Parnee

Someone turned the hardest parts of LLM inference engineering into a course where you have to fix the performance problems yourself. And you can do it on a Mac. The project is called tiny-llm, an open-source course for building a miniature vLLM-style inference engine around Qwen3. It starts with a simple implementation using matrix operations. You build attention, normalization, positional encoding, the Transformer layers, and the loop that generates tokens. You have a working model. Now the interesting engineering begins. During decoding, repeatedly processing the previous tokens wastes computation. So you implement a KV cache and reuse previously computed attention state. Next, you look at model weights. Keeping everything in dense BF16 consumes memory bandwidth. So you retain packed 4-bit weights and implement specialized matrix operations. Then you move into Metal kernels, SIMD matrix operations, fused RMSNorm, RoPE, SwiGLU, and tiled prefill attention. Once the single-request engine works, you tackle a different problem. What happens when multiple requests arrive, each with different prompt lengths and different amounts of cached state? You implement continuous batching, chunked prefill, paged KV storage, and attention that reads directly from noncontiguous cache pages. The four-week curriculum eventually extends into a small coding-agent harness with validated tool calls, checkpoints, context management, and evaluation. Every stage comes with exercises, reference implementations, tests, and measurements to help you understand the behavior of the system you're building. The course uses MLX, C++, and Metal on Apple Silicon. You don't need an NVIDIA GPU, and the introductory path supports Qwen3-0.6B on an 8GB Mac. Some later checkpoints remain under development and review, but there's already a substantial amount of real inference engineering to learn from. The GitHub repo has crossed 4,800 stars. GitHub: github.com/skyzh/tiny-llm

Binance's initial investigation suggests that the security incident may involve a compromised DevOps engineer’s laptop, which allowed an attacker to gain access to a service container and submit unauthorized withdrawal transactions. We have found no evidence of any wallet private key leak. We will share a detailed technical post-mortem once the investigation is complete and the facts are fully verified. The estimated total impact sits at around $6.3B. We have worked with blockchain partners to freeze affected assets, with ~$3 billion frozen on-chain so far. Binance will cover all losses from the SAFU fund and the company’s funds. User funds are safe. We will also publish an updated Proof of Reserves once the remediation is complete. As an immediate precaution, Binance has: - temporarily suspended deposits and withdrawals while security checks are completed; - revoked the affected address authorisation; - engaged law enforcement and blockchain intelligence partners to trace and recover funds. Our teams are working around the clock to resolve this issue safely and thoroughly. We are working closely with our law enforcement partners to confirm the identity of these criminals and bring them to justice. Please rely only on official Binance channels for updates. Please feel free to contact Binance Customer Support if you have any further questions. binance.com/en/support/annou…