Coinspaid Dev emerges as a dedicated blockchain infrastructure engineering brand

How to use traces to avoid breaking changes

Tracing is a valuable tool in software development that helps prevent issues when making changes by providing visibility into system behavior and performance. It involves monitoring and recording the flow of data or events as they move through a system. Tracing in a distributed system includes components such as tracer, span, context propagation, correlation ID, instrumentation, data store, and visualization and analysis tools. OpenTracing, OpenTelemetry, Zipkin, and Jaeger are widely used distributed tracing libraries. Digma is an observability tool that provides insights and feedback during development.

Apache Flink 2.3.0 Release Announcement

Apache Flink 2.3.0 is now available, implementing 15 FLIPs with major improvements across SQL, connectors, and runtime. New SQL operators FROM_CHANGELOG and TO_CHANGELOG bridge append-only and dynamic changelog tables. Materialized tables gain DDL parity with regular tables and fine-grained refresh control via a new START_MODE clause. The SinkUpsertMaterializer is reworked with an explicit ON CONFLICT clause and watermark-based compaction to reduce state size. A new native S3 filesystem plugin built on AWS SDK v2 replaces Hadoop/Presto-based connectors with non-blocking I/O and zero Hadoop dependencies. Runtime improvements include adaptive partition selection for backpressure handling, watermark alignment redesign for faster backlog processing, checkpointing during recovery from unaligned checkpoints, and application-level lifecycle management with a new Web UI Applications tab.

Polymarket confirms hackers stole $3M from users after third-party vendor was compromised

Polymarket confirmed hackers stole approximately $3 million in cryptocurrency from over 11 users after a third-party vendor was compromised, injecting malicious code into the prediction market's frontend. The stolen funds were bridged from Polygon to Ethereum and converted into roughly 1,893 ETH. Polymarket says it has contained the incident and is refunding affected users in full. The attack was a supply chain compromise — no core smart contracts were exploited. The breach is the latest in a string of bad news for Polymarket, which also faced a separate $520K smart contract drain in May, a Wall Street Journal investigation into deceptive promotional videos, a Google engineer insider trading charge, regulatory blocks in multiple countries, and a $345 million governance dispute.

Client-Side Load Balancing at a Million Requests Per Second

Zalando's engineering team built an in-process client-side load balancer (CSLB) to handle over a million requests per second of internal fan-out traffic for their Product Read API, replacing shared Skipper ingress hops. The implementation replicates Skipper's xxHash64 consistent-hash ring for cache locality, uses a Kubernetes watch-based informer for pod discovery, and adds N-ring fade-in to prevent cold-cache spikes on scale-up. A key innovation is occupancy-based bounded load using Little's Law (seconds of work per second) rather than in-flight counts or throughput, combined with a latency multiplier borrowed from Finagle. Results include eliminating Skipper's fleet from 50+ pods to 8, reducing their own pod fleet by 25%, and saving over $1,000/day. AZ-aware routing was prototyped but paused due to edge cases around bounded-load threshold miscalculation during dual fade-in. The post also covers pipeline improvements, retry hardening, FIFO buffering, and how detailed logging revealed mysterious node-level network freezes that had previously been invisible.

Event Sourcing: Aggregates, Dynamic Consistency Boundaries, or what?

Part eleven of an event sourcing series explores how to handle consistency boundaries without relying on DDD aggregates or Dynamic Consistency Boundaries (DCBs). The author argues that the best approach depends on the actual problems at hand. Two alternatives are discussed: replacing concurrent designs with non-concurrent ones (e.g., a draft-registration phase processed by a single-threaded algorithm), and using Azure Service Bus sessions to serialize workday validation, eliminating race conditions within a consistency boundary. The post emphasizes solving real problems holistically rather than applying patterns preemptively, and shows how task-based UIs and small data models reduce the likelihood of concurrency conflicts in the first place.

The EF’s new structure

The Ethereum Foundation (EF) has completed a major reorganization, reducing its workforce by 54 people (roughly 20%) and restructuring into five domain clusters: Protocol Layer, Access Layer, User Layer, Community Layer, and Institutional Layer, plus operations and management clusters. The Protocol Layer focuses on hardening and scaling Ethereum's core protocol while preserving censorship resistance, privacy, and security. The Access Layer ensures self-sovereignty is practically available for key user actions. The User Layer grounds EF work in real user needs. The Community Layer manages EF's public presence and external relationships. The Institutional Layer engages enterprises, governments, and nonprofits on Ethereum adoption. Departing employees receive severance of one month per year worked (or local legal minimum, whichever is higher) plus transition support including career coaching grants.

Optimizing distributed AI inference: Advanced deployment patterns

Bài viết hướng dẫn kỹ thuật sâu về ba phương pháp tối ưu hóa inference AI phân tán ở quy mô lớn: tách rời prefill/decode (P/D), chiến lược KV cache, và giải mã dự đoán (speculative decoding). P/D disaggregation đề xuất tỷ lệ worker 1:3 đến 1:5, sử dụng KV-transfer connector (NixlConnector, LMCacheConnector, MooncakeConnector) và routing thông minh (llm-d) giúp cải thiện TTFT lên tới 57 lần. KV cache được phân cấp (HBM/DRAM/NVMe), tối ưu chia sẻ tiền tố (prefix sharing) và tái sử dụng (reuse), cân nhắc lượng tử hóa FP8/FP4, cùng so sánh kiến trúc PagedAttention và RadixAttention. Phần speculative decoding so sánh EAGLE 3.1, self-speculative, Medusa heads, MTP, đồng thời cảnh báo rằng chế độ giải mã hạn chế (JSON mode, tool calls) có thể làm giảm tỷ lệ chấp nhận.

Lập trình viên chuyên phát triển hệ thống AI quy mô lớn cần đọc để tối ưu hóa hiệu suất và chi phí của các ứng dụng phân tán, từ cách phân tán tiền xử lý/giải mã đến lựa chọn cache KV hiệu quả và chiến lược dự đoán để giảm thời gian phản hồi mà không ảnh hưởng đến độ chính xác.