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Anthropic, the AI company renowned for its careful approach to technology, has faced two significant incidents in the same week, including an accidental leak of sensitive source code. These missteps highlight the fast-moving and high-stakes nature of the AI field.

AI recruiting startup Mercor was recently impacted by a supply chain attack traced to the compromised open source LiteLLM project. The company swiftly launched an investigation following claims by hackers that they had accessed internal data. LiteLLM's breach, discovered last week, has affected numerous organizations leveraging its popular library.

StrictlyVC San Francisco returns on April 30, 2026, bringing together key voices from TDK Ventures, Replit, and Forum AI for an evening of expert talks and networking. Find out what’s next for AI, fundraising, and startup growth from renowned industry leaders.

Cognichip is using AI to transform chip design, aiming to make the process faster and less expensive. The startup just secured a $60 million investment, betting that their technology can streamline the development of new semiconductors.

Meta is building a massive AI data center in Louisiana powered by 10 new natural gas power plants—enough energy to rival South Dakota’s entire capacity. Despite Meta’s prior promises on renewable energy and climate action, the company’s reliance on natural gas brings significant environmental concerns, particularly concerning CO2 and methane emissions.

Anthropic mistakenly took down over 8,000 GitHub repositories in an attempt to remove leaked source code from the internet—a move the company later admitted was an error. The takedown unintendedly affected legitimate copies of Anthropic’s own open-source software before the notices were mostly retracted and affected repositories restored.

MIT Professor Joseph Paradiso has built a career at the intersection of technology, art, health, and the environment. His pioneering work in wireless wearable sensors, artistic collaborations, and environmental monitoring has influenced a wide range of industries—from dance performance to medicine to wildlife tracking.

MIT Assistant Professor Matthew Jones is leveraging machine learning and experimental tools to better understand how and when tumors become resistant to treatment. His work focuses on decoding molecular mechanisms, such as extrachromosomal DNA amplification, to predict tumor behavior and ultimately improve patient care.

MIT researchers have developed a cutting-edge hybrid AI system that significantly improves long-range planning for complex visual tasks. This approach, which blends vision-language modeling with formal planners, enables robots to create reliable, adaptable plans—even in new or changing environments.

MIT's new cross-disciplinary class merges anthropology and computer science to guide students in building AI chatbots that foster social confidence in young users. By blending human-centered research with coding, the course encourages the development of chatbots that offer support rather than distraction, with projects ranging from real-life guidance bots to educational news interfaces.

MIT's Professor Jesse Thaler discusses how artificial intelligence and the mathematical and physical sciences are shaping each other's future, urging for interdisciplinary collaboration, innovative education, and strategic investment to advance both fields.

MIT, Mass General Brigham, and Harvard Medical School researchers have created a deep-learning tool to anticipate heart failure progression up to one year in advance. Their PULSE-HF model analyzes ECG data to predict the likelihood of a patient’s heart function worsening, helping prioritize care and potentially improving outcomes for those at greatest risk.