The rise of large language models and the lookout for new tech at Dwarves
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The past few weeks have been exciting for developers with an interest in deploying AI-powered applications by LLM, with Generative AI moving at an alarming pace. Meanwhile, the process of continuous translation, exploring PNPM systems, and unit testing in Golang within the Dwarves engineers has enriched the understanding and best practices for ensuring high-quality code.
????????????????: It is now possible to build AI-powered applications without having to spend months or years learning the ins and outs of machine learning. LLMs have some general embedded knowledge, but they mainly operate on the context that you give them via prompting.
Researching PNPM was originally from research on what package manager Next.js uses. PNPM is a package manager for Node.js that offers several advantages over other popular package managers, including saving disk space and boosting installation speed. It also creates a non-flat node_modules directory, which can be helpful for larger projects. It's worth exploring whether it's the right choice for your specific use case in this blog.
Explore the techniques for crafting effective tests in Go and outlines various strategies for writing effective and maintainable tests, including making code testable, writing clear test cases, using interfaces, avoiding file I/O and API calls, covering edge cases and boundary conditions, and ensuring test coverage. By following these strategies and best practices, you can write tests that are easier to run.
Dwarves engineer developed?continuous translation, an approach that allows for near-real-time communication across languages and provides solutions to integrate into a system. Continuous translation generates translations incrementally with?minimal latency rather than batching translations after complete utterances. It can reduce latency to under 20ms.
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British AI chipmaker #Graphcore unveiled their GPT-J model, by fine-tuning a smaller version of OpenAI's GPT-3 model using Graphcore's AI accelerators. Graphcore plans to release Fine-Tuned GPT-J as an open-source project, which will enable the wider community to use and further develop the model. GPT-J achieves state-of-the-art performance on several NLP benchmarks and can be trained on a single IPU-M2000 chip.
After working with LLM applications, Huyen Chip personally went down the rabbit hole of building her own applications. In the blog, Huyen discusses the key challenges of productionizing LLM applications, and how to compose multiple tasks with control flows, and covers some of the promising use cases. Although, we’re still in the early days of LLM applications, so not all of these changes will matter. Who knows that we’ll need humans to tune the prompts?
LLMs just hit a major milestone with the release of the new "Generative agents" paper. By using LLMs, generative agents were able to simulate human-like behavior in an interactive sandbox inspired by The Sims. The agent architecture extends Language Models to store a complete record of the agent's experiences using natural language, and synthesize those memories over time into higher-level reflections.
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