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2584 articles · Page 6 of 26
Mingyi World Elasticsearch
Mingyi World Elasticsearch
May 12, 2026 · Backend Development

From Zero to One: Building a Personalized E‑commerce Search with Easysearch

The article walks through constructing a fully personalized e‑commerce search system using Easysearch and Python Flask, detailing product modeling, behavior collection, profile building with time decay and LLM augmentation, and how to inject these signals into Elasticsearch DSL for real‑time, user‑specific ranking and recommendation.

EasysearchElasticsearchLLM
0 likes · 18 min read
From Zero to One: Building a Personalized E‑commerce Search with Easysearch
SuanNi
SuanNi
May 12, 2026 · Industry Insights

AI Job Market 2026: LLM and Agent Roles Dominate 58% of 8,720 Positions

Based on 8,720 AI job postings from 528 companies, the 2026 AI employment report reveals an average salary of $226K, with LLM and Agent roles accounting for 58% of demand, hybrid work fetching the highest pay, and top salaries concentrated in leading labs and major tech hubs.

2026AI jobsAgent
0 likes · 8 min read
AI Job Market 2026: LLM and Agent Roles Dominate 58% of 8,720 Positions
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
May 11, 2026 · Artificial Intelligence

Heuristic Learning: A New Reinforcement Learning Paradigm for Continual Learning

The article proposes Heuristic Learning (HL) as a way to tackle continual learning’s catastrophic forgetting by using coding agents that iteratively refine rule‑based policies, showing empirical gains on Atari, MuJoCo, and VizDoom tasks and outlining HL’s benefits, challenges, and future integration with neural networks.

LLMReinforcement Learningcoding agents
0 likes · 15 min read
Heuristic Learning: A New Reinforcement Learning Paradigm for Continual Learning
Bighead's Algorithm Notes
Bighead's Algorithm Notes
May 11, 2026 · Artificial Intelligence

Analyzing CN‑Buzz2Portfolio: A Chinese Market Dataset for LLM‑Driven Macro and Sector Asset Allocation

This article reviews the CN‑Buzz2Portfolio benchmark, which maps daily Chinese hot‑news streams to macro‑ and industry‑level ETF allocations, introduces a three‑stage CPA pipeline for evaluating large language models as autonomous financial agents, and reports extensive experiments on nine state‑of‑the‑art LLMs across two rolling market periods.

CN-Buzz2PortfolioCPA frameworkLLM
0 likes · 18 min read
Analyzing CN‑Buzz2Portfolio: A Chinese Market Dataset for LLM‑Driven Macro and Sector Asset Allocation
DeepHub IMBA
DeepHub IMBA
May 11, 2026 · Artificial Intelligence

2026 RAG Selection Guide: How to Choose Between Vector, Graph, and Vectorless

This article compares traditional Vector RAG, GraphRAG, and the newer Vectorless RAG, explains why Vector RAG fails on relational and structured queries, presents benchmark results, outlines each architecture's strengths and costs, and offers a decision framework and Adaptive RAG routing strategy for production systems.

Adaptive RetrievalGraphRAGKnowledge Graph
0 likes · 13 min read
2026 RAG Selection Guide: How to Choose Between Vector, Graph, and Vectorless
Old Zhang's AI Learning
Old Zhang's AI Learning
May 11, 2026 · Information Security

Critical CVE-2026-7482 'Bleeding Llama' in Ollama: Why You Must Upgrade Now

Ollama versions before 0.17.1 suffer a CVSS 9.1 heap out‑of‑bounds read vulnerability (CVE‑2026‑7482) that lets attackers upload malicious GGUF files, read server memory—including env vars and API keys—and exfiltrate data, affecting over 300,000 publicly exposed servers, so immediate upgrade and hardening are essential.

API VulnerabilityBleeding LlamaCVE-2026-7482
0 likes · 5 min read
Critical CVE-2026-7482 'Bleeding Llama' in Ollama: Why You Must Upgrade Now
Data Party THU
Data Party THU
May 11, 2026 · Artificial Intelligence

How a 1930‑Era AI Model Without Any Computer Knowledge Learned to Write Python

The talkie‑1930‑13b language model, trained exclusively on English texts published before 1931, surprisingly understands historical events, solves Python coding problems, and exhibits scaling‑law behavior, prompting a detailed comparison with its modern twin talkie‑web‑13b and an analysis of training pipelines, memory categories, and common deployment pitfalls.

AI memoryLLMPython code generation
0 likes · 10 min read
How a 1930‑Era AI Model Without Any Computer Knowledge Learned to Write Python
Su San Talks Tech
Su San Talks Tech
May 11, 2026 · Artificial Intelligence

Designing a Production‑Ready LLM Gateway: Architecture, Routing, Fallback, and Observability

This article outlines a production‑grade LLM Gateway design, detailing a three‑layer architecture, capability‑, cost‑, latency‑ and semantic‑based routing strategies, multi‑level fallback mechanisms, specialized load balancing, unified API adaptation, semantic caching, observability, and compares popular open‑source implementations.

FallbackLLMObservability
0 likes · 17 min read
Designing a Production‑Ready LLM Gateway: Architecture, Routing, Fallback, and Observability
FunTester
FunTester
May 11, 2026 · Artificial Intelligence

Why AI-Generated Code Produces More Bugs

Despite promises of faster development, AI‑generated code shows 1.7× more defects, up to 2× more security vulnerabilities, and forces 67% of developers to spend extra time debugging, because the probabilistic nature of large language models creates unavoidable hallucinations and context‑blindness.

AI codeLLMcode quality
0 likes · 7 min read
Why AI-Generated Code Produces More Bugs
Geek Labs
Geek Labs
May 11, 2026 · Artificial Intelligence

Train a 64M LLM from Scratch in 2 Hours for $3 and Master LLM Systems

This article introduces two open‑source projects—MiniMind, which lets you train a 64M‑parameter LLM in about two hours for under $3, and Happy‑LLM, a systematic tutorial that explains LLM theory and practice—detailing their features, training pipelines, benchmarks, data, and how they complement each other for comprehensive LLM learning.

