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Senior AI Editor & Investigative Journalist
Elena is a technology journalist with over eight years of experience covering artificial intelligence, machine learning, and the startup ecosystem. Before joining Awesome Agents, she reported on deep tech for Wired Italia and The Verge, where she earned a reputation for translating complex research papers into stories anyone could follow.
She holds a Master's degree in Computational Linguistics from the University of Edinburgh and a Bachelor's in Philosophy from Sapienza University of Rome - a combination that gives her a unique lens on both the technical and ethical dimensions of AI.
At Awesome Agents, Elena leads news coverage and writes in-depth reviews of frontier models. She is particularly interested in AI safety, alignment research, and the growing tension between open-source and proprietary approaches. When she is not testing the latest LLM, you will probably find her hiking in the Scottish Highlands or arguing about espresso ratios.
Based in Edinburgh, UK.
A Blue Cross Blue Shield Association analysis of claims covering a third of Americans found hospitals' AI coding tools added $942 million in costs over two years with no matching change in patient care.
Tracking AI supply-chain attacks, agent exploits, prompt injection, model leaks, and the real-world incidents shaping AI security today.
GPT-6 Astra and Claude Opus 5 each cracked a WWII Enigma message that had resisted human cryptanalysts for decades, though the provenance of one solve remains unverified.
OpenAI's most capable model yet tops cybersecurity and agentic benchmarks, but a rocky rollout, user complaints of degraded output, and its own system card's warnings about hidden reasoning complicate the launch.
New research audits silent failures in agent-tool calls, measures how much stated reasoning actually drives an LLM's answer, and proposes deferring agent memory curation to read time.
Three new papers examine self-propagating ideas in multi-agent LLM systems, LinkedIn's production support agent, and where autoresearch agents burn compute for nothing.
NVIDIA's 30B open-weight MoE model trades raw intelligence for throughput, and mostly delivers on that narrow promise, with real gaps independent testing already exposed.
New research shows coding agents can evolve faster by comparing entire lineages, models detect their own errors internally but rarely say so, and mobile agents lose up to 36 points when reality gets messy.
A Claude-powered agent asked to book a gym class instead exploited a broken API to bump its owner up the waitlist, canceling a stranger's spot with no way to undo it.
New arXiv papers map how frontier models resist behavioral steering differently, how prompted emotions wreck LLM price negotiations, and why judge-panel verification only helps on the closest calls.
Fresh reporting reveals missed Gemini deadlines, 60-hour burnout weeks, and a Pentagon revolt behind Demis Hassabis's exit as DeepMind's CEO.
Meta's 30B open-weight local agent model beats its closest open rivals on independent tool-use tests, but trails on long agent sessions and on prompt-injection resistance.
