Free AI & ML Developer Tools (Browser-Based, No Server)
DevKit includes 8 AI and machine learning tools that cover the full development pipeline — from cleaning training data and counting tokens to evaluating model performance with confusion matrices and detecting outliers. Every tool runs in your browser. Your data never leaves your device.
All 8 AI/ML Tools
Estimate token count for GPT-4, Claude, Llama, Gemini, and more. Shows cost estimates per model and pricing tier. Essential for optimizing prompt length before production API calls.
When to use: Use before deploying a prompt to estimate API cost, or when your prompt is getting truncated unexpectedly.
Strip HTML tags, remove URLs, normalize whitespace, deduplicate lines, and filter by minimum/maximum length. Export as plain text.
When to use: Use when preparing scraped web text, user-generated content, or any raw text corpus for fine-tuning or RAG.
Encode categorical labels to integers or one-hot vectors. Handles multi-class labels with preview of the encoding map. Export as CSV or JSON.
When to use: Use when converting text class labels to numeric form for sklearn, PyTorch, or TensorFlow models.
Calculate total token counts, storage size, and fine-tuning cost estimates from your dataset parameters. Supports multiple model providers.
When to use: Use before starting a fine-tuning run to estimate compute cost and whether your dataset meets minimum size requirements.
Visualize 2D embeddings as an interactive scatter plot. Paste x/y coordinate data with optional labels and see clusters and relationships visually.
When to use: Use when exploring sentence embeddings reduced to 2D via PCA or UMAP to identify semantic clusters.
Build a confusion matrix from your predictions. Auto-calculates accuracy, precision, recall, F1 score, and per-class metrics. Colour-coded heatmap.
When to use: Use after evaluating a classifier to understand where the model confuses classes and which errors are most common.
IQR, Z-score, and Modified Z-score methods. Paste your numeric data and see which values are flagged as outliers, with a box plot visualization.
When to use: Use during EDA (exploratory data analysis) to identify anomalous values before training or as a standalone anomaly detection step.
Apply Min-Max scaling, Z-score (standardization), or Robust scaling to your dataset. Shows before/after statistics and distribution.
When to use: Use when preprocessing features for models that are sensitive to scale (SVMs, neural networks, k-NN) before training.
The ML Development Pipeline — Where Each Tool Fits
| Pipeline Stage | DevKit Tool |
|---|---|
| Data collection & cleaning | Text Dataset Cleaner — strip HTML, deduplicate, filter by length |
| EDA (exploratory analysis) | Outlier Detection Tool — flag anomalies with IQR, Z-score |
| Feature engineering | Label Encoder — convert categorical labels to numeric; Data Normalization — scale features |
| Dataset sizing & cost planning | AI Dataset Size Calculator — tokens, storage, fine-tuning cost |
| Prompt engineering | Prompt Token Counter — estimate token count and API cost per model |
| Model evaluation | Confusion Matrix Visualizer — accuracy, precision, recall, F1 per class |
| Embedding exploration | Embedding Visualizer — visualize 2D projections as interactive scatter plot |
Why Use Browser-Based AI Tools?
- Your training data stays private. Proprietary datasets, internal model outputs, and fine-tuning data shouldn't be uploaded to third-party servers. Client-side tools eliminate that risk entirely.
- No Python environment required. Quick tasks like encoding labels, checking token counts, or building a confusion matrix don't need a Jupyter notebook. Open the tool, paste your data, get the result.
- Fast iteration. Browser tools are instant — no environment setup, no pip install, no kernel restart. For one-off tasks in the ML workflow, speed matters.
- Shareable with non-technical stakeholders. Send a link to the confusion matrix tool to a PM who needs to see model performance — no Python required on their end.
FAQ
What AI/ML developer tools are available in DevKit?
DevKit includes 8 AI/ML tools: Prompt Token Counter (GPT-4, Claude, Llama, Gemini), Text Dataset Cleaner, Label Encoder, AI Dataset Size Calculator, Embedding Visualizer, Confusion Matrix Visualizer, Outlier Detection Tool, and Data Normalization Tool — all free, all in-browser.
Is it safe to use these AI tools with real training data?
Yes. All tools run 100% client-side. Your dataset contents, training data, and model outputs never leave your browser. There's no backend processing or data storage. This is especially important for proprietary training data or fine-tuning datasets.
How do I count tokens for OpenAI GPT-4 or Claude?
Use DevKit's Prompt Token Counter. Paste your prompt, select the model (GPT-4, GPT-3.5, Claude, Llama, or Gemini), and it estimates the token count and cost. Useful for optimizing prompts to stay within context limits or budget.
What does the text dataset cleaner do?
The text dataset cleaner strips HTML tags, removes URLs, normalizes whitespace, deduplicates lines, and filters text by minimum/maximum length. Useful for cleaning scraped web text before using it for fine-tuning or RAG pipelines.
How do I visualize a confusion matrix?
Use DevKit's Confusion Matrix Visualizer. Enter the class names and the matrix values, and it auto-calculates accuracy, precision, recall, F1 score, and other metrics, displayed with colour-coded cells for easy interpretation.