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.
Demystify how LLM inference works by building a tiny inference engine from scratch. Covers tokenization, the transformer forward pass, KV cache, and decoding.
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: ...
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: ...
Introduction Every day, billions of gigabytes of video are captured by smartphones, dash‑cameras, drones, and wearables. This visual data is the fuel for modern breakthroughs in robotics, autonomous driving, remote sensing, and augmented reality. However, the most accurate video‑understanding models—think of them as the “brains” that can label every pixel in a video frame—are huge, requiring powerful GPUs and lots of memory. For devices that run on a battery or have limited compute (e.g., a car’s dash‑cam, a drone’s onboard computer, or a smartwatch), running these models locally is often impossible. The common workaround is cloud offloading: the device streams video to a server, the server runs the heavy model, and the result is sent back. While this solves the compute problem, it introduces a new one—latency. Even with fast 5G or Wi‑Fi, the round‑trip time (encoding, sending, inference, and returning the result) can be tens or hundreds of milliseconds, which is too slow for many real‑time applications such as lane‑keeping assistance or obstacle avoidance. ...