query time it composes only the context relevant to the task, so agents work on signal instead of noise. It runs independently of any specific data warehouse, model, or app platform, and serves context to existing agents over MCP. The typical use is internal, giving any team in the company reliable AI answers about its own business data.

 Visit the following resources to learn more:
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 # Multi-agent Context Sharing
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 Multi-agent context sharing covers how separate AI agents working on related tasks exchange the information each one needs. When one agent hands off a task to another, it has to pass along enough history and data for the second agent to continue without repeating work or losing important details. Poorly designed sharing can lead to agents working with outdated or incomplete pictures of the task, while passing too much context can waste tokens and slow the system down. Common approaches include shared memory stores, structured message passing between agents, and a coordinating agent that filters what each sub-agent receives.

 Visit the following resources to learn more:
@@ -4,4 +4,4 @@ The NanoBanana API is a tool designed to facilitate the integration and processi

 Visit the following resources to learn more:

- [@official@NanoBanana API](https://nanobananaapi.ai/)
+- [@official@NanoBanana API](https://nanobananaapi.ai/)
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 # PostHog
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 PostHog is a product analytics platform that also offers what it calls a context warehouse, a combined store of product event data, session replays, and business context from tools like Slack and support tickets. Rather than requiring a separate ETL pipeline to move data between systems, PostHog exposes this combined data directly to AI agents through its MCP server, letting an agent query product behavior and business context using the same interface a person would use.

 Visit the following resources to learn more:
@@ -4,6 +4,6 @@ Security and privacy concerns in AI revolve around the protection of data and th

 Visit the following resources to learn more:

- [@article@Examining Privacy Risks in AI Systems](https://transcend.io/blog/ai-and-privacy)
 - [@course@AI Security Labs: OWASP LLM, Agentic and MCP Top 10](https://ransomleak.com/catalogue/ai-security/)
+- [@article@Examining Privacy Risks in AI Systems](https://transcend.io/blog/ai-and-privacy)
 - [@video@AI Is Dangerous, but Not for the Reasons You Think | Sasha Luccioni | TED](https://www.youtube.com/watch?v=eXdVDhOGqoE)
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 # State & Historical Context
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 State and historical context refers to tracking what has already happened in a conversation or workflow so an agent can act consistently over time. This includes the sequence of user messages, past tool calls and their results, and any decisions the agent has already made. Without this tracking, an agent can repeat steps, contradict earlier answers, or lose track of a multi-step task. Systems typically store this as a structured log or state object that gets updated after each step and referenced before the next one.

 Visit the following resources to learn more:
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 # What is a Context Layer?
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 A context layer is the part of an AI system responsible for gathering, organizing, and delivering the right information to a model before it generates a response. It sits between raw data sources, such as databases, documents, and APIs, and the model itself, deciding what gets included and what gets left out. Some teams build this layer themselves using retrieval pipelines and memory stores, while others rely on dedicated context platforms. The goal is to give the model relevant, trustworthy information without overwhelming it with everything available.

 Visit the following resources to learn more: