Cloud-hosted transformer and large language model (LLM) inference creates a direct confidentiality problem: user prompts may contain sensitive code, business data, personal information, or regulated documents, yet remote serving exposes intermediate state to the cloud software stack and accelerator runtime. Fully homomorphic encryption (FHE) keeps accelerator-side execution ciphertext-only, but end-to-end LLM inference remains expensive because ... Read more ›
When I first heard about vector databases, I assumed they were just another database trend. Then I realized that many modern AI… Read more ›
89% of teams have monitoring but only 52% have evaluation. Here’s how to build real testing frameworks. Read more ›
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AI Latency, Streaming Inference, Distributed Systems, Real-Time AI, Infrastructure Engineering Read more ›
A practical guide to building a Retrieval-Augmented Generation (RAG) application using Spring AI, Gemini, Ollama, PostgreSQL, and PGVector. Read more ›
Certified MLOps Professional is a training program aimed at helping professionals develop the competencies required to streamline the… Read more ›
A practical guide to machine learning, neural networks, NLP, large language models, prompt engineering, and agentic AI — and how they… Read more ›
A vector database won’t fix a broken retrieval system. But a great retrieval system can make an average AI application exceptional. Read more ›
Why YAML-based worker agents beat LangGraph for theorem proving and complex research workflows Read more ›
End-to-end security verification, from requirements through architecture to code, requires datasets that span all three artifact types with fine-grained security labels. No existing dataset provides this combination. We present the EVerest dataset, a multi-artifact resource based on EVerest, an industry-driven open-source software stack for electric vehicle charging stations. The dataset includes 84 manually elicited security requirements anno... Read more ›
Conflict-free Replicated Data Types (CRDTs) ensure Strong Eventual Consistency without coordination, but typically assume benign participants and rely on validation or exclusion to handle Byzantine behavior. We address this problem through deterministic state reconstruction: rather than deciding which updates are admissible, all accepted updates are incorporated, while only a subset contributes to the reconstructed state. We instantiate this app... Read more ›
This paper studies learning-augmented online weighted vertex cover with advice and a parameter $\lambda \in (0,1)$. We consider two graph cases: bipartite graphs and general graphs. In both settings, the online algorithm must maintain a feasible vertex cover under irrevocable decisions. We show that these problems admit the same robustness--consistency tradeoffs as learning-augmented ski rental. For the bipartite graph model, we give a randomi... Read more ›
You’ve probably seen a dozen “vector database comparison” articles by now. Most of them compare managed cloud services on someone else’s… Read more ›
Top AI Engineers Don’t Start With LangChain. Read more ›
The hardest AI failures are the ones that look healthy on a dashboard. The request finished, the status code was fine, the latency stayed… Read more ›
You don’t need a PhD to understand the architecture behind GPT-4, Claude, and Gemini. You just need someone to stop making it complicated. Read more ›
Speech synthesis (TTS) has made massive leaps recently, with models like XTTS and OmniVoice enabling high-fidelity zero-shot voice cloning… Read more ›