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2026-11-06

llama.cpp releases21:50

b12170

The llama.cpp v1.21.70 release includes enhancements to the moe-cache system, introducing an LRFU/MQ-inspired hybrid cache with improved token handling, key computation, and decay calculations. Changes include adjusting the half-life parameter, increasing the number of classes to 8, refining score computation based on age, removing redundant branches, and improving documentation around unused slots and sorting. The release also features updated build binaries for multiple platforms including macOS, Linux, Windows, Android, and openEuler, with support for CUDA, Vulkan, ROCm, OpenVINO, SYCL, and other backends.

2026-11-05

Hacker News Show HN09:45

Show HN: Autorun-audit – find what runs when you open, CD into or install a repo

Article URL: https://github.com/ag3ntsc4rn/autorun-audit Comments URL: https://news.ycombinator.com/item?id=50374018 Points: 1 # Comments: 0

Hacker News Show HN07:50

Show HN: BrowserForge – AI agents use your real browser, you approve the clicks

Article URL: https://github.com/zoghlamimostafa/BrowserForge-MCP Comments URL: https://news.ycombinator.com/item?id=50373055 Points: 1 # Comments: 0

AWS Machine Learning Blog02:46

Use decision models with Amazon Bedrock AgentCore Evaluations

Some agent-evaluation questions are narrow and repeatable, and decision models answer exactly that kind of question. This post shows how to connect two decision models, Jev and Strands Decider, to Amazon Bedrock AgentCore Evaluations through code-based evaluators, and how to validate a decision-model check before you gate a release on it.

2026-10-26

LangChain23:32

Context Engineering (Agent Memory) for AI Agents Starts with Retrieval

Agents waste time and context on clumsy searches, oversized results and stale data. Learn the retrieval techniques LangChain's Deep Agents harness uses to get the right context in.

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arXiv cs.CL12:00

Latent Coherence: Do Frontier Language Models' Probability Judgments Obey the Law of Total Probability?

arXiv:2610.31833v1 Announce Type: new Abstract: LawZero's Scientist AI trains a Predictor toward the Bayesian posterior, with an objective that penalizes inconsistent probabilities among latent explanations. We ask whether current frontier language models, with no such training, are already consistent in this sense zero-shot: do their probability judgments satisfy the law of total probability, $P(X)=\sum_z P(z)P(X|z)$, when the latent $z$ must be inferred from records in the context? In separate single-turn calls, models are asked for $P(X)$, for $P(z)$ over the latent's states, and for $P(X|z)$; a causal family applies the same test to $P(y|do(x))$ and the back-door adjustment. Every query has an exact ground-truth probability, and a subset of items is asked in three wordings to separate

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