Webtagr - Technology News Summarizer

Cloud Run Jobs Now with GPUs: Unleashing Batch Processing Power

2025-06-04

Google Cloud Run now offers GPU support for its jobs, opening up new possibilities for batch processing and asynchronous tasks. This enables efficient model fine-tuning, large-scale batch AI inferencing, and high-throughput media processing. Early adopters like vivo, Wayfair, and Midjourney have reported significant cost savings and performance improvements. The service allows developers to focus on innovation, leaving infrastructure management to Google.

(cloud.google.com)

Development Batch Processing

Gemini's Text-to-SQL: Challenges and Solutions

2025-05-16

While Google's Gemini text-to-SQL functionality initially impresses, real-world applications reveal significant challenges. Firstly, the model needs to understand business-specific context, including database schema, data meaning, and business logic. Simple model fine-tuning struggles to handle the variations in databases and data. Secondly, the ambiguity of natural language makes it difficult for the model to accurately understand user intent, requiring adjustments based on context, user type, and model capabilities. Finally, differences between SQL dialects pose a challenge for generating accurate SQL code. Google Cloud addresses these challenges through intelligent data retrieval, semantic layers, LLM disambiguation, model self-consistency validation, and other techniques, continuously improving the accuracy and reliability of Gemini's text-to-SQL.

(cloud.google.com)

AI Text-to-SQL

Rapid Storage: Sub-Millisecond Latency Storage Built on Colossus

2025-04-10

Google's Rapid Storage leverages the Colossus architecture to achieve an incredible 20 million requests per second throughput, providing sub-millisecond latency for reads and writes, particularly beneficial for AI/ML applications. Using gRPC streaming and a stateful protocol, Rapid Storage dramatically improves data access efficiency, preventing storage latency from blocking accelerators during model pre-training, for example. Its robust fault tolerance ensures data consistency and continuity even with client or server failures, enabling unlimited appends and resuming interrupted operations. This makes it a powerful solution for large-scale data processing.

(cloud.google.com)

Tech

Google Cloud Unveils Major AI Hypercomputer Software Upgrades

2025-04-10

Google Cloud announced significant software upgrades to its AI Hypercomputer, dramatically improving AI model training and inference efficiency. Pathways on Cloud, a distributed runtime, is now available on Google Cloud, enabling elastic training and high-throughput inference. Cluster Director adds Slurm support and 360° observability features for high performance and reliability. GKE integrates Inference Gateway and Inference Quickstart, slashing inference costs and boosting throughput. vLLM now supports TPUs, further accelerating inference. Dynamic Workload Scheduler expands accelerator support, optimizing resource utilization. These upgrades empower developers to build and deploy AI applications faster and more cost-effectively.

(cloud.google.com)

Tech inference efficiency

Microsoft's Time Travel Debugger: A Deep Dive into TTD

2025-03-13

Microsoft's Time Travel Debugging (TTD) is a powerful user-mode record-and-replay framework enabling developers to debug programs as if traversing a timeline. It injects a DLL to capture every state of a process's execution, storing this in a .trace file. The core is the Nirvana runtime engine, which emulates CPU instructions for fine-grained control. Even with challenges like floating-point operations, memory models, peripheral emulation, and self-modifying code, Nirvana uses dynamic binary translation and code caching for efficiency and accuracy. The article describes a bug encountered while debugging an obfuscated 32-bit PE file using TTD, highlighting the advantage of using the TTD trace file for debugging.

(cloud.google.com)

Development Code Replay

Go Code Obfuscation: A State Machine Approach to Dynamic Data Decryption

2025-03-06

This Go code implements an advanced code obfuscation technique using a state machine and random indexes for dynamic data decryption. The data is randomly split into chunks, and a random index sequence controls the decryption order. A state-dependent decryption key is used to decrypt each chunk sequentially, finally reconstructing the original data. This approach significantly increases the difficulty of reverse engineering, effectively protecting code security.

(cloud.google.com)

Development code obfuscation

Russian Hackers Exploit Signal's 'Linked Devices' for Phishing Attacks

2025-02-19

Russian-aligned hackers are exploiting Signal's 'linked devices' feature for large-scale phishing attacks. Attackers create malicious QR codes disguised as legitimate Signal resources like group invites or security alerts. Scanning these codes links victims' accounts to attacker-controlled Signal instances, allowing real-time eavesdropping on conversations. This technique, used by groups like APT44, even targets Ukrainian military personnel. The stealthy nature and lack of effective defenses make this a high-risk, low-signature attack that can go undetected for extended periods.

(cloud.google.com)

Tech Signal vulnerability phishing attack

BigQuery's New Pipe Query Syntax: Easier to Read, Write, and Maintain

2025-02-13

Google BigQuery introduces a new pipe query syntax that simplifies writing and maintaining SQL queries with a linear structure. This syntax allows applying operators like SELECT, aggregate, GROUP BY, JOIN, and WHERE in any order and any number of times, resulting in clearer and more understandable query logic. It addresses issues in standard SQL such as strict clause ordering, the need for CTEs or nested subqueries for complex queries, and introduces new pipe operators like EXTEND, SET, DROP, and RENAME for greater flexibility in data manipulation.

(cloud.google.com)

Development Pipe Syntax

Dissecting ScatterBrain: A Deep Dive into Shadowpad's Sophisticated Obfuscator

2025-02-02

POISONPLUG.SHADOW (Shadowpad), a malware family first identified by Kaspersky, utilizes a custom obfuscating compiler, ScatterBrain, to evade detection. Google's Threat Intelligence Group (GTIG) and the FLARE team collaborated to reverse-engineer ScatterBrain, creating a standalone static deobfuscator. This deobfuscator tackles ScatterBrain's three protection modes (Selective, Complete, Complete "headerless"), neutralizing its control flow graph obfuscation, instruction mutations, and import table protection. This research significantly enhances the ability to analyze and counter sophisticated malware like Shadowpad.

(cloud.google.com)

Tech Deobfuscation

gRPC vs REST: Choosing the Right API Design Model

2025-01-23

This article delves into gRPC and REST, two primary API design models, and the role of OpenAPI. gRPC, based on the RPC model, hides data details, while REST, based on HTTP, is resource-oriented. Many APIs cleverly combine the strengths of both, using an entity-oriented approach but implemented with gRPC. The article compares three ways to use HTTP for APIs: REST, gRPC, and OpenAPI, outlining their advantages and disadvantages, ultimately suggesting choosing the best approach based on specific needs. gRPC offers superior performance but requires special software; OpenAPI is flexible but complex to design; REST is simple and straightforward but less commonly used. The choice involves weighing project requirements, team technology stack, and maintainability.

(cloud.google.com)

Development