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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.
Who Gets an FDE, and Who Doesn’t: The Great B2B + AI Debate Right Now
I was catching up with one of our favorite AI agent vendors the other day. Strong product. Strong traction. One of the ones we recommend. And they told me: going forward, only customers with 5,000 or more employees get a dedicated Forward Deployed Engineer. E…
Google explains why its all-in-one AI stack embraces competitors
'Differentiated, but open' Google Cloud Next Google Cloud’s Andi Gutmans said that the company holds a structural advantage over its largest rivals in the race to win value from AI agents in the enterprise, arguing that no competitor currently combines cloud …
Rilian raises $17.5 million to bring agentic AI to sovereign defence
The McLean, Virginia startup’s Caspian platform sits as a command layer above existing security stacks, deploying pre-trained AI agents into air-gapped and compliance-restricted environments. One of its co-founders is Nick Pompeo, son of former US Secretary o…
secret key management in authentication
Although I totally support limiting authentication options to asymmetric cryptography, it looks like I cannot extract my secret (private) key (identity). Windows security offers a choice of storing the key onto local machine (TPM enclave), mobile device (via QR/Bluetooth), hardware FIDO2 key. I do not have the latter to test but the second option does not work because Palantir platform rejects it in the end with “type of passkey does not meet enrollment security requirements”. As a result, my access is locked to a single PC: neither could I work with AIP from my MacBook, nor would I be able to restore access if the PC is lost or stolen. How could I access my identity (secret key), please? 1 post - 1 participant Read full topic
Allow admins to set the default model(s) for AI tools (FDE / Analyst)
These tools currently default to Claude Opus, which is rather expensive to run. We’re currently being challenged on opening these tools up to a wider audience, as there’s fear of run-away token costs. As an admin, I should be able to better control what the default model is, since regular users rarely change these. In addition, it would be great to have more fine-grained control over models served via AIP – models available in certain spaces, applications, etc. 1 post - 1 participant Read full topic
She raised concerns about her company's contracts with ICE. Then she lost her job
Billie Little had worked for Thomson Reuters for about two decades. She was fired after questioning whether federal immigration agents unlawfully used their products.
Budget Leak Reveals DHS Is Creating Smart Glasses for ICE
The Department of Homeland Security is developing smart glasses that would allow federal agents to identify people using biometric data in real time. Journalist Ken Klippenstein, citing a budget request from DHS, reports that these devices, slated to be relea…
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]