GoKawiil
Tech News
A commentator argues that since ChatGPT's 2022 launch, people have largely used AI for creative tasks like brainstorming, writing emails, and building strategies from scattered notes. The author contends this is the wrong use case, suggesting AI should instead handle routine, verifiable work where machines outperform humans.
A software professional describes a growing trend where colleagues use AI to retroactively generate design documents, pull request summaries, tickets, and even personal messages after work is already done. The writer argues these AI outputs are technically detailed but lack context, perspective, and human judgment, making them exhausting and frustrating to read despite AI's usefulness for improving writing speed and quality elsewhere.
A tech commentator argues that while AI tools like Claude, Codex/ChatGPT and Gemini are useful for coding and research, they should not be used to generate written documents from scratch. The reasoning is that writing itself is the process of thinking through a problem, and outsourcing that to AI means skipping the deep thinking required to produce genuinely useful, well-considered content. The author does allow for AI to be used afterward, to polish writing once the original thinking and drafting is complete.
An essay contends that people should almost never use AI to generate written text meant to convey ideas or arguments, even with detailed prompts or human editing afterward. The author distinguishes this from other AI uses they endorse, like transcription, data analysis, brainstorming, and copyediting, arguing only the act of drafting substantive prose should be avoided.
A new essay argues that writers should draft their own work entirely by hand, then feed it to a large language model purely to spot flaws, never to supply finished phrasing. The author lays out two guiding rules: never use a specific wording an LLM suggests, and treat the model strictly as a copyeditor rather than a co-writer, to avoid producing text that reads as generic machine output.
As AI agents increasingly write code, analyze documents, handle customer interactions, and coordinate workflows with minimal human input, organizations are confronting a governance gap: existing oversight processes were not built to keep pace with software that acts and decides in real time. The push is now toward supervision mechanisms that operate as fast as the agents themselves.
According to the New York Times, thousands of Kenyan workers who once earned income writing essays for foreign college students have seen that business evaporate since ChatGPT launched in 2022. One worker, Teresios Bundi, once earned $40-$70 per paper—far more than his public health training would have paid—but now struggles financially as demand has dried up. At its height, the essay-writing trade employed more than 40,000 people in Nairobi alone.
A personal essay recounts a university mishap where a professor accused a student of being carried by a group partner on an Ancient History project, despite the student having written the bulk of the work himself. The writer uses this anecdote to introduce a broader lesson on non-fiction writing: establish the central idea, or 'trunk,' before adding supporting details, or 'branches.'
OpenAI is testing a new 'Writing Style' feature for ChatGPT with a limited set of users. It works by connecting to apps like Gmail, Slack, Google Drive, and Notion, analyzing existing messages, emails, and documents to learn a person's natural tone and voice for future drafts.
An essay argues that readers are growing frustrated with writers who publish AI-generated text without disclosure, noting that the stylistic patterns of large language models are easy to spot. It cites a survey by Cynthia Dunlop of 668 developers, where 78% said they stop reading immediately upon detecting AI-written content, and 71% said they avoid that author's future work.
A developer commentary questions why so many software tools rely on JSON for configuration files despite JSON's inability to natively support comments. The piece argues this makes it difficult for users to document why specific settings were chosen, a basic need in configuration management.
An opinion piece argues that while large language models have rapidly improved at generating code, their prose remains stuck in an 'uncanny valley' of clichéd, robotic language lacking real insight. The author contends AI labs have already pushed hard to improve writing quality but hit a wall, unlike image, voice, and video models that scaled quickly with more compute and parameters. The piece frames writing as a 'wicked problem' with no clear definition or objectively correct solution, making it resistant to the kind of brute-force scaling that worked for other AI capabilities.