SShortSingh.
Back to feed

WSJ Accepts AI-Assisted Op-Ed, Sparking Debate on Disclosure in Publishing

0
·1 views

Billionaire investor Stanley Druckenmiller published an opinion piece in the Wall Street Journal that he later confirmed was drafted with AI assistance, describing the tool as similar to a calculator for writing. WSJ opinion editor Paul Gigot defended the decision, stating that AI is a 'fact of modern life' and that the paper's primary concern is whether the argument is original and the author credible. The publication determined no AI disclosure was necessary, marking a notable shift in how a major outlet treats AI involvement in contributed content. The stance contrasts with stricter policies at outlets like the Financial Times, which recently corrected a piece for undisclosed AI use, and the New York Times, which requires freelance submissions to be entirely human-created. The episode signals a broader industry shift in focus from whether AI was used to what it was used for and who takes responsibility for the final output.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

Gemini Omni 1.1 Flash Now Supports Video Extension Up to 40 Seconds

Google's Gemini Omni 1.1 Flash model generates video clips of 10 seconds by default, but a scene-extension feature allows developers to append additional 10-second segments, reaching a maximum total duration of 40 seconds across four segments. The generally available version, released on August 27, 2026, expanded the model's context window to the previous 10 seconds, enabling improved visual consistency and narrative continuity between segments. Extensions work via the Files API, where users upload an existing clip and pass its URI along with a text prompt describing the next scene. Developers can also chain segments using a previous interaction ID, which reduces token usage and better preserves scene state across multiple extension calls. Notable limitations include the inability to insert footage at the beginning or middle of a clip, a 10-second cap on directly uploaded input videos, and restrictions on adding dialogue when extending an uploaded video.

0
ProgrammingDEV Community ·

Four Common Myths About Free AI API Tiers Debunked With a Python Script

A developer has identified four widely held misconceptions about free-tier AI model endpoints, commonly seen in issue trackers and code reviews. The first myth is that free tiers use inferior models — in reality, they run the same weights as paid tiers but on a shared queue. The second myth involves trusting p50 latency as a health indicator, when p95, p99, and stall rates provide a far more accurate picture. The third myth is that immediately retrying a timed-out request helps recovery, whereas doing so concentrates load and can trigger a thundering herd effect, making timeouts more likely. A standard-library Python script is provided to help developers verify these myths on any OpenAI-compatible endpoint in under an hour.

0
ProgrammingDEV Community ·

Open-Source Job Search Platform Adds Local AI Support via Ollama Integration

A developer building an open-source, AI-assisted international job search platform has released the second iteration of the project, available on GitHub. The update transforms the app from a basic foundation into a functional search workspace where users can import resumes, set job preferences, and interact with an AI assistant. A key addition is support for multiple AI providers through an OpenAI-compatible adapter, allowing users to choose between hosted models or a locally run model via Ollama. API keys are stored server-side and encrypted with AES-256-GCM, and local Ollama users can opt out of providing a key entirely. The design philosophy prioritises user control over privacy, cost, and model performance rather than automating bulk job applications.

0
ProgrammingDEV Community ·

Why AI Demos Succeed But Products Fail: Four Core Engineering Gaps

Many AI projects that shine in demos collapse in production because a prototype only proves possibility, while a real product must deliver reliability at scale. A common root cause is that teams never define what a 'good' output looks like, making it impossible to evaluate or improve the system systematically. Poor context design is another major factor, as even capable models produce weak results when they lack essential information such as framework details, schemas, or coding conventions. Workflow design is also frequently neglected, with organizations treating the AI model as the entire solution rather than one component in a structured pipeline. Finally, companies often deploy AI on top of broken processes instead of fixing those processes first, compounding existing inefficiencies rather than resolving them.