AIHappy-LLMLLM
0 likes · 7 min read
Train a 64M LLM from Scratch in 2 Hours for $3 and Master LLM Systems
Wuming AI
Wuming AI
May 10, 2026 · Artificial Intelligence

Can Large Models Really Understand 1 M Tokens? Lessons from the RULER Benchmark

The article examines why a model’s advertised context window (e.g., 128 K or 1 M tokens) does not guarantee effective long‑context reasoning, summarizing the RULER framework that breaks long‑context ability into retrieval, interference resistance, multi‑hop tracking, aggregation, and multi‑answer recall, and offering practical guidance for evaluating and using such models.

AggregationEvaluationLLM
0 likes · 16 min read
Can Large Models Really Understand 1 M Tokens? Lessons from the RULER Benchmark
AI Architect Hub
AI Architect Hub
May 10, 2026 · Artificial Intelligence

RAG Series Recap: From Chunking to Prompt – A Complete Technical Roadmap

This article systematically reviews the nine‑stage RAG pipeline—from data cleaning and text chunking through embedding, vector indexing, retrieval, reranking, and finally prompt assembly—highlighting core concepts, practical code snippets, common pitfalls, and optimization tips for building production‑grade systems.

AILLMPrompt Engineering
0 likes · 22 min read
RAG Series Recap: From Chunking to Prompt – A Complete Technical Roadmap
DataFunSummit
DataFunSummit
May 10, 2026 · Artificial Intelligence

Why Memory Is the Bottleneck for AI Agents and How MemOS Overcomes It

The article analyzes the critical role of memory in AI agents, compares model‑driven and application‑driven approaches, details the five‑layer MemOS architecture with three‑level memory coordination, and presents performance gains such as 100‑200% monthly cloud‑service growth, up to 72% token savings, and a 30% improvement in answer quality.

AI AgentEnterprise AILLM
0 likes · 18 min read
Why Memory Is the Bottleneck for AI Agents and How MemOS Overcomes It
Java Tech Enthusiast
Java Tech Enthusiast
May 10, 2026 · Industry Insights

US Researcher’s 36‑Hour China AI Lab Tour Highlights Culture and Open‑Source Edge

During a 36‑hour visit to six leading Chinese AI labs, US researcher Nathan observed a collaborative, student‑driven culture, strong admiration for DeepSeek, pragmatic open‑source practices, and distinct market dynamics, contrasting sharply with the ego‑driven, less inclusive approaches typical of many US AI organizations.

AIAI CultureChina AI
0 likes · 11 min read
US Researcher’s 36‑Hour China AI Lab Tour Highlights Culture and Open‑Source Edge
Machine Heart
Machine Heart
May 10, 2026 · Artificial Intelligence

Stop Fragmenting Long Texts: HiLight Lets AI Highlight Key Points Directly

The HiLight approach inserts lightweight highlight tags into full-length inputs, training a small Emphasis Actor to score token importance and guide a frozen large language model, improving performance on tasks like recommendation and QA without modifying the solver, while keeping low latency and training cost.

EvaluationLLMPrompt Optimization
0 likes · 9 min read
Stop Fragmenting Long Texts: HiLight Lets AI Highlight Key Points Directly
AI Engineer Programming
AI Engineer Programming
May 10, 2026 · Artificial Intelligence

Lossless Context Management (LCM): Handling Unlimited Agent Tasks with Finite Windows

The article analyzes the limitation of finite LLM context windows for unbounded agent tasks, reviews existing truncation, summarization, and RAG approaches, and presents the Lossless Context Management (LCM) architecture with immutable storage, hierarchical DAG compression, three‑level summarization, and zero‑overhead processing for both short and large‑scale workloads.

AI agentsAgentic-MapLLM
0 likes · 9 min read
Lossless Context Management (LCM): Handling Unlimited Agent Tasks with Finite Windows
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
May 9, 2026 · Artificial Intelligence

Can 99% Sparse Transformers Run Faster? Insights from the Original Authors

A new ICML 2026 paper by Sakana AI and NVIDIA shows that applying lightweight L1 regularization can make Feed‑Forward Network activations in Transformers over 99% sparse, and with the TwELL storage format and a hybrid routing scheme this sparsity translates into up to 20.5% inference speedup, 21.9% training‑step acceleration, lower energy consumption and reduced peak memory across 0.5‑2 B‑parameter models while preserving downstream performance.

CUDAGPU OptimizationHybrid Routing
0 likes · 9 min read
Can 99% Sparse Transformers Run Faster? Insights from the Original Authors
DataFunSummit
DataFunSummit
May 9, 2026 · Artificial Intelligence

DeepEye: Building an Autonomous, Human‑Steerable Data Agent System

The article presents DeepEye, an open‑source autonomous data‑agent platform that combines LLM reasoning, workflow orchestration, and human‑in‑the‑loop control to enable end‑to‑end analysis of heterogeneous data, and introduces a six‑level capability taxonomy to guide its evolution from manual to fully autonomous operation.

Data AgentDeepEyeKnowledge Graph
0 likes · 18 min read
DeepEye: Building an Autonomous, Human‑Steerable Data Agent System
IT Services Circle
IT Services Circle
May 9, 2026 · Artificial Intelligence

How to Choose Between LangChain and LlamaIndex: Core Use‑Case Comparison for Agent Development

The article analyzes the design philosophies, key components, strengths, and weaknesses of LangChain and LlamaIndex, explains their distinct core scenarios—complex multi‑step agent orchestration versus private‑data RAG—and shows how they can be combined in real projects while outlining emerging ecosystem trends.

AgentLLMLangChain
0 likes · 13 min read
How to Choose Between LangChain and LlamaIndex: Core Use‑Case Comparison for Agent Development
James' Growth Diary
James' Growth Diary
May 9, 2026 · Artificial Intelligence

Agentic RAG Deep Dive: Letting the Agent Decide When and How Often to Retrieve

The article analyzes the shortcomings of traditional one‑shot RAG pipelines, introduces four Agentic RAG patterns that let an LLM‑driven agent control retrieval strategy, source selection, query rewriting and retry limits, and provides concrete TypeScript implementations with LangGraph, code snippets, and practical pitfalls.

Agentic RAGLLMLangGraph
0 likes · 16 min read
Agentic RAG Deep Dive: Letting the Agent Decide When and How Often to Retrieve
ZhiKe AI
ZhiKe AI
May 9, 2026 · Artificial Intelligence

Why Agent Loops Matter More Than Raw Model Power

The article explains how AI agents that operate in a reasoning‑action‑observation loop outperform single‑shot LLM inference by continuously observing, planning, and correcting errors, illustrated through a ticket‑booking example and detailed analyses of ReAct, Plan‑Execute, OODA, and Steering Loop architectures.

