A knowledge graph represents information as a set of nodes and the relationships between those nodes.
When your source data consists of assets like technical documentation, research publications, or highly
interconnected websites, a knowledge graph returns better results than a simple vector search. That’s
because a knowledge graph search can traverse links between nodes, finding semantically relevant results
two or more steps away from the first node.
Agentic AI is all about autonomy (think self-driving cars), employing a system of agents to constantly
adapt to dynamic environments and independently create, execute and optimize results.
When agentic AI is applied to business process workflows, it can replace fragile, static business
processes with dynamic, context-aware automation systems.
As organizations race to implement Artificial Intelligence (AI) initiatives, they’re encountering an unexpected bottleneck:
the massive cost of data infrastructure required to support AI applications.
I’m seeing organizations address these challenges through innovative architectural approaches. One promising direction is
the adoption of leaderless architectures combined with object storage. This approach eliminates the need for expensive data
movement by leveraging cloud-native storage solutions that simultaneously serve multiple purposes.
Another key strategy involves rethinking how data is organized and accessed. Rather than maintaining separate infrastructures
for streaming and batch processing, companies are moving toward unified platforms that can efficiently handle both workloads.
This reduces infrastructure costs and simplifies data governance and access patterns.
An increasing number of start-ups and end-users find that using cloud object storage as the persistence layer saves money and
engineering time that would otherwise be needed to ensure consistency.
According to a National Institute of Standards and Technology (NIST) paper, “A Data Protection Approach for Cloud-Native
Applications” (authors: Wesley Hales from LeakSignal; Ramaswamy Chandramouli, a supervisory computer scientist at NIST),
WebAssembly could and should be integrated across the cloud native service mesh sphere in particular to enhance security.
During DeepSeek-R1’s training process, it became clear that by rewarding accurate and coherent answers, nascent model
behaviors like self-reflection, self-verification, long-chain reasoning and autonomous problem-solving point to the
possibility of emergent reasoning that is learned over time, rather than overtly taught — thus possibly paving the way
for further breakthroughs in AI research.
The Linux-based Azure Cosmos DB emulator is available as a Docker container and can run on a variety of platforms, including
ARM64 architectures like Apple Silicon. It allows local development and testing of applications without needing an Azure
subscription or incurring service costs. You can easily run it as a Docker container, and use it for local development and
testing.
A new paper today describes a success in making a brand-new enzyme with the potential to digest plastics. But it also shows how even a
simple enzyme may have an extremely complex mechanism—and one that’s hard to tackle, even with the latest AI tools.
We have a simple proposal: all talking AIs and robots should use a ring modulator. In the mid-twentieth century, before it was easy to
create actual robotic-sounding speech synthetically, ring modulators were used to make actors’ voices sound robotic.
Cloud solutions offer unparalleled flexibility and ease of scaling, while on-premises setups provide unmatched control and security for
sensitive workloads.
ASAN detects a lot more types of memory errors, but it requires that you recompile everything. This can be limiting if you suspect that
the problem is coming from a component you cannot recompile (say because you aren’t set up to recompile it, or because you don’t have
the source code). Valgrind and AppVerifier have the advantage that you can turn them on for a process without requiring a recompilation.
In order to build high-quality data lineage, we developed different techniques to collect data flow signals across different technology
stacks: static code analysis for different languages, runtime instrumentation, and input and output data matching, etc.
GPT-5 will be a system that brings together features from across OpenAI’s current AI model lineup, including conventional AI models,
SR models, and specialized models that do tasks like web search and research.
A ChatGPT jailbreak flaw, dubbed “Time Bandit,” allows you to bypass OpenAI’s safety guidelines when asking
for detailed instructions on sensitive topics, including the creation of weapons, information on nuclear
topics, and malware creation.
An internal email reviewed by WIRED calls DOGE staff’s access to federal payments systems “the single
greatest insider threat risk the Bureau of the Fiscal Service has ever faced.”
Microsoft.Testing.Platform is a lightweight and portable alternative to VSTest for running tests in all contexts, including continuous
integration (CI) pipelines, CLI, Visual Studio Test Explorer, and VS Code Text Explorer. The Microsoft.Testing.Platform is embedded
directly in your test projects, and there’s no other app dependencies, such as vstest.console or dotnet test needed to run your
tests.
OpenAI is entering the final stages of designing its long-rumored AI processor with the aim of decreasing the company’s dependence on
Nvidia hardware, according to a Reuters report released Monday. The ChatGPT creator plans to send its chip designs to Taiwan Semiconductor
Manufacturing Co. (TSMC) for fabrication within the next few months, but the chip has not yet been formally announced.
JUring is a high-performance Java library that provides bindings to Linux’s io_uring asynchronous I/O interface using Java’s
Foreign Function & Memory API. Doing Random reads JUring achieves 33% better performance than Java NIO FileChannel operations
for local files and 78% better performance for remote files.
The Linux 5.10 release included a change that is expected to significantly increase the
performance of the ext4 filesystem; it goes by the name “fast commits” and introduces a
new, lighter-weight journaling method.
New data reveals how efficiently the major cloud providers run and cool their data centers
– from AWS’s and Azure’s tropical struggles to Google’s industry-leading performance.
In this paper, we introduce the No-Order File System (NoFS), a simple, lightweight file
system that employs a novel technique called backpointer based consistency to provide
crash consistency without ordering writes as they go to disk.
In order to measure the engineering effectiveness of Yelp, we need to measure the
effectiveness of its organizations and the teams that make up those organizations. But
how do we know what a team is responsible for? We needed a way to assign an owner to
something (let’s call this an entity) that we want to measure. Once an entity has an
owner, we can collect metrics on that entity and derive the health score (i.e.,
effectiveness) for that owner. These metrics can then be aggregated by team,
organization, or even the entire Engineering division, so that we can identify areas
that we can collectively improve. And this is how the Ownership microservice was born.
