An analysis of the quality of AI-generated summaries of a technical paper, comparing outputs from Gemini, ChatGPT, Claude, Grok, Perplexity, and NotebookLM. The author finds Gemini to be the best, highlighting the importance of context in prompting and the potential usefulness of AI summaries as 'extended abstracts'.
>"I cover briefly what SVD is, and why it is central to making attention work really well."
PII Guard is an LLM-powered tool that detects and manages Personally Identifiable Information (PII) in logs — designed to support data privacy and GDPR compliance. It uses the gemma:3b model running locally via Ollama.
A Google engineer's testimony shows how page quality is scored and confirms the existence of a popularity signal that uses Chrome data.
This course provides an introduction to the Model Context Protocol (MCP), covering its theory, design, and practical application. It includes foundational units, hands-on exercises, use case assignments, and collaboration opportunities. The course aims to equip students with the knowledge and skills to build AI applications leveraging external data and tools using MCP standards.
A summary of a workshop presented at PyCon US on building software with LLMs, covering setup, prompting, building tools (text-to-SQL, structured data extraction, semantic search/RAG), tool usage, and security considerations like prompt injection. It also discusses the current LLM landscape, including models from OpenAI, Gemini, Anthropic, and open-weight alternatives.
"A fully autonomous, AI-powered DevOps platform for managing cloud infrastructure across multiple providers, with AWS and GitHub integration, powered by OpenAI's Agents SDK."
git-gpt-commit v0.9.0 is a Git extension that uses OpenAI's GPT models to automatically generate commit messages. New features include support for GPT-4o, language selection, prefix toggle, API key management, and configuration management.
Edge Delta announces its new MCP Server, an open standard for streamlining communication between AI models and external data sources. It enables intelligent telemetry data analysis, adaptive pipelines, and effortless cross-tool orchestration directly within your IDE.
Edge Delta’s MCP Server acts as a bridge between developer tools and the Edge Delta platform, enabling generative AI to be integrated into observability workflows. Key benefits include:
* **Instant Root Cause Analysis:** Quickly identify the causes of errors using logs, metrics, and probable root causes.
* **Adaptive Pipelines:** AI-driven suggestions for optimizing telemetry pipeline configurations.
* **Effortless Orchestration:** Seamless integration of Edge Delta anomalies with other tools like Slack and AWS KB.
The server is built on Go and requires minimal authentication (Org ID + API Token). It can be easily integrated into IDEs with a simple configuration. The author anticipates that, despite current limitations like context window size and latency, this technology represents a significant step forward, similar to the impact of early algorithmic breakthroughs.
Edge Delta’s MCP Server acts as a bridge between developer tools and the Edge Delta platform, enabling generative AI to be integrated into observability workflows. Key benefits include:
* **Instant Root Cause Analysis:** Quickly identify the causes of errors using logs, metrics, and probable root causes.
* **Adaptive Pipelines:** AI-driven suggestions for optimizing telemetry pipeline configurations.
* **Effortless Orchestration:** Seamless integration of Edge Delta anomalies with other tools like Slack and AWS KB.
The server is built on Go and requires minimal authentication (Org ID + API Token). It can be easily integrated into IDEs with a simple configuration. The author anticipates that, despite current limitations like context window size and latency, this technology represents a significant step forward, similar to the impact of early algorithmic breakthroughs.
MarkItDown is an open-source Python utility that simplifies converting diverse file formats into Markdown, designed to prepare data for LLMs and RAG systems. It handles various file types, preserves document structure, and integrates with LLMs for tasks like image description.
