LLVM AI tool policy: human in the loop

The proposed LLVM AI tool policy mandates a "human-in-the-loop" approach, requiring contributors to fully understand and defend LLM-generated code during the review process. To protect maintainer bandwidth from "extractive contributions," the policy bans autonomous agents and requires transparent labeling of tool-assisted work via commit trailers. Contributors remain technically and legally accountable for all output, ensuring that LLM usage does not offload the burden of validation onto project maintainers.

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How AI labs are solving the power problem

AI labs are bypassing overwhelmed electrical grids by adopting "Bring Your Own Generation" (BYOG) strategies to accelerate datacenter deployment. Technical solutions include aeroderivative gas turbines, modular RICE units, and SOFC fuel cells, often paired with BESS or flywheels to manage high-inertia training load fluctuations. While onsite generation carries a higher TCO, the potential for $10-12 billion in annual revenue per GW makes deployment speed the critical competitive moat.

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AI-generated videos showing young and attractive women promote Poland's EU exit

A disinformation campaign on TikTok utilized generative AI to create synthetic avatars of young women advocating for "Polexit." Despite low-quality rendering and audio-visual desynchronization, the campaign achieved significant reach by targeting younger demographics and repurposing established accounts to bypass initial algorithmic hurdles. This incident highlights the "Hydra effect" in AI-driven propaganda, where generative tools enable the rapid deployment of new accounts to replace those banned for spreading disinformation.

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Why C++ programmers keep growing fast despite competition, safety, and AI

C++ and Rust are the fastest-growing languages because AI-driven demand for compute and power efficiency consistently outstrips hardware capacity. C++26 addresses safety concerns by introducing a hardened standard library, contracts, and the elimination of undefined behavior for uninitialized variables. While AI accelerates rote tasks, it remains dependent on C++ for high-performance infrastructure like CUDA and serves as a productivity multiplier rather than a replacement for skilled developers.

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Laptops are about to become a casualty of the AI grift

Government subsidies for hyperscale AI data centers are distorting the semiconductor supply chain by prioritizing AI-grade silicon over consumer hardware. Major manufacturers are diverting DRAM and NAND production to server-side infrastructure, leading to significant price hikes and spec "shrinkflation" for laptops and mobile devices. This focus on AGI-oriented compute is siphoning capital away from narrow AI and edge computing, potentially creating a policy-driven bubble that undermines the broader hardware ecosystem.

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Exposing LLM-Generated Logical Flaws in Reasoning via Automated Theorem Proving

MATP is an evaluation framework that verifies LLM reasoning by translating natural language into First-Order Logic (FOL) for validation via automated theorem provers. It addresses subtle logical errors in multi-step reasoning that traditional fact-checking and self-consistency methods often miss. Benchmarks show MATP outperforms prompting-based baselines by over 42% in step verification, highlighting significant coherence gaps between specialized reasoning models and general-purpose LLMs.

ArXiv

Can AI Recognize Its Own Reflection?

An evaluation of GPT-4, Claude, and Gemini reveals that while these LLMs can identify default AI-generated text, they suffer from high false positive rates (up to 32%) on human-written work. Adversarial prompting significantly degrades detection accuracy, with Gemini-generated deceptive text successfully bypassing GPT-4. Consequently, current LLMs are unreliable for high-stakes academic integrity assessments.

ArXiv

Bayesian Geometry of Transformer Attention

Researchers used "Bayesian wind tunnels" to prove that transformers achieve high-precision Bayesian inference ($10^{-3}$–$10^{-4}$ bits) in controlled environments where memorization is impossible. Mechanistically, residual streams serve as belief substrates, FFNs perform posterior updates, and attention handles content-addressable routing via a low-dimensional value manifold. This geometric design explains the architectural necessity of hierarchical attention for Bayesian reasoning, contrasting sharply with the failure of flat MLP architectures.

ArXiv

Eliminate Branches by Melding IR Instructions

MERIT is a compiler transformation that eliminates branch mispredictions by aligning and melding structurally similar IR instructions from divergent execution paths. By utilizing sequence alignment and operand-level guarding instead of hardware predication, it overcomes the limitations of traditional if-conversion for irregular, data-dependent branches. Implemented as an LLVM pass, MERIT achieves a 10.9% geometric mean speedup and up to 32x peak performance gains by reducing instruction overhead.

ArXiv

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

MTTR-A is a runtime reliability metric designed to quantify cognitive recovery latency in LLM-based multi-agent systems (MAS) following reasoning drift. By adapting classical dependability theory, it measures the time required to restore coherence, supported by complementary metrics like MTBF and NRR. Empirical validation using LangGraph benchmarks establishes a quantitative foundation for assessing cognitive uptime and reflex recovery strategies in distributed agentic architectures.

ArXiv

A local-first financial auditor using IBM Granite, MCP, and SQLite

This privacy-centric financial platform utilizes a local-first agentic architecture powered by Ollama and MCP. It employs granite3.3:8b to interpret natural language and orchestrate SQL-backed tools, offloading arithmetic to SQLite to ensure 100% mathematical accuracy. The system integrates LLM-based vendor normalization and a FastAPI backend to transform raw bank data into structured, auditable insights.

Repo

LLMRouter – first LLM routing library with 300 stars in 24h

LLMRouter is an open-source library for intelligent LLM routing that dynamically selects optimal models based on task complexity, cost, and performance. It supports over 16 routing strategies—including single-round, multi-round, agentic, and personalized models—leveraging techniques such as KNN, MLP, and graph-based routing. The framework includes a unified CLI for training and inference, a plugin system for custom routers, and a comprehensive data generation pipeline supporting 11 major benchmarks.

Repo

ChatGPT and Claude-style smart scrolling for React Native message lists

react-native-streaming-message-list is a React Native library designed to replicate the smart scrolling behavior of ChatGPT and Claude for LLM chat interfaces. It features a FlatList-compatible component that handles streaming responses without scroll jank by using dynamic placeholders and anchored message positioning. Built with react-native-reanimated, it allows developers to manage growing assistant responses while keeping the preceding user message visible.

Repo

OpenCode plugin for interactive plan annotation

Plannotator is an interactive plan review tool for AI coding agents that provides a visual UI for annotating and refining agent-generated plans. It integrates with Claude Code and OpenCode, allowing users to approve or request changes via structured feedback before implementation. This human-in-the-loop workflow enables precise control over agentic coding tasks through visual editing and team collaboration.

Repo

GitHub Action for AI/LLM Security Scanning in CI/CD

AgentAudit is a GitHub Action that automates security scanning for AI agent endpoints within CI/CD pipelines to detect prompt injection, jailbreaking, and data exfiltration. It features configurable scan modes and severity-based failure thresholds, providing detailed risk scores and reports to facilitate automated security gating for LLM applications.

Repo