LLVM AI tool policy: human in the loop

The LLVM project's new AI Tool Use Policy mandates a "human in the loop" for all LLM-assisted contributions. Contributors must thoroughly review and be fully accountable for LLM-generated content, prepared to answer questions during review to avoid offloading validation work to maintainers. The policy also requires transparency by labeling tool-generated content, prohibits autonomous AI agents, and aims to prevent "extractive" contributions that burden maintainers, while also holding contributors responsible for copyright compliance.

Read more

The 70% AI productivity myth: why most companies aren't seeing the gains

While vendors claim 70-90% productivity gains from AI tools, this text argues such benefits are limited to about 10% of developers, mainly in AI-native or greenfield contexts. Independent studies show experienced engineers can be slower with AI, often misperceiving their speed, and face significant challenges like legacy systems and a substantial "AI fluency tax." The shift to working with stochastic, fallible LLM-based systems represents a new paradigm, yielding more realistic organizational productivity gains of 10-15% over extended ROI timelines for most.

Read more

Show HN: Summit – local AI meeting insights

Summit AI Notes is a macOS application that provides a privacy-first meeting assistant, performing 100% local recording and AI processing of meeting audio directly on the user's device. This on-device processing ensures confidentiality and NDA compliance by preventing sensitive conversations from being uploaded to cloud servers, a key differentiator from many cloud-based transcription services. It generates structured summaries with action items and key insights, leveraging local AI models for transcription and summarization without relying on external LLM APIs.

Read more

Show HN: Brainrot Translator – Convert corporate speak to Gen Alpha and back

This AI-powered tool, the "Brainrot Translator," facilitates translation between corporate jargon and Gen Alpha slang. It also provides humorous, slang-infused "Cooked Analysis" for images and "Dating Roast Samples" for dating profiles, offering critiques and suggestions for improvement.

Read more

AI code analysis is getting good

Mitchell Hashimoto shared a positive experience where a user, despite lacking domain knowledge, effectively leveraged AI to generate a Python script for crash file analysis, leading to the identification and resolution of four critical bugs in Ghostty. This highlights AI's potential for complex code analysis and high-quality bug reporting when guided by critical human thinking and careful interaction, contrasting with the "slop" often produced by less skilled AI users. The discussion also notes varied experiences with LLM-generated submissions in other open-source projects, underscoring the necessity of human judgment.

Read more

Can AI Recognize Its Own Reflection?

This study evaluates GPT-4, Claude, and Gemini's efficacy in detecting AI-generated text within computing education contexts. While easily identifying default AI-generated content, models struggled with human-written text (up to 32% error) and were highly susceptible to deceptive prompts, with Gemini's output completely fooling GPT-4. These findings indicate that current LLMs are too unreliable for high-stakes academic misconduct judgments due to their instability and susceptibility to simple prompt alterations.

ArXiv

LLM Efficiency: From Hyperscale Optimizations to Universal Deployability

Current LLM efficiency methods like MoE, speculative decoding, and complex RAG are largely inaccessible to most organizations due to their demanding infrastructure and expertise requirements, creating a deployment disparity. The text advocates for a new research agenda focused on "robust simplicity" and "Overhead-Aware Efficiency (OAE)" to democratize LLM deployment. This involves retrofitting models without retraining, lightweight fine-tuning, economical reasoning, and dynamic knowledge management without heavy RAG pipelines, aiming to reduce inequality and carbon waste.

ArXiv

Professional software developers don't vibe, they control

A study on experienced developers' use of AI agents in software development finds they value agents for productivity but retain agency in design and implementation due to insistence on fundamental software quality attributes. Developers leverage their expertise to control agent behavior and complement agent limitations, identifying suitable tasks and emphasizing best practices for effective agent integration. The findings suggest opportunities for improved agentic interfaces and usage guidelines.

ArXiv

Stable-Pretraining-v1: Foundation Model Research Made Simple

stable-pretraining is a modular, extensible PyTorch-based library designed to streamline foundation model and SSL research by unifying essential utilities like probes and collapse detection. It addresses complex codebases and engineering overhead, offering comprehensive logging for enhanced debugging and reproducibility. The library aims to accelerate discovery and expand research possibilities by lowering barriers and scaling large experiments.

ArXiv

MTTR-A: Measuring Cognitive Recovery Latency in Multi-Agent Systems

Addressing cognitive failures in LLM-based MAS, this work introduces MTTR-A, a runtime reliability metric quantifying cognitive recovery latency. MTTR-A measures the time to detect reasoning drift and restore coherent operation, adapting classical dependability theory to agentic orchestration. The paper also defines MTBF and NRR, establishing theoretical bounds for cognitive uptime, and empirically validates recovery behavior using a LangGraph benchmark.

ArXiv

Show HN: Replacing my OS process scheduler with an LLM

BrainKernel is a TUI process manager that leverages an LLM for context-aware analysis of running processes, distinguishing between critical system tasks and bloatware based on parentage, I/O, and behavior history. It supports Groq or Ollama for LLM inference, offering features like "Roast Mode," "Focus Mode" to suspend distractions, and "Diplomatic Immunity" for protected applications. The tool maintains low CPU usage via Delta Caching and incorporates safety mechanisms like PID Safety Lock.

Repo

Show HN: Cover letter generator with Ollama/local LLMs (Open source)

Cover Letter Maker is an open-source Next.js web application that generates personalized cover letters using local AI. It operates entirely on-device, ensuring privacy by not sending data to external servers. Users provide job details and a PDF resume, which the app parses to create tailored, human-sounding cover letters. It supports various OpenAI-compatible local LLM servers like Ollama, LM Studio, and vLLM, and offers multi-language support.

Repo

Show HN: MCP Mesh – one endpoint for all your MCP servers (OSS self-hosted)

MCP Mesh is an open-source control plane for MCP traffic, unifying authentication, routing, and observability between MCP clients (e.g., LLM agents) and MCP servers. It replaces M×N integrations with a single governed endpoint, enforcing RBAC, policies, and audit trails. The mesh offers full OpenTelemetry observability and supports runtime strategies as gateways to optimize tool selection and execution for LLM-powered applications.

Repo

Introduction to Machine Learning Systems

This project establishes AI engineering as a foundational discipline, focusing on designing, building, and evaluating efficient, reliable, and robust intelligent systems for real-world deployment. It offers an open learning stack comprising a textbook, TinyTorch for understanding ML framework internals, and hardware kits for deploying models on edge devices, bridging core ML concepts with systems engineering principles like MLOps and hardware acceleration.

Repo

PowerMem – Persistent memory layer for AI agents

PowerMem is an intelligent memory system designed to enable LLMs to persistently remember historical conversations, user preferences, and contextual information. It utilizes a hybrid storage architecture combining vector retrieval, full-text search, and graph databases, integrating the Ebbinghaus forgetting curve theory for dynamic memory management. The system demonstrates significant improvements in accuracy, response speed, and token reduction compared to full-context methods. Key features include LLM-based intelligent memory extraction, multimodal support, multi-agent memory isolation and collaboration, user profile management, and optimized data storage with knowledge graph capabilities.

Repo