"Learning Software Architecture" というタイトルの記事から。 ・ソフトウェア設計は、実際のプロジェクトを通じて実践で学ぶのが一番 ・プログラミングにおいてコード以上に重要なのはアーキテクチャ ・アーキテクチャ以上に重要なのは組織の社会的な問題 ・科学（研究に近い）向けのコードが産業界のコードと異なるのは、開発者の知識の差ではない ・これは、3ヶ月後に論文を出す、といったインセンティブ構造の違いによる ・プロジェクトのインセンティブ構造自体を設計できる機会は稀だが、できれば非常に効果的になる ・構造を変えられない場合は、諦めて制約の中で最善を尽くすしかない ・筆者は rust-analyzer の開発において、コントリビューターの層に合わせたアーキテクチャを採用した ・中核部分には優秀な開発者を惹きつけるため、依存関係を減らしてビルドを徹底的に高速化 ・周辺機能には週末だけプログラミングできる層を集めるため、各機能を完全に独立させた ・周辺機能の品質基準も正常系が動けばOKとして、クラッシュが全体に波及しない仕組みを作っておいた （つまり、コントリビューターが取り組みやすい構造を設計に取り入れて、社会構造に対応しようとした） ・ただ、適応を優先して実験的に作ったコードが、そのまま本番運用され続けるリスクもあるが ・なお、ソフトウェア設計の真理が1冊にまとまった本は存在しない ・それでも、おすすめの書籍や講演はいくつかある ・APoSDとか、Software Engineering at Googleとか

# Learning Palantir Foundry 🚀 "How many screens do I need to open just to understand one customer?" Object Views answer that pain by bundling everything about a single object into one screen. 📌 Title and Feature URL Title: オブジェクトビュー URL: 📝 Overview Object Views act as the central hub for everything related to a specific object. They consolidate properties, linked objects, metrics and analytics, dashboards, and operational applications into a single unified interface. For example, an Airport object view can integrate flight timelines, delay-handling workflows, and location data in one place. In practice, an Object View becomes the daily "home screen" that frontline users open to start their work. 🔧 How It Works Object Views are highly configurable by builders: - They support multiple formats and sizes, so appearance and interaction patterns can be tailored to the task. - They combine properties (attributes), linked objects, metrics, analytics, and dashboards into one display. - They can be embedded throughout the platform wherever the object appears. - Configuration happens in the Ontology Manager under the "Object views" tab, and version tabs at the top let you switch between format variations. - Selecting "Edit views" opens the configuration editor or the underlying Workshop module. - The system also supports version management, panel variations, commenting, and Marketplace product integration. 🛠 Practical Usage - In Ontology Manager, select the target object type and use the "Object views" tab to preview and configure. - Lay out core information, related objects, operation history, and embedded dashboards so everything the team needs is on one screen. - Beyond viewing, embed action types so users can trigger status changes or assignments directly from the view. - Create multiple formats to show different layouts per role (for example, sales view vs. maintenance view). 🎯 Use Cases - Customer 360: one launchpad combining transaction history, inquiries, related orders, and account owners for sales. - Equipment record: a maintenance home screen with sensor values, service history, related parts, and open tickets. - Case management: a single view of stakeholders, due dates, approval status, and next actions on a case object. ⚠️ Caveats - Views depend on the quality of the underlying ontology modeling (object types and link types); a weak foundation limits view quality. - Overloading a view confuses users, so design role-specific layouts that show only what each role needs. - Editing requires appropriate permissions to the Ontology Manager and the underlying Workshop module. #PalantirFoundry# #DataPlatform#

# Learning Palantir Foundry 🚀 Put business logic right on the ontology. Functions cure the "numbers don't match across departments" problem by centralizing logic in one place. 📌 Title and Feature URL Title: ファンクション URL: 📝 Overview Functions let you write server-side logic that executes in isolated environments, powering operational apps like dashboards and decision-support tools. They are designed to work with Foundry ontologies, so they can read object properties, traverse links, and perform flexible ontology edits. 🔧 How It Works - Supported languages: TypeScript (full feature support) and Python (beta, with growing support especially for serverless and deployed execution). - Serverless execution: spins up on demand when invoked and bills only during execution, with a 60-second total wall-clock timeout (30s CPU plus a 30s network buffer). Multiple versions can run simultaneously, making upgrades safer. - Deployed execution: reserves dedicated resources for cases serverless cannot meet, runs a single version at a time, and bills continuously while deployed. - Capability differences: ontology read/write, Workshop integration, and external API calls work in both languages. Pipeline Builder is Python, while model embedding and semantic search are TypeScript. 🛠 Practical Usage - Derived properties: display function-computed values as table columns. - Function-backed Actions: implement complex edits spanning multiple objects. - Workshop integration: run functions to compute or display variables. - API gateway: invoke query functions programmatically to reuse the same logic everywhere. 🎯 Use Cases - Implement derived-KPI logic once and return identical results to Workshop, OSDK, and the API. - Query external systems to enrich ontology objects. - Build complex validation or bulk updates as function-backed Actions. ⚠️ Caveats - The 60-second timeout applies uniformly across execution modes, so optimize for efficiency. - Available capabilities depend on the invocation context (for example, model embedding and semantic search are TypeScript only), so decide on language early. #PalantirFoundry# #DataEngineering#

