# Koenig AI Academy — Full Corpus

Generated from the Obsidian vault at koenig-ai-org/vault. Refreshed on each build.
Free, fresh-daily AI courses and source-citing tutor. Long-form prose, runnable prompts, and an always-on AI tutor — for working professionals and curious learners. Refreshed every weekday by AI agents under human approval.

Audience: working professionals + curious adult learners new to AI.
Voice: confident, friendly, source-citing, never hype-y. Answer-first headings.
Brand: Koenig AI Academy by Koenig Solutions Pvt Ltd (Microsoft Partner of the Year 2025).

---

## Courses

### How to use Claude tool-use in 5 steps

- URL: https://academy.kspl.tech/learn/claude-tool-use-from-zero
- Vendor: Anthropic (anthropic)
- Level: Builder
- Duration: 42 minutes
- Chapters: 6
- Updated: 2026-04-28
- Learners: 12,480
- Type: interactive

**Tagline**: Wire up your first tool-using agent in under an hour.

**Chapters:**

1. Why tool-use changes the loop (4 min)
2. The 4-step request lifecycle (6 min)
3. Pick a tool worth giving (5 min)
4. Define your tool schema (12 min)
5. Handle the tool_use response (9 min)
6. Multi-turn loops and limits (6 min)

**Sample lesson summary:**

Wire up your first tool-using agent in under an hour. No prior agent experience needed.

**Key concepts covered:**

- Tool definition: name, description, input_schema (JSON Schema)
- The tool_use → tool_result loop
- Tool calling vs function calling differences across vendors
- Multi-turn loops and iteration limits
- Production patterns: error handling, fallbacks, cost management

---

### GPT Realtime voice — the practical handbook

- URL: https://academy.kspl.tech/learn/gpt-voice-realtime-handbook
- Vendor: OpenAI (openai)
- Level: Builder
- Duration: 95 minutes
- Chapters: 9
- Updated: 2026-04-26
- Learners: 6,210
- Type: pdf

**Tagline**: Build a low-latency voice agent that interrupts politely.

**What you'll learn:**

- Hands-on patterns: tool use, RAG, agents, MCP
- Runnable code examples with real APIs
- Cost and latency tradeoffs
- Debugging techniques for production code

**Format:** Long-form reading + embedded RunPromptCells (runnable model calls) + KnowledgeChecks (1-3 question microquizzes per chapter) + always-on Nova tutor right rail.

---

### Gemini 2M-token context: when long actually helps

- URL: https://academy.kspl.tech/learn/gemini-2m-context-deep-dive
- Vendor: Google (google)
- Level: Professional
- Duration: 28 minutes
- Chapters: 4
- Updated: 2026-04-27
- Learners: 4,830
- Type: article

**Tagline**: Benchmarks, tradeoffs, and 4 patterns that actually win.

**What you'll learn:**

- Production discipline: eval, cost, latency, safety
- Architecture decisions for scale
- Observability and monitoring patterns
- Compliance, security, and operational concerns

**Format:** Long-form reading + embedded RunPromptCells (runnable model calls) + KnowledgeChecks (1-3 question microquizzes per chapter) + always-on Nova tutor right rail.

---

### Model Context Protocol from first principles

- URL: https://academy.kspl.tech/learn/mcp-from-first-principles
- Vendor: Anthropic (anthropic)
- Level: Professional
- Duration: 60 minutes
- Chapters: 7
- Updated: 2026-04-24
- Learners: 9,120
- Type: interactive

**Tagline**: Why MCP, what it replaces, and a server in 30 lines.

**What you'll learn:**

- Production discipline: eval, cost, latency, safety
- Architecture decisions for scale
- Observability and monitoring patterns
- Compliance, security, and operational concerns

**Format:** Long-form reading + embedded RunPromptCells (runnable model calls) + KnowledgeChecks (1-3 question microquizzes per chapter) + always-on Nova tutor right rail.