AI agentsAgent LoopLLM
0 likes · 15 min read
Why Agent Loops Matter More Than Raw Model Power
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
May 8, 2026 · Artificial Intelligence

Dynamic Memory Forest: Precisely Tracking Long‑Range Dialogue Trajectories for Highly Coherent Responses

The paper introduces the Dynamic Memory Forest (DMF) framework, inspired by human memory consolidation and growth, which transforms fragmented long‑term dialogue histories into structured memory trees and employs entropy‑driven walks to retrieve coherent, context‑aware responses, outperforming full‑history and other memory baselines on multiple open‑domain chat datasets.

Dynamic Memory ForestEntropy‑Driven RetrievalLLM
0 likes · 10 min read
Dynamic Memory Forest: Precisely Tracking Long‑Range Dialogue Trajectories for Highly Coherent Responses
James' Growth Diary
James' Growth Diary
May 8, 2026 · Artificial Intelligence

How Claude Code’s Agent Swarms Use Unix Domain Sockets to Run 10 AIs Concurrently

This article deep‑dives into Claude Code’s Agent Swarms, explaining why Unix Domain Sockets replace HTTP for intra‑process communication, how three‑stage address parsing, filesystem‑based mailbox queues, various spawn modes, AgentId design, graceful shutdown, plan‑mode approval and common pitfalls together enable reliable, low‑latency coordination of multiple LLM agents.

Agent SwarmsClaude CodeIPC
0 likes · 14 min read
How Claude Code’s Agent Swarms Use Unix Domain Sockets to Run 10 AIs Concurrently
AI Engineer Programming
AI Engineer Programming
May 8, 2026 · Artificial Intelligence

Is Non-Vector RAG the Next Generation of Retrieval‑Augmented Generation?

The article analyses the relevance and accuracy shortcomings of traditional vector‑based RAG, explains how non‑vector approaches like PageIndex let LLMs navigate document trees for relevance classification and auditability, and evaluates their complexity, latency, metadata risks, and suitable use cases compared with hybrid retrieval.

Hybrid RetrievalLLMRAG
0 likes · 8 min read
Is Non-Vector RAG the Next Generation of Retrieval‑Augmented Generation?
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
May 7, 2026 · Artificial Intelligence

How TileLang Enables Efficient Small Operators in Large LLMs (DeepSeek V4 Report)

The article analyzes TileLang, the DSL behind DeepSeek V4, showing how its Fragment and Parallel abstractions, host‑side codegen via TVM‑FFI, and Z3 prover integration let developers implement fused small operators with hand‑written performance, faster development, and easier maintenance.

DSLDeepSeekGPU compiler
0 likes · 11 min read
How TileLang Enables Efficient Small Operators in Large LLMs (DeepSeek V4 Report)
inShocking
inShocking
May 7, 2026 · Artificial Intelligence

What to Store and When to Skip: Lessons from Claude Code’s Memory Mechanism

The article dissects Claude Code’s memory system, showing that the real challenge is deciding what information to keep and when to discard, and it details design principles, index‑content separation, LLM‑based retrieval, expiration handling, write‑path isolation, and practical improvements applied to the author’s own agent platform.

Claude CodeLLMMemory Management
0 likes · 16 min read
What to Store and When to Skip: Lessons from Claude Code’s Memory Mechanism
AI Explorer
AI Explorer
May 7, 2026 · Artificial Intelligence

Goose Open‑Source AI Agent: A Desktop Assistant That Goes Beyond Code

Goose is an open‑source, Rust‑based AI agent that runs locally, handling the entire development workflow—from installing dependencies to running tests—while supporting 15+ LLM providers via the ACP protocol and offering desktop, CLI, and API interfaces for developers, analysts, and ops engineers.

AI AgentGooseLLM
0 likes · 6 min read
Goose Open‑Source AI Agent: A Desktop Assistant That Goes Beyond Code
DeepHub IMBA
DeepHub IMBA
May 7, 2026 · Frontend Development

Self‑Healing Playwright Tests with LLM‑Driven Locator Recovery

This article shows how to combine Playwright with an LLM (Groq) to build a self‑healing test framework that detects broken selectors, extracts a trimmed DOM snapshot, asks the model for a replacement locator, validates confidence, caches results, and integrates the logic via a Playwright fixture.

GroqJavaScriptLLM
0 likes · 17 min read
Self‑Healing Playwright Tests with LLM‑Driven Locator Recovery
Alimama Tech
Alimama Tech
May 7, 2026 · Artificial Intelligence

Dual‑Phase RL‑LLM Framework DARA for Few‑Shot Online Advertising Budget Allocation

The DARA framework splits online advertising budget allocation into a few‑shot LLM reasoning stage and a fine‑grained optimizer stage, enhanced by a dynamically updated RL‑fine‑tuning algorithm (GRPO‑Adaptive), achieving significantly lower ROI variance than traditional baselines in both real and simulated environments.

LLMOnline AdvertisingReinforcement Learning
0 likes · 16 min read
Dual‑Phase RL‑LLM Framework DARA for Few‑Shot Online Advertising Budget Allocation
Woodpecker Software Testing
Woodpecker Software Testing
May 7, 2026 · Artificial Intelligence

When AI Starts Testing AI: The 2026 Open‑Source Landscape of AI Testing Tools

In 2026, AI testing has shifted from traditional web and API checks to evaluating large‑model applications, agent workflows, and multimodal systems, with open‑source projects such as Apache OpenTAP 3.0, TestGPT‑OS, LlamaTest, and AegisEval providing programmable runtimes, hallucination detection, prompt‑injection defense, and drift monitoring, while also highlighting remaining challenges in multimodal support, long‑context stability, and compliance.

AI testingAegisEvalApache OpenTAP
0 likes · 8 min read
When AI Starts Testing AI: The 2026 Open‑Source Landscape of AI Testing Tools
Data Party THU
Data Party THU
May 7, 2026 · Artificial Intelligence

Step‑by‑Step Guide to Building a Multi‑Agent Trading System for End‑to‑End Intelligent Decisions

This article walks through constructing a multi‑agent trading platform—analysts, researchers, traders, risk managers, and a portfolio manager—using LangChain, LangGraph, and LLMs (gpt‑4o, gpt‑4o‑mini), with real‑time data tools, shared and long‑term memory, ReAct loops, structured debates, and a final executable trade proposal.

