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Sign the intent, the decision, and the execution
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AI — Latest Models & Research
AI
Sign the intent, the decision, and the execution
Most governance tools produce a single signed receipt after the fact. The action happened, here is...
DEV Community
AI
I replaced myself with DeepSeek as tech lead for Claude Code
Claude Code can write code better than most of us. But it still needs someone to lead — decide what...
DEV Community
AI
I Built a Voice AI Agent in 72 Hours — Here's Every Decision I'd Make Differently
A post-mortem on local speech-to-text, LLM intent classification, compound commands, and why I...
DEV Community
AI
Fail-Open LLM Architecture: Why Your Reviewer Stage Should Never Block a Decision
The problem You added a reviewer LLM to your pipeline for good reasons. Quality matters,...
DEV Community
AI
Your Docs Pipeline Is a Security Risk — Zenzic v0.6.1rc1 Fixes That
Most documentation builds trust Markdown blindly. Zenzic v0.6.1rc1 introduces a zero-subprocess, engine-agnostic security layer for your docs pipeline.
DEV Community
AI
The gap between detecting hallucinations and handling them
After posting about TraceMind's hallucination detection, someone left a comment that stopped...
DEV Community
AI
What's actually in a modern favicon set (and why everyone gets it wrong)
What's actually in a modern favicon set (and why everyone gets it wrong) A tiny Python...
DEV Community
AI
The AI Agent Economy Needs Payment Rails — Here's What I Built
500K+ AI agent wallets exist on x402. Stripe launched machine payment protocols. Google announced...
DEV Community
AI
Construa uma crew CrewAI que publica na The Colony em 5 minutos
Dê aos seus agentes CrewAI um lugar para compartilhar descobertas e receber feedback de ~400 outros agentes de IA — usando crewai-colony.
DEV Community
AI
SceneCritic: A Symbolic Evaluator for 3D Indoor Scene Synthesis
Large Language Models (LLMs) and Vision-Language Models (VLMs) increasingly generate indoor scenes through intermediate structures such as layouts and scene graphs, yet evaluation still relies on LLM or VLM judges that score rendered views, making judgments sensitive to viewpoint, prompt phrasing, and hallucination. When the evaluator is unstable, it becomes difficult to determine whether a model
arXiv
AI
Visual Preference Optimization with Rubric Rewards
The effectiveness of Direct Preference Optimization (DPO) depends on preference data that reflect the quality differences that matter in multimodal tasks. Existing pipelines often rely on off-policy perturbations or coarse outcome-based signals, which are not well suited to fine-grained visual reasoning. We propose rDPO, a preference optimization framework based on instance-specific rubrics. For e
arXiv
AI
CLAD: Efficient Log Anomaly Detection Directly on Compressed Representations
The explosive growth of system logs makes streaming compression essential, yet existing log anomaly detection (LAD) methods incur severe pre-processing overhead by requiring full decompression and parsing. We introduce CLAD, the first deep learning framework to perform LAD directly on compressed byte streams. CLAD bypasses these bottlenecks by exploiting a key insight: normal logs compress into re
arXiv