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
llayer applies the Unix philosophy to large language model orchestration by building framework-free agents with bash, curl, and jq. The architecture decomposes the agent lifecycle into three fundamentals: an append-only JSONL history file for state and memory, a jq stream reducer for context window management, and a standard bash while loop for control flow. This stateless text pipeline enables time-travel debugging via simple file slicing, zero abstraction tooling through native bash functions, and seamless POSIX tool integration for filtering or benchmarking. The system functions as a REPL-style loop that ingests user input, constructs context, evaluates it against a local model like Ollama, handles tool dispatches, and outputs results. All interactions are recorded immutably in a structured JSONL event schema, prioritizing transparency, composability, and minimalist design.
- Append-only JSONL history for auditing and replayability
- Modular command chaining for stateless and stateful interactions
- Docker Compose integration for local Ollama inference
- Transparent POSIX tool pipeline for data filtering and token benchmarking
- Minimalist schema with explicit event types and sources
- Append-only JSONL history for auditing and replayability
- Modular command chaining for stateless and stateful interactions
- Docker Compose integration for local Ollama inference
- Transparent POSIX tool pipeline for data filtering and token benchmarking
- Minimalist schema with explicit event types and sources
Vercel has released Skills.sh, an open-source tool designed to provide AI agents with a standardized way to execute reusable actions, or skills, through the command line. The project introduces what Vercel describes as an open agent skills ecosystem, where developers can define, share, and run discrete operations that agents can invoke as part of their workflows.
The ollama 0.14-rc2 release introduces experimental functionality allowing LLMs to use tools like bash and web searching on your system, with safeguards like interactive approval and command allow/denylists.
Anurag Singh details seven small Python scripts designed to automate common tasks and improve productivity on both Mac and Windows machines:
**Script Name** | **Purpose** | **Key Features/Notes** |
|---|---|---|
| **Inbox Cleaner** | Cleans email inbox | Archives or deletes emails older than a specified number of days. Works with IMAP, compatible with most email providers. |
| **S3 Backup** | Backs up files to cloud storage | Uploads files to an S3-compatible storage provider. Minimalist, suitable for daily/weekly backups. |
| **Screenshot Sorter** | Organizes screenshots | Finds screenshots, sorts them by date into a tidy folder structure. Includes a dry-run mode. |
| **Bulk Rename** | Renames multiple files | Renames files using patterns or sequentially. Uses regular expressions for advanced renaming. |
| **Arrange It** | Organizes files into folders | Moves files into categorized folders based on their extension (Images, Docs, Spreadsheets, etc.). |
| **Clipboard Save** | Saves clipboard history | Saves every new clipboard item to a daily text file. Creates a personal history of copied text. |
**Script Name** | **Purpose** | **Key Features/Notes** |
|---|---|---|
| **Inbox Cleaner** | Cleans email inbox | Archives or deletes emails older than a specified number of days. Works with IMAP, compatible with most email providers. |
| **S3 Backup** | Backs up files to cloud storage | Uploads files to an S3-compatible storage provider. Minimalist, suitable for daily/weekly backups. |
| **Screenshot Sorter** | Organizes screenshots | Finds screenshots, sorts them by date into a tidy folder structure. Includes a dry-run mode. |
| **Bulk Rename** | Renames multiple files | Renames files using patterns or sequentially. Uses regular expressions for advanced renaming. |
| **Arrange It** | Organizes files into folders | Moves files into categorized folders based on their extension (Images, Docs, Spreadsheets, etc.). |
| **Clipboard Save** | Saves clipboard history | Saves every new clipboard item to a daily text file. Creates a personal history of copied text. |
This blog post details how to build a natural language Bash agent using NVIDIA Nemotron Nano v2, requiring roughly 200 lines of Python code. It covers the core components, safety considerations, and offers both a from-scratch implementation and a simplified approach using LangGraph.
This blog post details a personal code review tool built around `llm` and `git diff`. It describes installation, how it works, how the author uses it, and its advantages over GitHub's Copilot review tool.
The glamourous AI coding agent for your favourite terminal
The article discusses using AI for code review, emphasizing that it should be used as a tool to flag potential issues for human review, similar to how a spell checker works. It highlights a tool created by Bill Mill to aid in this process, which uses a command-line interface to connect to LLMs. The author stresses the importance of discernment when accepting AI suggestions and provides the system prompt used in the tool.
This GitHub repository contains a collection of example files demonstrating various use cases and configurations for the llamafiles tools, including examples:
* **System Administration:** Scripts and configurations for Ubuntu, Raspberry Pi 5, and macOS.
* **LLM Interaction:** Examples of prompts and interactions with LLMs like Mixtral and Dolphin.
* **Text Processing:** Scripts for summarizing text, extracting information, and formatting output.
* **Development Tools:** Examples related to Git, Emacs, and other development tools.
* **Hardware Monitoring:** Scripts for monitoring GPU and NVMe drive status.
* **System Administration:** Scripts and configurations for Ubuntu, Raspberry Pi 5, and macOS.
* **LLM Interaction:** Examples of prompts and interactions with LLMs like Mixtral and Dolphin.
* **Text Processing:** Scripts for summarizing text, extracting information, and formatting output.
* **Development Tools:** Examples related to Git, Emacs, and other development tools.
* **Hardware Monitoring:** Scripts for monitoring GPU and NVMe drive status.