ChromaDBLLMLangChain
0 likes · 46 min read
Step‑by‑Step Guide to Building a Multi‑Agent Trading System for End‑to‑End Intelligent Decisions
PaperAgent
PaperAgent
May 7, 2026 · Artificial Intelligence

190 Must-Read AI Agent Papers + 321 Google Implementation Cases – Free Resource Pack

The article provides a free compiled resource containing 190 essential AI Agent papers—from fundamentals to cutting‑edge topics—along with 321 Google‑released implementation cases and 500 open‑source agent applications, all with source code to help beginners and researchers quickly understand the field and reproduce results.

AI AgentLLMMemory
0 likes · 6 min read
190 Must-Read AI Agent Papers + 321 Google Implementation Cases – Free Resource Pack
Machine Heart
Machine Heart
May 7, 2026 · Artificial Intelligence

How TACO Lets CLI Agents Self‑Evolve to Drop Useless Context

TACO is a plug‑and‑play, training‑free framework that lets terminal‑based autonomous agents automatically learn compression rules to filter low‑value output while preserving critical decision cues, achieving higher task success rates and better token efficiency across multiple terminal‑related benchmarks.

Code IntelligenceLLMSelf‑Evolving Rules
0 likes · 14 min read
How TACO Lets CLI Agents Self‑Evolve to Drop Useless Context
LuTiao Programming
LuTiao Programming
May 6, 2026 · Backend Development

Can You Build an MCP Server with Spring Boot? Complete Java Guide to Standardized AI APIs

This article explains why the Model Context Protocol (MCP) is becoming the universal AI interface standard, compares three implementation approaches, and provides a step‑by‑step tutorial for Java developers to create a production‑ready MCP server with Spring Boot, including tool definition, registration, controller, LLM integration, and best‑practice optimizations.

AIJavaLLM
0 likes · 10 min read
Can You Build an MCP Server with Spring Boot? Complete Java Guide to Standardized AI APIs
DeepHub IMBA
DeepHub IMBA
May 6, 2026 · Information Security

Why MCP’s Protocol Layer Allows Prompt Injection and Hijacks Agent Context

The Model Context Protocol (MCP) embeds every tool’s description into an LLM’s context window, creating a structural “Context Poisoning” vulnerability that lets malicious or bloated tool metadata hijack agent reasoning, inflate tokens, and bypass traditional input validation.

AI Agent SecurityContext PoisoningLLM
0 likes · 10 min read
Why MCP’s Protocol Layer Allows Prompt Injection and Hijacks Agent Context
Bighead's Algorithm Notes
Bighead's Algorithm Notes
May 6, 2026 · Artificial Intelligence

AI‑Trader: Real‑time Benchmark for Autonomous LLM Agents in Financial Markets

The AI‑Trader benchmark evaluates large language model agents in fully autonomous, real‑time US stock, Chinese A‑share, and cryptocurrency markets, revealing that general intelligence alone does not guarantee profitable trading, while robust risk‑control mechanisms drive cross‑market stability and excess returns.

Autonomous AgentsLLMRisk Management
0 likes · 17 min read
AI‑Trader: Real‑time Benchmark for Autonomous LLM Agents in Financial Markets
Geek Labs
Geek Labs
May 6, 2026 · Artificial Intelligence

Build a GPT from Scratch and Decode AI Coding Jargon with Two Top GitHub Projects

The article introduces two practical GitHub repositories—how-to-train-your-gpt, a step‑by‑step guide that builds a LLaMA‑style GPT model across 12 chapters, and dictionary-of-ai-coding, a plain‑language glossary of AI‑coding terms—showing how they together provide a complete understanding of modern LLM fundamentals and terminology.

AIGPTGitHub
0 likes · 9 min read
Build a GPT from Scratch and Decode AI Coding Jargon with Two Top GitHub Projects
DataFunTalk
DataFunTalk
May 6, 2026 · Artificial Intelligence

From Vibe Coding to Agentic Engineering: Why Karpathy Says He’s Falling Behind

In a December 2025 interview, Andrej Karpathy explains how Vibe Coding lowered the software‑creation barrier, why Agentic Engineering shifts responsibility from models to humans, and what engineers must master to manage AI agents safely and effectively.

AI agentsAgentic EngineeringEngineering Management
0 likes · 15 min read
From Vibe Coding to Agentic Engineering: Why Karpathy Says He’s Falling Behind
PaperAgent
PaperAgent
May 6, 2026 · Artificial Intelligence

How to Detect Introspective Awareness in LLMs – Boosting Detection Rates by 53% and 75%

Anthropic and MIT researchers reveal that large language models can sense injected steering vectors, a capability that emerges during post‑training (especially DPO), and they present a two‑stage detection circuit whose performance improves by up to 75% when reject directions are ablated or bias vectors are trained.

DPOIntrospective AwarenessLLM
0 likes · 15 min read
How to Detect Introspective Awareness in LLMs – Boosting Detection Rates by 53% and 75%
Tech Ocean
Tech Ocean
May 5, 2026 · Artificial Intelligence

Choosing Between LangChain, LangGraph, and Deep Agents: A Visual Guide

The article compares LangChain, LangGraph, and Deep Agents, outlining their roles, core differences, strengths, and weaknesses, and then maps typical LLM‑agent development scenarios to the most suitable framework, providing a concise decision matrix for developers.

AI developmentAgent FrameworkDeep Agents
0 likes · 7 min read
Choosing Between LangChain, LangGraph, and Deep Agents: A Visual Guide
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
May 5, 2026 · Artificial Intelligence

LLMBeginner: A Project‑Based Roadmap for Zero‑Base Mastery of Large Language Models

The LLMBeginner project from the MLNLP community offers a staged, project‑oriented learning path—covering big‑picture concepts, deep learning and reinforcement learning fundamentals, LLM theory and practice, and agent development—to guide beginners from fragmented resources to systematic mastery, with both concise and detailed versions hosted on GitHub.

AgentDeep LearningGitHub
0 likes · 5 min read
LLMBeginner: A Project‑Based Roadmap for Zero‑Base Mastery of Large Language Models
AI Explorer
AI Explorer
May 5, 2026 · Artificial Intelligence

Achieving 95% SimpleQA Accuracy on a Single RTX 3090 with Local Deep Research

Local Deep Research is an open‑source AI assistant that runs entirely on a consumer RTX 3090, reaches about 95% accuracy on the SimpleQA benchmark, uses a plugin‑based architecture with multiple LLM and search back‑ends, stores data in an encrypted SQLCipher database, and can be launched in minutes via Docker for privacy‑focused researchers and developers.

