You "vibe coded" the import. Understand Adam's optimization dynamics, why it fails spectacularly, and how to fix it.
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Using mathematical optimization to solve a pickup-and-delivery problem with time windows.
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In a world where human time is expensive, learn how to use it only when really necessary
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Building a great AI agent isn’t just about choosing the right models. The harness is the architecture surrounding the model. How it renders context, executes...Building a great AI agent isn’t just about choosing the right models. The harness is the architecture surrounding the model. How it renders context, executes actions, manages state, and decides when a task is done shapes outcomes just as much as the model itself. Harness design alone can account for double-digit swings in benchmark results and significant differences in token cost…
Customization is what enables developers to take a general model and tailor it to use cases, domains, languages, and more. However, customization comes with a...Customization is what enables developers to take a general model and tailor it to use cases, domains, languages, and more. However, customization comes with a few challenges. It requires infrastructure, technical expertise, and software specific to the workflow, as well as resources such as GPUs and the ability to use them effectively. It also depends on specialized domain knowledge: What…
Apache Spark 4.2 launched last week, and it signals an expansion of Spark’s decade-plus role at the center of enterprise data processing
With new features for AI workloads, including governed metrics, vector retrieval primitives, real-time processing, improved Python support and native geospatial analytics, Spark 4.2 builds on a recent history of new AI and streaming features, reflecting how many engineering teams use the platform today. The release builds on Spark’s traditional role as a data processing engine by adding more of the capabilities needed to support production AI applications.
The launch introduced several features that enable developers to do more without leaving the platform, and for teams already using Spark, that could mean fewer systems to manage.
One team’s definition of a business metric isn’t always the same as another’s. Over time, those differences can lead to conflicting reports and uncertainty about which number to trust.
That becomes even more problematic when AI applications start consuming the same enterprise data that analysts and business intelligence tools use. If different teams define the same metric differently, AI systems can produce inconsistent results for the same question.
Spark 4.2 introduces governed metric views to address that issue. Organizations can define a business metric once and reuse that definition across applications. A metric view makes dimensions and measures first-class objects that Spark understands, so the engine can preserve the intended aggregation semantics regardless of who or what is querying it.
Organizations can define a business metric once and reuse that definition across applications.
One of the more significant additions is native vector search, which reduces the need to move data between Spark and a separate vector database.
Spark 4.2 introduces vector distance and similarity functions, vector normalization, vector aggregation, and NEAREST BY, a new SQL operator for top-K similarity searches. By bringing vector search into Spark, developers can keep more of their retrieval pipeline on the same platform.
By bringing vector search into Spark, developers can keep more of their retrieval pipeline in the same platform.
Spark 4.2 makes it easier to move data between Spark and Arrow-native tools. With support for the Arrow C Data Interface and the PyCapsule protocol, Spark DataFrames can be passed directly to tools like Polars and DuckDB without copying or serializing the underlying data, as long as both sides support the standards.
Python also gets a few other updates. PySpark has been expanded; Arrow-optimized UDF execution is now the default, and Python Data Sources now include built-in time and memory profiling to help developers troubleshoot custom connectors.
Spark Connect, which separates the client from the Spark server via a gRPC- and Arrow-based protocol, receives several updates in 4.2. The key idea is that a client builds a logical plan, the server handles analysis, optimization, and execution, and the results come back as Arrow batches. The client doesn’t need a full Spark runtime or a colocated JVM.
This update includes several changes to Spark Connect, the project’s client-server interface. AI applications can send processing requests to a remote Spark cluster while the work continues to run inside Spark. The release improves RDD API compatibility, error handling, and status reporting along that path.
Streaming gets several updates in Spark 4.2, including Auto CDC and Real-Time Mode. Many AI applications depend on continuously updated data rather than scheduled batch jobs. Auto CDC brings first-class change data capture to Spark Declarative Pipelines, handling the merge logic for keeping target tables current as source data changes — something that previously required hand-written, error-prone code. The new CHANGES SQL clause allows teams to retrieve data changes through a single SQL interface.
Spark 4.2 also adds built-in GEOMETRY and GEOGRAPHY types along with ST_* functions for location-aware analytics, without requiring external spatial extensions. For teams doing anything with location data — logistics, real estate, IoT — this removes another reason to move data out of Spark.
Spark 4.2 brings more of the AI and data stack into the platform itself. Features that once depended on separate tools can now be handled directly in Spark.
For teams that currently use Spark for ETL and then hand data off to other systems for retrieval, governance, or real-time processing, this release starts to blur that line. As more AI applications run directly on operational data, Spark is becoming part of the serving layer rather than simply preparing data for it.
Spark is becoming part of the serving layer rather than simply preparing data for it.
The post Spark 4.2 has a feature that could retire your vector database appeared first on The New Stack.
Mixture-of-experts (MoE) models have quickly become a foundational component of modern, large-scale AI systems. They are widely adopted because they enable...Mixture-of-experts (MoE) models have quickly become a foundational component of modern, large-scale AI systems. They are widely adopted because they enable substantially larger model capacity while activating only a subset of parameters for each token, offering an unparalleled approach for scaling performance within a practical compute budget. As model scales continue to grow…
NVIDIA CUDA 13.3 brings new capabilities and performance optimizations to developers across the CUDA ecosystem. The launch of NVIDIA CUDA Tile programming in...NVIDIA CUDA 13.3 brings new capabilities and performance optimizations to developers across the CUDA ecosystem. The launch of NVIDIA CUDA Tile programming in C++, enables high-level, tile-based kernel development that automatically manages complex low-level GPU details for optimal performance and portability. Additionally, CUDA Tile programming is now supported on Compute Capability 9.0…
A massive-scale X-ray free-electron laser (XFEL) enables tracking structural and electron dynamics in novel systems, including fusion materials, semiconductors,...A massive-scale X-ray free-electron laser (XFEL) enables tracking structural and electron dynamics in novel systems, including fusion materials, semiconductors, batteries, and catalysis. It produces ultrashort X-ray pulses that can record the movements of atoms and electrons. These instruments can detect the smallest change in material structure caused by defects and other influences.
In a previous post, we introduced the Universal Sparse Tensor (UST), enabling developers to decouple a tensor’s sparsity from its memory layout for greater...In a previous post, we introduced the Universal Sparse Tensor (UST), enabling developers to decouple a tensor’s sparsity from its memory layout for greater flexibility and performance. We’re excited to announce the integration of the UST into nvmath-python v0.9.0 to accelerate sparse scientific and deep learning applications. This post provides a walkthrough of key UST features…
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