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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.
Crime Analytics Global Market Analysis Report 2026: $15.68 Bn Opportunities, Trends, Competitive Landscape, Strategies, and Forecasts, 2020-2025, 2025-2030F, 2035F
Key opportunities in the crime analytics market include the growing integration of AI and predictive policing, increased deployment of real-time monitoring tools, expansion of cloud-based analytics, and emphasis on data-driven threat detection, driving growth…
GitHub-First Development on Foundry Code Repos: Mirroring, CI/CD, and AI Agent Workflows
This is a x-post of our inaugural [Engineering Blog post](https://www.gallatin.ai/news/github-first-development-on-foundry-code-repos-mirroring-ci-cd-and-ai-agent-workflows) @ Gallatin GitHub-First Development on Foundry Code Repos: Mirroring, CI/CD, and AI Agent Workflows Josh Bouganim - Senior Software Engineer At Gallatin, we’ve built a set of tools that lets us keep our primary repository on GitHub while automatically mirroring changes to a Palantir Foundry Code Repo. It provides automated branch protection management, tag-based release deployment, Foundry build status monitoring, a gradlew wrapper for local development, and CLI-based function execution. This makes the entire develop-test-deploy cycle UI-free for engineers and scriptable for AI coding agents. Why We Built This We’re building [*Navigator*]( https://www.gallatin.ai/product ) , a logistics and supply chain management platform powered by Palantir Foundry. Our backend runs almost entirely on Found
Dell expands AI factory with NVIDIA to push enterprise AI beyond experiments
At Dell Technologies World 2026, Dell Technologies announced a major expansion of its Dell AI Factory with NVIDIA, introducing new infrastructure, AI-ready data platforms, and ecosystem partnerships designed to help enterprises scale AI deployments beyond exp…
Armada Announces Agreement with Johnson Controls for Galleon Forge One; Raises $230M in Oversubscribed Series B with a Pre-Money Valuation of $2B to Accelerate Deployment of the U.S. AI Stack and Support Explosive Customer Demand Growth Across Industries
Framework agreement with Johnson Controls outlines plans for modular data center production at dedicated factory in Arizona, expected to create more than 500 jobs SAN FRANCISCO, May 19, 2026 /PRNewswire/ -- "The AI race will not be won by one-off projects," s…
The Deployment Company, Back to the 70s, Apple and Intel
OpenAI is forming a new company to deploy AI, and the other labs aren't far behind, reinforcing the thesis that AI's impact will require top-down implementation. Then, Apple has economic reasons to work with Intel.
OpenAI just acquired the consulting firm it was born alongside. The model company is now the services company.
Tomoro was created in 2023 in alliance with OpenAI. The Edinburgh and London-based firm built AI concierges for Virgin Atlantic, in-game support agents for Supercell, and deployment systems for Fidelity International, Tesco, Red Bull, Mattel, and the NBA. It …
OpenAI Just Launched a Consulting Arm to Help Companies Deploy AI
The OpenAI Deployment Company launches with $4 billion, 19 investors, and a Palantir-style playbook to embed engineers inside enterprises.
Removing FK Columns used in Links in Ontology as Code Repos Breaks Install
I am attempting to install a new version of my ontology as code repo via markeplace. I am all the way through to the final checks and got 47 errors that prevent install similar to: “The foreign key specified by a link type doesn’t exist as a property on the specified object type.” These error appear to point at RIDs in the existing ontology, but they are hard to make out. I opened a seperate ticket on that topic here . These FK relationships have been removed in my `mts` file and I have remapped the entire ontology to the new dataset schemas as part of the install process. This seems like a valid developer path as database schemas evolve over time. The behavior I could expect is the existing link types would be removed upon install, not that I am blocked on upgrade. Is there a workaround or fix for this that can be issued? It’s currently blocking deployment to our customer’s dev environment. We were hoping to get through UAT this week as well with a production deployment next w
Met's Palantir deployment turns heat on its own officers
HN Discussion — 1 comments · 5 points.