DockerLLMLocal Deep Research
0 likes · 6 min read
Achieving 95% SimpleQA Accuracy on a Single RTX 3090 with Local Deep Research
AI Engineer Programming
AI Engineer Programming
May 5, 2026 · Artificial Intelligence

Deep Dive into Agent Harness: Turning LLM Failures into Robust AI Agents

The article dissects the concept of an Agent Harness— the full software infrastructure that wraps LLMs— covering its twelve components, engineering layers, context management, error handling, and validation loops, and explains how proper harness design can prevent common agent failures and dramatically improve performance.

AI agentsAgent HarnessLLM
0 likes · 24 min read
Deep Dive into Agent Harness: Turning LLM Failures into Robust AI Agents
Linyb Geek Road
Linyb Geek Road
May 5, 2026 · Artificial Intelligence

Optimizing Retrieval and Generation Latency in High‑Concurrency RAG Agents

The article dissects latency in high‑concurrency RAG Agent pipelines, showing how retrieval, re‑ranking, and LLM generation each contribute milliseconds of delay, and presents system‑level tactics—from ANN index tuning and partitioned search to vLLM PagedAttention, continuous batching, speculative decoding, model quantization, routing, semantic caching, and pipeline parallelism—to dramatically cut end‑to‑end response time.

ANNLLMRAG
0 likes · 15 min read
Optimizing Retrieval and Generation Latency in High‑Concurrency RAG Agents
PaperAgent
PaperAgent
May 4, 2026 · Artificial Intelligence

Why Claude 4.6 Scores Only 66%: Claw‑Eval‑Live Shows Terminal Skills Aren’t Enough

The article explains that modern AI agents must be judged on actual task execution and audit evidence, and Claw‑Eval‑Live reveals that while agents can use terminals, they still fail dramatically on cross‑system workflows such as HR, management, and operations, with no model surpassing a 70% pass rate.

AI agentsClaw-EvalEvaluation
0 likes · 7 min read
Why Claude 4.6 Scores Only 66%: Claw‑Eval‑Live Shows Terminal Skills Aren’t Enough
AI Engineer Programming
AI Engineer Programming
May 4, 2026 · Artificial Intelligence

RAG in the Long-Context Era: Challenges, Benchmarks, and Context Engineering

The article analyzes how expanding LLM context windows to millions of tokens reshape Retrieval‑Augmented Generation, detailing chunking trade‑offs, embedding retrieval limits, attention U‑shaped distribution, benchmark results, and the emerging practice of Context Engineering for optimal end‑to‑end pipelines.

Embedding RetrievalLLMPrompt Engineering
0 likes · 10 min read
RAG in the Long-Context Era: Challenges, Benchmarks, and Context Engineering
AI Architecture Hub
AI Architecture Hub
May 4, 2026 · Artificial Intelligence

Karpathy Unpacks the AI Programming Revolution: From Vibe Coding to Agentic Engineering

In a detailed interview, Andrej Karpathy traces the evolution of AI‑assisted software development, contrasting early Vibe Coding with the emerging Agentic Engineering paradigm, explains Software 3.0’s workflow, highlights the limits of current LLMs, and outlines future opportunities for AI‑native engineers.

AI programmingAI-native engineerAgentic Engineering
0 likes · 24 min read
Karpathy Unpacks the AI Programming Revolution: From Vibe Coding to Agentic Engineering
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
May 3, 2026 · Artificial Intelligence

Running a 400B Mixture‑of‑Experts LLM on iPhone 17 Pro: Inside Flash‑MoE

The article details how the open‑source Flash‑MoE engine streams a 400‑billion‑parameter Mixture‑of‑Experts language model on an iPhone 17 Pro, achieving interactive‑level token throughput by eliminating Python dependencies, crafting a custom Metal pipeline, and streaming weights directly from SSD.

Apple SiliconFlash-MoEGCD
0 likes · 7 min read
Running a 400B Mixture‑of‑Experts LLM on iPhone 17 Pro: Inside Flash‑MoE
PaperAgent
PaperAgent
May 3, 2026 · Artificial Intelligence

Skill Graphs Reveal Why Training Diversity Beats Quantity for Terminal Agents

The paper shows that, instead of increasing the number of training tasks, controlling the diversity of scene‑skill combinations via a large‑scale Skill Graph dramatically improves terminal‑agent performance, with Qwen3‑32B surpassing a 480B model on the Terminal‑Bench 2.0 benchmark.

LLMQwen3Skill Graphs
0 likes · 9 min read
Skill Graphs Reveal Why Training Diversity Beats Quantity for Terminal Agents
Shuge Unlimited
Shuge Unlimited
May 3, 2026 · Artificial Intelligence

Combining OpenSpec and Superpowers: A 4‑Step Workflow to Eliminate Luck in AI Coding

This article analyses how OpenSpec’s hard‑coded specification engine and Superpowers’ LLM‑driven execution loop complement each other, presenting a detailed four‑step workflow, concrete code snippets, and a side‑by‑side comparison that shows how the combined approach resolves both definition and execution quality issues in AI‑assisted programming.

AI programmingDelta SpecLLM
0 likes · 17 min read
Combining OpenSpec and Superpowers: A 4‑Step Workflow to Eliminate Luck in AI Coding
Spring Full-Stack Practical Cases
Spring Full-Stack Practical Cases
May 3, 2026 · Artificial Intelligence

9 Advanced Retrieval‑Augmented Generation (RAG) Architectures Explained

This article introduces Retrieval‑Augmented Generation (RAG) and systematically details nine distinct RAG architectures—standard, conversational with memory, corrective (CRAG), adaptive, self‑RAG, fusion, HyDE, agentic, and Graph RAG—highlighting their workflows, real‑world examples, advantages, and trade‑offs.

AI architectureGraphRAGLLM
0 likes · 17 min read
9 Advanced Retrieval‑Augmented Generation (RAG) Architectures Explained
Machine Heart
Machine Heart
May 3, 2026 · Operations

Is LLM4OR the Next Hot Application? Exploring Its First Enterprise Decisions

The article examines how LLM4OR merges large language models with operations research to turn manufacturing and supply‑chain business language, data fields, and on‑site rules into computable optimization models, outlining its potential entry points in enterprise decision‑making and the challenges of modeling.

