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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.
Meta's 6,500-person Applied AI team, created in March with a join-or-quit mandate, is in revolt: 1,600+ employees signed a petition against keystroke surveillance, bathroom-stall flyers appeared across US campuses, and UK workers began organizing with UTAW.
Three new papers expose a 50-point gap in agent tool knowledge, show tree search tripling inference throughput, and map the research between AGI and superintelligence.
A hands-on review of all seven MAI models - from the April transcription and image launch to Build 2026's MAI-Thinking-1, MAI-Code-1-Flash, and the multimodal upgrades.
A Chinese cybercrime network sold $88/week phishing kits that used Google's own Gemini AI to generate fake sites impersonating banks, carriers, and government agencies at scale.
Anthropic commits $150 million to place 1,000 AI-trained fellows inside US nonprofits for a year at $85,000 each, no degree required.
A new impossibility theorem proves feedback-based training can't guarantee honest AI, while two papers cut agent memory costs 78% and multi-agent latency 7x.
Three new arXiv papers expose how context bloat tanks agent performance, agent memory bleeds private data, and misaligned behavior spreads through multi-agent systems.
Claude Fable 5 delivers the strongest coding and long-context results Anthropic has ever shipped publicly, but its safety classifiers block enough legitimate work to make that power conditional.
New research reveals MCP error messages triple agent attack success rates, ranks eight models on sycophancy with Claude scoring best, and finds self-evolving agents make 30-42% false edits.
Three papers: strategic attack timing exposes gaps in AI control evaluations, Perplexity's agents slash task time by 87%, and Lean4 formal proofs make agent workflows more reliable.
Anthropic published internal data showing Claude writes 80% of its own codebase - and called for a coordinated global AI pause - four days after filing a $965B IPO.
OpenAI's life sciences reasoning model gets a June update with global access and new NGS plugins - strong benchmarks, but still locked behind a Trusted Access Program with no public pricing.
