Identity gets an upgrade 🔐 @OntologyNetwork is all about trust, identity, and data for Web3 with tools built to make apps more secure, more private and easier to plug into real-world use. Less hype, more rails. Dive into $ONT on ChangeNOW:

🧠 One answer to "how do you give agents accurate business context?": auto-build ontologies and a knowledge graph from existing data, then serve it via MCP. An open-source project from AWS. Title: Context Ontology Accelerator (aws/context-ontology-accelerator) URL: A semantic context layer that gives AI agents validated business context. Three highlights stand out. 🔎 A Scan → Model → Serve pipeline Connect diverse data sources to discover schemas and ingest documents (Scan), induce formal ontologies and build a unified knowledge graph (Model), and expose it via VKG SPARQL federation and MCP (Serve). It derives semantic structure from existing data, no manual knowledge engineering. ✅ Consistency validated by reasoning engines HermiT and ELK validate ontology consistency, enabling rule-based checks from formal constraints. Agents query validated business rules instead of relying on memorized training data. 🏗 AWS-native and production-minded Deployed via AWS CDK, a VKG powered by Ontop, and namespace RBAC (owner/maintainer/data-steward/data-analyst). API design via Smithy, UI in React + Cloudscape. A solid foundation for running agents within validated context while keeping explainability. #KnowledgeGraph# #AIAgents#

# Learning Palantir Foundry 🚀 Answer "where did this dashboard number come from?" in an instant. Data Lineage is an exploration tool that visualizes the entire flow of your data. 📌 Title and Feature URL Title: Data Lineage URL: 📝 Overview Data Lineage is an interactive visualization tool that comprehensively shows how data flows through the Foundry platform. It helps you understand data movement, dependencies, and transformations across your entire data ecosystem. Because you can trace the lineage from sources through pipelines, the Ontology, and apps as a graph, it sharply reduces the cost of incident response and audit explanations. 🔧 How It Works It represents data dependencies through a graph-based visualization. - Find datasets using project names, table identifiers, or row labels, and browse data directly from Foundry Projects - Expand or collapse ancestor (upstream) and descendant (downstream) relationships for any dataset - View multiple table attributes at once, down to schema details, build timestamps, and source code - Apply custom color schemes to highlight pipeline characteristics such as stale datasets - Create shareable pipeline snapshots to communicate within the team 🛠 Practical Usage - Trace upstream from a dashboard or output dataset to pinpoint the source of a number - When an upstream schema changes, trace downstream to map the blast radius - Color-code stale datasets to discover neglected pipelines - Drill down from a high-level overview into granular technical details like transformation code and execution history - Share pipeline snapshots to document data workflows across functions 🎯 Use Cases - Instantly answering "what is the origin of this dashboard's number" - Identifying the impact scope of upstream schema changes in advance to prevent incidents - Presenting data lineage during audits to cut explanation costs - Finding stale or unused datasets to tidy up pipelines ⚠️ Caveats - This overview page does not explicitly discuss performance or graph-complexity constraints with extremely large pipelines - Lineage covers data flow within the Foundry platform; processing outside the platform is out of visualization scope - The accuracy of lineage depends on transforms and pipelines being properly configured within Foundry #PalantirFoundry# #DataLineage#

# Learning Palantir Foundry 🚀 "How do we connect to an on-prem Oracle or SAP in a closed network without poking holes in the firewall?" Data Connection is what clears that first hurdle of enterprise adoption. 📌 Title and Feature URL Title: Data Connection URL: 📝 Overview Data Connection is an application that synchronizes external system data into Foundry for use across the data integration, modeling, and ontology layers. It also supports outbound connections through webhooks and data exports to write data back to external systems. It handles many source types and abstracts away the messy parts — authentication, scheduling, and monitoring — so you can configure pipelines from simple interfaces. 🔧 How It Works Foundry standardizes data connections around three principles: - Robustness: automatic retries, processing in small batches, and integrated health monitoring that warns of failures. Data should be ingested "as-is" from the most primitive source, making Foundry's versioned pipelines the single source of truth for all transformations rather than depending on external preprocessing. - Extensibility: beyond standard integrations (databases, FTPS, HDFS, S3, SFTP), the system accommodates new source types. Because core functions like scheduling and orchestration are standardized, only connection-specific adjustments are needed. - Usability: the system abstracts complexity, letting users configure through simple interfaces instead of manually managing authentication, scheduling, and monitoring. - Key components include agent setup, source configuration, batch/streaming syncs, webhooks, and exports. 🛠 Practical Usage - Access Data Connection from the workspace navigation or the application portal. - Configure a source, then set up a batch or streaming sync to ingest data "as-is." - Concentrate post-ingestion transformations in Foundry pipelines, avoiding preprocessing on the source side. - For write-back, configure outbound integrations using webhooks or exports. 🎯 Use Cases - Connect on-prem Oracle/SAP in a closed network via an agent model (outbound-only) without firewall changes. - Scheduled batch ingestion from diverse sources such as databases, SFTP, and S3. - Outbound integration to write processed results back into external systems. ⚠️ Caveats - The design assumes ingesting data "as-is" and centralizing transforms in Foundry pipelines; transforming on the source side undermines traceability. - Configuring agents and sources requires proper network and authentication setup. - Do not assume external service limits or terms; verify the constraints of each connected source in advance. #PalantirFoundry# #DataIntegration#

