@omarsar0 writes on X that the fastest path to genuinely understanding agent harnesses is to build one from scratch in TypeScript or Python, starting with a minimal ReAct implementation prompted from Google's original paper, targeting three clean components—an LLM inference module (multi-model, OpenRouter-backed, with separable system prompt), an MCP tools module for interoperability, and a simple agent loop that ties them together—then logging every input/output at each boundary and iterating against a small set of diverse test tasks so each change is inspectable. The punchline: skip the framework first, because only once you've felt the loop, the tokens, and the tool calls in your own code do the "next steps"—memory, skills, subagents—stop being black boxes you configure and become modules you actually know how to tune.
- LLM module: wraps inference across multiple frontier models via OpenRouter; system prompt either embedded or isolated for context-engineering experiments
- Tools module: implement as MCP (Model Context Protocol) tools for cross-harness interoperability, or as bespoke functions if experienced
- Agent loop: ReAct pattern (alternating reasoning traces and action calls) encapsulating both LLM and tools; exit conditions handled via system-prompt instructions (non-deterministic), code-level checks (deterministic), or both
- Logging strategy: capture loop in/out, every LLM call in/out, and every tool-call in/out; run a fixed diverse task suite after each modification
- Scaling path: keep architecture modular so memory, skills, and subagent orchestration can be bolted on once the core loop is understood
- Shortcut alternatives (if not building from scratch): Pi SDK or LangChain harness tooling
- LLM module: wraps inference across multiple frontier models via OpenRouter; system prompt either embedded or isolated for context-engineering experiments
- Tools module: implement as MCP (Model Context Protocol) tools for cross-harness interoperability, or as bespoke functions if experienced
- Agent loop: ReAct pattern (alternating reasoning traces and action calls) encapsulating both LLM and tools; exit conditions handled via system-prompt instructions (non-deterministic), code-level checks (deterministic), or both
- Logging strategy: capture loop in/out, every LLM call in/out, and every tool-call in/out; run a fixed diverse task suite after each modification
- Scaling path: keep architecture modular so memory, skills, and subagent orchestration can be bolted on once the core loop is understood
- Shortcut alternatives (if not building from scratch): Pi SDK or LangChain harness tooling
Abhijith N Arjunan writes that Qwen Code is an open-source AI coding agent that effectively replaces Claude Code, offering better flexibility and being completely free to use. The tool allows users to connect with almost every AI provider, including local AI tools, and can be configured with various models like DeepSeek or OpenRouter. Setup is straightforward, and the tool supports features like subagents, hooks, skills, and sandbox environments. While Qwen Code may not yet match the stability of Claude Code in some areas, it provides greater freedom and is continuously updated.
Rohit Edathil writes about Dagic, a minimal workflow Directed Acyclic Graph (DAG) definition language and asynchronous execution engine implemented in Python designed for LLM agents. It provides a middle ground between standard tool calling and full code execution by allowing models to describe workflows through assignments and function calls that are parsed, type-checked, and executed concurrently without the security risks of arbitrary code execution.
- Performs ~7x more efficiently than per-call tools in math benchmarks regarding token usage
- Executes independent branches of a workflow concurrently using Python's asyncio
- Provides static type checking for tool arguments to prevent mid-run failures
- Avoids the need for sandboxing arbitrary model-generated code by restricting execution to host-registered functions
- Performs ~7x more efficiently than per-call tools in math benchmarks regarding token usage
- Executes independent branches of a workflow concurrently using Python's asyncio
- Provides static type checking for tool arguments to prevent mid-run failures
- Avoids the need for sandboxing arbitrary model-generated code by restricting execution to host-registered functions
- **Inference** – Platforms and engines for running models, plus user interfaces.
- **Models** – LLMs (general, coding, multimodal, image, audio), model providers, and specific model highlights.
- **RAG** – Retrieval-Augmented Generation tools.
- **Safeguards** – Safety and content filtering.
- **Agents & Tools** – Agent frameworks, Model Context Protocol, coding agents, computer/browser automation, memory management, and testing/evaluation.
- **Research, Training & Fine-tuning** – Security, sandboxing, and model development.
- **Hardware** – Local hardware options.
- **Tutorials** – Guides covering models, prompt/context engineering, inference, agents, and RAG.
- **Communities** – Places to connect and share knowledge.
- **Models** – LLMs (general, coding, multimodal, image, audio), model providers, and specific model highlights.
