News & Intel
LiveDaily AI-curated intelligence on Palantir Foundry, Ontology, AIP, Apollo, contracts, and community feedback. Updated automatically via GitHub Actions every day at 7 AM UTC.
Need access to Foundry
Hi, I’m currently taking the “Speedrun Your First AIP Workflow” course on Palantir Learn. I’ve completed the setup steps but don’t have access to a Foundry instance. Is there a way to request access to a training or sandbox environment to follow along hands-on? Thanks in advance! 3 posts - 3 participants Read full topic
Best approach for incremental processing when S3 source provides full snapshots
We’re ingesting data from an S3 bucket where an upstream Spark job writes a full snapshot (single file, fully replaced) on a scheduled basis. Each snapshot contains all rows — both unchanged and new/modified records. Our data has a unique key and a last-modified timestamp column. Our challenge is that we don’t want to process the entire snapshot through our downstream Pipeline Builder pipelines every time, as they include compute-heavy transforms and LLM calls. Our current approach is to use a Python transform to compare each new snapshot against the previous state, detect only the new and modified rows (delta), and append only the delta to a master dataset. This allows downstream Pipeline Builder pipelines to read the master dataset with the incremental input toggle enabled, processing only the newly appended rows. The Pipeline Builder output is then set to Snapshot Replace for deduplication. However, since the Snapshot Replace output is a replace transaction, any further downstream
Enhancing Mission Analysis: Integrating Artificial Intelligence Into the Military Decision-Making Process
Abstract This article details an experiment at the U.S. Army Command and General Staff College (CGSC) testing the integration of Artificial Intelligence (AI) agents, built on the Palantir Vantage platform, into Step 2 (Mission Analysis) of the Military Decisi…
AI Native Market Research and Forecast Report 2026-2032 with Deep Dives Into Cursor/Anysphere, Lovable, Replit, Tessl, Windsurf, and the Hyperscale Plays from Microsoft, Google, and AWS
The main market opportunities are in AI-native development platforms transforming software engineering. Organizations are urged to innovate with AI-augmented systems, autonomous agents, and bespoke applications. Key areas include hyper-agile engineering, AI-d…
Palantir exec: the biggest mistake retailers are making with AI? Trying to do it all with one agent
AI, as most people understand it, is a single exchange—prompt in, answer out. But retail decisions are never single exchanges.
Feature Request: Add Data Expectations to Pipeline Builder DSL
Summary Pipeline Builder supports configuring data expectations (Primary Key, Row Count, Value is not null, etc.) through the UI, but these are not exposed in the DSL . I’d like to request that expectations be added as a first-class concept in the Pipeline Builder DSL so they can be managed programmatically. Motivation When building production-grade pipelines, data expectations are essential guardrails. Currently, if you author or modify a Pipeline Builder pipeline via the DSL (e.g., through AIP / AI-assisted development, or programmatic pipeline management), there is no way to define expectations in code . They must be manually configured through the UI after the fact. This creates several problems: Expectations are invisible in the DSL — When reading a pipeline’s definition via get_pipeline_builder_definition , expectations are not included. There’s no way to audit or review them programmatically. AI-assisted pipeline development can’t add expectation
Does the loop in Logic execute sequentially for each element or no?
Is the ‘loop’ transform in AIP Logic async or does it run sequentially for all the elements? 1 post - 1 participant Read full topic
Better integration of AIP in Contour
Hello, With the new AIP releases such as AIP Analyst, we can see a good integration with some features of Foundry, however some other ones seems to be left aside such as Contour. Contour is one of OG tools but still pretty popular around - particularly for newcomers or non technical individuals - it is a direct competitor to BI Tools (PowerBI, Qlik) but it does not receive as much improvements as other platform apps ? For example, when working with AIP Analyst, I would like to be able to finalize my analysis with a Contour dashboard so all the work done (tokens spent) can be conclude with an asset. Right now, I have good analysis done by Analyst but I can only print it in PDF ? As well, the integration of AIP Assist with Contour is quite weak - a lot of hallucinations and no cross interactivity. In my opinion, only the +++ feature of Expression works well. 1 post - 1 participant Read full topic
Why No One Has Enough FDEs, Why CS Can’t Fill The Gaps, And Why Agents Most Can’t Deploy Themselves — Today
Everyone rolling out AI Agents at scale is saying the same thing right now: we can’t hire enough forward deployed engineers. And they’re right. But most companies are drawing the wrong conclusion from that fact. The shortage of FDEs isn’t just a hiring proble…
Cannot Bind Slate Variables to Foundry Function—Custom Endpoints Access Request
Hello, I’ve read through existing posts about calling Foundry Functions (including the thread about AIP Logic Function endpoints), but my situation has some key differences. I’m hoping to get clarity on whether Custom Endpoints access is required, or if there’s another supported pattern I’ve missed. What I’ve Built I have a Python Foundry Function deployed from a Functions repository (not AIP Logic). It’s published to the Ontology and Function Registry: Function Name: market_metrics_kpi Repository Type: Python Functions (not AIP Logic, not TypeScript) Parameters: market (Optional string), quarter (Optional string), asset_type (Optional string) Output: Custom dataclass MarketMetricsKPIResult containing: avgVacancy (float) sumAbsorption (int) sumUnderConstruction (int) marketCount (int) chartData (object with market and vacancy arrays) The Function queries Ontology objects ( MarketMe
Why Palantir (PLTR) Remains a Standout in Agentic AI at Scale
If you click 'Accept all', we and our partners, including 251 who are part of the IAB Transparency & Consent Framework, will also store and / or access information on a device (in other words, us… [+1046 chars]
Palantir Stock 2026: Buy or Sell PLTR as AI Platform Drives 61% Revenue Growth Despite High Valuation Concerns
NEW YORK — Palantir Technologies Inc. (NASDAQ: PLTR) shares traded near $128 as of April 10, 2026, with Wall Street issuing a Moderate Buy consensus amid explosive commercial momentum from its Artificial Intelligence Platform (AIP) and steady government contr…
How to execute code in a Foundry Agent?
