DevTalk LA

This week's topic: Playwright

As described in Thoughtworks Technology Radar Vol. #29.

With Playwright you can write end-to-end tests that run in Chrome, Firefox and WebKit. By using the Chrome DevTools Protocol (CDP) Playwright can offer new features and eliminate many of the issues seen with WebDriver. Using the same API eliminates many of the issues that WebDriver has and introduces a set of new features. Chromium-based browsers implement CDP directly. To support Firefox and Webkit, though, the Playwright team has to submit patches to these browsers, which may sometimes limit the framework.

Playwright’s many features include: Built-in auto-waits, which result in tests that are more reliable and easier to understand; browser contexts, which let you test that persisting sessions across tabs work properly; and the ability to simulate notifications, geolocation and dark mode settings. Our teams are impressed with the stability Playwright brings to the test suite and like that they get feedback more quickly by running tests in parallel. Other features that set Playwright apart include better support for lazy loading and tracing. Although Playwright has some limitations — component support is currently experimental, for example — our teams consider it the go-to test framework and in some cases are migrating away from Cypress and Puppeteer.

Zoom link will be added about 10 min before the event starts.

Discussion Resources :

Article and video resources used to drive the discussion will be added soon after the previous week's meetup.

This week's topic: Azure OpenAI Service

As described in Thoughtworks Technology Radar Vol. #29.

With the huge interest in generative AI, many solutions have sprung up to access the major models. If considering or already using Azure, then it’s worth assessing Azure OpenAI Service. It provides access to OpenAI’s GPT-4, GPT-35-Turbo and Embeddings models through a REST API, a Python SDK and a web-based interface. The models can be adapted to tasks such as content generation, summarization, semantic search and natural language to code translation. Fine-tuning is also available via few-shot learning and the customization of hyperparameters. In comparison to OpenAI’s own API, Azure OpenAI Service benefits from Azure’s enterprise-grade security and compliance features; it’s also available in more regions, although availability is limited for each of the larger geographic regions, and, as of writing, India is not included.

Zoom link will be added about 10 min before the event starts.

Discussion Resources :

Article and video resources used to drive the discussion will be added soon after the previous week's meetup.

This week's topic: Open-source LLMs for coding

As described in Thoughtworks Technology Radar Vol. #29.

GitHub Copilot is a valuable tool for coding assistance while developing software. Under the hood, LLMs can power seamless developer experiences through inline code assistance, code fine-tuning, conversational support in the IDE and much more. Most of these models are proprietary and can only be used via subscription services. The good news is you can use several open-source LLMs for coding. If you’re in a space where you need to build your own coding assistance service (such as a highly regulated industry), look at models like StarCoder and WizardCoder. StarCoder is trained with a large data set maintained by BigCode, and Wizardcoder is an Evol-Instruct tuned StarCoder model. We’ve used StarCoder in our experiments and found it to be useful for generating structured software engineering elements such as code, YAML, SQL and JSON. Based on our experiments, we found both the models to be receptive to in-context learning using few-shot examples in the prompt. Nonetheless, for specific downstream tasks (such as SQL generation for a specific database like Postgres) the models needed fine-tuning. Recently, Meta unveiled its Code Llama, a code-specialized version of Llama 2. Be sure to proceed with caution when using these open-source models. Consider their license, the license of the code and of the data set used to train the model. Carefully assess these aspects before you choose any of these coding LLMs for your organization.

Zoom link will be added about 10 min before the event starts.

Discussion Resources :

Article and video resources used to drive the discussion will be added soon after the previous week's meetup.

This week's topic: LLM-powered autonomous agents

As described in Thoughtworks Technology Radar Vol. #29.

As development of large language models continues, interest in building autonomous AI agents is strong. AutoGPT, GPT-Engineer and BabyAGI are all examples of LLM-powered autonomous agents that drive an underlying LLM to understand the goal they have been given and to work toward it. The agent remembers how far it has progressed, uses the LLM in order to reason about what to do next, takes actions and understands when the goal has been met. This is often known as chain-of-thought reasoning — and it can actually work. One of our teams implemented a client service chatbot as an autonomous agent. If the bot cannot achieve the customer’s goal, it recognizes its own limitation and redirects the customer to a human instead. This approach is definitely early in its development cycle: autonomous agents often suffer from a high failure rate and incur costly AI service fees, and at least one AI startup has pivoted away from an agent-based approach.

Zoom link will be added about 10 min before the event starts.

Discussion Resources :

Article and video resources used to drive the discussion will be added soon after the previous week's meetup.