Generative AI coding tools and agents do not work for me

The author, a software engineer, does not find generative AI coding tools and agents useful because they do not make him work faster, as he still needs to thoroughly review and understand the generated code before incorporating it into his projects. He believes that relying on AI-generated code without proper review is irresponsible, as he is ultimately responsible for the code and any potential malfunctions, and that using AI tools would not increase his productivity without compromising the quality of his work.

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Building Effective AI Agents

Anthropic has found that the most successful large language model (LLM) implementations use simple, composable patterns, rather than complex frameworks or specialized libraries. The company recommends starting with simple LLM APIs and only increasing complexity when needed, and provides guidance on when to use workflows, agents, and frameworks to build effective agentic systems.

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Accumulation of Cognitive Debt When Using an AI Assistant for Essay Writing Task

A study on the cognitive effects of using AI assistants like ChatGPT for essay writing found that participants who relied on these tools showed weaker neural connectivity and decreased learning skills compared to those who used search engines or wrote without any assistance. The study's results suggest that over-reliance on AI assistants can lead to a decline in cognitive abilities, with participants in the AI group performing worse than their counterparts in areas such as neural activity, language use, and scoring.

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Time Series Forecasting with Graph Transformers

Time series forecasting is a crucial aspect of business analytics, and traditional methods often focus on individual time series data, neglecting valuable predictive signals from related data sources. Graph-structured data, which represents interconnected entities, can be utilized to improve forecasting accuracy by incorporating related information, and techniques such as graph transformers and relational deep learning can be applied to extract and leverage this data.

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AI will shrink Amazon's workforce in the coming years, CEO Jassy says

Amazon CEO Andy Jassy stated that the company's corporate workforce will shrink in the coming years as it adopts more generative artificial intelligence tools and agents, with employees needing to learn how to use AI tools to get more done with smaller teams. This announcement comes after Amazon has already laid off over 27,000 employees since 2022, and the company is using AI broadly across its internal operations, including in its fulfillment network to improve efficiency.

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Future of Work with AI Agents

The introduction of a novel auditing framework has enabled the assessment of which occupational tasks workers want AI agents to automate or augment, and how those desires align with current technological capabilities. The framework's application to over 844 tasks across 104 occupations reveals diverse expectations for human involvement and highlights the need to align AI development with human desires, preparing workers for shifting workplace dynamics.

ArXiv

Developing RAG Based LLM Systems from PDFs: An Experience Report (2024)

The paper discusses the development of Retrieval Augmented Generation (RAG) systems using PDF documents, which combines large language models with information retrieval to enhance response transparency, accuracy, and contextuality. The authors share their experience, including technical challenges and solutions, and provide insights for researchers and practitioners, with the goal of improving the reliability of generative AI systems in various sectors.

ArXiv

Is there a half-life for the success rates of AI agents?

The performance of AI agents on longer tasks can be explained by a simple mathematical model, which suggests that their success rate declines exponentially with task length, and each agent can be characterized by its own "half-life". This model, which fits the data well, implies that failure on longer tasks is likely due to the increasing number of subtasks, where failing any one subtask fails the entire task.

ArXiv

Future of Work with AI Agents: Auditing Automation and Augmentation Potential

The introduction of a novel auditing framework has enabled the assessment of which occupational tasks workers want AI agents to automate or augment, and how those desires align with current technological capabilities. The framework's application has revealed diverse expectations for human involvement across occupations and highlighted the need to align AI development with human desires, preparing workers for shifting workplace dynamics and a potential shift from information-focused to interpersonal skills.

ArXiv

Rethinking Text-Based Protein Understanding: Retrieval or LLM?

Protein-text models have shown promise in generating and understanding proteins, but current benchmarks and metrics have significant limitations, including data leakage issues and inaccurate performance assessments. To address these issues, a new evaluation framework and retrieval-enhanced method have been proposed, which outperform existing approaches in protein-to-text generation and demonstrate accuracy and efficiency in training-free scenarios.

ArXiv

please add an option to block or disable "AI"

Codecov has a feedback repository where users can provide feedback, ask questions, and receive community support, as well as best-effort support from Codecov team members. The company's core product repositories, including the API layer, task processing layer, front end, and shared functions, are available on GitHub, and users can also access a self-hosted repository to run Codecov locally.

Repo

vibetunnel - turn any browser into a terminal and command your agents on the go

VibeTunnel is a tool that allows users to access their Mac terminal from any device with a web browser, enabling remote monitoring and control of terminal sessions, including AI agents and development environments. The tool offers features such as zero-configuration setup, secure tunneling, and session recording, making it easy to use and share terminal sessions with others.

Repo

Show HN: Rulebook AI – rules and memory manager for AI coding IDEs

This template provides a cross-platform framework for AI coding assistants, such as Cursor, CLINE, RooCode, Windsurf, and Github Copilot, to operate consistently and follow best practices. By leveraging established software engineering principles and a structured documentation system, developers can supercharge their AI coding workflow, ensuring predictable and high-quality output across different platforms and projects.

Repo

Show HN: DeepTeam – Open-Source Red-Teaming Framework for LLM Security

DeepTeam is an open-source LLM red teaming framework that simulates adversarial attacks to identify vulnerabilities in large-language model systems, including bias, PII leakage, and misinformation. The framework provides 40+ vulnerabilities and 10+ adversarial attack methods, and allows users to customize and test their own vulnerabilities, with the goal of catching safety risks and security vulnerabilities before they can be exploited.

Repo

Show HN: MCP Kit – a toolkit for building, mocking and optimizing AI agents

MCP Kit is a Python toolkit for developing and optimizing multi-agent AI systems, providing seamless integration between AI agents and various data sources, APIs, and services. It offers features such as flexible target systems, framework adapters, configuration-driven architecture, and advanced response generation, making it a comprehensive tool for building, testing, and deploying multi-agent systems.

Repo