Agentic FactoryEnterprise OptimizationLLM
0 likes · 9 min read
Is LLM4OR the Next Hot Application? Exploring Its First Enterprise Decisions
Test Development Learning Exchange
Test Development Learning Exchange
May 2, 2026 · Operations

Give Your Test Scripts a Brain: 15 Cutting‑Edge AI Decorators for 2026

The article showcases fifteen practical AI‑powered Python decorators that transform brittle if‑else test code into intelligent, self‑healing automation—covering smart retry, semantic assertions, data generation, flaky detection, traffic replay, dynamic timeouts, sensitive data masking, root‑cause analysis, and more—complete with concrete code samples and explanations.

AI testingCI/CDIntelligent Testing
0 likes · 18 min read
Give Your Test Scripts a Brain: 15 Cutting‑Edge AI Decorators for 2026
Architect
Architect
May 2, 2026 · Backend Development

From a 30‑Minute DIY Agent to Harness as the New Backend – What Gaps Remain for an Agent‑Ready System?

The article examines a minimal 30‑minute Agent loop demo, then analyzes how Harness can serve as the backend by introducing a runtime capability registry, worker lifecycle management, diverse triggers, and unified tracing, outlining four concrete design actions to close the gaps for agent‑ready systems.

AgentCapability RegistryHarness
0 likes · 18 min read
From a 30‑Minute DIY Agent to Harness as the New Backend – What Gaps Remain for an Agent‑Ready System?
Smart Workplace Lab
Smart Workplace Lab
May 2, 2026 · Industry Insights

Prompt Engineer Layoffs: How to Re‑Engineer Your Career Path

As large language models mature, prompt‑writing roles are disappearing, prompting engineers to shift from crafting prompts to designing end‑to‑end AI workflows; this article outlines a three‑step system‑reconstruction protocol, common pitfalls, and practical guidelines for transitioning into workflow architecture.

AI WorkflowLLMPrompt Engineering
0 likes · 6 min read
Prompt Engineer Layoffs: How to Re‑Engineer Your Career Path
SuanNi
SuanNi
May 2, 2026 · Artificial Intelligence

How Karpathy Envisions Software 3.0: Agents as the New Programming Paradigm

Karpathy argues that AI agents are reshaping software development by turning the LLM context window into a programmable layer, redefining the basic unit of work, and introducing a verifiability‑driven framework that separates domains where models excel from those where they still stumble.

AI agentsAgentic EngineeringKarpathy
0 likes · 14 min read
How Karpathy Envisions Software 3.0: Agents as the New Programming Paradigm
Tech Ocean
Tech Ocean
May 2, 2026 · Artificial Intelligence

Switch Over 20+ LLM Providers with a Single String in Deep Agents

The article explains how Deep Agents compresses model provider switching into a one‑line "provider:model" string, compares this approach with passing a LangChain model instance, and provides installation, configuration, and best‑practice guidance for both standard and domestic AI services.

AI agentsConfigurationDeepAgents
0 likes · 11 min read
Switch Over 20+ LLM Providers with a Single String in Deep Agents
AI Explorer
AI Explorer
May 2, 2026 · Artificial Intelligence

How a New AI Probe Can Reverse‑Engineer LLM Parameter Counts

Researcher Li Bojie’s “Uncompressible Knowledge Probe” uses random, black‑box API queries to gauge how much irreducible knowledge a large language model retains, allowing an indirect estimate of its effective parameter count and prompting a broader debate on model evaluation and transparency.

AI evaluationLLMParameter Estimation
0 likes · 5 min read
How a New AI Probe Can Reverse‑Engineer LLM Parameter Counts
AI Engineer Programming
AI Engineer Programming
May 2, 2026 · Artificial Intelligence

From Demo to Production: How to Evaluate RAG Effectively

This guide outlines a comprehensive RAG evaluation framework covering failure modes, multi‑layer metrics, test‑set construction, open‑source tools, CI/CD quality gates, production monitoring, and special considerations for agentic RAG to ensure reliable, trustworthy retrieval‑augmented generation systems.

AIEvaluationLLM
0 likes · 18 min read
From Demo to Production: How to Evaluate RAG Effectively
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
May 1, 2026 · Artificial Intelligence

Why Most Apps Shouldn't Exist, Understanding Remains Humanity’s Last Moat, and CPUs Will Become Sidekicks – Karpathy’s 2026 AI Forecast

In a 2026 Sequoia Ascent interview, Andrej Karpathy argues that large language models are not merely speed‑up tools but a new computing paradigm that renders many legacy apps obsolete, elevates understanding as humanity’s final competitive edge, and relegates CPUs to auxiliary roles, while outlining software evolution, jagged intelligence, and the rise of agentic engineering.

AI economicsAI paradigmAgentic Engineering
0 likes · 11 min read
Why Most Apps Shouldn't Exist, Understanding Remains Humanity’s Last Moat, and CPUs Will Become Sidekicks – Karpathy’s 2026 AI Forecast
AI Explorer
AI Explorer
May 1, 2026 · Artificial Intelligence

A New Multi‑Agent LLM Framework Redefines AI‑Driven Financial Trading

TradingAgents introduces a multi‑agent LLM framework that transforms AI from a single‑point price predictor into a collaborative trading team, offering roles such as analyst, researcher, trader, and risk manager, with open‑source code, Docker deployment, and over 59,000 GitHub stars.

AI financeDockerLLM
0 likes · 7 min read
A New Multi‑Agent LLM Framework Redefines AI‑Driven Financial Trading
Machine Heart
Machine Heart
May 1, 2026 · Artificial Intelligence

How a 400B Mixture‑of‑Experts Model Runs on the iPhone 17 Pro

The article details the Flash‑MoE project that streams the 400 billion‑parameter Qwen3.5‑397B‑A17B mixture‑of‑experts model on an iPhone 17 Pro, achieving up to 0.6 tokens per second with a custom Metal‑GPU pipeline, zero‑Python code, and SSD‑backed weight streaming that keeps only 5.5 GB in RAM.