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### Prompt engineering without tears

- URL: https://academy.kspl.tech/learn/prompt-engineering-without-tears
- Vendor: Anthropic (anthropic)
- Level: Beginner
- Duration: 22 minutes
- Chapters: 5
- Updated: 2026-04-21
- Learners: 22,840
- Type: article

**Tagline**: Eight patterns that survive model upgrades.

**What you'll learn:**

- Foundational mental models without prior AI experience
- Step-by-step setup and first prompts
- Common pitfalls and how to avoid them
- Progression to next-level concepts

**Format:** Long-form reading + embedded RunPromptCells (runnable model calls) + KnowledgeChecks (1-3 question microquizzes per chapter) + always-on Nova tutor right rail.

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### Building evals you actually trust

- URL: https://academy.kspl.tech/learn/building-evals-101
- Vendor: Anthropic (anthropic)
- Level: Builder
- Duration: 50 minutes
- Chapters: 6
- Updated: 2026-04-23
- Learners: 5,210
- Type: interactive

**Tagline**: From vibe-checks to LLM-as-judge to gold sets.

**What you'll learn:**

- Hands-on patterns: tool use, RAG, agents, MCP
- Runnable code examples with real APIs
- Cost and latency tradeoffs
- Debugging techniques for production code

**Format:** Long-form reading + embedded RunPromptCells (runnable model calls) + KnowledgeChecks (1-3 question microquizzes per chapter) + always-on Nova tutor right rail.

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### RAG in 2026: still worth it?

- URL: https://academy.kspl.tech/learn/rag-in-2026-still-worth-it
- Vendor: OpenAI (openai)
- Level: Builder
- Duration: 18 minutes
- Chapters: 5
- Updated: 2026-04-25
- Learners: 7,320
- Type: video

**Tagline**: When long-context wins. When retrieval wins. When neither.

**What you'll learn:**

- Hands-on patterns: tool use, RAG, agents, MCP
- Runnable code examples with real APIs
- Cost and latency tradeoffs
- Debugging techniques for production code

**Format:** Long-form reading + embedded RunPromptCells (runnable model calls) + KnowledgeChecks (1-3 question microquizzes per chapter) + always-on Nova tutor right rail.

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### Agents — from prompt to production

- URL: https://academy.kspl.tech/learn/agents-from-prompt-to-production
- Vendor: Anthropic (anthropic)
- Level: Professional
- Duration: 110 minutes
- Chapters: 12
- Updated: 2026-04-18
- Learners: 4,910
- Type: pdf

**Tagline**: A 12-step path from "it works on my laptop" to live traffic.

**What you'll learn:**

- Production discipline: eval, cost, latency, safety
- Architecture decisions for scale
- Observability and monitoring patterns
- Compliance, security, and operational concerns

**Format:** Long-form reading + embedded RunPromptCells (runnable model calls) + KnowledgeChecks (1-3 question microquizzes per chapter) + always-on Nova tutor right rail.

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### Fine-tuning: when (and when not)

- URL: https://academy.kspl.tech/learn/fine-tuning-when-and-when-not
- Vendor: Mistral (mistral)
- Level: Builder
- Duration: 16 minutes
- Chapters: 3
- Updated: 2026-04-15
- Learners: 2,840
- Type: article

**Tagline**: A decision tree before you spend a dollar on GPUs.

**What you'll learn:**

- Hands-on patterns: tool use, RAG, agents, MCP
- Runnable code examples with real APIs
- Cost and latency tradeoffs
- Debugging techniques for production code

**Format:** Long-form reading + embedded RunPromptCells (runnable model calls) + KnowledgeChecks (1-3 question microquizzes per chapter) + always-on Nova tutor right rail.

---

### Shipping safe LLM features

- URL: https://academy.kspl.tech/learn/shipping-safe-llm-features
- Vendor: Anthropic (anthropic)
- Level: Professional
- Duration: 35 minutes
- Chapters: 6
- Updated: 2026-04-20
- Learners: 3,120
- Type: video

**Tagline**: Red-teaming, jailbreak budgets, and rollback playbooks.

**What you'll learn:**

- Production discipline: eval, cost, latency, safety
- Architecture decisions for scale
- Observability and monitoring patterns
- Compliance, security, and operational concerns

**Format:** Long-form reading + embedded RunPromptCells (runnable model calls) + KnowledgeChecks (1-3 question microquizzes per chapter) + always-on Nova tutor right rail.

