AI 2027

The impact of superhuman AI over the next decade is predicted to be enormous, exceeding that of the Industrial Revolution, with AI agents transforming industries and professions. By 2025, AI agents will be able to function like employees, taking instructions and making substantial changes on their own, but will still be unreliable and expensive, with the potential for misuse, such as assisting in the design of bioweapons, despite efforts to "align" them with human values.

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Senior Developer Skills in the AI Age

The author, a seasoned software engineer, has been experimenting with AI-powered coding tools and has found them to be highly effective in improving productivity and output quality. By leveraging their experience and expertise, senior developers can harness the power of AI-assisted coding tools to achieve significant benefits, and the author shares their experiences and best practices to help others in the software development community adopt this technology.

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The Slow Collapse of Critical Thinking in OSINT Due to AI

The increasing reliance on AI tools in OSINT (Open-Source Intelligence) analysis is leading to a decline in critical thinking skills among analysts, as they begin to trust the tools' outputs without thoroughly verifying the information. This shift from thinking to trusting can have serious consequences, including the risk of false confidence, inaccurate information, and compromised integrity, ultimately threatening the effectiveness of OSINT operations.

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AI cheats: Why you didn't notice your teammate was cheating

The video game cheating scene has evolved rapidly, with cheats transitioning from memory reading aimbots to colorbots and now AI-powered aim assist, which can detect enemies in any game. These advanced cheats are difficult to detect and often require a second PC and external hardware, making them expensive and time-consuming to use, but still pose a significant threat to fair play in online gaming.

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I stopped using AI code editors

The author stopped using AI-powered code editing tools after realizing they were losing their competence and intuition in programming due to over-reliance on these tools. They draw a parallel with their experience of using Tesla's Full Self-Driving feature, which also led to a decline in their driving skills, and warn that relying on AI in coding can have similar effects, eroding one's ability to develop the intuitive flair and situational awareness that comes with experience and practice.

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Measuring AI Ability to Complete Long Tasks

Researchers have proposed a new metric, the 50%-task-completion time horizon, to quantify AI capabilities in terms of human capabilities, finding that current AI models can complete tasks with 50% success rate in around 50 minutes, a time frame that has been doubling approximately every seven months. If this trend continues, AI systems may be able to automate many software tasks that currently take humans a month within the next 5 years, driven by improvements in reliability, adaptability, logical reasoning, and tool use capabilities.

ArXiv

Search-R1: Training LLMs to Reason and Leverage Search Engines with RL

The Search-R1 model, an extension of DeepSeek-R1, enables large language models (LLMs) to autonomously generate search queries during reasoning, leveraging reinforcement learning and real-time retrieval. Experiments show that Search-R1 improves performance by up to 26% over strong baselines on seven question-answering datasets, demonstrating its effectiveness in retrieval-augmented reasoning.

ArXiv

Banked Memories for Soft SIMT Processors

A soft SIMT processor with banked memories has been designed and evaluated, achieving high bandwidth and speed, and its performance has been compared to simpler multi-port memories across 51 benchmarks. The results show that while multi-port memories offer higher performance for smaller memories, banked memories are more suitable for larger datasets due to their lower footprint cost, and can also be applied to other FPGA applications.

ArXiv

A Study of Undefined Behavior Across Foreign Function Boundaries in Rust Libs

Developers using the Rust programming language to build secure applications often interoperate with other languages, which can introduce bugs that conflict with Rust's aliasing models. A large-scale evaluation of Rust libraries found 46 instances of undefined or undesired behavior in 37 libraries, highlighting the need for new tooling to detect these errors in multi-language applications.

ArXiv

DeepSeek: Inference-Time Scaling for Generalist Reward Modeling

Reinforcement learning has been used to improve large language models, but a key challenge is obtaining accurate reward signals, which can be addressed through improved reward modeling and learning methods. This work proposes a new approach, DeepSeek-GRM, which uses pointwise generative reward modeling and Self-Principled Critique Tuning to enable scalable and effective reward generation, resulting in improved performance and inference-time scalability.

ArXiv

Show HN: MCP Server to let agents control the browser

Skyvern is a platform that automates browser-based workflows using large language models (LLMs) and computer vision, allowing it to operate on websites it has never seen before and adapt to changes in website layouts. It provides a simple API endpoint to automate manual workflows on a large number of websites, and can be run locally or through a managed cloud version, with features such as anti-bot detection mechanisms and CAPTCHA solvers.

Repo

How to write good prompts for generating code from LLMs

Potpie is an open-source platform that creates AI agents specialized in a user's codebase, enabling automated code analysis, testing, and development tasks. The platform offers pre-built agents for tasks such as debugging, code review, and testing, as well as the ability to create custom agents, and can be integrated into existing development workflows through a VSCode extension and API.

Repo

Show HN: OCR pipeline for ML training (tables, diagrams, math, multilingual)

This OCR system is designed to extract structured data from complex educational materials, such as exam papers, and optimize it for machine learning training, supporting multilingual text, mathematical formulas, tables, diagrams, and charts. The system achieves high accuracy, over 90-95%, and generates AI-ready outputs in JSON or Markdown format, including human-readable descriptions of mathematical expressions, table summaries, and figure captions.

Repo

Claude Squad – A terminal app that manages multiple Claude Code instances

Claude Squad is a terminal application that allows users to manage multiple instances of Claude Code and other local agents in separate workspaces, enabling simultaneous work on multiple tasks. The application provides features such as background task completion, isolated git workspaces, and a simple TUI interface for easy navigation and management.

Repo

Show HN: Aider-script – create and run reusable LLM prompt templates

Aider-script is a CLI tool that streamlines using aider for common tasks by allowing reusable prompt templates with variables and automatic file loading. The tool uses Markdown templates with a frontmatter section for configuration, and supports features like case conversion filters and previewing generated messages before running them.

Repo