Flash-MoELLMMetal
0 likes · 7 min read
How a 400B Mixture‑of‑Experts Model Runs on the iPhone 17 Pro
James' Growth Diary
James' Growth Diary
May 1, 2026 · Artificial Intelligence

10 Real-World LangGraph Production Pitfalls That Can Crash Your App

The article details ten production‑grade pitfalls encountered when using LangGraph—ranging from misusing thread IDs and unbounded state growth to uncaught tool errors, infinite loops, concurrency conflicts, subgraph field mismatches, HITL timeouts, and misconfigured LangSmith tracing—each illustrated with concrete code, root‑cause analysis, and concrete remediation steps.

AI agentsCheckpointLLM
0 likes · 14 min read
10 Real-World LangGraph Production Pitfalls That Can Crash Your App
Machine Heart
Machine Heart
May 1, 2026 · Artificial Intelligence

LLMs Write and Evolve Code to Redefine Quantitative Factor Mining – The CogAlpha ACL Paper

The CogAlpha framework upgrades Alpha discovery from static formulas to executable Python code, organizes a 7‑layer, 21‑agent research hierarchy, iteratively evolves factor candidates, and on CSI300 10‑day prediction outperforms 21 baselines with a 16.39% annual excess return and an IR of 1.8999, demonstrating that large models can actively participate in the discovery process.

ACL 2026Alpha MiningEvolutionary Algorithms
0 likes · 9 min read
LLMs Write and Evolve Code to Redefine Quantitative Factor Mining – The CogAlpha ACL Paper
Machine Heart
Machine Heart
May 1, 2026 · Artificial Intelligence

From PPO to MaxRL: The Evolution of Reinforcement Learning for LLM Inference

This article surveys the rapid evolution of reinforcement‑learning algorithms for large‑language‑model inference from early REINFORCE and PPO to newer approaches such as GRPO, RLOO, DAPO, CISPO, DPPO, ScaleRL and MaxRL, highlighting their design motivations, mathematical formulations, empirical trade‑offs and open research challenges.

GRPOLLMMaxRL
0 likes · 27 min read
From PPO to MaxRL: The Evolution of Reinforcement Learning for LLM Inference
Machine Heart
Machine Heart
May 1, 2026 · Artificial Intelligence

API‑Only Probes Reveal GPT, Claude, Gemini Parameter Counts – Community Buzz

A new arXiv paper introduces Incompressible Knowledge Probes that estimate large language model sizes via black‑box API calls, fitting a log‑linear relation on 89 open‑source models and producing controversial parameter estimates for GPT‑5.5, Claude Opus, Gemini and others, sparking heated community debate.

AI scalingClaude OpusGPT-5.5
0 likes · 7 min read
API‑Only Probes Reveal GPT, Claude, Gemini Parameter Counts – Community Buzz
21CTO
21CTO
May 1, 2026 · Artificial Intelligence

IBM Launches Bob AI: How the New Coding Assistant Boosts Developer Productivity

IBM unveiled Bob AI, an LLM‑powered coding assistant that reportedly raised productivity by 45% for 80,000 internal users, offers multimodal model selection, embeds security to catch new risk categories, and promises measurable gains such as 10× ROI, 300 k automated test payloads, while facing concerns over CLI‑based malware execution and IDE data‑theft vulnerabilities.

AI coding assistantBob AIIBM
0 likes · 6 min read
IBM Launches Bob AI: How the New Coding Assistant Boosts Developer Productivity
ZhiKe AI
ZhiKe AI
May 1, 2026 · Artificial Intelligence

From Chatbot to Action: How Large‑Model Agents Turn Queries into Real‑World Tasks

The article explains that large‑model agents differ from traditional chatbots by perceiving goals, planning steps, invoking tools, and executing actions autonomously, covering their definition, core modules, ReAct reasoning‑acting loop, single‑ versus multi‑agent systems, current industry trends, and the reliability, safety, observability, and cost challenges they face.

AI AgentAI engineeringLLM
0 likes · 18 min read
From Chatbot to Action: How Large‑Model Agents Turn Queries into Real‑World Tasks
AI Engineer Programming
AI Engineer Programming
May 1, 2026 · Artificial Intelligence

From Naive Retrieval to Knowledge Runtime: The Full Evolution of RAG

The article traces the evolution of Retrieval‑Augmented Generation from its 2020 Naive baseline through Advanced, Modular, Graph, and Agentic generations, detailing architectural shifts, optimization techniques, self‑correction mechanisms, and future challenges such as long‑context handling and multimodal retrieval.

GraphLLMRAG
0 likes · 14 min read
From Naive Retrieval to Knowledge Runtime: The Full Evolution of RAG
AI Explorer
AI Explorer
May 1, 2026 · Artificial Intelligence

Boost AI Coding with Karpathy’s Four Principles in CLAUDE.md

The article presents Karpathy’s four “sins” of LLM coding and shows how a simple CLAUDE.md file implements four guiding principles—thinking before coding, simplicity, surgical edits, and goal‑driven execution—to make Claude Code produce cleaner, more reliable code, with easy installation and broad applicability.

AI programmingCLAUDE.mdClaude Code
0 likes · 7 min read
Boost AI Coding with Karpathy’s Four Principles in CLAUDE.md
PaperAgent
PaperAgent
Apr 30, 2026 · Artificial Intelligence

DeepSeek Unveils Open‑Source Multimodal Model: “Thinking with Visual Primitives”

DeepSeek releases an open‑source multimodal LLM that introduces a visual‑primitive framework—elevating bounding boxes and points to token level—to close the reference gap, achieve extreme KV‑cache compression, and outperform GPT‑5.4, Claude‑Sonnet‑4.6 and Gemini‑3‑Flash on counting, spatial reasoning, maze navigation and path‑tracing benchmarks.

DeepSeekLLMMultimodal
0 likes · 13 min read
DeepSeek Unveils Open‑Source Multimodal Model: “Thinking with Visual Primitives”
Woodpecker Software Testing
Woodpecker Software Testing
Apr 30, 2026 · Artificial Intelligence

2026 Open-Source Landscape of AI Testing Tools

The article surveys the 2026 open‑source ecosystem for AI testing, detailing programmable runtimes, AI‑specific quality dimensions, testing‑as‑code practices, observability integration, real‑world case studies, and remaining challenges such as multimodal support and long‑context stability.

