Goose is a local, extensible, open-source AI agent designed to automate complex engineering tasks. It can build projects from scratch, write and execute code, debug failures, orchestrate workflows, and interact with external APIs. Goose is flexible, supporting any LLM and seamlessly integrating with MCP-enabled APIs, making it a powerful tool for developers to accelerate innovation.
The article discusses how Visa leverages retrieval-augmented generation (RAG) and deep learning to enhance operations. It describes Visa's 'Secure ChatGPT,' which offers a multi-model interface for secure internal use, and how RAG improves policy-related data retrieval. The article also explores Visa's data infrastructure and AI's role in fraud prevention.
Alibaba's Qwen team aims to find out with its latest release, QwQ. Despite having a fraction of DeepSeek R1's claimed 671 billion parameters, Alibaba touts its comparatively compact 32-billion 'reasoning' model as outperforming R1 in select math, coding, and function-calling benchmarks.
The article discusses the OVON agentic framework for mitigating hallucinations in Large Language Models (LLMs). It explains the structured, collaborative pipeline involving front-end and reviewer agents, the use of 'Conversation Envelopes' and 'Whispers' for efficient data exchange, and novel KPIs for measuring success. The article also addresses future directions and the importance of trust in AI systems.
China appears to think homegrown AI startup DeepSeek could become a notable tech success story for the country. After DeepSeek's sudden rise to fame with the release of its open 'reasoning' model, R1, the company is now operating under new, tighter government-influenced restrictions.
This article discusses the concept of Generative UI, explaining how it builds upon Generative AI to create more interactive and user-friendly experiences. It outlines the technical considerations for implementing Generative UI, including linking applications with LLMs, data sources, and tools like React Server Components and vector databases.
This article details how to implement Generative User Interfaces (Generative UI) using LangGraph, specifically focusing on integrating React components with LangGraph graphs to create dynamic and interactive applications.
Google is upgrading Google Assistant users on mobile to Gemini, offering a new AI-powered assistant experience. The classic Google Assistant will no longer be accessible on most mobile devices later this year. Updates are also coming to tablets, cars, headphones, watches, and home devices.
This article describes a workflow using Large Language Models (LLMs) to automate the process of normalising spreadsheet data, making it tidy and machine-readable for easier analysis and insights.
A look at this year’s crop of LoRA alternatives, including SVF, SVFT, MiLoRA, PiSSA, and LoRA-XS, all based on SVD (Singular Value Decomposition). The article compares these techniques to the original LoRA method for fine-tuning Large Language Models.
| Method | Description | Key Feature(s) | Reference |
|--------------|---------------------------------------------|---------------------------------------------|-|
| LoRA | Freezes the model and trains a small pair of low-rank “adapter” matrices. | Saves memory and compute cycles by reducing the number of trainable parameters. | arxiv.org/abs/2106.09685 |
| SVF | Uses SVD on the model’s weight matrices and fine-tunes the singular values directly. | More economical in parameters than LoRA; makes tuned models composable. | arxiv.org/abs/2501.06252v2 |
| SVFT | Adds more trainable weights on the diagonal and evaluates various alternatives. | Provides more trainable values than just the diagonal, useful for better fine-tuning. | arxiv.org/abs/2405.19597 |
| PiSSA | Tunes only the large principal values. | Designed to approximate full fine-tuning by adapting the principal singular components. | arxiv.org/abs/2404.02948 |
| MiLoRA | Tunes only the small principal values. | Retains base model’s knowledge while adapting to new tasks. | arxiv.org/abs/2406.09044 |
| LoRA-XS | Similar to PiSSA but with a slightly different mechanism. | Shows good results with significantly fewer parameters than LoRA. | arxiv.org/abs/2405.17604 |
| DoRA | Splits weights into magnitudes and directions then tunes those. | | arxiv.org/abs/2402.09353 |
| AdaLoRA | Complex mechanism for finding the best tuning rank for a given budget of trainable weights. | | arxiv.org/abs/2303.10512 |
| Method | Description | Key Feature(s) | Reference |
|--------------|---------------------------------------------|---------------------------------------------|-|
| LoRA | Freezes the model and trains a small pair of low-rank “adapter” matrices. | Saves memory and compute cycles by reducing the number of trainable parameters. | arxiv.org/abs/2106.09685 |
| SVF | Uses SVD on the model’s weight matrices and fine-tunes the singular values directly. | More economical in parameters than LoRA; makes tuned models composable. | arxiv.org/abs/2501.06252v2 |
| SVFT | Adds more trainable weights on the diagonal and evaluates various alternatives. | Provides more trainable values than just the diagonal, useful for better fine-tuning. | arxiv.org/abs/2405.19597 |
| PiSSA | Tunes only the large principal values. | Designed to approximate full fine-tuning by adapting the principal singular components. | arxiv.org/abs/2404.02948 |
| MiLoRA | Tunes only the small principal values. | Retains base model’s knowledge while adapting to new tasks. | arxiv.org/abs/2406.09044 |
| LoRA-XS | Similar to PiSSA but with a slightly different mechanism. | Shows good results with significantly fewer parameters than LoRA. | arxiv.org/abs/2405.17604 |
| DoRA | Splits weights into magnitudes and directions then tunes those. | | arxiv.org/abs/2402.09353 |
| AdaLoRA | Complex mechanism for finding the best tuning rank for a given budget of trainable weights. | | arxiv.org/abs/2303.10512 |
