financial accounting sap 1

 AI-driven financial and accounting "Assistants."

These tools are designed to automate end-to-end business processes, from planning and sales to accounting and compliance.

The diagram is organized into three main horizontal tiers, each focusing on a different area of the financial lifecycle.

1. Top Tier: Planning, Sales, and Billing

This section focuses on the front-end of the financial cycle—generating revenue and planning for it.

· Financial Planning Assistant (Under "Planning"):

  · Function: Transforms financial planning by automating forecasts and budgets.

  · Key Features: It stress-tests assumptions in seconds, pinpoints variance drivers, and enables real-time adjustments through natural language conversation.

· Sales Operation Assistant (Under "Selling"):

  · Function: Autonomously resolves delivery issues and keeps orders moving.

  · Key Features: Clears credit blocks and flags supply risks early to protect revenue.

· Billing Assistant (Under "Billing"):

  · Function: Automates comprehensive invoicing.

  · Key Features: Built-in compliance, detects anomalies before they impact financials, validates pricing upfront, and processes adjustments with minimal manual effort.

2. Middle Tier: Core Accounting and Treasury Operations

This section handles the management of cash flow, payables, and receivables.

· Accounts Payable Assistant (Under "Accounts payable"):

  · Function: Ensures accurate, secure payments and faster vendor reconciliation.

  · Key Features: Provides greater visibility into vendor performance and overall spend.

· Cash and Treasury Assistant (Under "Treasury operations"):

  · Function: Monitors cash positions in real time.

  · Key Features: Automates liquidity and funding decisions, delivers predictive cash forecasts, and proactively identifies hedge risks with actionable recommendations.

· Accounts Receivable Assistant (Under "Accounts receivable"):

  · Function: Autonomously resolves disputes and analyzes the accounts receivable portfolio.

  · Key Features: Prioritizes collections and powers intelligent customer outreach based on risk and payment behavior.

3. Bottom Tier: Closing, Consolidation, and Compliance

This section covers the back-end, statutory, and regulatory aspects of finance.

· Financial Closing Assistant (Under "Period close"):

  · Function: An intelligent close assistant.

  · Key Features: Automates journals, accruals, and clearing to accelerate the period close with full audit confidence.

· Financial Consolidation Assistant (Under "Consolidation"):

  · Function: Ensures accurate and on-time group close.

  · Key Features: Detects and resolves errors automatically.

· Governance Assistant (Under "Governance"):

  · Function: Proactively tracks regulatory changes.

  · Key Features: Detects risk early and automates compliance actions across different jurisdictions.

· Tax and Compliance Assistant (Under "Tax compliance"):

  · Function: Automates e-invoicing and applies legal/tax changes at scale.

  · Key Features: Validates statutory reporting with confidence.

Bottom Process Bar (Workflow)

At the very bottom of the screen, there is a blue bar representing the continuous lifecycle of business processes, showing how these assistants connect:

· Planning -> Order to invoice -> Invoice to cash -> Treasury and [risk management] -> Record to [report] -> Consolidation -> Governance, risk, and [compliance].

Overall Theme:

The entire diagram illustrates a modern, heavily automated Enterprise Resource Planning (ERP) or financial management system. By utilizing AI "assistants" for every step of the financial process, the software promises to reduce manual effort, speed up closing times, provide real-time predictive insights, and ensure regulatory compliance.

knowing and doing

The gap between "Knowing" and "Doing."

The title at the bottom left roughly translates to: "Cognition (thinking/understanding) is actually a skill."

Here is the breakdown in simple, layman's terms:

The Two Main Characters

· 知道 (Knowing): This is your brain. It's reading a book, watching a YouTube tutorial, or understanding a theory.

· 做到 (Doing): This is your hands. It's actually performing the task successfully in real life.

The Left Side: The "Knowing" Trap (Red Dashed Lines)

· Cognitive Learning (认知学习): You study the theory.

· The Trap (Red dotted arrow pointing down): You think, "I understand this, so I can do it." The diagram labels this "Neglecting massive practice" (忽视大量练习).

· No Positive Feedback (无正向反馈 - Red X marks): Because you haven't practiced, when you try to jump from Knowing to Doing (or apply your knowledge), it doesn't work. You fail. You get no positive feedback.