Palantir is still so misunderstood by most. I am such a massive bear on software because I think LLMs are going to render almost all of it useless eventually, but Palantir is the rails for the LLM train to mow down everything. Every corporate AI meeting or talk I’ve attended has the same problem, and ontology is the solution. Most don’t even know yet.

Why going direct is about telling a story much bigger than your company's: @pmarca: "The story of you and your startup is not inherently an interesting story, but there is almost certainly an interesting story that involves your startup, and this is sort of the cheat code of it." @bhorowitz: "The grand wizard of this is Alex Karp. If you watch his interviews, he never talks about Palantir. The only thing he ever says about Palantir, Marc pointed this out to me, is 'ontology' and 'orchestration,' two words that nobody knows what they mean." "Nobody knows what Palantir does as a result, but it doesn't matter because it's the future of the US military, Palantir. Superintelligence, Palantir. Whatever the story is that's really good, Alex will go tell that story. Neurodivergence." "Whatever is interesting, he'll just start talking about. And then because he's this founder of Palantir, the CEO of Palantir, like that just works." "When something happens in the world, something happens involving US military, AI in the military, or this or that, geopolitics with China, he's the first phone call, right? Because he's the guy who's been out there talking about that." @eriktorenberg: "Ryan Petersen... has done a phenomenal job of that." @pmarca: "The difference between talking about freight versus talking about 'the global supply chain is completely collapsing' during COVID, and 'we're all gonna starve to death.'" "And then therefore, he's the guy who literally goes on 60 Minutes to explain to the world that in fact, yes, we all are about to starve to death." @typesfast

🦈 Before that press release goes live, why not test it against "hundreds of public voices" first? A slightly futuristic engine now simulates an entire crowd's reaction for $1 in 10 minutes. Title: aaronjmars/MiroShark URL: 📦 Overview MiroShark is a "Universal Swarm Intelligence Engine." For any scenario—a press release, a news headline, a policy draft, or a question—it simulates in real time how hundreds of AI agents would react. The agents post, argue, trade, and shift their positions as simulated time passes. ❓ Challenges Solved Organizations want to test how the real public will receive an idea before committing resources. MiroShark removes the need for lengthy focus groups and expensive market research, enabling validation for under $1 in less than 10 minutes. 💡 How It Works It runs in five phases. ・Generate an ontology from the input documents ・Build a Neo4j knowledge graph of entity relationships ・Ground 100+ personas using demographics, web enrichment, and graph attributes ・Have agents interact hourly across Twitter, Reddit, and prediction markets ・Generate reports that cite the actual simulated posts and trades Posts are ingested via NER, embeddings, and entity resolution, then retrieved by fusing vector, BM25, and graph traversal. 🎯 Use Cases ・PR crisis testing and market-reaction forecasting ・Ad campaign pre-testing and policy impact analysis ・Personal decision scenarios and historical counterfactuals You can also inject breaking news mid-run, or fork a running simulation (counterfactual branching). 📊 Highlights ・1.3k GitHub stars and 265 forks, AGPL-3.0 licensed ・Each simulation runs at roughly $1, about 10 minutes, with 100+ agents ・Python backend, Vue.js frontend, Neo4j database; LLMs via OpenRouter (local Ollama also supported) #AIAgents# #Simulation#

$PLTR / $PEGA Pegasystems was firing a few shots Palantir's way during the JPM conference today. "What's ironic is, I think what Palantir does is as subject to attack by the LLMs as anything. I guarantee you Claude can build a brilliant, you know, ontology, you know, which is how they talk about what they do... At the end of the day, I think they gotta send a whole slew of those FDE's in to actually get anything to work." Two things are ironic about this. 1⃣ Seeing that an LLM can help in building something like an Ontology, but missing AI-FDE's existence (a huge oversight) and how LLMs help multiply FDE efficacy. 2⃣ Headcount and #'s say otherwise. 4001 Employees - Mar 31 2025 4164 Employees - June 30 2025 4414 Employees - Sept 30 2025 4429 Employees - Dec 31 2025 4395 Employees - Mar 31 2026 While Palantir headcount is up 10% Y/Y. However, it's flat since Sept 30th. Find me on this chart (or on any Palantir metric) a sign of weakness due to failing to hiring and deploying far more FDEs. I'll wait.