Supametas AI
Automation ToolsScrapes and converts messy web data into structured JSON or Markdown ready for an LLM's RAG pipeline.
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Most people arrive knowing what they need done, not which category it lives in. 65 jobs grouped into 12 kinds of work. Pick the kind on the left, or search.
Tick Compare on any two to four cards to put them side by side.
Scrapes and converts messy web data into structured JSON or Markdown ready for an LLM's RAG pipeline.
A Python tool for searching, cleaning, and quantifying datasets before training an LLM.
Chats with locally running LLMs through Ollama and lets you RAG the page you're viewing.
An open-source Python framework for building and deploying real-world ML workflows at scale.
Steps through a dataset record by record and links AI prompts to specific columns as it goes.
Focus on ML, not infrastructure.
Uploads your chatbot conversations automatically to build a shared open-source training dataset.
LLM Pricing Comparison tool lacks comprehensive coverage.
A dataset inspection tool that surfaces PII, duplicates, and bias before training an LLM.
KNIME simplifies data workflows but lacks built-in machine learning.
Filters large datasets with plain-language queries and surfaces the top options automatically.
Scores and prioritizes sales leads with machine learning, then syncs the results into major CRMs.
Adds LLM-powered autocomplete to Overleaf, using a server you host yourself.
Turn enterprise documents into AI-ready knowledge. Build repeatable development workflows.
On-demand context is available, but the tool requires manual selection of data points for each request.
An Azure-focused consultancy delivering data, AI and Copilot integration projects for enterprises.
Open-source, self-hosted AI gateway.
AI-driven demand planning that forecasts market shifts and lines up inventory with changing demand.
Counts tokens to stay within OpenAI model limits.
Not ideal for large datasets, lacks advanced analytics.
Speeds up financial crime investigations by up to 70% with a centralized, AI-assisted platform.
Build and verify formulas, run variance commentary — but FormulAI does not generate sample datasets.
GoDiary detects your workouts without user input, combining GPS and machine learning for autonomous tracking. Great for runners and cyclists.
Deploy AI models at sub-second cold starts, run them on any GPU. Perfect for rapid inference and training.
Search over 1,000 AI and data science jobs worldwide. Perfect for job seekers and recruiters alike.
Unified access to LLMs via a single API.
Huntr is a bug bounty platform for AI/ML.
Suitable for researchers needing visual connections, unsuitable for keyword searchers.
AI platform that tunes in-app purchase pricing and ad timing to grow app and game revenue.
AI-powered Jupyter Notebook for database and Excel automations.
JIT.codes lets you experiment with AI models and build projects fast. It’s great for rapid prototyping.
Expert teams turn raw data into quality datasets.
Open-source AI gateway, tracks and caps LLM spend.
Ouro brings together data and ideas, but relies on AI agents for some tasks.
Free daily AI football predictions.
Optimize LLM spend and route requests to the right model, even air-gapped.
BigML simplifies machine learning, offering a comprehensive platform for building models. It falls short on extensive customization.
Linq Alpha offers intelligent search for hedge funds.
Online tool to count tokens in OpenAI prompts.
Gives feedback on how LLM applications affect wellbeing, helping keep AI labs accountable.
A CRM that automates data entry and adds an AI co-pilot named Duke for daily tasks.
Route LLM traffic through one gateway, observe calls in traces. But lacks built-in prompt management.
Gooey.AI helps build multilingual AI solutions, but lacks integration with custom datasets.
Predicts win odds for each map in a FACEIT veto using a model trained on 30 million matches.
Smarter Testing, Faster Deployments
Trace and evaluate LLMs with OpenTelemetry, manage prompts, and compare models.
Calculates win odds for FACEIT matches and can auto-veto the weakest map for you.
LM Studio lets you run LLMs offline on your laptop. Supports various models.
Models, training data and machine learning.
Data scientists and ML engineers prepare data, train and evaluate models, run experiments and move working models into production. The tools here cover that pipeline: model hubs and libraries (HuggingFace), no-code and AutoML platforms (BigML, Lobe, Abacus.AI), GPU compute (Vast.ai, Lightning AI, Modal) and local LLM runners like LM Studio. Compare reproducibility and how easily you can export a trained model.
AutoML platforms such as BigML and Abacus.AI choose algorithms and tune settings for you, which is fast for standard tabular problems. Writing models yourself gives more control over features, architecture and evaluation. Many teams use AutoML for a baseline, then build custom models when it falls short.
Apps such as LM Studio download open models and run them on your own machine, so data stays local. You need enough memory for the model size you choose, and smaller quantised models trade some quality for speed. This suits private data and offline experiments.
Marketplaces and platforms such as Vast.ai, Lightning AI and Modal rent GPU time by usage instead of hardware ownership. Compare availability, startup time, storage and how they handle interrupted jobs. Keep checkpoints saved, since cheaper capacity can be taken away mid-run.