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…
The Power of Prophecy, from Apollo to AI
Divination is not just good for business; it’s a good business in itself. Prophets are merchants of prediction. The Delphic Oracle sat on Mount Parnassus, on the northern coast of the Gulf of Corinth, around ninety miles northwest of Athens,
Zero-shot TimesFM on Foundry: Model Adapter + Live Inference pattern — and three questions
Shipped this last week and figured I’d share the shape, because honestly I couldn’t find anyone on here who had done TimesFM on Foundry yet and I’d rather compare notes than keep guessing alone. If someone has a cleaner version of this I’d genuinely like to hear it. One note before the code — yes, Model Studio already has a native time-series forecasting trainer (AutoGluon under the hood, ensembles AutoETS, DeepAR, PatchTST, TFT, TiDE, Theta). If you have labeled history per series, start there. This post is for the neighbouring case: cold-start series, brand-new sources, no labeled history yet — where supervised per-series training doesn’t really apply and a zero-shot foundation model can carry you until you’ve accumulated enough training data to go back to Model Studio. The shape, short version: checkpoint as a Foundry dataset → Model Adapter → Live Deployment → named Function → Workshop widget. No fine-tuning. Bence TimesFM is good enough zero-shot for daily-granularity foreca
As the Counter-Drone Era Goes Mainstream, This Nasdaq AI Defense Stock Just Landed a World Cup Deployment Order
From Latin American public safety forces to Tier-1 U.S. defense primes, AI-driven sensing and autonomous vehicles are moving from pitch decks to procurement Issued on behalf of VisionWave Holdings, Inc. Companies mentioned in this commentary include: VisionWa…
Oil And Gas Analytics Market Size to Worth USD 86.60 Billion by 2035 | Research by SNS Insider
Oil And Gas Analytics Market Size, Share, Trends & Segmentation, By Offering, Deployment, Application, Analytics Type, End-user, Region, And Forecast 2035...
How to optimize time series analysis with foundryTS
Hi all, I would like to perform a fleet analysis based on timeseries data. For that purpose I will use foundryTS library to perform queries on the timeseries. I understand that the analysis can be quite heavy in terms of computation time if I want to analyse 100 aircraft that performed 500.000 flights over the last 5 years. That’s the reason why I’m looking for some best practices with respect to timeseries analysis with foundryTS. Here is my use case: for each flight, I need to extract some features during the take off phase. I need 3 parameters to identify the events during the flight (altitude and engine temperatures), then I want to average 60 parameters over a window of 4 seconds around the identified events. I tried several strategies on a sample of aircraft before launching the analysis on the complete fleet. First strategy: for each msn and each flight (interval is one flight), I first perform an interpolation at 1Hz to align all the data on the same timestamp, then I a
Shyam Sankar: AI narratives are misleading, human agency is crucial for ethical deployment, and user feedback must guide technology development | Shawn Ryan Show
AI's role in empowering American workers could redefine productivity and challenge traditional organizational power structures. The post Shyam Sankar: AI narratives are misleading, human agency is crucial for ethical deployment, and user feedback must guide t…
Manufacturing with the Connected Edge
Industrial and defense environments generate massive amounts of data that can’t wait for the cloud. Latency is often measured in milliseconds, and resiliency is paramount. A manufacturing plant can’t go down due to flaky Wi-Fi or a public cloud outage. “Traditional” approaches — shipping servers, hiring local IT, bespoke development, managing one-off deployments — simply don’t scale. Critical operations require infrastructure that is purpose-built for complexity at the edge. In the era of AI-enabled automation, the challenge isn’t building one edge device; it’s operating hundreds or thousands of devices, consistently and securely, without dedicated IT teams at every site. This requires a holistic architecture that spans three core dimensions: Data : Aggregating structured and unstructured streams from sensors like video, PLC(s), SCADA , MES(s), PLM(s), ERP(s) etc. into a unified namespace , which is accessible in real-time. Logic : Processing and analyzing that data locally,
Forward-Deployed Engineers Emerge as One of AI’s Fastest-Growing Jobs
Artificial intelligence is often feared for automating work. Yet as companies move from experimentation to the deployment of AI workflows, new job categories are emerging. Across the AI industry, startups and enterprise software firms are hiring engineers who…