Steve Losh shares his personal journey and practical advice for learning Common Lisp in 2018, covering recommended resources, tools like Quicklisp and Slime, and common pitfalls. He emphasizes the language's stability, interactive development experience, and unique features like macros and conditions, while offering a structured path for programmers new to the language.
Background
Steve Losh is a programmer and author known for writing about Lisp, Vim, and software practices. Common Lisp is a mature, powerful dialect of the Lisp programming language family, standardized in 1994 and known for its interactive development (REPL), macro system, and multi-paradigm nature. It is relatively niche today compared to Python or JavaScript, but has a dedicated community. This 2018 article is a guide for experienced programmers who want to learn Common Lisp practically, bypassing academic or outdated materials. It recommends specific tools (SBCL implementation, Quicklisp package manager, Portacle IDE) and resources (Practical Common Lisp book, 21st-century Common Lisp advice) to avoid early frustration with tooling and documentation.
Max Weinbach says he had early access to OpenAI's new model GPT-5.6 Sol, calling it his favorite model by far. He highlights that it never gives up and will keep reasoning until it's done. OpenAI announced that GPT-5.6 Sol, along with Terra and Luna, will launch publicly on Thursday, with preview access expanding globally now.
The US government ordered Anthropic to suspend access to its Fable 5 and Mythos 5 models for all customers, citing a potential jailbreak technique that involved asking the model to review a codebase for vulnerabilities—a capability Anthropic says is available in other public models. Access was abruptly cut off on June 12.
Andrej Karpathy announces the release of Claude Fable 5, the same underlying model as Mythos but with added safeguards. He calls it a major step forward, particularly for long problem-solving sessions on difficult tasks, and describes it as state-of-the-art on nearly all benchmarks with exceptional performance in software engineering, research, and vision.
Roman Storm warns that the legal theory in his case could set a precedent making open-source developers liable for how others use their code, potentially criminalizing the mere publication of privacy, messaging, or crypto tools. He notes that developer Michael Lewellen cannot publish lawful code due to prosecution fears, and argues this chilling effect extends beyond any single case.
Meta's engineering culture is deteriorating under Mark Zuckerberg and Scale AI CEO Alexandr Wang, who have introduced keyboard tracking, reassignments to data labeling, and AI-centric performance metrics. Critics argue this incentivizes performative AI use, drives away experienced engineers, and contributed to a major Instagram hijacking incident caused by AI-written and AI-reviewed code.