News

3 Experiments That Reveal the Shocking Inner Life of AI Introduction: Is Anybody...

Researchers used a technique called "concept injection" to test whether AI can notice its own internal states. The results are sur...

Ripple Swell Conference 2025: How to Watch, Date, and Expected Impact on XRP

The Swell Conference that will be hosted by Ripple will take place on Nov 4th and 5th in New York City. The two-day event brings t...

From Logs to Insights: How Product Teams Use OpenSearch Every Single Day

Comparing Efficiency Strategies for LLM Deployment and Summarizing PowerInfer‑2’...

This article situates PowerInfer‑2 among other frameworks that improve LLM efficiency through compression, pruning, and speculativ...

Performance Evaluation of PowerInfer‑2: Offloading, Prefill, and In‑Memory Effic...

PowerInfer‑2 achieves up to 29× speedups over llama.cpp and 13× over LLMFlash by leveraging neuron‑level pipelines and NPU‑centric...

How PowerInfer‑2 Turns Your Smartphone Into an AI Workstation

The cost model leverages SMT‑based solving (Z3) to achieve optimal decoding speed under CPU, I/O, and memory constraints.

Crypto Market Crash as $595.8M in Longs is Liquidated, Bitcoin Slides to $105,00...

The crypto market experienced a notable decline on Monday. Long positions worth more than $595.8 million were sold off in a single...

Solana Price Eyes Rebound as Institutional Demand Tops $3.2B YTD

Solana price has faced a notable decline recently, dipping below $170 after a significant market sell-off. The cryptocurrency has...

Why Log Semantics Matter More Than Sequence Data in Detecting Anomalies

This study explores how semantic information within log messages enhances anomaly detection, often outperforming models that rely...

Transformer Models Outperform Traditional Algorithms in Log Anomaly Detection

This study evaluates a Transformer-based model for log anomaly detection across multiple datasets, comparing it with simpler basel...

How Transformer Models Detect Anomalies in System Logs

This study evaluates a transformer-based framework for detecting anomalies in large-scale system logs. Experiments were conducted...

Transformer-Based Anomaly Detection Using Log Sequence Embeddings

This paper introduces a flexible Transformer-based model for detecting anomalies in system logs. By embedding log templates with a...