Data centre operators and researchers are shifting from copper wiring toward photonics to cut the energy used by power distribution, cooling, and heat from electronic networking. A typical 100MW data centre uses about 400 tonnes of copper, including up to 70 tonnes in servers and up to 20 tonnes in network wiring. This changes data-centre infrastructure by replacing some copper interconnects with light-based optical links while requiring new manufacturing, installation, and thermal-reliability practices before wider deployment.
Qualcomm announced the Snapdragon 8 Elite Gen 6, and also released the Snapdragon 8 Elite Extreme Gen 6 alongside it. The chips are built on a 2nm process. The Extreme model mainly changes AI processing, video capture, and gaming performance versus the standard Gen 6 while keeping most other core specs the same.
CoolIT says that above 250 kW per rack, liquid-air hybrid cooling becomes impractical and it shifts to near-total heat capture to enable fanless liquid-cooled AI servers. It claims air can fall below 1% of the load, with a 70/30 liquid-air split leaving 75 kW to be handled by air. This changes server design toward integrating all components into a single liquid loop built from modular coldplate blocks rather than relying on air cooling for non-CPU parts.
Parallel used GPT-6 Astra to let its agents research and synthesize labor-market data faster. The work took half the time and cost half as much as with prior models. As a result, Parallel could complete the same research task more cheaply and on a shorter timeline.
Open-source parsing and bitmap libraries were rebuilt and re-benchmarked, showing large performance gains across six widely used codebases in 2026 after long flat periods. simdjson’s serialization speed rose 2.1x on citm_catalog.json from February 2026 onward. The result is faster URL/number/JSON/unicode parsing and bitmap operations for many downstream users, likely enabled by more accessible optimization work in that period.
NVIDIA, NTU, and MIT released SoL-Pi, an MIT-licensed extension for the open-source Pi coding agent that uses auto-research loops to tune a harness for efficiency. On the 51-task EdgeBench benchmark, SoL-Pi cut recorded token traffic by 44.7% to 49.0% versus Pi and reduced API cost by about 33%. SoL-Pi ships on GitHub and is deployable on an unmodified Pi release (tested with Pi 0.85.1 and Node.js 22.19+), with efficiency improvements carried through to closely matched scores.
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