# 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#

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Sakana AI CEOの David Ha（@hardmaru）が、Tim Romero 氏のポッドキャスト Disrupting Japan Podcast に出演しました。 日本におけるAIの未来は、米国や中国とは少し違った形を取り得るのか。Sakana AIの内側から何が見えているのか。そうしたテーマで、長めの対談を行っています。 Davidは、Sakana AI の事業展開戦略、メガバンクとの取り組みがPoCから本番運用へと進みつつある現状、巨大なフロンティアモデル一つに依存するよりも複数のモデルを束ねるオーケストレーションのほうが長期的には合理的であるという見方、そして実務における「AI主権」とはスタック全体を自前で所有することではなく、グローバルなサプライチェーンの中で、AIを国内で開発・適応・運用していく能力を確保することだと、Davidは語りました。 対話のなかでは、当社の研究成果やプロダクトにも多く触れました。Sakana Fuguは、強化学習で訓練したオーケストレーションモデルで、複数ステップのタスクを多様なLLMに振り分けます。Namazuは、日本の知識や価値観を反映するようにポストトレーニングを施した、オープンウェイトモデルのシリーズです。東京のRSI Labは、The AI Scientistから続く取り組みとして、再帰的自己改善とAIによる科学的発見を専門に扱う研究組織です。共同創業者のLlion Jonesは、Transformerそのものを超えるアーキテクチャの探求という、より長期的な目標を追い続けています。 最後に「日本について一つだけ変えられるとしたら」と問われたDavidの答えは、「希望（hope）」でした。 「皆が集団として希望を感じていることが必要だと思います。その希望こそが、物事を動かしていく。日本に希望をもたらすのは何でしょうか。お金でしょうか。減税でしょうか。社会保障の充実でしょうか。おそらく、そのどれでもないはずです。もし魔法の杖を一本持てるとしたら、日本の人々が未来をもっと楽観的に捉えられるようにしたい。希望によって変化が起こり、その変化がさらなる希望をもたらすのではないでしょうか。」 ⸻⸻⸻⸻⸻⸻ Our CEO David Ha joined Tim Romero on Disrupting Japan Podcast for a long conversation on how the future of AI may take a different shape in Japan than in the U.S. or China, and on what that looks like from inside Sakana AI. David walks through Sakana AI’s go-to-market strategy, how Japanese megabanks have moved real AI workflows from proof-of-concept into production, why orchestration across many models is likely to win out over a single massive frontier model, and what sovereign AI actually means in practice: not owning the entire stack, but having the capability to develop, adapt, and run AI domestically as part of a global supply chain. The conversation also covers the research directions behind the company. Sakana Fugu is our orchestration model, trained with reinforcement learning to route multi-step tasks across a variety of underlying LLMs. Namazu is our line of post-trained open-weight models tuned to reflect Japanese knowledge and values. The RSI Lab in Tokyo is dedicated to recursive self-improvement and AI-driven scientific discovery, continuing the work that began with The AI Scientist. And our co-founder Llion Jones keeps pushing on a longer-horizon goal of moving beyond the Transformer architecture itself. Asked at the end what one thing he would change about Japan, David’s answer was “hope.” “To really move the needle in terms of innovation, like the era in Japan when you have companies like Sony or Panasonic come out, there are a few factors. One is that the nation needs to have a collective sense of hope. A nation needs to feel hopeful. It could be poor. Everyone can be dirt poor, but they have hope. And that hope leads to things being done. So, what creates hope in Japan? Is it more money? Is it less taxes? Or is it more benefits? Probably not. I think it’s stories and narratives. If I have a magic wand, if the population in Japan is more optimistic about the future of this country, suddenly they’re persuaded, to be more optimistic. People should be optimistic. With optimism comes change. And change leads to more optimism.” Listen to the full episode: 🎏