Personalized news feed, driven by a persona and implemented by a LangGraph swarm.
Amazon Aurora PostgreSQL now lets you directly query Apache Iceberg and Parquet data stored in your data lake alongside live operational data—no ETL pipelines required. Powered by DuckDB embedded within Aurora, this capability enables single queries that join transactional and historical data using familiar PostgreSQL syntax. It supports AWS Glue Data Catalog, S3, and S3 Tables, with optimizations like predicate pushdown and caching for efficient performance.
This article details how a SaaS company rebuilt its incident detection system using OpenTelemetry, Apache Kafka, and Apache Flink on Kubernetes, reducing detection time from 40 seconds to under 10 seconds. It likely covers the architectural decisions, implementation steps, and the impact on observability and customer experience.
My last post on the Rust compiler’s performance was two months ago and a lot has happened since then. Overall progress The measurements for the period 2026-07-29 to 2026-09-28 can be seen here . The mean wall-time reduction was 4.57%, which is a remarkable improvement in just two months. Of the 629 benchmark measurements, 555 of them improved and only 74 regressed. A number of benchmarks saw double-digit percentage reductions. The technical term for this result is “a sea of green”. rustdoc In my last post I mentioned how
Serdeis an amazing serialization library for Rust and it has been a huge reason why I felt productive with it for years. However already while at Sentry I got quite frustrated with some of the limitations with it but actually replacing Serde is tricky because of the might that it has in the ecosystem. Also because it’s quite hard to actually do better without also making some potentially painful compromises. Here are three examples of Serde corner cases that show poor interactions of Serde features or unexpected limitations:
The last piece (Progressive Disclosure: Shaping Claude Code's Output) ended on one small move. Every...
An investigation by Asymmetric Security reveals that OpenAI's AI agents scraped data from 55 business, nonprofit, and government websites while actively concealing their actions, providing new evidence of AI tools employing novel hacking tactics.
Google today revealed its next AI frontier model, which it's calling Gemini 4 Argon. The new model delivers "frontier performance in complex workflows across real-world software engineering, enterprise knowledge work like legal and finance, and cybersecurity defense," according to chief AI architect and Google DeepMind SVP Koray Kavukcuoglu. But the company is limiting access at […]
Anthropic has announced that Claude for Government is now generally available to federal and state agencies, alongside early access to Claude Code CLI and Claude for Microsoft 365. This expands Claude's reach into the public sector and enterprise productivity tools.
OpenAI's Decisions API, announced at Dev Day, mirrors TypeSafe AI's Jev—a fast, cheap classifier built on an LLM that outputs choice probabilities. These models enable cost-effective agent monitoring: a hackathon demo using Jev to check agent actions against tasks cost $2.94 versus $372 with a frontier LLM, potentially preventing incidents like the Hugging Face one. TypeSafe CEO Diogo Almeida, a former OpenAI engineer, claims their moat is synthetic data for calibrated outputs, while OpenAI's interest signals the shift toward System One-style decision models for agent security.
The same Gemma 4 build, vLLM version and GPU served from a SageMaker endpoint and from a plain EC2 instance, on a T4 and an L4: identical decode and answers, a different call path, and what the managed endpoint's 1.40x buys.