AI testingDevOpsLLM
0 likes · 8 min read
2026 Open-Source Landscape of AI Testing Tools
DataFunTalk
DataFunTalk
Apr 30, 2026 · Artificial Intelligence

How GenericAgent Cuts Token Costs by 10× While Boosting AI Agent Performance

The technical report on GenericAgent, a self‑evolving LLM‑based agent, shows that by maximizing context information density and using a minimal atomic toolset with hierarchical memory, it achieves up to ten‑fold token savings, 100% task accuracy, and progressive efficiency gains across multiple benchmarks.

AI benchmarksGenericAgentLLM
0 likes · 15 min read
How GenericAgent Cuts Token Costs by 10× While Boosting AI Agent Performance
AI Explorer
AI Explorer
Apr 30, 2026 · Artificial Intelligence

How an LLM‑Powered Open‑Source Tool Automates Multi‑Market Stock Analysis

The article examines the open‑source "daily_stock_analysis" project, detailing its zero‑cost, fully automated architecture that integrates LLMs with multiple market data sources to generate a concise decision dashboard and push notifications via popular channels, dramatically reducing manual research time for investors.

AI automationGitHub ActionsLLM
0 likes · 7 min read
How an LLM‑Powered Open‑Source Tool Automates Multi‑Market Stock Analysis
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Apr 30, 2026 · Artificial Intelligence

When Is Claude Code’s Memory Injected into system_prompt? Interview Insight

The article explains that Claude Code loads persisted memory once at REPL startup via _build_system(), inserts it as the 10th segment of system_prompt, enforces a 200‑line limit on MEMORY.md, deliberately avoids side‑effects in get_memory_dir(), and only refreshes the prompt with the /model command.

Claude CodeLLMMemory Injection
0 likes · 11 min read
When Is Claude Code’s Memory Injected into system_prompt? Interview Insight
AI Waka
AI Waka
Apr 29, 2026 · Artificial Intelligence

Mastering Agent Harness: The Core Architecture Behind Modern AI Systems

The article explains how Agent Harness structures the interaction between user intent and LLM output, detailing its components, long‑conversation handling, layered memory, tool integration, and a four‑stage pipeline demonstrated by an Essay Harness prototype, highlighting design trade‑offs and practical implementation details.

Agent HarnessLLMMemory Architecture
0 likes · 22 min read
Mastering Agent Harness: The Core Architecture Behind Modern AI Systems
CodeTrend
CodeTrend
Apr 29, 2026 · Artificial Intelligence

qwen2API: Turning Qwen Web Chat into OpenAI, Claude, and Gemini Compatible APIs

The qwen2API project offers a FastAPI backend and React+Vite frontend that expose the Qwen web chat as OpenAI Chat Completions, Anthropic Messages, and Gemini GenerateContent interfaces, featuring tool calling, image generation, account pool management, multiple deployment options, and various execution engines.

AnthropicFastAPIGemini
0 likes · 6 min read
qwen2API: Turning Qwen Web Chat into OpenAI, Claude, and Gemini Compatible APIs
AI Explorer
AI Explorer
Apr 29, 2026 · Artificial Intelligence

Open-Source ML Intern: One-Click Paper Reading, Training & Deployment – Hype or Real Deal?

ml‑intern, an open‑source AI agent from Hugging Face, automates the full ML workflow—reading papers, generating code, training and deploying models—using an asynchronous event‑driven loop with submission and event queues, supporting interactive and headless modes, Slack notifications, and multiple LLM back‑ends.

AI AgentHugging FaceLLM
0 likes · 5 min read
Open-Source ML Intern: One-Click Paper Reading, Training & Deployment – Hype or Real Deal?
Woodpecker Software Testing
Woodpecker Software Testing
Apr 29, 2026 · Artificial Intelligence

Testing AI Agents: How Test Teams Must Transform

With autonomous AI agents now deployed in 63% of leading tech firms, traditional deterministic testing fails, prompting test teams to shift from case writers to architects of behavioral contracts, observability stacks, early design involvement, and trustworthiness assessment across accuracy, robustness, explainability, fairness and ethics.

AI agentsLLMObservability
0 likes · 7 min read
Testing AI Agents: How Test Teams Must Transform
Kuaishou Tech
Kuaishou Tech
Apr 29, 2026 · Operations

Boosting Oncall Interception from 15% to 55%: KOncall’s AI‑Driven Evolution at Kuaishou

Kuaishou’s R&D efficiency team built the KOncall intelligent on‑call platform, integrating LLM‑based retrieval‑augmented generation, Redis Pub/Sub streaming, OCR multimodal parsing, FAQ knowledge ops, and custom reranking, which raised automated query interception from 15% to 55% and processed over 116 000 requests, turning on‑call from a bottleneck into a capability starter.

AI operationsLLMOCR
0 likes · 26 min read
Boosting Oncall Interception from 15% to 55%: KOncall’s AI‑Driven Evolution at Kuaishou
java1234
java1234
Apr 29, 2026 · Artificial Intelligence

What Exactly Is an AI Agent and How Does It Differ from a Chatbot?

The article explains that an AI Agent combines a large language model, a clear goal, and callable tools in a multi‑step reasoning loop, detailing its perception‑plan‑act architecture, differences from plain chat, common misconceptions, and practical questions for evaluating such systems.

AI AgentAgent LoopLLM
0 likes · 8 min read
What Exactly Is an AI Agent and How Does It Differ from a Chatbot?
SuanNi
SuanNi
Apr 28, 2026 · Artificial Intelligence

Zero‑Code Fine‑Tuning Hundreds of Large Models with the LLaMA‑Factory MLU Image

This article provides a step‑by‑step guide to deploying the LLaMA‑Factory MLU image on Cambricon MLU hardware, covering environment checks, downloading the modified source package, configuring Python dependencies, and running both the Web UI and command‑line fine‑tuning for models such as Qwen2.5‑0.5B.

CLICambriconLLM
0 likes · 7 min read
Zero‑Code Fine‑Tuning Hundreds of Large Models with the LLaMA‑Factory MLU Image
Architect
Architect
Apr 28, 2026 · Artificial Intelligence

Agent Harness Context: Chat Log vs. Workset – How Runtime Management Shapes Long‑Running Agents

The article argues that an agent harness’s context window should be treated as a bounded workset rather than an ever‑growing transcript, and explains how pagination, compression, tool‑output limits, session isolation, and sub‑agent design together determine whether long‑running agents remain reliable and efficient.

Agent HarnessLLMSub-Agent
0 likes · 24 min read
Agent Harness Context: Chat Log vs. Workset – How Runtime Management Shapes Long‑Running Agents