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| Tool | Client | Backend | Model | |------|-|-|-| | Mutahunter | β
| β
| β |
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### Language support
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| Tool | Python | Javascript | Java | Cpp | |------|-|-|-|-| | Mutahunter | β
| β
| β
| β |
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### Supports local model
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| Tool | Supports local model | |------|------| | Mutahunter | β
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### Supports offline use
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| Tool | Supports offline use | |------|------| | Mutahunter | β |
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### Pricing
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| Tool | Model | Tiers | |------|-------|------| | Mutahunter | free | Free: Free |
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## Enhanced IDE
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Enhanced IDE tools provide advanced features and integrations to improve the development experience within integrated development environments.
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## Remember to bookmark and share
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This page will be updated regularly with new information, revisions and enhancements. Be sure to share it and check back frequently.
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---
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developer
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your
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own
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## Table of contents
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## Building your own tools
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One of my favorite things about being a developer is creating my own tools.
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In some cases, this means building a tool that only I can access - one that supports login with GitHub but only allows `zackproser` through.
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Most recently, I've been building [Panthalia](https://github.com/zackproser), but this post is about the benefits of building your own tools as a developer.
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## Building tools is good practice
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You're still building software and picking up new experiences when you work on pet projects.
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You're more free when you're hacking on something for yourself.
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You can feel free to try out a new framework, language or architecture.
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## Eating your own dogdood makes you sensitive to dogfood taste
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When you are both a tool's creator and primary user, you quickly pick up on which UX patterns suck.
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Having a janky workflow around saving your content will eventually bother you enough that you'll fix it and make it more robust.
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I believe this helps you to have more compassion for the experience of end users when you're building something that is intended for wider consumption.
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## It's fun
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Especially as the functionality you originally desired begins to take shape.
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## It's a singular feeling
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To one day realize that the tool you've been hacking on for a while has finally turned a corner and is able to solve the original use case you set out to solve.
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## Tool development can be lucrative
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Some developers polish relentlessly until their project starts getting real adoption and becomes a real revenue stream for them.
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But this isn't the only way that building your own tools can make you money.
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The skills and experience you pick up while hacking are applicable to full-time jobs, part-time gigs and contracts. In my experience, the best developers are those who are constantly scratching their own technical itches by trying things out in code.
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A little bit down the road, when you're using at your day job what you learned in your spare personal hacking time, you'll see the value of contantly building things for practice.
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---
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## Table of contents
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In the rapidly evolving world of artificial intelligence (AI) and machine learning, there's a concept that's revolutionizing the way machines understand and process data: embeddings. Embeddings, also known as vectors, are floating-point numerical representations of the "features" of a given piece of data. These powerful tools allow machines to achieve a granular "understanding" of the data we provide, enabling them to process and analyze it in ways that were previously impossible. In this comprehensive guide, we'll explore the basics of embeddings, their history, and how they're revolutionizing various fields.
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## Extracting Features with Embedding Models
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At the heart of embeddings lies the process of feature extraction. When we talk about "features" in this context, we're referring to the key characteristics or attributes of the data that we want our machine learning models to learn and understand. For example, in the case of natural language data (like text), features might include the semantic meaning of words, the syntactic structure of sentences, or the overall sentiment of a document.
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To obtain embeddings, you feed your data to an embedding model, which uses a neural network to extract these relevant features. The neural network learns to map the input data to a high-dimensional vector space, where each dimension represents a specific feature. The resulting vectors, or embeddings, capture the essential information about the input data in a compact, numerical format that machines can easily process and analyze.
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There are various embedding models available, ranging from state-of-the-art models developed by leading AI research organizations like OpenAI and Google, to open-source alternatives like Word2Vec and GloVe. Each model has its own unique architecture and training approach, but they all share the common goal of learning meaningful, dense representations of data.
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