Article URL: https://www.goldmansachs.com/static-libs/pdf-redirect/prod/index.html?path=/pdfs/insights/goldman-sachs-research/an-ai-job-apocalypse/report.pdf&originalQuery=&referrer= Comments URL: https://news.ycombinator.com/item?id=48769110 Points: 6 # Comments: 2
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Dory, an AI-native SQL workspace, has added Snowflake as a first-class data source, enabling teams to query, explore, and collaborate on Snowflake data directly within the platform. The integration supports core database workflows including running SQL via a console, browsing schemas and tables, previewing data, and inspecting columns. Users can authenticate via password or key-pair methods, with key-pair authentication recommended for production environments as it avoids storing plain passwords. Snowflake joins Dory's existing lineup of native database connections, participating in the same workspace, query, and schema-browsing flows as other supported sources. The update is aimed at analysts, engineers, operators, and AI agents who need a unified environment to work against shared database context.
At AWS Community Day Brasília last Saturday, a developer ran a live workshop demonstrating AI-assisted open source contribution using an AI coding agent called Kiro. The chosen project was ScanAPI, a Brazilian Python tool for API testing that was the first Brazilian project to receive GitHub sponsorship. After finding that top candidate issues already had open pull requests, the presenter used Kiro to identify issue #916, a Docker Hub image hash dependency bug introduced in release 2.13 that left the project's build broken and vulnerable. Kiro's autonomous mode completed the full contribution workflow in roughly five minutes, from forking the repository to submitting Pull Request #1001 for maintainer review. The presenter emphasized that while AI can dramatically speed up the contribution process, technical understanding and human oversight remain essential responsibilities.
A web developer recounts how a corporate WordPress site launch went wrong after hardcoded local development URLs caused broken buttons and missing images in production. The incident was logged as a formal company entry, highlighting how even minor oversights at handoff can permanently damage client trust. Unlike standard launch checklists that focus on functionality, a proper handoff checklist should anticipate what the client might discover that the team missed. Key areas to audit include test posts, unused or watermarked media, temporary pages, and critical settings like search engine visibility. The article provides manual, tool-free methods for each check to help developers deliver clean, professional sites to clients.
TypeScript's async generator model relies on three explicit type parameters — T for yielded values, TReturn for the final return value, and TNext for values sent back via .next() — but the compiler cannot infer TNext automatically. This limitation exists because yield expressions are bidirectional: a generator yields values outward while callers can simultaneously send values back through the same yield point, creating an inference deadlock. Without explicit annotations, TypeScript defaults to AsyncGenerator<any, any, undefined>, leaving type safety gaps at critical boundaries in streaming, pagination, or event-processing code. Unlike regular functions where parameter types can be inferred from usage, async generators receive their 'inputs' asynchronously through an external iteration protocol, breaking standard inference patterns. Developers must therefore manually annotate all three type parameters whenever a generator consumes input through yield to ensure proper type safety across the codebase.
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