· The "Knowing" Loop ("知道"闭环): You get stuck in a cycle of just consuming information and feeling smart, but you never actually build the skill. The loop is broken (marked with an X).

The Right Side: The Path to Mastery (Blue Solid Lines)

· The Bridge (Blue arrow pointing down): To get from Knowing to Doing, you "Must practice massively" (必须大量练习).

· Doing to Knowing (Blue arrow pointing up): As you do it, you understand it better.

· Positive Feedback (有正向反馈 - Blue checkmarks): When you practice, you start getting it right. You see results. This success gives you positive feedback, which makes you want to keep going.

· The "Doing" Closed Loop ("做到"闭环): This is the successful cycle. You learn a bit, you practice a lot, you succeed, you get feedback, and you level up your skill.

---

The Perfect Real-Life Example: Swimming

· Cognitive Learning (Knowing): You read a book about swimming. You know you need to kick your legs and move your arms. You know it.

· Ignoring Practice (The Red X path): You jump into the deep end thinking you can swim because you read the book. You sink.

· Massive Practice (The Blue Path): You get in the shallow end. You practice kicking. You practice breathing. You get water up your nose, but eventually, you float.

· Positive Feedback: You realize, "Hey, I'm actually moving forward!" You keep practicing and eventually master it.

The Main Takeaway

Just because you read something or understand it (Knowing) doesn't mean you can do it (Doing). You have to put in the boring, repetitive work of massive practice to build the actual skill. Don't get stuck in the "Knowing" loop!

sap stuff 2 by chris

SAP Knowledge Graph (SKG) 是 SAP 构建“自主企业”（Autonomous Enterprise）的语义基石。它并非一个简单的数据库，而是一个由 SAP 维护、覆盖超过 730万个数据字段 和 45.2万张表的结构化语义地图。它的核心使命是为 AI（如 Joule 及底层的大语言模型）提供精准的业务上下文，让 AI 能够理解企业数据之间错综复杂的关联，从而做出可靠的决策，而不仅仅是生成流畅的文本。

结合您提供的架构图与路线图，以下是 SAP Knowledge Graph 在 SAP 生态中的详细运作机制与战略价值：

⚙️ 核心运作机制：从用户提问到精准执行的闭环

您提供的“How SAP Knowledge Graph works”流程图，清晰地展示了 Joule 如何借助 SKG 完成一次智能交互：

1. 意图理解与转换：当用户发出自然语言请求（User utterance）后，系统会调用 大语言模型（LLM） 进行意图提取和转换。

2. 技能与工具选择：Agent Harness（智能体执行框架） 根据提取的意图，在技能库（Skills）中选择合适的工具或执行路径。

3. 知识图谱搜索：关键在于，系统并非直接在底层数据表上盲目查询，而是通过 SAP Knowledge Graph 搜索通用的工具或业务对象模型。

4. 查询翻译与执行：确定目标后，系统将业务意图翻译为标准的 SQL 或 OData 查询语句，并在底层数据平台（如 HANA Cloud）上执行，最终将结构化的结果返回给用户。

🧩 核心组件：SKG 与 SAP 数据平台的协同

SKG 的高效运作依赖于 SAP 数据平台内多个组件的紧密协作：

· SAP HANA Cloud（原生引擎）：SKG 的图引擎和向量引擎直接内置于 HANA Cloud，而非外部数据层。这种原生集成确保了处理大规模图数据和复杂查询时的极致效率。HANA Cloud 还支持 MCP（模型上下文协议），使 Joule Agents 能无缝访问其多模型引擎，理解非结构化、空间、图及向量数据间的深层语义。

· SAP Business Data Cloud (BDC)（数据供给）：BDC 负责提供高质量、受治理的数据产品（Data Products）。SKG 的质量直接取决于 BDC 供给的数据产品的质量，BDC 是 SKG 的“燃料”来源。同时，BDC 也提供了对客户自定义数据产品的直接访问和查询能力。

· SAP Domain Models（领域专家）：这是基于 SAP 代码、数据和流程训练的原生基础模型。当 Joule 处理自然语言查询时，会调用 Domain Models 来理解 SAP 专有术语和业务逻辑，并利用 SKG 将输出与相关工件联系起来，从而提供更精准的答案和代码建议。

