The Best Free Browser Tools for AI and ML Engineers

BY TOOLS.FUN · MARCH 28, 2026 · 5 min read

AI and machine learning engineers spend a lot of time outside model training: cleaning datasets, managing configs, comparing experiment outputs, and wrangling serialized data formats. The tools at tools.fun handle the utility work that fills the gaps between your Jupyter notebooks and your ML pipelines — fast, free, and always available in the browser.

Part of the Tools for Data Engineers series. See the hub article for the complete guide.

JSON Formatter & Validator

ML experiment configs, model metadata, HuggingFace model cards, and API responses from inference endpoints are all JSON. When a malformed config crashes your training job, paste it into the JSON formatter to find the exact syntax error. The formatter also makes deeply nested configs from frameworks like Hydra and YAML-converted-to-JSON readable in seconds.

Best for: Reading HuggingFace model API responses, validating MLflow experiment config JSON, and inspecting Vertex AI pipeline component metadata.

Pro tip: Paste your entire config.json from a transformers model directory here to quickly inspect tokenizer settings and model architecture parameters without opening Python.

Base64 Encoder / Decoder

ML pipelines frequently encode binary data — model weights, image bytes, audio samples — as base64 for API transport. When debugging an inference API that accepts base64-encoded image inputs, use this tool to manually encode a test image or decode a response payload to verify the binary content is correct before writing the full pipeline integration.

Best for: Encoding test images for vision model API calls, decoding base64-encoded model artifacts from cloud storage, and inspecting serialized feature vectors in base64 format.

Regex Tester

Data cleaning in ML relies heavily on regex. From normalizing text for NLP preprocessing to extracting structured fields from unstructured log data, getting your patterns right before applying them to millions of rows is critical. Test your patterns here against real samples from your dataset before building them into your preprocessing pipeline.

Best for: Building text normalization patterns for NLP preprocessing, extracting structured data from log files for training, and validating label format consistency in annotation datasets.

Unix Timestamp Converter

Time-series models, event logs, and model training metadata all involve timestamps. When your training data has inconsistent timestamp formats — some Unix epoch, some ISO 8601, some milliseconds — use the timestamp converter to quickly verify what each format represents and standardize your understanding before writing the ETL transformation.

Best for: Auditing time-series training data for timestamp consistency, verifying MLflow run start and end times, and checking model deployment event logs.

Character & Word Counter

LLM prompt engineering lives and dies by token counts. While you ultimately need a tokenizer for exact token counts, character count is a fast proxy for estimating prompt size. Use the character counter to check that your prompts, system instructions, and few-shot examples stay within the context window budget before sending to the API.

Best for: Estimating LLM prompt token usage before API calls, checking dataset text field lengths for sequence model input limits, and sizing system prompts for Claude, GPT, or Gemini.

Pro tip: As a rough rule, 1 token ≈ 4 characters for English text. Divide the character count by 4 to get a quick token estimate.

Text Diff Tool

Comparing model configurations between experiments, reviewing changes to preprocessing pipelines, or auditing prompt template versions between A/B tests — the diff tool makes these comparisons immediate and visual. No Git, no Jupyter widgets, no custom difflib scripts required.

Best for: Comparing Hydra config files between experiment runs, reviewing changes to data preprocessing steps, and auditing LLM prompt template versions.

MD5 Hash Generator

Dataset versioning and artifact tracking in ML require content hashes. Use MD5 to quickly generate checksums for dataset splits, model artifacts, or config files to verify reproducibility. While SHA-256 is preferred for security-critical uses, MD5 is perfectly adequate for ML artifact deduplication and cache key generation.

Best for: Generating dataset split checksums for reproducibility tracking, creating cache keys for preprocessed features, and verifying that two model config files are identical.

Hex Converter

Working with low-level model serialization formats, binary model weights, or custom ONNX graph operations sometimes requires understanding hex-encoded byte sequences. The hex converter translates between hex, decimal, binary, and ASCII, making it easier to interpret binary data without writing custom Python inspection scripts.

Best for: Inspecting ONNX model binary headers, debugging custom C++ CUDA kernel outputs, and understanding quantized model weight representations.

List Deduplication Tool

Dataset quality in ML is everything. Duplicate training examples bias models. The deduplication tool lets you quickly paste a list of labels, URLs, or text samples and strip duplicates before building your dataset pipeline. This is especially useful for curating few-shot examples for LLM prompting or cleaning up category label lists.

Best for: Cleaning up annotation label lists, deduplicating training data URLs before scraping, and ensuring few-shot example uniqueness in LLM prompts.

Color Picker & Converter

ML papers, experiment dashboards, and data visualization notebooks all require consistent color schemes. Use the color picker to select and convert colors between HEX, RGB, and HSL formats for matplotlib, seaborn, or plotly charts. Maintain a consistent visual language across your research figures and presentations.

Best for: Building consistent color palettes for ML experiment plots, picking accessible colors for confusion matrix heatmaps, and matching brand colors in model demo UIs.

Password Generator

ML pipelines touch sensitive infrastructure: cloud storage buckets, model registries, experiment tracking servers, and GPU cluster access. Generate strong API keys and service account passwords here for MLflow, Weights & Biases, or your on-premise training cluster, then store them in your secrets manager of choice.

Best for: Generating API tokens for MLflow and W&B integrations, creating strong database passwords for feature stores, and producing secure service account credentials for ML pipeline infrastructure.

JSON to YAML Converter

Hydra, Kubernetes ML workloads, and many MLOps tools prefer YAML configs while model APIs often return JSON. Convert between formats instantly without writing Python conversion scripts. This is especially useful when translating HuggingFace model config JSON files into YAML format for Kubernetes training job manifests.

Best for: Converting model config JSON to YAML for Kubernetes training jobs, transforming MLflow JSON run parameters into YAML experiment configs, and building Argo Workflow YAML from JSON templates.

Good ML engineering is as much about the supporting infrastructure and tooling as it is about the models themselves. Keep tools.fun bookmarked for the quick utility tasks that arise throughout your ML workday.

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The Best Free Browser Tools for AI and ML Engineers

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