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Show HN: I've asked Claude to improve codebase quality 200 times

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A "Frozen" Dictionary for Python

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Show HN: GPULlama3.java Llama Compilied to PTX/OpenCL Now Integrated in Quarkus

Auto-grading decade-old Hacker News discussions with hindsight

A Developer Accidentally Found CSAM in AI Data. Google Banned Him for It

Learnings from Cultivating Machine Learning Engineers as a Team Manager

As an AI team manager, Vivek Gupta stays broadly informed to guide AI experts effectively and drive the team. Engineers need feedback on both technical and interpersonal skills, Gupta mentioned at Dev Summit Boston. He stresses learning time, asking for help, and cross-team collaboration. Mentorship, data handling, and human-in-the-loop validation are key to success for machine learning engineers. By Ben Linders

Five AI Security Myths Debunked at InfoQ Dev Summit Munich

Katharine Jarmul challenged five common AI security and privacy myths in her InfoQ Dev Summit Munich 2025 keynote: that guardrails will protect us, better model performance improves security, risk taxonomies solve problems, one-time red teaming suffices, and the next model version will fix current issues. She said that current approaches to AI safety rely too heavily on technical solutions. By Karsten Silz

Presentation: Securing AI Assistants: Strategies and Practices for Protecting Data

Andra Lezza explains the criticality of data security for AI copilots, detailing the OWASP AI Exchange threat model and the OWASP Top 10 LLM risks. She reviews two copilot architectures - independent (single domain) and integrated (multi-tenant) - listing specific threats, controls, and best practices like granular authorization, templates, and DevSecOps to secure the entire AI data supply chain. By Andra Lezza

Podcast: Platform Engineering for AI: Scaling Agents and MCP at LinkedIn

QCon AI New York Chair Wes Reisz talks with LinkedIn’s Karthik Ramgopal and Prince Valluri about enabling AI agents at enterprise scale. They discuss how platform teams orchestrate secure, multi-agentic systems, the role of MCP, the use of foreground and background agents, improving developer experience, and reducing toil. By Karthik Ramgopal, Prince Valluri

Agentic Postgres: Postgres for Agentic Apps with Fast Forking and AI-Ready Features

Tiger Data, the company behind TimescaleDB, has launched Agentic Postgres, a Postgres-based database designed for both AI agents and developers. It extends Postgres with fast forking, an MCP server, native BM25 and vector search, and includes a CLI for terminal access. By Sergio De Simone

OpenAI's New GPT-5.1 Models are Faster and More Conversational

OpenAI recently released upgrades to their GPT-5 model. GPT‑5.1 Instant, the default chat model, has improvements to instruction following. GPT‑5.1 Thinking, the reasoning model, is faster and gives more understandable responses. GPT‑5.1-Codex-Max, the coding model, is trained to use compaction to perform long-running tasks. By Anthony Alford

Replit Introduces New AI Integrations for Multi-Model Development

Replit has introduced Replit AI Integrations, a feature that lets users select third-party models directly inside the IDE and automatically generate the code needed to run inference. By Daniel Dominguez

Marco Rubio bans Calibri font at State Department for being too DEI

Disney Inks Blockbuster $1B Deal With OpenAI, Handing Characters Over To Sora

Pneumatic-suction robot clears 75,000 lb of cargo an hour

The Number of People Using AI at Work Is Suddenly Falling

Mistral’s Vibe CLI now supports a 200K token context window (previously 100K)

Soft robots can now see with a self-focusing squishy eye

New in llama.cpp: Live Model Switching

Videos of sexually suggestive, AI-generated children are racking up millions of likes on TikTok, study finds

Oracle just revived fears that tech giants are spending too much on AI

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🆙 Upscayl - #1 Free and Open Source AI Image Upscaler for Linux, MacOS and Windows.

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A lightning-fast search engine API bringing AI-powered hybrid search to your sites and applications.

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🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

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Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.

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High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)