The Dispatch is a software project that leverages Large Language Models (LLMs) to generate automated reports on software projects. It aims to offer clear visibility into the development process and document institutional knowledge.
The project's setup process involves installing Python, pip, and Redis, and setting environment variables. The system is modular and designed to generate reports on a weekly basis or on-demand.
Notable considerations include:
There are no open pull requests. The most significant closed pull request is PR #3, which integrated the new OpenAI assistants API and maintained backward compatibility with the old system. This PR included a substantial amount of code changes and seems to be a critical update for the project.
#2311.6250: Towards Auditing Large Language Models: Improving Text-based Stereotype Detection - Introduces a novel stereotype classifier for English text and a dataset for training it. The classifier is used to assess the stereotypic behaviour of the GPT family of models.
2311.12604: Fairness-Aware Domain Generalization under Covariate and Dependence Shifts - Proposes a novel domain generalization approach that addresses domain shifts by considering both covariate and dependence shifts. The approach surpasses state-of-the-art methods.
#2311.6251: A Unified Framework for Fair Spectral Clustering With Effective Graph Learning - Proposes a novel graph construction method with a node-adaptive graph filter to learn graphs from noisy data. The model outperforms state-of-the-art fair clustering methods.
#2311.6252: Current Topological and Machine Learning Applications for Bias Detection in Text - Utilizes the RedditBias database to analyze textual biases. Findings suggest BERT, particularly mini BERT, excels in bias classification.
#2311.6253: Intention and Context Elicitation with Large Language Models in the Legal Aid Intake Process - Demonstrates a proof-of-concept using LLMs to elicit and infer clients' underlying intentions and specific legal circumstances through free-form, language-based interactions.
#2311.6254: Attention: Large Multimodal Model is Watching your Geo-privacy - Demonstrates the capabilities of advanced AI tools, specifically a GPT-4 based model, in identifying and potentially compromising geographic privacy through online shared content.
#2311.6255: Certification of Distributional Individual Fairness - Proposes a novel convex approximation of IF constraints that exponentially decreases the computational cost of providing formal guarantees of local individual fairness.
#2311.6256: Data Equity: Foundational Concepts for Generative AI - Explores frameworks for data equity in the context of Generative AI (genAI). The proposed definitions, framework and recommendations are intended to proactively shape the development of promising genAI technologies.
#2311.6257: AI Ethics and Ordoliberalism 2.0: Towards A 'Digital Bill of Rights' - Suggests introducing hard-law legislation with a more effective oversight structure and merging existing AI guidelines with an ordoliberal-inspired regulatory and competition policy.
#2311.6258: AI-enhanced Auto-correction of Programming Exercises: How Effective is GPT-3.5? - Investigates the potential of AI in providing personalized code correction and generating feedback. The results show that GPT-3.5 can successfully generate effective and high-quality feedback in 59% of cases.
There are currently no open pull requests for this software project.
There have been a total of 3 closed pull requests, with 3 of them being closed recently.
Summary
Detailed Reports
.gitignore, various files in the missions directory, and several template files. The changes involved a significant number of line additions and modifications, with a total of +826 lines added, ~1159 lines modified, and -333 lines removed.The most significant activity in the project's pull requests is the merging of PR #3, which dealt with integrating the new OpenAI assistants API and maintaining backward compatibility with the old system. This PR included a substantial amount of code changes and seems to be a critical update for the project. There are no unresolved or problematic open pull requests at the moment, and the other closed pull requests do not have detailed information available.
The Dispatch is a software project that utilizes Large Language Models (LLMs) to provide companies with insights into their ongoing software projects. It aims to create "automated internal journalism" by generating reports that describe the state and progress of software projects. These reports are generated by analyzing data from various software development tools such as git repositories, JIRA, Notion, Slack, Sentry, etc. The reports also include a "state of the industry" section, which offers a summary of new developments within the industry and among competitors.
The value of The Dispatch lies in its ability to offer managers clear visibility into the development process and to document institutional knowledge that might otherwise be lost after project completion or staff turnover.
The Dispatch's technical approach is inspired by the Graph of Thoughts LLM prompting pattern. It breaks down the report generation process into multiple tasks, such as fetching data, analyzing changes, generating graphs, web searching, self-evaluation, and determining the next steps. The system is designed to be modular, allowing administrators to add, remove, and configure tasks easily. Reports are expected to be generated on a weekly basis, but the system can be triggered to create reports at any time.
Instructions for setting up The Dispatch for local development include installing Python, pip, and Redis, as well as setting environment variables such as ADMIN_PASSWORD, OPENAI_API_KEY, and GITHUB_TOKEN. The setup process involves installing dependencies, configuring the SQLite database, seeding initial data, and running services like Redis and RQ Worker. The application can then be accessed locally through a web browser.
Overall, The Dispatch appears to be a project focused on enhancing project management and documentation through automated reporting, with a technical setup that could be optimized for easier local development.