LingBot-VLA, a Vision-Language-Action model trained on extensive real-world robotic data, demonstrates superior performance and generalization across multiple platforms with enhanced efficiency. The model is supported by an efficient codebase and open access to code, base model, and benchmark data.
This article explains how the Cursor IDE indexes a codebase, focusing on its approach to understanding code structure and relationships beyond simple text indexing. It details the use of Language Server Protocol (LSP) and the creation of a graph database to represent the codebase, enabling features like code navigation, refactoring, and accurate code completion.
Lobster is a **Clawdbot-native workflow shell** designed to be a **typed, local-first "macro engine"** for building composable and safe automations. It allows users to define pipelines of tools and skills that Clawdbot (or other AI agents) can invoke with a single step, saving tokens and enabling determinism and resumability.
**Key Features & Goals:**
* **Typed Pipelines:** Uses JSON objects/arrays instead of text pipes for data flow.
* **Local-First:** Executes workflows locally, enhancing privacy and control.
* **No New Authentication:** Leverages existing authentication mechanisms; doesn't require new OAuth tokens.
* **Composability:** Workflows can be chained and reused.
* **Approval Gates:** Includes mechanisms for human or automated approval before execution.
* **Workflow Files:** Supports YAML/JSON workflow files for defining complex pipelines.
**Quick Start:**
Requires `pnpm install`, `pnpm test`, and `pnpm lint`. Workflows can be run from the command line using `node ./bin/lobster.js`.
**Example Use Case:**
The documentation provides examples of monitoring GitHub pull requests and detecting changes, demonstrating how Lobster can be used to automate tasks and provide insights.
**Key Features & Goals:**
* **Typed Pipelines:** Uses JSON objects/arrays instead of text pipes for data flow.
* **Local-First:** Executes workflows locally, enhancing privacy and control.
* **No New Authentication:** Leverages existing authentication mechanisms; doesn't require new OAuth tokens.
* **Composability:** Workflows can be chained and reused.
* **Approval Gates:** Includes mechanisms for human or automated approval before execution.
* **Workflow Files:** Supports YAML/JSON workflow files for defining complex pipelines.
**Quick Start:**
Requires `pnpm install`, `pnpm test`, and `pnpm lint`. Workflows can be run from the command line using `node ./bin/lobster.js`.
**Example Use Case:**
The documentation provides examples of monitoring GitHub pull requests and detecting changes, demonstrating how Lobster can be used to automate tasks and provide insights.
Unusually detailed post explains how OpenAI handles the Codex agent loop. The article dives into the technical aspects of OpenAI's Codex CLI coding agent, including the agent loop, prompt construction, caching, and context window management.
The article details how their Codex CLI coding agent functions. OpenAI engineer Michael Bolin explains the "agent loop" – the process by which the AI receives user input, generates code, runs tests, and iterates with human supervision.
* **Agent Loop Mechanics:** The agent builds prompts with prioritized components (system, developer, user, assistant) and sends them to OpenAI’s Responses API.
* **Prompt Management:** The system handles growing prompt lengths (quadratic growth) through caching, compaction, and a stateless API design (allowing for "Zero Data Retention"). Cache misses can significantly impact performance.
* **Context Window:** Codex automatically compacts conversations to stay within the AI model's context window.
* **Open Source Focus:** OpenAI open-sources the CLI client for Codex, unlike ChatGPT, suggesting a different approach to development and transparency for coding tools.
* **Challenges Acknowledged:** The article doesn't shy away from the engineering challenges, like performance issues and bugs encountered during development.
* **Future Coverage:** Bolin plans to release further posts detailing the CLI’s architecture, tool implementation, and sandboxing model.
The article details how their Codex CLI coding agent functions. OpenAI engineer Michael Bolin explains the "agent loop" – the process by which the AI receives user input, generates code, runs tests, and iterates with human supervision.
* **Agent Loop Mechanics:** The agent builds prompts with prioritized components (system, developer, user, assistant) and sends them to OpenAI’s Responses API.
* **Prompt Management:** The system handles growing prompt lengths (quadratic growth) through caching, compaction, and a stateless API design (allowing for "Zero Data Retention"). Cache misses can significantly impact performance.
* **Context Window:** Codex automatically compacts conversations to stay within the AI model's context window.
* **Open Source Focus:** OpenAI open-sources the CLI client for Codex, unlike ChatGPT, suggesting a different approach to development and transparency for coding tools.
* **Challenges Acknowledged:** The article doesn't shy away from the engineering challenges, like performance issues and bugs encountered during development.
* **Future Coverage:** Bolin plans to release further posts detailing the CLI’s architecture, tool implementation, and sandboxing model.
This article details how to combine Clawdbot with Docker Model Runner (DMR) to build a privacy-focused, high-performance personal AI assistant with full control over data and costs. It covers configuration, benefits, recommended models, and how to get involved in the ecosystem.
"Yahoo Scout looks like a more web-friendly take on AI searchIt’s somewhere between 10 blue links and a full-blown AI assistant, and so far, it feels like the right mix."
Rafael Ben-Ari has created AI-generated newspapers, including a tech news feed and a retrocomputing paper based on SimCity 2000, using a suite of LLM agents for reporting and editing. This allows for highly niche publications tailored to specific interests.
Logs, metrics, and traces aren't enough. AI apps require visibility into prompts and completions to track everything from security risks to hallucinations.
Based on the discussion, /u/septerium achieved optimal performance for GLM 4.7 Flash (UD-Q6_K_XL) on an RTX 5090 using these specific settings and parameters:
- GPU: NVIDIA RTX 5090.
- 150 tokens/s
- Context: 48k tokens squeezed into VRAM.
- UD-Q6_K_XL (Unsloth quantized GGUF).
- Flash Attention: Enabled (-fa on).
- Context Size: 48,000 (--ctx-size 48000).
- GPU Layers: 99 (-ngl 99) to ensure the entire model runs on the GPU.
- Sampler & Inference Parameters
- Temperature: 0.7 (recommended by Unsloth for tool calls).
- Top-P: 1.0.
- Min-P: 0.01.
- Repeat Penalty: Must be disabled (llama.cpp does this by default, but users warned other platforms might not).
- GPU: NVIDIA RTX 5090.
- 150 tokens/s
- Context: 48k tokens squeezed into VRAM.
- UD-Q6_K_XL (Unsloth quantized GGUF).
- Flash Attention: Enabled (-fa on).
- Context Size: 48,000 (--ctx-size 48000).
- GPU Layers: 99 (-ngl 99) to ensure the entire model runs on the GPU.
- Sampler & Inference Parameters
- Temperature: 0.7 (recommended by Unsloth for tool calls).
- Top-P: 1.0.
- Min-P: 0.01.
- Repeat Penalty: Must be disabled (llama.cpp does this by default, but users warned other platforms might not).
Pixlpal is a hackable, ESP32-S3-based desktop device with an 11.25-inch LED matrix, high-fidelity audio, and Home Assistant integration, designed to be a smart AIoT desktop companion.
