@gql

HawDB is an embedded graph database built in Rust that enables applications to efficiently store, query, and explore connected data using native Cypher and #PostgreSQL-compatible #SQL. It combines query optimization, reliable ACID transactions with crash recovery, optional full-text and vector search, and graph analytics in a lightweight, configurable package that runs directly within an application. Developers can experiment with the online playground

https://hawdb.ai/playground/

Data centers are the buildings full of computers where the internet lives, supporting everything from photos and social media to AI chatbots, and they now drive huge power and water use because each AI query requires supercomputer-class computation and generates massive heat that needs evaporative cooling.

They already consume about 5% of US power generation, with projections of 15% by 2030 and 50% by 2040, while offering relatively few long-term jobs for communities and sometimes operating under non-disclosure agreements.

Because fiber-optic networks allow data to cross the country in milliseconds, these facilities can be sited in industrial areas with proper power and networking, made more grid-friendly through flexible workloads and adjacent energy stations that use excess renewable energy and shave peak loads, and required to transparently cover their full lifetime infrastructure costs.

https://news.uchicago.edu/story/what-are-data-centers-and-why-do-they-need-so-much-power-expert-explains

A University of Illinois English professor’s new book draws on interviews with more than 100 AI and machine learning researchers to cut through sensational claims about job loss, existential threats, or miracle cures, instead highlighting the everyday reality of building practical tools such as GPS navigation and email spam filters through iterative math, modeling, and revision. The work shows that much of the surrounding hype stems from intense competitive pressures to publish rapidly at conferences and promote results across social media and other platforms, which can encourage overstatement and underplay of limitations like the alignment problem—when systems produce unintended harmful outcomes from flawed training data. To counter this, the book recommends framing AI as targeted automation for specific needs, training researchers to explain technical details clearly to nonexperts, adjusting academic incentives toward quality over quantity, and directing the public toward researchers’ own technical explanations rather than business or media spin.

https://www.barnesandnoble.com/w/ai-through-the-experts-eyes-john-r-gallagher/1148351867?ean=9780822968047

Tuhin Chakrabarty, a Stony Brook University computer science assistant professor who combines literary background with technical expertise, researches generative AI’s impacts on creative labor, human authorship, copyright and the publishing market, finding that AI-generated books are increasingly dominating bestseller rankings and crowding out human creators. His CREATE research group focuses on untangling the labor, economic and accountability dynamics of AI-driven creativity, with the goal of developing equitable regulations and building responsible AI systems that center human creativity, ownership and agency.

https://news.stonybrook.edu/computer-science/as-ai-learns-to-write-tuhin-chakrabarty-studies-what-humans-stand-to-lose/

Recent college graduates have faced a tougher job market in recent years, with unemployment rising relative to the broader workforce. In summer 2026, however, there was no major increase in unemployment among 22- to 25-year-olds with bachelor’s degrees who had left school. Their unemployment rate averaged 7.3%, which was higher than in 2022, 2023, and 2025 but lower than the 7.8% recorded in 2024. Unemployment was also only slightly higher in occupations where artificial intelligence can be used extensively, and the difference was not statistically significant. Recent graduates did not lose significant ground compared with older college graduates or similarly aged workers without college degrees. A broader measure that included graduates who wanted jobs but had stopped actively searching reached 10.4%, the highest level in the period studied, although this increase was also not statistically significant. At the same time, other research suggests that graduates in majors highly exposed to AI, especially computer science, have been slower to find their first jobs and may be entering lower-paying industries such as retail and restaurants. Remote work may also be contributing to the difficulty because younger workers can benefit from in-person mentoring, feedback, and informal learning, although remote-friendly jobs are often also more exposed to AI. Overall, the summer of 2026 provides little evidence of a widespread AI-driven unemployment crisis for new college graduates, but the effects may become clearer as AI becomes more deeply integrated into workplaces.
https://anderson-review.ucla.edu/for-new-college-grads-the-ai-jobpocalypse-didnt-arrive