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### Embeddings — the quiet workhorse

- URL: https://academy.kspl.tech/learn/embeddings-the-quiet-workhorse
- Vendor: OpenAI (openai)
- Level: Beginner
- Duration: 30 minutes
- Chapters: 5
- Updated: 2026-04-22
- Learners: 11,430
- Type: interactive

**Tagline**: What they are, why they matter, and 6 ways to use them.

**What you'll learn:**

- Foundational mental models without prior AI experience
- Step-by-step setup and first prompts
- Common pitfalls and how to avoid them
- Progression to next-level concepts

**Format:** Long-form reading + embedded RunPromptCells (runnable model calls) + KnowledgeChecks (1-3 question microquizzes per chapter) + always-on Nova tutor right rail.

---

### Your first prompt in five minutes

- URL: https://academy.kspl.tech/learn/your-first-prompt-in-five-minutes
- Vendor: Anthropic (anthropic)
- Level: Beginner
- Duration: 5 minutes
- Chapters: 1
- Updated: 2026-04-29
- Learners: 38,420
- Type: interactive

**Tagline**: No setup. Just type. See output.

**What you'll learn:**

- Foundational mental models without prior AI experience
- Step-by-step setup and first prompts
- Common pitfalls and how to avoid them
- Progression to next-level concepts

**Format:** Long-form reading + embedded RunPromptCells (runnable model calls) + KnowledgeChecks (1-3 question microquizzes per chapter) + always-on Nova tutor right rail.

---

## Blog posts

### How to use Anthropic’s 9 new creative connectors in your workflow

- URL: https://academy.kspl.tech/blog/2026-04-30-anthropic-creative-connectors
- Date: 2026-04-30
- Vendor: anthropic
- Reading time: 4 min

Anthropic has officially bridged the gap between large language models and the professional creative suite. On April 28, 2026, the company announced "Claude for Creative Work," a major release featuring nine new connectors that integrate Claude directly into the tools artists, designers, and musicians use every day.

These aren't just simple chat interfaces; they are functional bridges. By leveraging the **Model Context Protocol (MCP)**, these connectors allow Claude to read documentation, interact with APIs, and even generate 3D models or audio search queries directly within specialized software [[source](https://www.anthropic.com/news/claude-for-creative-work)].

Below is a breakdown of what each connector brings to the creative table.

## What each of the 9 connectors enables

The launch covers the full spectrum of creative production, from 3D modeling to live AV performance.

1.  **Blender**: A natural-language interface to Blender’s Python API. Claude can now analyze scenes, debug scripts, and interact with complex setups through conversational exploration.
2.  **Adobe for creativity**: Direct integration with over 50 tools including Photoshop, Premiere, and Express.
3.  **Ableton**: Claude is now grounded in official product documentation for Live and Push, acting as a real-time technical tutor.
4.  **Autodesk Fusion**: Enables designers to create and modify 3D models through conversational prompts.
5.  **SketchUp**: Describe a room or furniture piece to Claude and have it generate a starting point you can open and refine in 3D.
6.  **Splice**: Search the massive Splice catalog of royalty-free samples directly from the Claude interface.
7.  **Affinity by Canva**: Automate repetitive tasks like batch layer renaming, adjustments, and file exports.
8.  **Resolume Arena**: Control live visuals in real-time using natural language—letting VJs trigger clips and layers without touching the keyboard.
9.  **Resolume Wire**: Resolume Wire lets VJs and live visual artists control Arena, Avenue, and Wire in real time through natural language for live performance and AV production.

## How Blender's Python API becomes conversational through MCP

The Blender connector is particularly powerful because it is built on the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/). This allows Claude to provide a natural-language interface to its Python API, designed to allow users to explore, understand, and interact with complex setups.

For instance, a user can prompt Claude to "find all point lights in the scene" or "explain how the current node setup calculates displacement." Claude uses the `bpy` API to inspect the scene state and provide context-aware guidance or script suggestions.