🗺️ 战略路线图：从 SAP 管理到客户自治

根据您的路线图截图，SKG 的演进路径清晰可见，其核心趋势是开放与融合：

· Today（当前）：主要提供 SAP 管理的参考内容，覆盖 S/4HANA、SuccessFactors、Ariba 等核心应用。

· Q4 2026：计划推出 SAP 管理的客户知识图谱 Beta 版，并支持 HANA Cloud 的多租户和与第三方图谱的联邦查询。

· 2027：客户知识图谱将正式可用（GA），并能整合来自 SAP 和非 SAP 来源的客户特定数据，这是一个重大突破。同时，Joule Studio 将集成 SKG，用于上下文响应。

· Future（未来）：目标是构建一个统一的知识图谱平台，融合 SAP 管理与客户自管理的内容，形成一个真正开放、可扩展的企业知识网络。

🤖 机器学习与 Joule：SKG 如何赋能 AI 智能体

· GraphRAG 与检索增强预测 (RAP)：SKG 结合了 GraphRAG（基于图的检索增强生成） 和 RAP，使 AI 不仅能检索信息，更能基于图结构进行推理和预测，驱动自主的业务行动。

· Joule Studio 的发现与探索：作为构建企业级智能体的环境，Joule Studio 的发现步骤（Discovery step） 会查询 SKG、Domain Models 等，将用户需求映射到现有系统架构和 SAP 最佳实践上，实现基于意图的开发。

⚔️ 关键对比：SKG 与 Claude 的本质区别

您提到的“Joule work compare to Claude”，核心在于理解两者的分工与互补：

· Claude 是“推理引擎”：它是 Anthropic 提供的通用大语言模型，负责核心的语言理解和逻辑推理能力。SAP 已选择 Claude 作为其 AI 体系的“主要推理模型”。

· SKG 是“企业语义层”：它提供的是Claude 无法从公开数据中学到的企业内部知识——特定的业务流程、合规规则、数据模型和系统间的依赖关系。

· 协同工作：Joule 是“编排器”。当 Joule 处理任务时，会调用 Claude 进行推理，同时从 SKG 中获取精准的企业上下文来“锚定”这些推理，确保输出结果基于真实、特定的企业数据，而非泛泛而谈。

💎 业务价值：精度、成本与准确性

· 精度飞跃：与传统孤立数据模型相比，使用上下文知识图谱的 AI 决策准确率可提升高达 94%。

· 成本优化：研究表明，基于依赖关系的图谱构建方法能达到 LLM 生成图谱 94% 的性能，同时显著降低成本并提升可扩展性。SAP 主动管理业务上下文，也减少了企业自身的数据科学投入。

· 消除“幻觉”：SKG 将 AI 锚定在正确的企业数据上，确保 AI 应用避免产生幻觉，提供可信的决策依据。

💎 总结

SAP Knowledge Graph 是 SAP 从传统 ERP 迈向“自主企业”的核心基础设施。它通过提供一个统一、机器可读的企业语义层，解决了大语言模型在企业级应用中“不懂业务”的根本性难题。它让 Claude 等强大的推理引擎拥有了“企业常识”，让 Joule 等智能体能够精准地理解意图、调用工具并执行任务。随着路线图向客户自管理和混合图谱演进，SKG 将成为企业构建差异化 AI 能力的战略资产。

sap stuff 1 by chris

你列出的这组术语，共同描绘了 SAP 正在从“数据仓库”向“以数据产品为核心的 AI 就绪型数据平台”转型的路径。其核心载体是 SAP Business Data Cloud (BDC)，而 SAP Datasphere 是其中的数据集成与语义建模骨干。下面逐层拆解这些方法论之间的关联。

---

平台基座：BDC 与“融合数据平台 / 湖仓一体”

过去 SAP 的数据栈是分散的：BW 做数仓，Datasphere 做云上集成，分析在 SAC。BDC 的定位是把它们整合成一个完全托管的 SaaS 层，统一 SAP 与非 SAP 数据。

在这个基座中，“converged data platform”（融合数据平台）与“lake house”是同一架构目标的两面：Datasphere 提供治理、语义与建模能力，Databricks 的 Lakehouse 提供大规模处理与 AI/ML 算力，两者通过 BDC 连接，而不是互相复制数据。

数据进入方式：“data pipelining”与“zero-copy share”

