Meta has unveiled four new chips it designed to handle tasks like training and running AI models and serving recommendations across its social media platforms and other services. The new chips are part of Meta’s Meta Training and Inference Accelerator (MTIA) family and are designed to be used in data centers. Meta has been designing

The AI economy is projected to reach $17 trillion by 2028, fundamentally altering how organizations architect their infrastructure. Driven by this shift, 95% of major global enterprises are on a mission-critical sprint to become their own AI and data platforms within the next two years.  Yet only 13% of enterprises have successfully found the formula. Their secret to mainstreaming

JetBrains has introduced Tracy, an AI tracing library for the Kotlin and Java languages. Announced March 11 and accessible from GitHub, Tracy helps developers trace, monitor, and evaluate AI-powered features directly from their Kotlin or Java projects, JetBrains said. The open-source Kotlin library provides a unified API to capture structured traces and helps developers debug failures,

Google has added a plan mode to Gemini CLI, its open-source software engineering agent that runs on the command line. With plan mode, announced March 11, Gemini CLI focuses first on analyzing a request, planning complex changes, and understanding the codebase or dependencies in a read-only mode that is safe from accidental changes or executions.

Take a wild ride with us, as we use a large language model to convert a Python app to Rust. Also, could Pandas finally compel you to ditch Excel? And, is Python’s native JIT the Python performance booster we’ve all been waiting for? All this and more, in this week’s report. Top picks for Python

For the past few years, I’ve watched a specific story sell itself in boardrooms: “Software will soon be free.” The pitch is simple: Large language models can write code, which is the bulk of what developers do. Therefore, enterprises can shed developers, point an LLM at a backlog, and crank out custom business systems at

When Romy Gai, FIFA’s chief business officer, described the operational challenge of running a 48-team World Cup across Canada, Mexico and the United States, he was not talking about technology. He was talking about complexity. Previous World Cups relied on local organising committees to absorb much of the logistical load. For 2026, FIFA is running operations directly.

Managing the economics of multi-agent AI now dictates the financial viability of modern business automation workflows. Organisations progressing past standard chat interfaces into multi-agent applications face two primary constraints. The first issue is the thinking tax; complex autonomous agents need to reason at each stage, making the reliance on massive architectures for every subtask too

At a glance Problem: Debugging AI agent failures is hard because trajectories are long, stochastic, and often multi-agent, so the true root cause gets buried. Solution: AgentRx (opens in new tab) pinpoints the first unrecoverable (“critical failure”) step by synthesizing guarded, executable constraints from tool schemas and domain policies, then logging evidence-backed violations step-by-step. Benchmark

Xscape Photonics has closed a $37 million funding round, bringing its total Series A investment to $81 million. The round was led by Addition, a new investor in the company, with continued participation from IAG Capital Partners and NVIDIA, among others. The fresh capital doubles the company’s valuation and deepens its runway as it advances