Vercel has open-sourced bash-tool, a Bash execution engine for AI agents, enabling them to run filesystem-based commands to retrieve context for model prompts. It allows agents to handle large local contexts without embedding entire files, by running shell-style operations like find, grep, and jq.
Lighweight CLI to interact with MCP servers
This article presents a compelling argument that the Manifold-Constrained Hyper-Connections (mHC) method in deep learning isn't just a mathematical trick, but a fundamentally physics-inspired approach rooted in the principle of energy conservation.
The author argues that standard neural networks act as "active amplifiers," injecting energy and potentially leading to instability. mHC, conversely, aims to create "passive systems" that route information without creating or destroying it. This is achieved by enforcing constraints on the weight matrices, specifically requiring them to be doubly stochastic.
The derivation of these constraints is presented from a "first principles" physics perspective:
* **Conservation of Signal Mass:** Ensures the total input signal equals the total output signal (Column Sums = 1).
* **Bounding Signal Energy:** Prevents energy from exploding by ensuring the output is a convex combination of inputs (non-negative weights).
* **Time Symmetry:** Guarantees energy conservation during backpropagation (Row Sums = 1).
The article also draws a parallel to Information Theory, framing mHC as a way to combat the Data Processing Inequality by preserving information through "soft routing" – akin to a permutation – rather than lossy compression.
Finally, it explains how the Sinkhorn-Knopp algorithm is used to enforce these constraints, effectively projecting the network's weights onto the Birkhoff Polytope, ensuring stability and adherence to the laws of thermodynamics. The core idea is that a stable deep network should behave like a system of pipes and valves, routing information without amplifying it.
The author argues that standard neural networks act as "active amplifiers," injecting energy and potentially leading to instability. mHC, conversely, aims to create "passive systems" that route information without creating or destroying it. This is achieved by enforcing constraints on the weight matrices, specifically requiring them to be doubly stochastic.
The derivation of these constraints is presented from a "first principles" physics perspective:
* **Conservation of Signal Mass:** Ensures the total input signal equals the total output signal (Column Sums = 1).
* **Bounding Signal Energy:** Prevents energy from exploding by ensuring the output is a convex combination of inputs (non-negative weights).
* **Time Symmetry:** Guarantees energy conservation during backpropagation (Row Sums = 1).
The article also draws a parallel to Information Theory, framing mHC as a way to combat the Data Processing Inequality by preserving information through "soft routing" – akin to a permutation – rather than lossy compression.
Finally, it explains how the Sinkhorn-Knopp algorithm is used to enforce these constraints, effectively projecting the network's weights onto the Birkhoff Polytope, ensuring stability and adherence to the laws of thermodynamics. The core idea is that a stable deep network should behave like a system of pipes and valves, routing information without amplifying it.
Researchers are studying large language models as if they were living things, discovering secrets by applying biological and neurological analysis techniques. This approach is revealing unexpected behaviors and limitations of LLMs.
The Tiiny AI Pocket Lab, the world's smallest mini PC, debuted at CES 2026. This portable AI supercomputer packs 80GB of RAM and 1TB of SSD storage into a pocket-sized device, offering on-device AI processing for privacy and convenience.
Minimal Claude Code alternative. Single Python file, zero dependencies, ~250 lines.
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.
mcp-cli is a lightweight CLI that enables dynamic discovery of MCP servers, reducing token consumption and making tool interactions more efficient for AI coding agents.
The article emphasizes the importance of optimizing AI coding agent context to improve efficiency and performance. The author shares four key techniques: maintaining an updated AGENTS.md file, providing documentation links, sharing IaC stack context, and starting new threads for new tasks.
**Bullet Points:**
- **Always update AGENTS.md**: Store coding rules and preferences across threads to improve consistency and reduce errors.
- **Provide documentation links**: Ensure agents use up-to-date API and syntax information by linking to current docs.
- **Provide IaC stack as context**: Share infrastructure details (e.g., database tables) to reduce token usage and improve speed.
- **Start new threads for new contexts**: Avoid context noise by initiating fresh threads when switching tasks or projects.
**Bullet Points:**
- **Always update AGENTS.md**: Store coding rules and preferences across threads to improve consistency and reduce errors.
- **Provide documentation links**: Ensure agents use up-to-date API and syntax information by linking to current docs.
- **Provide IaC stack as context**: Share infrastructure details (e.g., database tables) to reduce token usage and improve speed.
- **Start new threads for new contexts**: Avoid context noise by initiating fresh threads when switching tasks or projects.
This document provides guidelines for maintaining high-quality Python code, specifically for AI coding agents. It covers principles, tools, style, documentation, testing, and security best practices.