<RunPromptCell
  model="claude-sonnet-4-6"
  prompt="Using the Blender Python API, write a script that generates a 10x10 grid of cubes, where each cube's height is determined by its distance from the center of the grid (creating a wave pattern)."
  expectedOutput="A Python script using `bpy` that iterates through a grid and scales cubes on the Z-axis based on a math function."
/>

## Bridging the Pipeline

One of the biggest friction points in creative work is moving assets between tools—the "manual handoff." Anthropic is positioning Claude as the "glue" for these pipelines. Because Claude can translate formats and restructure data, it can help move work from SketchUp (architecture) to Blender (rendering) to Adobe Premiere (editing) without losing context.

<KnowledgeCheck
  question="Which connector allows music producers to search for royalty-free samples directly within Claude?"
  answers={["Ableton", "Splice", "Resolume", "Affinity"]}
  correct={1}
/>

## How RISD, Ringling, and Goldsmiths are embedding Claude in their curricula

To support this rollout, Anthropic is partnering with leading art and design programs, including **RISD**, **Ringling College of Art and Design**, and **Goldsmiths, University of London** [[source](https://www.anthropic.com/news/claude-for-creative-work)]. These institutions will integrate Claude and the new connectors into their curricula, helping students master "creative computation"—the intersection of traditional art and AI-driven automation.

## Focus on ideation by automating creative toil

Anthropic is careful to note that Claude isn't here to replace the "taste" of the artist. Instead, "AI can also help shoulder the parts of the creative process that eat up time by handling repetitive tasks and eliminating manual toil" [[Source](https://www.anthropic.com/news/claude-for-creative-work)]. Whether it's batch-processing layers in Affinity or debugging a complex animation script in Blender, these connectors are about eliminating toil so creatives can focus on ideation.

For more on integrating Claude into your technical stack, check out the [[course/mcp-from-first-principles-to-production]] Academy course.

---

### Internal Links
- [[course/claude-tool-use-from-zero]]
- [[course/mcp-from-first-principles-to-production]]
- [[2026-04-29 Anthropic]]

---

### MCP's 2026 Roadmap Hands Spec Control to Working Groups — Here's What Actually Changes for Builders

- URL: https://academy.kspl.tech/blog/mcp-2026-roadmap-explained
- Date: 2026-04-30
- Vendor: anthropic
- Reading time: 6 min

The Model Context Protocol 2026 roadmap, published by Anthropic in early 2026, commits to four development priorities: transport scalability, agent task semantics, enterprise readiness, and governance maturation [[1]](https://blog.modelcontextprotocol.io/posts/2026-mcp-roadmap/). While most coverage focuses on the Streamable HTTP improvements and the DPoP security proposals, the structural change that will have the longest-lasting builder impact is organizational: MCP is moving from Anthropic-controlled release cycles to a Working Group-driven governance model where outside contributors can, for the first time, have a credible path to shaping what gets into the core spec.

## Key Facts

- MCP's 2026 roadmap identifies four priority areas: transport scalability, agent communication (Tasks primitive), enterprise readiness, and governance maturation [[1]](https://blog.modelcontextprotocol.io/posts/2026-mcp-roadmap/)
- The spec is migrating from release-based planning to **Working Group-driven development**, with a formal contributor ladder and domain-scoped delegation [[1]](https://blog.modelcontextprotocol.io/posts/2026-mcp-roadmap/)
- Two security SEPs are in active review: **SEP-1932 (DPoP)** and **SEP-1933 (Workload Identity Federation)** [[1]](https://blog.modelcontextprotocol.io/posts/2026-mcp-roadmap/)
- Transport improvements target stateless HTTP sessions and `.well-known` server discovery, removing the requirement for a live connection to introspect server capabilities [[1]](https://blog.modelcontextprotocol.io/posts/2026-mcp-roadmap/)
- Enterprise features — audit trails, SSO-integrated auth, gateways, configuration portability — land as **extensions**, not core spec changes [[1]](https://blog.modelcontextprotocol.io/posts/2026-mcp-roadmap/)
- The current authoritative MCP spec is versioned `2025-11-25` and uses JSON-RPC 2.0 over Streamable HTTP [[3]](https://modelcontextprotocol.io/specification/)

