Google has announced that Gemini Code Assist will be discontinued on July 17. The service, which offered AI-powered code review capabilities, is being shut down by the company. The announcement was made via Google's official cloud documentation page. Users of the tool will need to seek alternative solutions before the shutdown date.
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A detailed production-grade walkthrough has been published for deploying a NestJS backend to DigitalOcean with zero downtime. The setup uses Ubuntu 24.04, Node.js v18, PM2 in cluster mode, and Nginx as a reverse proxy, with GitLab CI/CD automating the deployment pipeline. The guide recommends installing Node.js directly from official binaries rather than using NodeSource scripts, which can install unintended versions. Security best practices are emphasized, including running deployments under a dedicated non-root user called 'deployer' instead of root. The walkthrough also covers SSL configuration via Certbot and proper Nginx proxy settings to ensure uninterrupted request handling during deployments.
Backend developers commonly use database abstractions ranging from raw SQL to ORMs, but these tools vary widely in how much visibility they offer into actual query execution. A 'black box' approach hides the generated queries, making it difficult to diagnose performance issues or incorrect results during production incidents. In contrast, a 'glass box' approach prioritizes readable, deterministic queries that are pre-compiled and inspectable, reducing runtime surprises. The article argues that opacity in the data layer turns routine debugging into guesswork, especially under time pressure. Choosing transparent data access patterns can ease onboarding, improve performance tuning, and make refactoring safer for development teams.
A developer built an autonomous AI research tool called Luka and directed it at Goldbach's conjecture, one of mathematics' oldest unsolved problems. Luka computed Goldbach partition counts for over 2.4 million even integers and found that numbers congruent to 1 (mod 3) consistently produce 0.26% more prime-pair representations than those congruent to 2 (mod 3). This asymmetry contradicts the Hardy–Littlewood formula, which predicts equal counts for both residue classes, and was confirmed with an exceptionally low p-value of 4.07 × 10⁻²⁰⁴. The developer attributes the bias to Chebyshev's known tendency to favor primes in certain residue classes, a effect that appears to amplify when convolved through Goldbach's bilinear structure. The findings, shared on DEV Community along with open-source Python code, are presented as a proof of concept for AI-assisted mathematical discovery rather than a formal peer-reviewed proof.
A software developer built an autonomous AI research engine called Luka and directed it toward the twin prime conjecture, one of mathematics' long-standing unsolved problems. Luka analyzed verified twin prime counts across 33 data points spanning eight orders of magnitude, from 10⁶ to 10¹⁴. It found that the residual between the widely used Hardy-Littlewood approximation and actual twin prime counts follows a consistent power law with an R² of 0.9907, later refined to 0.9997 with an additional logarithmic term. Further analysis revealed this pattern reflects a known second-order asymptotic error in the simplified approximation formula rather than a new property of twin primes themselves. Luka also tested and statistically falsified a recent oscillatory model called PRIT, whose predictions deviated from actual values by factors of 100 to 700.
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