数据进入 Datasphere 空间有两条根本不同的路径，理解这个区分是关键：

· Data Pipelining（复制流） ：物理搬运。Replication Flow 把源系统数据（初始快照 + 增量变更）写入 Datasphere 的本地表（file space），适合需要物理落地、精细转换的场景。

· Zero-Copy Share（零拷贝共享） ：逻辑访问。通过 Delta Sharing，数据留在源端，消费方（Databricks、Snowflake、Fabric 等）直接查询，不产生 ETL 开销和数据重复。

SAP 的方向明确是“少复制、多共享”，零拷贝协议正在成为跨平台集成的主流方式。

核心单元：“数据产品”与“DPG / DPCA / DPG”

BDC 的集成单元不再是原始表，而是受治理的数据产品。SAP 应用（S/4HANA、SuccessFactors 等）把自己的业务数据发布为带有语义的数据产品，Datasphere 负责协调与建模，SAC 负责消费。

你提到的 DPG（Data Product Generator） 在这里承担具体的技术转换角色：

· 功能：把本地 BW/BW/4HANA 的 InfoProvider（InfoObject、aDSO、CompositeProvider、MultiProvider 等）发布为 BDC 中的数据产品，或通过数据订阅将数据抽取到 Datasphere 的托管对象存储中。

· 前提：BW 系统需要先被“lift”到 BDC 的私有云组件中（BW 7.5 SP24+ / BW/4HANA 2021 SP04+ / 2023 SP00+），并有一个包含 Datasphere 的 BDC cockpit 租户。

· 价值：把 BW 几十年的建模资产从“手动管道”转为“自动化快车道”，直接生成可被 AI/ML 场景消费的零拷贝数据产品。

DPCA：在 SAP 官方语境中，并未出现与“Data Product Creation Ability”直接对应的标准缩写；公开搜索结果多指向汽车行业项目中的“DPCA”（神龙汽车），而非 SAP 产品能力。它可能是内部或特定语境下的简称。如果指的是“数据产品创建能力”这一通用概念，其实现载体就是 Datasphere 内的数据产品目录、建模空间，以及 DPG 的自动化生成能力。

语义层与 AI：“知识图谱 / 本体 / 代理记忆”

这是 BDC 区别于传统数仓的关键：不只是存数据，还要存“关系”和“含义”。

· Knowledge Graph（SAP 知识图谱） ：基于图论，从 SAP Virtual Data Model 中提取元数据，自动构建反映 S/4 业务关系的本体（Ontology）。它存储在 HANA Cloud 的 Triple Store（RDF 三元组存储） 中，可通过 SPARQL 查询。

· Ontology Manager（本体管理器） ：计划随 BDC 推出（目标 2027 Q1），让客户跨 SAP 与第三方数据构建本体，把知识图谱的通用业务上下文聚焦到具体任务上。

· Cloud Ontology：指的就是上述在云上管理、可扩展的本体层，它是 AI Agent 做准确决策的“语义护城河”。

· Agent Memory（代理记忆） ：HANA Cloud 的 knowledge graph engine 与 agentic memory 结合，让 AI Agent 能记住对话上下文，并随模型变化持续同步知识，实现“有记忆的学习”。简言之，知识图谱提供结构化的业务关系，代理记忆提供运行时的经验积累，两者共同支撑 BDC 上可推理、可审计的 AI 工作流。

BW 现代化迁移路径

你提到的“BW from on-prem to BDC”有一条阶梯式的工具链：

· DPG：处理数据层——把 InfoProvider 转为数据产品。

· Query Template Generator：处理报表层——自动把 BW 查询及其依赖对象转换为 Datasphere 中的分析模型（维度、事实视图、分析模型），可直接被 SAC 消费，大幅降低报表重写工作量。

· BW Bridge：提供云上的 BW 功能过渡，允许逐步下线本地仓库，而不是“一刀切”迁移。

“Datasphere - Consolidate in BDC of lake house”

这句话概括了 Datasphere 在 BDC 湖仓架构中的角色：它是“整合点”（consolidation point） 。非 SAP 和 SAP 的原始数据可以在 Datasphere 空间中被建模、赋予业务语义、施加治理，然后以受治理的数据产品或零拷贝共享的形式，供 Databricks 等 Lakehouse 引擎消费；反过来，Lakehouse 的处理结果也可以回流到 Datasphere 的治理层。Datasphere 不取代 Lakehouse，而是充当语义与治理的汇聚层。

