Urgent.News

600+ sources. One page. See who else covered it.

Editions

Tech

Ocypus Sigma L36 Pro Review: How is this LCD AIO so cheap?

The Ocypus Sigma L36 Pro is a high-performance AIO that includes a fancy 3.5-inch display and a low price tag. We’ve tested this liquid cooler paired with AMD’s Ryzen 9 9950X3D CPU to benchmark thermal efficiency.

Ocypus Sigma L36 Pro Review: How is this LCD AIO so cheap?

The Ocypus Sigma L36 Pro is a budget-friendly high-end AIO with a 3.5-inch display, coming from relatively new manufacturer Ocypus. This company has previously made attention-grabbing products, such as the Iota C70 computer case and A62 digital air cooler. The Sigma L36 Pro features a detachable 3.5-inch IPS screen with VGA resolution (640x480) and 4:3 aspect ratio, which is a common trend in modern liquid coolers.

One impressive feature is the fans' static pressure, which is almost twice as strong as many competitors, rated at up to 5.8 mmH20. However, the most striking aspect is its price, which typically ranges from $135 or less. Despite its large display and powerful fans, the Sigma L36 Pro is notably cheap, making it a potentially attractive option for those seeking an AIO cooler with added features.

Written by urgent.news from Tom's Hardware's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

Read the original at tomshardware.com →

More in Tech

The IR Is a sqlglot AST

Every data tool has an intermediate representation, whether it admits to one or not. It is the thing a model becomes after parsing and before execution, and it quietly decides what the tool can do.

  • The IR is a sqlglot AST combined with Arrow schema
  • Remote engines connect over ADBC wire format
  • Storage defaults to DuckLake with SQLite control plane

Beyond the Demo: Building Production-Ready AI Agents — A Guide to Benchmarking, Cost Optimization, and Tooling in 2026

Originally published on tamiz.pro . Most AI agents ship from a notebook, impress in a demo, and quietly fail in production.

  • Production AI agents need observability, evaluation rigor, and cost discipline beyond demos.
  • Benchmarks should include task-level tests, edge cases, and a weighted scoring rubric.
  • Cost optimization techniques include tiered model routing, prompt compression, and token budgeting.

More from Saturday 15 August →