- **RAG** – Retrieval-Augmented Generation tools.
- **Safeguards** – Safety and content filtering.
- **Agents & Tools** – Agent frameworks, Model Context Protocol, coding agents, computer/browser automation, memory management, and testing/evaluation.
- **Research, Training & Fine-tuning** – Security, sandboxing, and model development.
- **Hardware** – Local hardware options.
- **Tutorials** – Guides covering models, prompt/context engineering, inference, agents, and RAG.
- **Communities** – Places to connect and share knowledge.
MindMux presents brain.md, an open-source toolkit that provides a persistent memory layer for coding agents by storing project knowledge as plain Markdown files within a repository. This system ensures that critical decisions and constraints are durable across different LLM sessions and machines via version control. A zero-dependency CLI manages the reading and writing of these files to maintain data integrity through an append-only timeline.
- Uses Markdown instead of databases like SQLite to facilitate easier diffing in git history.
- Features a "correct by construction" design that prevents malformed edits by making the CLI the exclusive writer.
- Supports integration with several agents including Claude Code, Codex, Cursor, and Pi.
- Uses Markdown instead of databases like SQLite to facilitate easier diffing in git history.
- Features a "correct by construction" design that prevents malformed edits by making the CLI the exclusive writer.
- Supports integration with several agents including Claude Code, Codex, Cursor, and Pi.
The Brave LLM Context API provides an advanced web search service specifically designed to ground Large Language Models (LLMs) in RAG pipelines or agentic workflows. It delivers pre-extracted content—such as text, tables, and code snippets—in a compact format optimized for machine consumption rather than human reading. Users can manage context through configurable token budgets and refine results using relevance thresholds or custom source ranking via Goggles.
- Supports location-aware queries including point-of-interest (POI) and map data.
- Features freshness filtering based on page modification or publication dates.
- Includes a "strict" threshold mode to prioritize high-relevance content over breadth.
- Supports location-aware queries including point-of-interest (POI) and map data.
- Features freshness filtering based on page modification or publication dates.
- Includes a "strict" threshold mode to prioritize high-relevance content over breadth.
@0xabad1dea@infosec.exchange writes about an incident where AI-assisted mathematical proofs appear to exploit bugs in theorem provers, specifically highlighting a case involving the Collatz conjecture and Lean 4. The discussion explores whether large language models are inadvertently discovering software vulnerabilities through pattern matching or learning from existing technical discussions about those bugs, while broader debates address the inherent limitations of formal verification when facing hardware faults, modeling errors, and human mistakes in specifications.
An examination of the hype surrounding autonomous AI agent frameworks and why they may add unnecessary complexity to software development. The author argues that for most production use cases, structured workflows using LLM function calling are more reliable than fully autonomous agents.
- Complexity vs control in agentic systems
- Limitations of current models regarding long-term autonomy
- Advantages of explicit programming over unpredictable loops
- Complexity vs control in agentic systems
- Limitations of current models regarding long-term autonomy
- Advantages of explicit programming over unpredictable loops
Local large language models often struggle with ambiguous prompts because they lack the massive datasets and scale used by cloud-based AI to infer user intent. To improve accuracy, users can implement a custom system prompt that instructs the model to ask up to three targeted clarifying questions before performing complex tasks like coding or writing. This approach reduces errors caused by incorrect assumptions and helps refine user instructions through active dialogue.
>"""When tasked with coding, writing, editing, or summarizing, ask the user up to three targeted clarifying questions. Proceed with the task once you've received answers and understand the prompt fully. If the task is a simple factual question or conversational message, respond directly.
"""
>"""When tasked with coding, writing, editing, or summarizing, ask the user up to three targeted clarifying questions. Proceed with the task once you've received answers and understand the prompt fully. If the task is a simple factual question or conversational message, respond directly.
"""
Unlike cloud AI services like Claude or Gemini, local LLMs lack built-in workspace features for persistent memory. You can bridge this gap using "context journaling" via system prompts and RAG.
* LM Studio presets for concise system prompts.
* RAG document uploads for background/project history.
* Markdown journal structure (Background, Projects, Corrections).
* “Corrections” section to prevent recurring model errors.
* Session exports for prompt effectiveness records.
* LM Studio presets for concise system prompts.
* RAG document uploads for background/project history.
* Markdown journal structure (Background, Projects, Corrections).
* “Corrections” section to prevent recurring model errors.
* Session exports for prompt effectiveness records.
