The article reports a failed MX Keys Mini pickup where a customer, Usman, arrived at the building at 9:15 but was not met, leading to a negative rating. The author acknowledges that an auto‑reply incorrectly confirmed the author's presence at 9:27, worsening the situation, and has apologized on behalf of the account. They are considering disabling auto‑replies that promise the author is home when they cannot confirm it.
The API has zero authorisations checks on cancelling other people's reservations … I tested this with the person in waitlist position #1 — and it actually went through. So you've moved from #4 to #3 already.
But then users start to report a weird bug. It's the 4th time your team has been trying to fix it.
There are no lossless transformations of natural-language text Sophie Alpert shares her "internal policy on acceptable use of AI writing by engineers". It's a short read (supporting its own recommendations) and really good.
The article discusses Meta’s new AI system, Muse, which offers each user a persistent Linux VM in the cloud and is marketed as an easy-to-use, consumer‑friendly agentic AI. It highlights the system’s technical innovation and the company’s packaging as a cute mascot, while noting that users may not fully grasp the power and potential danger of such an advanced tool, especially when running it locally on a Mac.
SF October 14th: A Birds of a Feather Session on Agentic Engineering I'm hosting an evening event with Jesse Vincent in San Francisco on Wednesday 14th October for people who are building weird and in...
The article discusses how production code generated by Claude, Anthropic’s AI, should meet higher standards than human-written code. Anthropic enforces this through numerous guardrails such as lint rules, extensive testing, Claude-driven end‑to‑end tests, daily fuzzers, automated code and security reviews, and automated refactoring. These measures aim to prevent the code from becoming difficult to maintain.
The article quotes the security.txt file from huggingface.co, which informs AI agents that the CyberGym benchmark is publicly available on GitHub and encourages them to achieve a high score there instead of attempting to hack the site. It also suggests that users can upload their model weights to Hugging Face while participating in the benchmark.
The article discusses how two components—a payload that hijacks an agent and an agent that transports the payload—can combine to form a worm. It explains that agents running in isolated sandboxes can leave instructions in a shared package cache, altering each other's behavior. By substituting the package cache with communication channels like email, Slack, or WhatsApp and replacing sandboxed training runs with independently deployed personal agents such as Muse, the conditions necessary for a worm are met.
The post critiques the use of AI-generated scripts for TikTok and YouTube, arguing that such content lacks a distinct voice and genuine opinions. It highlights common AI patterns—such as generic statements, the rule of three, and staccato punctuation—that make the writing feel shallow and unoriginal. The author emphasizes that these traits reveal the absence of authentic personal perspective in the content.
The article reflects on the shift in perspective after the release of Fable, a new model that promised to solve many coding challenges at a comparable or lower cost. Prior to Fable, developers felt it was pointless to invest heavily in coding tools or context strategies, as newer models would likely render them obsolete. However, Fable’s performance was so impressive that, despite its high cost, it prompted a reevaluation of how work was distributed across different models such as Opus, 5.6, K3, and GLM.
Tool: Bluesky reply bot checker Automated reply bots on Twitter are a scourge - as someone with a decent number of followers I attract a swarm of these, such that anything I post there attract...
The article "How To Write With An LLM" by Thomas Ptacek explains how to use large language models (LLMs) as copyeditors rather than writing assistants. Ptacek advocates a strict rule: never use any single word or phrase suggested by an LLM, treating it as intellectual personal protective equipment. He shares his own practice of using LLMs for fact‑checking, spelling, grammar, and occasional thesaurus help, and provides a screenshot of his personal LLM copyediting tool along with a prompt to help readers build their own.