The Internet Archive has launched the Vintage Artificial Intelligence collection, a curated group of emulated software packages from roughly the 1970s through the 1990s. The programs do not actually think, but they portray thinking machines, simulated personalities, autonomous characters and computer-controlled opponents across home computers and game consoles. The collection is available for research, education and play. The collection includes numerous ports of Joseph Weizenbaum’s ELIZA, whose DOCTOR script imitated a psychologist through question-and-answer exchanges and led some users to treat it as a real conversational partner. Versions run on systems including the Apple II, Atari 800, Radio Shack Color Computer and PalmPilot. Other conversational programs include Dr. Z, Talking Sam, Hyper Psych, DR. SPOC, Abuse and Serbian-language parody Pisko. Racter, a commercial chatbot released in 1985, generated sentences and stories and was marketed as a program capable of conversation. Games and simulations in the collection used rules, scripted events and variable character behavior to create impressions of independent digital life. Activision’s Alter Ego let players guide a persona through life stages and consequences, while Little Computer People simulated inhabitants living inside a computer. Murder on the Zinderneuf, Suspended, Deadline and The Hobbit featured characters acting beyond the player’s immediate control. Robot War, Worms and Fortress let players program automated competitors before sending them into arenas.

The comparison of generative AI to playing Guitar Hero is criticized for being inadequate. The author expresses strong opposition to AI, arguing against the analogy presented by their father.

LLMs are commonly utilized for learning complex topics and have various applications, including building proofs of concept, internal tools, and dashboards. Some users find the explanatory style of LLMs overly simplistic and sometimes distracting due to excessive emoji use.

Generative AI is transforming software engineering, but misconceptions about its capabilities are leading to poor decision-making regarding AI adoption and tooling. Research indicates that developers spend less time writing code than commonly believed, with studies from Microsoft and other organizations supporting this finding.

Generative large language models (LLMs) are not used in the writing process. The author emphasizes a preference for original work over AI-generated content and criticizes the commercialization of LLMs.

Codeberg has updated its terms to prohibit projects that are primarily created using generative AI. This decision reflects the organization's democratic process and membership rights.

AI companies are consuming up to 70 percent of the global supply of high-end computer memory due to the resource demands of large language models like ChatGPT and Claude. This increased demand is driving up prices for computer memory and storage significantly.

Brown University students experienced a 50% drop in final exam scores after a professor mandated in-person testing due to suspicions of AI-assisted cheating. The situation highlights the prevalence of academic dishonesty among high-achieving students under pressure.

Generative AI is enabling candidates to create polished résumés and deliver convincing interview responses regardless of their actual skills. Traditional hiring practices that rely on flawless résumés and structured answers are becoming less reliable due to these advancements.

Predatory coding startups are promoting generative AI as a pathway to quick success, often misleading young people about the potential for financial gain. Many of these companies resemble multi-level marketing schemes, offering false hope rather than viable career opportunities.