
-
- *Description*: The `llm` command-line tool leverages large language models, such as OpenAI's GPT-3, to make it easier to incorporate AI functionalities into your command-line tasks. You can use it to generate text, answer questions, and assist with coding or other language-based tasks directly from your terminal.
- **Link**: [llm on GitHub](https://github.com/simonw/llm)
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Nutrient
Nutrient - The #1 PDF SDK Library. Bad PDFs = bad UX. Slow load times, broken annotations, clunky UX frustrates users. Nutrient’s PDF SDKs gives seamless document experiences, fast rendering, annotations, real-time collaboration, 100+ features. Used by 10K+ devs, serving ~half a billion users worldwide. Explore the SDK for free.
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`ttok` is a CLI tool for working with tokenizers. It's a tool under development aimed at helping users understand how tokenization processes text, which is essential for preparing data for machine learning models, especially in NLP applications.
- *Link*: [ttok GitHub Repository](https://github.com/simonw/ttok)
### 3. symbex
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`Symbex` is a CLI tool for performing symbolic execution on Python bytecode. It can analyze Python code to find potential bugs, vulnerabilities, or logical errors by exploring all possible execution paths.
- *Link*: [symbex GitHub Repository](https://github.com/simonw/symbex)
(Truncated)
llm -m 4o "Tell me about these cli tools and link to them:
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- *Description*: `symbex` is a CLI tool designed for searching codebases. It employs symbolic execution to analyze code paths and find functions, variables, or other code elements. This can be particularly useful for developers looking for higher-precision searches in large codebases.
- **Link**: [symbex on GitHub](https://github.com/paulgb/symbex)
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So it's a pretty simple wrapper of LLM model in use (currently gpt-4o), it does not add much technical stuff in it.
It does not use database for any "random search", but yes, columns.ai is a data analytics tool that allows you to connect supported live data sources like Google Spreadsheet, Airtable, Notion Database to create visual stories.
The analytics engine is home built (https://github.com/varchar-io/nebula) but it is not a database. And I don't use LLM agents, just build logic how to purify data returned by LLM, and fit them into an optimized visualization.
Hope I answered your question!
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CodeRabbit
CodeRabbit: AI Code Reviews for Developers. Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.
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