Qwen3-Coder-Next is an 80B MoE model with 256K context designed for fast, agentic coding and local use. It offers performance comparable to models with 10-20x more active parameters and excels in long-horizon reasoning, complex tool use, and recovery from execution failures.
The article details the release of Qwen3-Coder-Next, a new 80-billion-parameter open-source large language model (LLM) from Alibaba’s Qwen team. This model is designed for coding tasks and utilizes an ultra-sparse Mixture-of-Experts (MoE) architecture, activating only 3 billion parameters at a time for increased efficiency. It boasts a massive 262,144 token context window and innovative techniques like Gated DeltaNet and Best-Fit Packing to overcome traditional LLM limitations. Qwen3-Coder-Next was trained using an "agentic training" pipeline, learning from real-world coding scenarios and feedback. It supports 370 programming languages and demonstrates competitive performance against leading models like OpenAI’s Codex and Anthropic’s Claude, while also exhibiting strong security features. The release is positioned as a significant advancement in open-weight AI and a challenge to proprietary coding models.
This article details seven advanced feature engineering techniques using LLM embeddings to improve machine learning model performance. It covers techniques like dimensionality reduction, semantic similarity, clustering, and more.
The article explores how to leverage LLM embeddings for advanced feature engineering in machine learning, going beyond simple similarity searches. It details seven techniques:
1. **Embedding Arithmetic:** Performing mathematical operations (addition, subtraction) on embeddings to represent concepts like "positive sentiment - negative sentiment = overall sentiment".
2. **Embedding Clustering:** Using clustering algorithms (like k-means) on embeddings to create categorical features representing groups of similar text.
3. **Embedding Dimensionality Reduction:** Reducing the dimensionality of embeddings using techniques like PCA or UMAP to create more compact features while preserving important information.
4. **Embedding as Input to Tree-Based Models:** Directly using embedding vectors as features in tree-based models like Random Forests or Gradient Boosting. The article highlights the importance of careful handling of high-dimensional data.
5. **Embedding-Weighted Averaging:** Calculating weighted averages of embeddings based on relevance scores (e.g., TF-IDF) to create a single, representative embedding for a document.
6. **Embedding Difference:** Calculating the difference between embeddings to capture changes or relationships between texts (e.g., before/after edits, question/answer pairs).
7. **Embedding Concatenation:** Combining multiple embeddings (e.g., title and body of a document) to create a richer feature representation.
The article explores how to leverage LLM embeddings for advanced feature engineering in machine learning, going beyond simple similarity searches. It details seven techniques:
1. **Embedding Arithmetic:** Performing mathematical operations (addition, subtraction) on embeddings to represent concepts like "positive sentiment - negative sentiment = overall sentiment".
2. **Embedding Clustering:** Using clustering algorithms (like k-means) on embeddings to create categorical features representing groups of similar text.
3. **Embedding Dimensionality Reduction:** Reducing the dimensionality of embeddings using techniques like PCA or UMAP to create more compact features while preserving important information.
4. **Embedding as Input to Tree-Based Models:** Directly using embedding vectors as features in tree-based models like Random Forests or Gradient Boosting. The article highlights the importance of careful handling of high-dimensional data.
5. **Embedding-Weighted Averaging:** Calculating weighted averages of embeddings based on relevance scores (e.g., TF-IDF) to create a single, representative embedding for a document.
6. **Embedding Difference:** Calculating the difference between embeddings to capture changes or relationships between texts (e.g., before/after edits, question/answer pairs).
7. **Embedding Concatenation:** Combining multiple embeddings (e.g., title and body of a document) to create a richer feature representation.
This post discusses the limitations of using cosine similarity for compatibility matching, specifically in the context of a dating app. The author found that high cosine similarity scores didn't always translate to actual compatibility due to the inability of embeddings to capture dealbreaker preferences. They improved results by incorporating structured features and hard filters.
A blog about Emacs, Rust, and low-level systems programming.
A guide on running OpenClaw (aka Clawdbot aka Moltbot) in a Docker container, including setup, configuration, and accessing the web UI.
Crush is a Go-based CLI application that brings AI assistance to your terminal. It provides a terminal user interface (TUI) for AI coding.
Abstract:
>"The rapid development of advanced AI agents and the imminent deployment of many instances of these agents will give rise to multi-agent systems of unprecedented complexity. These systems pose novel and under-explored risks. In this report, we provide a structured taxonomy of these risks by identifying three key failure modes (miscoordination, conflict, and collusion) based on agents' incentives, as well as seven key risk factors (information asymmetries, network effects, selection pressures, destabilising dynamics, commitment problems, emergent agency, and multi-agent security) that can underpin them. We highlight several important instances of each risk, as well as promising directions to help mitigate them. By anchoring our analysis in a range of real-world examples and experimental evidence, we illustrate the distinct challenges posed by multi-agent systems and their implications for the safety, governance, and ethics of advanced AI."
>"The rapid development of advanced AI agents and the imminent deployment of many instances of these agents will give rise to multi-agent systems of unprecedented complexity. These systems pose novel and under-explored risks. In this report, we provide a structured taxonomy of these risks by identifying three key failure modes (miscoordination, conflict, and collusion) based on agents' incentives, as well as seven key risk factors (information asymmetries, network effects, selection pressures, destabilising dynamics, commitment problems, emergent agency, and multi-agent security) that can underpin them. We highlight several important instances of each risk, as well as promising directions to help mitigate them. By anchoring our analysis in a range of real-world examples and experimental evidence, we illustrate the distinct challenges posed by multi-agent systems and their implications for the safety, governance, and ethics of advanced AI."
This article explores the emerging category of AI-powered operations agents, comparing AI DevOps engineers and AI SRE agents, how cloud providers are responding, and what engineers should consider when evaluating these tools.
LlamaBarn is a macOS menu bar app for running local LLMs. It provides a simple way to install and run models locally, connecting to apps via an OpenAI-compatible API.
