Intel Foundry revenue hit $5.8B in Q2, up 31% YoY, as the company lifted FY26 capex to $20B from $18B. The US foundry buildout is converting from strategic narrative into recognized revenue, and capacity coming online meets a CPU cycle still running hot. $INTC

Digital Foundry has criticized Activision’s new Call of Duty: Black Ops port for PS4 and PS5, saying it doesn’t make good use of the PS5’s hardware. The PS5 version runs at 1080p at 60 FPS, still has no anti-aliasing, and doesn’t include the visual improvements many players expected from a native PS5 release. “A 1080p60 presentation would be potentially acceptable for the PS4 version… but for a brand new PS5 conversion, it’s disappointingly poor and well below what the hardware is capable of.” The team also called the port “a deeply odd state of affairs,” saying it’s surprising that so few improvements were made for modern hardware.

Digital Foundry says GTA 6 is not expected to run at 60 FPS on any PS5 console, including the PS5 Pro. According to the team, the biggest problem is the CPU, not the GPU. The game’s large open world, advanced AI, heavy traffic, and high number of NPCs are expected to be too demanding for current PS5 hardware to deliver stable 60 FPS. The PS5 Pro should still offer better image quality and improved graphics, but Digital Foundry doesn’t think it will be enough to reach 60 FPS.

# 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 🚀 Are you recomputing billion-row tables in full every single day? Process only the delta, and your compute costs drop dramatically. 📌 Title and Feature URL Title: Incremental Transforms URL: 📝 Overview Incremental transforms enable efficient data processing by handling only the data added or changed since the last run, instead of reprocessing the entire dataset. They're enabled with the `@incremental()` decorator, which automatically chooses between incremental and snapshot execution based on how the inputs changed. 🔧 How It Works The `@incremental()` decorator wraps a transform function to give it delta-processing capability. - It converts the standard input/output objects into incremental variants: `IncrementalTransformInput`, `IncrementalTransformOutput`, and `IncrementalTransformContext` - Input read modes can be `added` (new rows since last run, the default), `previous` (state from the last run), or `current` (the full current dataset) - Output write modes are `modify` (append to existing output) or `replace` (overwrite entirely); the default is `modify` for incremental runs and `replace` for snapshot runs - Key parameters include `require_incremental` (fail if incremental isn't possible), `semantic_version` (bumping it triggers a snapshot rebuild), `snapshot_inputs` (exempt specific inputs from incremental constraints), and `strict_append` (enforce append-only safety) 🛠 Practical Usage - Add `@incremental()` to large append-heavy log or transaction tables to replace daily full recomputes with delta processing - When you change logic, bump `semantic_version` to safely trigger a snapshot rebuild - Use `require_incremental` to force delta execution when you don't want a silent full reprocess - Use `strict_append` when you need strict append-only guarantees 🎯 Use Cases - Slashing soaring compute costs from daily full recomputes of billion-row tables via delta processing - Serving as the core cost-optimization technique that determines the economics of large-scale projects - Daily ingestion of append-only transaction histories and event logs - Streamlining pipelines whose upstream grows only through additions (APPEND/UPDATE) ⚠️ Caveats - Preview features always run non-incrementally - Unless requirements are met (all non-snapshot inputs contain additions only via APPEND/UPDATE, the input list stays stable, `semantic_version` is unchanged, etc.), the transform automatically runs in snapshot mode and fully replaces the output - Updated or deleted input files must be marked as snapshot inputs - The `previous` mode requires schema validation matching the previous output structure - Transform logic must support both incremental and snapshot execution paths #PalantirFoundry# #DataEngineering#

# Learning Palantir Foundry 🚀 Bring complex logic that no-code can't reach into your data platform, along with full software-engineering quality control. That's what Code Repositories delivers. 📌 Title and Feature URL Title: Code Repositories (Python Transforms) URL: 📝 Overview Code Repositories is a web-based integrated development environment (IDE) for creating and collaborating on production-ready code within Foundry. It provides a friendly UI over the underlying Git repositories, so teams can work without command-line access. With platform-specific features, you can apply software development practices directly to data engineering. 🔧 How It Works Version control and collaboration are at its core. - Common Git tasks (branching, committing, release tagging) execute through the web UI - Pull requests drive code review, with "highly configurable" permissions that support quality assurance such as mandatory reviews - IntelliSense, linting, error checking, and contextual help dialogs are available across all repository types - Transforms repositories let you author data transformation logic in Python, Java, or SQL with preview and debugging - Functions repositories natively integrate the Ontology and run low-latency business logic in TypeScript or Python 🛠 Practical Usage - Use PySpark to implement billion-row entity resolution and complex business rules in code - Require PR reviews so a second reviewer and CI checks must pass before merge - Add unit tests to guard transform logic against regressions - In Functions repositories, leverage Ontology-data-type autocomplete to write logic safely - Bring machine learning workflows into the platform via model development repositories 🎯 Use Cases - Implementing complex reconciliation and business rules in PySpark that Pipeline Builder can't express - Structurally eliminating "regressions from editing production directly" through mandatory reviews and branch-based workflows - Implementing derived KPIs and validation logic as Functions reused across apps - Managing ML model training and inference code under governance ⚠️ Caveats - The docs note that Japanese translations are machine-generated and unverified, so localized content may have accuracy limitations - Each repository type (Transforms/Functions/Model) supports different languages and purposes, so pick the one that fits your goal - Being a pro-code environment, the quality benefits only materialize if your organization establishes review, CI, and test practices #PalantirFoundry# #DataEngineering#

According to Digital Foundry, Call of Duty: Black Ops 2 looks better on PS5 Pro when playing the PS4 version rather than the native PS5 version. The PS4 version produces a sharper image thanks to the PS5 Pro’s PS4 image enhancement feature. The PS5 version uses a lower internal resolution and relies more on upscaling, which makes the image look softer.

Claude in Microsoft Foundry is now generally available, hosted on Azure. Prompt caching and extended thinking are supported today, with more capabilities on the way. Read more: