Machine-Learning

An engineer with a laptop working alongside a factory floor operations dashboard.

Forward Deployed Engineer (FDE): The New AI Engineer at the Frontline

Forward Deployed Engineers embed inside customer teams to turn messy enterprise data into shipped AI systems. Here’s how the role emerged, what FDEs actually do, and why it matters now.

Abstract diagram of a neural network with corrupted input nodes highlighted in red.

Poisoning Attacks on LLMs: How Adversaries Corrupt Models and What Defenders Can Do

Poisoning attacks corrupt the data, weights, or fine-tuning process of large language models. This post breaks down the attack surface, walks through recent incidents, and lays out the defenses working teams can ship today.

A stylized illustration of transformer blocks and a KV cache heatmap.

Build Your Own Inference Engine: From Scratch

Demystify how LLM inference works by building a tiny inference engine from scratch. Covers tokenization, the transformer forward pass, KV cache, and decoding.

Optimizing Decentralized AI Inference with WebAssembly and Zero Knowledge Proofs

Table of Contents Introduction Background: Decentralized AI Inference Why WebAssembly (Wasm) for Edge AI? Zero‑Knowledge Proofs (ZKP) in AI Inference Architecture Overview: Combining Wasm and ZKP Practical Implementation Steps 6.1 Compiling AI Models to Wasm 6.2 Setting Up a Decentralized Runtime 6.3 Generating ZKPs for Inference Correctness Example: TinyBERT + zk‑SNARK Verification Performance Considerations Security and Trust Model Real‑World Use Cases 11 Challenges and Future Directions 12 Conclusion 13 Resources Introduction Artificial intelligence (AI) is no longer confined to massive data‑center clusters. The rise of edge devices, IoT sensors, and decentralized networks has opened a new frontier: performing inference where the data lives. Yet, moving heavy neural networks to untrusted or resource‑constrained environments introduces two major challenges: ...

Optimizing High-Throughput Inference Pipelines for Distributed Vector Search and Retrieval Augmented Generation

Introduction The explosion of large‑language models (LLMs) and multimodal encoders has turned vector search and retrieval‑augmented generation (RAG) into core components of modern AI products—search engines, conversational agents, code assistants, and recommendation systems. While a single GPU can serve an isolated model with modest latency, real‑world deployments demand high‑throughput, low‑latency inference pipelines that handle millions of queries per second across geographically distributed data centers. This article dives deep into the engineering challenges and practical solutions for building such pipelines. We will: ...