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github.com · 2026-10-27 · Ranked: agent match · community signal · fresh 1.00 · score 3.49 · Context

Show HN: AGBoss Dependency injection and static validation for AI agents

infoq.com · 2026-10-27 · Ranked: practitioner analysis · fresh 0.98 · score 3.37

Presentation: Serving LLMs at Scale: The Hidden KV Cache Advantage

Khawaja Shams shares a distributed systems perspective on scaling LLM inference stacks efficiently. He discusses practical strategies for optimizing GPU utilization, managing KV cache memory cliffs, leveraging tiered... Context & related coverage →

github.com · 2026-10-27 · Ranked: agent match · community signal · fresh 0.99 · score 3.25 · Context

A Salesforce AI agent that can't publish a hallucination

codingcat.dev · 2026-10-27 · Ranked: community signal · fresh 0.98 · score 3.09 · Context

Dockerless Serverless GPUs: How I Built a Fast, Cheap AI SaaS with RunPod Flash

infoq.com · 2026-10-27 · Ranked: practitioner analysis · fresh 0.99 · score 3.04

Uber Moves Beyond CPU Cores with Performance-Aware Scheduling for Stateful Workloads

Uber has introduced Normalized Compute Units (NCUs) to make CPU allocation more consistent across its highly heterogeneous server fleet. By Craig Risi Context & related coverage →

arxiv.org · 2026-10-26 · Ranked: agentic + evaluation match · research watch · fresh 0.88 · score 2.83

Evaluating DeepSeek V4.1 Flash-1017 for loss of control and harmful manipulation risks

As frontier language models grow increasingly capable and autonomous, independent third-party evaluations are critical for characterizing their systemic safety risks. While closed-source releases are routinely accompa... Context & related coverage →

arxiv.org · 2026-10-26 · Ranked: research watch · fresh 0.89 · score 2.41

Understanding Jev-Style System One Models: Does Structure Reduce Selection Bias?

Selection bias in large language models (LLMs) can persist despite instructions to ignore presentation order or prompts that elicit reasoning. Recent efforts to reproduce Jev-style System One behavior have produced a... Context & related coverage →

arxiv.org · 2026-10-26 · Ranked: eval match · research watch · fresh 0.89 · score 2.40

Learning When to Commit: Schedule Distillation for Fast Diffusion Language Models

Diffusion large language models (dLLMs) can generate multiple tokens in parallel, but often require many decoding rounds to complete a response. Efficient generation depends not only on which tokens are easy to predic... Context & related coverage →

arxiv.org · 2026-10-26 · Ranked: agentic + harness match · research watch · fresh 0.89 · score 2.31

I-HITL: Inalienable Human-in-the-Loop

Human oversight is a pillar of AI safety. It is often exercised by a human in the loop (HITL) who approves critical actions taken by an AI agent. AI agents' increasing ability to bypass guardrails, though, raises the... Context & related coverage →

arxiv.org · 2026-10-26 · Ranked: eval match · research watch · fresh 0.85 · score 2.30

CalibraRL: Confidence-Calibrated Reinforcement Learning for Mitigating Hallucinations in RAG

Large language models (LLMs) often produce plausible but incorrect answers, motivating reinforcement learning methods that reward correct answers, penalize incorrect answers, and allow abstention. However, fixed outco... Context & related coverage →