I would like to let my agent execute arbitrary code within a containerized/safe environment, depending on the user permissions. The goal could be to make specific queries to the ontology, or to read/write files stored in the ontology, etc. How can I do this ? 3 posts - 2 participants Read full topic
Custom Text Extraction for Tracked-Deletion Text in Word Documents
Hey everyone! I’m working on a Foundry pipeline where users upload redlined Word documents (.docx), and an LLM in AIP Logic compares proposed deletions against gold-standard language stored in an ontology object. The AI reasoning side is mostly sorted — my blocker is the preprocessing step. The Problem: Foundry’s native text extraction and AIP’s built-in document handling don’t appear to have a way to detect and tag tracked-deletion text (strikethrough / <w:del> nodes in the underlying Word XML). I need a custom function that: Reads the raw .docx file Parses the underlying XML (or converts to HTML) to identify tracked deletions Returns plain text with deleted segments specially tagged (e.g. wrapped in <strikethrough> tags) before the text ever reaches AIP Logic What I’ve Tried: Python Functions API — media references don’t appear to be supported yet, which is a blocker since uploaded docs come in as media references via a media set TypeScript
C3 AI debuts agentic tool that transforms natural language prompts into enterprise-grade systems
C3.ai Inc. has lifted the lid off a new “agentic” software development platform it’s calling C3 Code, which is capable of transforming natural language requests into production-ready applications fully autonomously. Unveiled today, C3 Code is said to shift th…
Alternatives to Completion strategies
We are currently using file synchronization via Agent Worker. Due to a Foundry update, the Completion strategies (deleting files after synchronization) have been discontinued, so we would like to know about alternative methods. https://www.palantir.com/documentation/static/foundry/data-connection/file-based-syncs/#completion-strategies Is my understanding of the alternative methods correct as follows? (1) Creating a REST API Source Create a REST API Source in the Foundry Data Connection app and configure the SSH connection information (hostname, port 22, SSH username, password). Register the password as a secret. (2) Configuring Agent Proxy Egress Policy Create an Agent Proxy Egress Policy to allow SSH communication from the Foundry Worker to the Agent Host. If the Agent Host has a private IP address, configure Host Override to allow access via the domain name. (3) Implement File Deletion Transform Delete files on the Agent Host via SSH (The files to be deleted are the
how api use my ontoloty and links in llm context?
I’m trying to learn and use AIP to build my application. But I don’t know how AIP use the `ontology context` to fill the prompt. Can anyone help me, plz! 1 post - 1 participant Read full topic
Completion strategiesの代替手段
We are currently using file synchronization via Agent Worker. Due to a Foundry update, the Completion strategies (deleting files after synchronization) have been discontinued, so we would like to know about alternative methods. https://www.palantir.com/documentation/static/foundry/data-connection/file-based-syncs/#completion-strategies Is my understanding of the alternative methods correct as follows? (1) Creating a REST API Source Create a REST API Source in the Foundry Data Connection app and configure the SSH connection information (hostname, port 22, SSH username, password). Register the password as a secret. (2) Configuring Agent Proxy Egress Policy Create an Agent Proxy Egress Policy to allow SSH communication from the Foundry Worker to the Agent Host. If the Agent Host has a private IP address, configure Host Override to allow access via the domain name. (3) Implement File Deletion Transform Delete files on the Agent Host via SSH (The files to be deleted are the