Context Engineering

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Fastino Releases GLiNER2.5-Decide: A 340M Open-Weight Decision Model That Runs on CPU

agentic ai, ai, AI (Artificial Intelligence), AI Infrastructure, AI Shorts, Applications, Artificial Intelligence, Context Engineering, deep learning, Editors Pick, Knowledge Graphs, Language Model, Large Language Model, Machine Learning, New Releases, Open Source, Software engineering, Staff, Tech News, Technology

Fastino Labs has released GLiNER2.5-Decide, a 340M-parameter open-weight decision model. It takes text and a schema of typed questions and returns structured answers. Each answer comes with a probability distribution, a confidence score, and constraint-feasibility metadata. It targets the frequent judgment calls inside agent pipelines: routing, triage, tool selection, and guardrails. Is it deployable? Yes, […]

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Nokia Open-Sources AnyJev: A Training-Free Layer That Turns Any Open LLM Into a Calibrated Decision Model

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Nokia’s applied research team has open-sourced AnyJev, a Python library that turns an open LLM into a decision model. It needs no training. It targets a common production job: picking one answer from a fixed set instead of writing a sentence. Is it deployable? Yes, it installs from PyPI, ships under Apache-2.0, and has transformers

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Can LLMs Engineer Their Own Agent Harness? ByteDance Seed’s HarnessDev Says Only 34 of 64 Changes Generalize

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An agent harness is the code around a model: execution loop, tools, context, state, recovery, and verification. Per the Terminal-Bench 2.1 leaderboard, GPT-5 solves 35.2% of tasks inside Terminus 2 but 49.6% inside Codex CLI with identical weights. Most benchmarks keep that harness fixed. HarnessDev proposed by team of researchers from ByteDance Seed, Singapore University

Can LLMs Engineer Their Own Agent Harness? ByteDance Seed’s HarnessDev Says Only 34 of 64 Changes Generalize Read More »

Google AI Introduces EnvHarness: A Programmable Layer That Turns Static Agent Environments Into Adaptive Training Worlds

agentic ai, ai, AI (Artificial Intelligence), AI Agents, AI Infrastructure, AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Context Engineering, Editors Pick, Harness, Language Model, Large Language Model, Machine Learning, New Releases, Open Source, Software engineering, Staff, Tech News, Technology, Uncategorized

A team of researchers from Google Cloud AI Research, Washington University in St. Louis and UNC Chapel Hill has released EnvHarness, a programmable layer that turns a static agent benchmark into one that adapts to the policy training on it. LLM agents now learn less from curated text and more from interactive environments, but those

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DeepSeek AI Releases DeepSeek Harness in Developer Preview: An MIT-Licensed Agent Harness Where Everything is a Plugin

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DeepSeek released DeepSeek Harness v0.1 in developer preview and published the full source code under the MIT license. The project ships as dsh at deepseek-ai/deepseek-harness. A harness is the layer between a model and the environment it acts in — the tools, files, sandboxes, and control loop that let an agent keep working. DeepSeek frames

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Context Rot in Large Language Models

ai, AI (Artificial Intelligence), AI Agents, Artificial Intelligence, attention dilution, Context Engineering, context rot, context window, effective context, Large Language Models, llm reliability, long context llm, lost in the middle, prompt engineering, Retrieval-augmented generation, Uncategorized

Why it matters: Context rot makes LLMs fail long before the window fills. See why, how to measure it, and the context engineering fixes that keep answers reliable.

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Prompt Engineering vs Loop Engineering vs Graph Engineering: What Changes at Each Layer

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Three terms now compete for the same line in AI engineering job descriptions. Prompt engineering is the established one. Loop engineering entered the AI vocabulary in late 2025 and dominated developer discussion through June 2026. Graph engineering followed roughly six weeks later. They get used interchangeably. Should they be? The three are not competing techniques.

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Prompt Engineering vs Loop Engineering vs Graph Engineering: What Changes at Each Layer

agentic ai, ai, AI (Artificial Intelligence), AI Agents, AI Shorts, Applications, Artificial Intelligence, Context Engineering, Editors Pick, Generative AI, Language Model, Machine Learning, Software engineering, Staff, Technology, Uncategorized

Three terms now compete for the same line in AI engineering job descriptions. Prompt engineering is the established one. Loop engineering entered the AI vocabulary in late 2025 and dominated developer discussion through June 2026. Graph engineering followed roughly six weeks later. They get used interchangeably. Should they be? The three are not competing techniques.

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Meet EverOS: An Open Source Markdown-First Agent Memory Runtime With Hybrid BM25 + Vector Retrieval and Self-Evolving Skills

agentic ai, ai, AI (Artificial Intelligence), AI Agents, Artificial Intelligence, Context Engineering, Editors Pick, New Releases, Open Source, Software engineering, Staff, Technology

EverMind has released EverOS, an open-source memory runtime for AI agents. It ships under an Apache 2.0 license. It targets a problem agent builders hit early: large language models are stateless. The conversation ends, and the context is gone. EverOS proposes a different substrate. Instead of locking memory inside a vector database, it writes memory

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Google Cloud Introduces Open Knowledge Format (OKF): A Vendor-Neutral Markdown Spec for Giving AI Agents Curated Context

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Foundation models keep getting stronger, yet they still stall on the same thing: context. A model can write code or analyze a dataset, but only with the right internal knowledge. That knowledge includes table schemas, metric definitions, runbooks, join paths and it lives scattered across catalogs, wikis, and a few senior engineers’ heads. Google Cloud

Google Cloud Introduces Open Knowledge Format (OKF): A Vendor-Neutral Markdown Spec for Giving AI Agents Curated Context Read More »