Framework

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A practical guide to mnemo, a Rust-based sidecar service providing structured, persistent memory for LLMs without cloud dependencies.

mnemo: Local-First Knowledge Graph for Persistent LLM Memory

mnemo is a local-first memory layer for LLMs, offering persistent, structured context via a sidecar service. It extracts entities and relationships into a knowledge graph from raw text, and retrieves ranked context for LLM prompts, supporting fully local setups with Ollama or integration with OpenAI.

Microsoft Research's text-space optimizer enables self-evolving agent capabilities, demonstrated in a multimodal paper-figure extraction task.

SkillOpt: Optimizing Agent Skills with Trainable Natural-Language Descriptions

SkillOpt, from Microsoft Research, is a text-space optimizer that treats agent skill documentation as a trainable external state. This approach allows agents to self-evolve their capabilities, as shown by @omarsar0's integration, which improved paper-figure extraction quality by 20 points.

Understanding the autonomous, script-based approach to AI task management compared to static and sub-agent methods.

Anthropic Dynamic Workflows: Definitions, Claude Code, and Orchestration Patterns

Explore Anthropic's dynamic workflows, where Claude autonomously determines action sequences. This entry defines dynamic workflows, details their implementation in Claude Code as JavaScript scripts for large-scale orchestration, and compares them to static workflows, subagents, and other AI patterns.

Beyond generic outputs: strategies for eliciting disagreement, handling long contexts, and refining drafts with LLMs.

Prompting Claude for Critical Feedback and Deeper Insights

User-derived strategies for optimizing Claude's performance in writing and research. Learn how to prompt for critical feedback, effectively manage long contexts, and leverage editing over generation to achieve more specific, insightful AI outputs.

This project isn't just a clever name; it's a robust, distributed AI architecture inspired by the iconic sitcom.

Munder Difflin: Beyond The Office's Humor, a Serious Open-Source Multi-Agent System Emerges

Explore Munder Difflin, an open-source multi-agent system drawing inspiration from "The Office." This project offers a practical, distributed AI architecture, demonstrating how pop culture can spark serious software innovation.

A practical guide to implementing a rigorous, preregistered workflow for computational research with zero third-party dependencies.

How Science Superpowers Transforms AI Agents into Disciplined Scientific Collaborators

Science Superpowers guides AI agents through a rigorous, preregistered workflow for scientific collaboration, ensuring precision, reproducibility, and protection against p-hacking. This guide details its functionality, emphasizing its zero third-party dependency design and installation across various agent harnesses like Cursor, Claude Code, and Gemini CLI.

YouTube personality PewDiePie unveils a new artificial intelligence tool designed to manage and coordinate AI agents for various tasks.

PewDiePie Creates AI Agent Orchestrator

PewDiePie, the renowned YouTube personality, has developed an AI agent orchestrator. This new tool allows for the management and coordination of multiple AI agents, potentially revolutionizing content creation and automation.

A novel runtime harness approach improves frozen LLM agents by converting interaction failures into reusable interventions, outperforming model-centric training.

Life-Harness: Adapting the Interface for Deterministic LLM Agents

Introducing Life-Harness, a lifecycle-aware runtime harness that significantly improves frozen LLM agents without modifying model weights. By adapting the interface to convert recurring interaction failures into reusable interventions across various categories, Life-Harness achieved an average 88.5% relative improvement across 116 out of 126 model-environment settings on seven deterministic benchmarks.

Discover how a novel framework, inspired by diffusion models, enables training of massive Transformers with significantly reduced memory footprint.

How DiffusionBlocks Overcomes the Deep Learning Memory Wall

Explore the "memory wall" in deep learning and how DiffusionBlocks, by reinterpreting residual networks as diffusion processes, offers a principled, block-wise training method. Learn how it dramatically cuts memory usage for large Transformer models, making them accessible on standard hardware.

Explore Genspark AI, an open-source Super Agent framework for multi-step task automation, offering local operation, diverse LLM integration, and versatile outputs.

What is Genspark AI and How Does It Work?

Discover Genspark AI, an open-source Super Agent framework that orchestrates multiple LLMs to plan, reason, and execute complex tasks. Learn about its local operation, customizability, and ability to generate dynamic Sparkpages, presentations, spreadsheets, and more, all without subscription costs or vendor lock-in.

Learn to use MLLM-Jailbreak-Bench, a reproducible and model-agnostic framework for measuring harmful output in multimodal large language models.

How to Evaluate Multimodal LLM Safety with MLLM-Jailbreak-Bench

Discover MLLM-Jailbreak-Bench, an evaluation framework for assessing multimodal LLM safety across five attack categories. Understand how to measure Attack Success Rate, refusal quality, and calibration error to identify real safety gaps and avoid false positives. Get started with installation and quick-start instructions.

A systematic study reveals 'constraint decay' as agents lose 30 points in assertion pass rates when facing production-grade requirements across eight web frameworks.

Why LLM Agents Fail at Structural Constraints in Backend Code

Learn how LLM agents fail to maintain structural constraints like ORM and architectural patterns in multi-file backend generation. This paper identifies constraint decay, framework sensitivity, and data-layer defects as key challenges for autonomous coding.