整体逻辑串联

把这些概念串起来，SAP 的方法论脉络是：

BDC 作为统一 SaaS 基座 → 数据通过复制流或零拷贝进入 → 以“数据产品”为集成单元 → DPG 把 BW 资产产品化，Query Template Generator 把报表云化 → Datasphere 做语义整合与治理 → 知识图谱/本体提供 AI 所需的业务上下文 → 代理记忆让 Agent 在上下文中持续学习 → 最终在湖仓一体的算力上运行可审计的 AI 与分析。

---

如果你希望进一步展开其中某一条线——例如 DPG 的具体配置步骤、零拷贝 Delta Sharing 与 Databricks 的对接方式，或者知识图谱在 Agent 工作流中的实际使用模式——可以告诉我，我再做针对性梳理。

slow learner

I’m a slow student. By the time my classmates had read every article for the semester, I was still confused, and I only started reading the relevant articles when I began the project. But while others read once, I read ten times—that’s my secret. That's why I spend more time on reading .. but reading is still my life hobby. I am a lazy worker but a hardworking reader.

Knowledge economy by Kenway etc

## Summary of the chapter

Kenway, Bullen and Robb’s **“Global Knowledge Politics and ‘Exploitable Knowledge’”** is the concluding chapter of the 2004 edited volume *Innovation & Tradition: The Arts, Humanities and the Knowledge Economy* (pp. 135–149). Although it is sometimes described as an article, it is formally a book chapter.

### Central argument

The chapter argues that the **knowledge economy is not a neutral or purely technical concept**. It is part of a global political project that determines:

- which kinds of knowledge are considered valuable;

- how knowledge should be produced and distributed;

- who owns and controls it; and

- who receives its economic and social benefits.

The authors’ use of **“exploitable knowledge”** is deliberately critical. It points to the narrowing of knowledge into something that can be codified, measured, owned, transferred, commercialized, or converted into economic advantage.

### Key points

1. **Knowledge has become an economic policy priority** to

   Governments increasingly treat universities and research institutions as engines of national competitiveness. Research is expected to support innovation, economic growth, industry collaboration, and commercialization.

2. **Universities are being reorganized as market-oriented institutions**

   Knowledge-economy policy encourages competition, entrepreneurship, private-sector partnerships, intellectual property ownership, and closer relationships between academic research and commercial demand. This challenges traditional academic commitments to open inquiry, public education, and knowledge as a public good.

3. **“Global” knowledge relations are unequal**

   The international knowledge economy is not a level playing field. Powerful national governments, international policy organizations, major research institutions, and corporations largely define what counts as valuable knowledge and capture much of its economic value. Other countries and institutions are often expected to adapt to standards and priorities established elsewhere.

4. **Commercial utility becomes the dominant test of knowledge**

   Knowledge that is difficult to patent, commodify, quantify, or apply immediately tends to be devalued. This creates particular problems for the arts and humanities, whose contributions may be interpretive, critical, historical, aesthetic, ethical, social, or cultural rather than directly marketable.

5. **Arts and humanities knowledge is more than an economic input**

   The chapter resists the idea that humanities scholarship and artistic practice should be justified only through their contribution to productivity or commercial innovation. These fields help societies interpret experience, preserve memory, question assumptions, imagine alternatives, communicate across cultures, and develop ethical judgment.

6. **“Innovation” should not be reduced to technology**

   Technological invention is only one form of innovation. The arts and humanities can generate intellectual, social, institutional, cultural, and creative innovation. They can also provide important critical perspectives on the knowledge economy itself.

7. **Tradition and innovation are interconnected**

   The arts and humanities often work through reinterpretation rather than simple replacement: earlier texts, ideas, practices, and cultural forms are continually reconsidered in new contexts. Their “traditional” methods therefore should not automatically be treated as obsolete or economically irrelevant.

8. **Strategic adaptation must not mean complete submission to the market**

   The chapter does not necessarily reject collaboration, entrepreneurship, or engagement with new technologies. Instead, it cautions against allowing commercial criteria to determine the entire purpose of scholarship and education. Universities should participate in the knowledge economy while also defending intellectual autonomy, public accountability, cultural diversity, and broader conceptions of value.