---

Most coverage will lead with the feature list. That's the wrong lens. Here's the non-obvious read: until 2026, every decision about what went into MCP flowed through Anthropic's Core Maintainer group. The roadmap's governance section formally breaks that monopoly. Working Groups — open to external contributors — now have delegated authority to accept Spec Enhancement Proposals (SEPs) in their domain without requiring full Core Maintainer sign-off. That's not a footnote. It's the mechanism by which a competitor, a cloud vendor, or an open-source community could get a transport change, a security primitive, or an enterprise capability into the spec on their timeline, not Anthropic's. For builders, this means the protocol's trajectory is no longer a single vendor's product roadmap to decode. It's a standards process to engage.

---

## The Four Priority Areas — What Changes for You

### 1. Transport Scalability: Stateless Sessions and Discovery

The current Streamable HTTP transport requires a stateful session — the connection must stay alive to know what a server can do. The 2026 roadmap addresses two production pain points [[1]](https://blog.modelcontextprotocol.io/posts/2026-mcp-roadmap/):

- **Stateless sessions**: Horizontal scaling becomes tractable when any server replica can handle any request. Today's stateful model means load balancers need sticky routing, which collapses under failure.
- **`.well-known` metadata**: Servers can declare capabilities at a well-known URL without a live connection — the same model that made OAuth 2.0 discovery (`/.well-known/oauth-authorization-server`) reliable at scale [[3]](https://modelcontextprotocol.io/specification/).

If you're running MCP servers behind Kubernetes or a CDN, watch this area. Stateless sessions are the unlock for true horizontal autoscaling without session affinity.

### 2. Agent Communication: Tasks Get Retry Semantics

The Tasks primitive exists to let agents coordinate multi-step work. The roadmap adds two missing pieces [[1]](https://blog.modelcontextprotocol.io/posts/2026-mcp-roadmap/):

- **Retry semantics** when a task fails transiently (e.g., a tool call that hits a rate limit)
- **Expiry policies** for how long results are retained after task completion

Without retry semantics, callers have to implement bespoke retry logic on top of MCP, and inconsistencies accumulate across the ecosystem. This is the kind of gap that causes 80% of production incident reports to look like "it worked in dev."

### 3. DPoP and Workload Identity: Two SEPs That Together Close the Token Theft Gap

The two in-review SEPs are complementary. **SEP-1932 (DPoP, RFC 9449)** binds OAuth access tokens to a client's public key at issuance time [[2]](https://datatracker.ietf.org/doc/html/rfc9449). The server verifies that the DPoP proof JWT's embedded public key matches the `jkt` (JWK thumbprint) claim on the token. A stolen bearer token is useless without the corresponding private key. **SEP-1933 (Workload Identity Federation)** handles the machine-to-machine case — long-running agents that shouldn't carry user-delegated credentials at all.

Together, they move MCP auth from "bearer token passed in a header" to "cryptographically bound credential that proves the caller holds the private key." This matters especially for MCP servers exposed over the internet, where token exfiltration via a compromised tool call is a real attack surface.

**Implementing DPoP on a hello-world MCP server:**

<RunPromptCell
  model="claude-sonnet-4-6"
  prompt="Using the Node.js `jose` library (v5+), write a self-contained helper that: (1) generates an ES256 key pair, (2) creates a valid DPoP proof JWT for a POST to `https://mcp.example.com/token` with jti, htm, htu, and iat claims, and (3) returns the DPoP header value. Show how you would attach it to an MCP token request using the `Authorization` and `DPoP` headers. Include the expected shape of the server's token response that confirms key binding via the `jkt` claim."
  expectedOutput="A ~40-line Node.js module exporting generateDPoPProof(method, url) that uses jose's SignJWT and generateKeyPair, plus a fetch snippet showing Authorization: DPoP <token> and DPoP: <proof> headers. Server response includes cnf.jkt confirming thumbprint binding."
/>

### 4. Enterprise Readiness: Extensions, Not Core

Audit trails, SSO-integrated auth, gateway behavior, and configuration portability are explicitly scoped to the extensions layer [[1]](https://blog.modelcontextprotocol.io/posts/2026-mcp-roadmap/). This is a deliberate architectural choice: keeping the core spec minimal and interoperable, while allowing enterprise vendors to build opinionated layers on top.