# Learning Palantir Foundry 🚀 Action Types are what decisively separate Foundry from read-only BI. Approvals and assignments become safe, validated, structured writes. 📌 Title and Feature URL Title: アクションタイプ URL: 📝 Overview An Action Type defines a set of changes a user can apply to ontology objects, properties, and links in a single transaction. It encapsulates both the data modifications and any side effects triggered on submission, letting users think in terms of overall goals rather than individual property edits. 🔧 How It Works - Write-back to the ontology: when an action runs, all changes are committed to the ontology and reflected across every app. The latest object data, including user edits, is captured in the object type's write-back dataset. - Parameters and defaults: parameters standardize input, supporting default values, filtered dropdown results, and overrides. - Rules: define when and how an action executes, including object relationships and property constraints. - Submission criteria and validation: validation rules control execution eligibility and error handling before changes persist. - Action logs: a full audit trail of every executed action supports accountability and compliance. 🛠 Practical Usage - Run an "Assign Employee" style action that changes a role property, auto-creates a manager-employee link, and notifies stakeholders in one transaction. - Embed submission criteria like "only a director may submit amounts over 1M yen" as validation, replacing Excel-plus-email approvals with structured operations. - Reuse the same validation logic and workflow consistently across every user-facing app. 🎯 Use Cases - Standardize status changes, approvals, and assignments as permissioned, criteria-bound operations. - Let non-technical users safely execute multi-step changes spanning several objects. - Use the action log of every operation as an audit trail for internal controls. ⚠️ Caveats - Actions execute only after passing their validation rules, so submission-criteria design drives the quality of your controls. - Changes propagate immediately across the ontology and all apps, so do not leave rule and parameter design ambiguous. #PalantirFoundry# #Ontology#

# Learning Palantir Foundry 🚀 The first move that turns a dataset into a business object. How you design Object Types largely decides downstream app performance and UX. 📌 Title and Feature URL Title: オブジェクトタイプ URL: 📝 Overview An Object Type defines the schema for a real-world entity or event. A single occurrence is an object instance (e.g., employee "Melissa Chang"), while a group is an object set (e.g., all tenured employees). This mirrors how datasets handle rows and filtered row collections. 🔧 How It Works - Primary keys and identity: objects need a primary key to uniquely identify instances. Mapping a data source to the object type lets you create and display objects in applications. - Properties: define an object's characteristics, with options such as edit-only properties, required properties, and shared properties reused across multiple object types. - Property types: support time series data, geospatial information, and struct types (nested, complex properties). - Display and search: title/display settings and search indexing improve discoverability inside apps. - Value Types: custom value types with versions, permissions, and constraints standardize representation across the ontology. 🛠 Practical Usage - Connect an employee directory or enterprise data to an Employee object type, converting raw datasets into actionable ontology instances. - Nail down primary key design first and index for search to secure downstream app performance and UX. - Use struct properties to auto-map hierarchical data, combined with shared properties for reuse. 🎯 Use Cases - Turn a customer master into a Customer object so the whole company shares one identity. - Model sensor-equipped assets with time series properties to retain operating history. - Model sites and stores with geospatial properties for map-based search and aggregation. ⚠️ Caveats - Primary key design, property types, and search indexing largely determine later app performance and UX, so treat them as your most important modeling decisions. - You must correctly map a data source to the object type before objects can be created or displayed. #PalantirFoundry# #Ontology#

『The Little Book of Reinforcement Learning』という強化学習の入門書リポジトリ。 リポジトリ内で書籍のPDFデータ本体が公開されている。モンテカルロ法からPPOまで、実際に動かせるコードが用意されている。

【📢受講生募集開始｜世界モデル Deep Learning 応用講座 2026】 本日より、「世界モデル Deep Learning 応用講座 2026」の受講生募集を開始しました。 「世界モデル」は、AIエージェントを取り巻く環境のモデルを、観測から学習によって獲得する枠組みです。 近年では、動画生成やロボティクス、AIエージェントなどにもつながる技術として注目を集めています。 本講座では、世界モデルを軸に、 🔹深層生成モデル・潜在表現 🔹モデルベース強化学習 🔹状態表現・潜在予測モデル（JEPA） 🔹3D表現・動画生成 など、基礎となる技術から最新の手法まで体系的に学びます。 さらに、講義内容に対応した実践的な演習を通じて、実際に手を動かしながら最新技術への理解を深めます。世界モデルに関するコンペティションも複数回開催予定で、講座後半ではグループで最終課題にも挑戦します。 📚全12回｜毎週月曜 18:45〜20:30 💻完全オンライン（Zoom） 📅開講：11/2（月）〜2027/2/1（月） 🎓対象：学生（深層学習の基礎知識・実装経験がある方） 📅ID登録・学年更新締切：10/16（金）14:00 📅申込締切：10/20（火）14:00 世界モデルを研究・開発に活かしたい学生の方、Physical AIや次世代のAI技術に関心のある方は、ぜひご応募ください！ ▼詳細・お申し込みはこちら

「研究者はこれを「偶発的学習（incidental learning）」と呼んでおり、これこそ多くの偶然の発見の背後にあるメカニズムなのである」 Google検索のAIモードはユーザーの好奇心を殺す？ - YAMDAS現更新履歴