### Overall conclusion

The chapter’s main warning is that when **“exploitable” becomes the principal definition of valuable knowledge**, education and research are narrowed to serve short-term economic interests. Kenway, Bullen and Robb call for a more democratic and inclusive understanding of knowledge—one that recognizes commercial and technological innovation but also protects the critical, cultural, creative, and public purposes of the arts and humanities.

The chapter is **not primarily about Indigenous knowledge, genetic-resource patents, or modern patent-disclosure rules**, despite the similarity between those topics and the broader concept of knowledge politics.

**Sources:** [Monash University chapter record](https://research.monash.edu/en/publications/global-knowledge-politics-and-exploitable-knowledge/) and [Peter Lang volume description](https://www.peterlang.com/document/1098627).

Story inspired by king Edgar of England by Christine

EDGAR THE PEACEFUL:

Or, How to Rule a Country Without Actually Getting Crowned for 14 Years and Making Other Kings Row Your Boat

Chapter 1: The Family Business

Before Edgar could even think about being king, he had to live in the shadow of his ancestors. First, there was Grandpa Alfred, who famously spent his time hiding in swamps and burning cakes while fending off the Danish. Then came Edgar's uncle, Athelstan (924–939). Athelstan was the ultimate overachiever. He took the family business and expanded it, becoming the first true King of England. He was so bossy that he beat a combined army of Scots, Vikings, and Picts at the Battle of Brunanburh in 937, a battle so epic that people are still writing books about it. (He was the undisputed, undisputed overlord of Britain, okay?).

Chapter 2: My Brother, the Party Animal

Then, the throne passed to Edgar’s older brother, Edwy (Eadwig). Edwy was, to put it mildly, a bit of a disaster.

Edwy had a major PR problem. At his own coronation banquet—where he was supposed to be shaking hands and eating roast swan with the nobility—he snuck off. Why? Because he was "wickedly enticing" two women (a mother and a daughter, no less!) to hang out with him. According to the text, he "wallowed between the two of them in evil fashion."

Enter the ultimate buzzkill: Dunstan (the Bishop of Worcester, and later Archbishop of Canterbury). Dunstan, a man who probably slept in a hairshirt and frowned for fun, tracked Edwy down, aggressively pulled him out of the room, and dragged him back to his own party by the ear.

Edwy was furious. Because he was king, he immediately exiled Dunstan to Flanders.

Chapter 3: The Introvert Takes Over

This is where Edgar steps in. The text notes he was "a very different man" from his brother. While Edwy was causing scandals, Edgar was playing the long game.

Edgar looked around, realized his brother was a trainwreck, and basically just took over the country. But Edgar had a weird quirk: he didn't want to be officially crowned. He waited a remarkable 14 years to actually put a crown on his head. Why? Maybe he was waiting for the right lighting. Maybe he had imposter syndrome. Or maybe, like a true Machiavellian genius, he just wanted to see if he could run the whole country without the official paperwork.

Chapter 4: The Scandalous Rebound

Remember those two women Edwy snuck off with at his coronation? Edgar, being a master troll, married the younger of the two women. He essentially stole his older brother’s controversial girlfriend, made her his Queen, and secured the ultimate sibling power move.

Chapter 5: The Grand Flex

Edgar was actually a fantastic administrator. He founded the English Civil Service (making clerks do all the boring paperwork), unified the kingdom, and built a massive navy just to flex on the Vikings.

But Edgar’s greatest moment came in 973 (the year written on your notes!). After 14 long years, he finally decided it was time for the ultimate coronation at Bath. To prove he was the absolute boss of the British Isles, Edgar invited all the kings of Scotland, Wales, and the Isle of Man to his party.

He didn't just ask them to show up, though. According to the text, Edgar made eight kings get into a boat on the River Dee. He sat in the front, steering the rudder, while the Kings of Scotland (like Kenneth II) and Wales physically rowed him down the river.

Imagine the scene: The King of Scots, sweating through his royal robes, pulling an oar, while Edgar sits in the back, pointing at ducks and saying, "Faster, lads, I want to see if we can beat the current!"

Epilogue

Edgar died in October 959 (according to the margin note, though the text confusingly mentions his coronation was in 973—history is messy!). He left behind a unified, peaceful England, a strong navy, a foundation for the civil service, and a legacy of making other kings do his cardio.

He was a very different man from his brother, indeed.