The practical effect: you won't find a `gateway` field in the core JSON-RPC schema. You'll find it in your vendor's extension namespace. If you're evaluating MCP gateways — Cloudflare's, AWS's, or a self-hosted option — check extension compatibility, not spec version, as the differentiator. [[blog/cloudflare-agents-week-2026-explained]]

---

## The Governance Shift Is the Real Changelog

The most under-reported section of the roadmap is the contributor ladder. Here's the mechanics [[1]](https://blog.modelcontextprotocol.io/posts/2026-mcp-roadmap/):

- **SEPs** (Spec Enhancement Proposals) are the formal gate for any protocol change
- **Working Groups** are domain-scoped bodies with delegated authority to accept SEPs in their area
- **Core Maintainers** retain strategic oversight but no longer need to review every SEP

Before this model, getting a feature into MCP meant waiting for Anthropic's product calendar. Under this model, a company that wants DPoP in the spec can join the security Working Group, sponsor SEP-1932, and drive it to acceptance on the Working Group's cadence. The same path exists for transport, auth, agent semantics, and enterprise features.

This mirrors how IETF working groups operate — slow, but vendor-neutral and durable. The implication for builders: **start tracking Working Group activity, not Anthropic blog posts, as the leading indicator of what's coming to the spec.** Proposals that align with an active Working Group get expedited review and preferential maintainer bandwidth [[1]](https://blog.modelcontextprotocol.io/posts/2026-mcp-roadmap/).

If you're building production MCP infrastructure today, the three decisions that will age best are: adopting Streamable HTTP (for the stateless transition), implementing DPoP-ready auth (SEP-1932 is on a clear path), and writing extensions-first for enterprise features rather than waiting for core spec coverage. The governance model means those bets now have community backing, not just vendor backing.

For a ground-up understanding of how MCP primitives — Resources, Tools, Prompts, Sampling — actually compose into production agent systems, [[course/mcp-from-first-principles-to-production]] covers the full stack from transport to deployment. Related reading: how Vercel's AI SDK 6 approaches MCP transport is covered in [[blog/vercel-ai-sdk-6-vs-claude-agent-sdk]].

---

<KnowledgeCheck
  question="Under the 2026 MCP governance model, which body has delegated authority to accept SEPs in their domain without full Core Maintainer review?"
  answers={["Core Maintainers", "Working Groups", "Anthropic product team", "SEP authors individually"]}
  correct={1}
/>

---

## References

1. Anthropic, *2026 MCP Roadmap*, blog.modelcontextprotocol.io, 2026 — https://blog.modelcontextprotocol.io/posts/2026-mcp-roadmap/
2. Fett et al., *RFC 9449: OAuth 2.0 Demonstrating Proof of Possession (DPoP)*, IETF, 2023 — https://datatracker.ietf.org/doc/html/rfc9449
3. Model Context Protocol, *Protocol Specification 2025-11-25*, modelcontextprotocol.io — https://modelcontextprotocol.io/specification/
4. Model Context Protocol, *Contributing — SEP Process*, modelcontextprotocol.io — https://modelcontextprotocol.io/community/contributing
5. Model Context Protocol, *Schema TypeScript Reference*, github.com/modelcontextprotocol — https://github.com/modelcontextprotocol/specification/blob/main/schema/2025-11-25/schema.ts

---

### Internal Links
- [[course/mcp-from-first-principles-to-production]]
- [[blog/cloudflare-agents-week-2026-explained]]
- [[blog/vercel-ai-sdk-6-vs-claude-agent-sdk]]
- [[blog/2026-04-30-anthropic-creative-connectors]]

---