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General AI agent executing tasks and integrating thousands of apps; very high traffic.
Tools, model specs and courses for LLM engineers-VRAM calculator, benchmarks and model directory-with free and paid tiers.
Enterprise AI observability and security platform to monitor, evaluate, and govern agentic and ML systems with guardrails.
Open-source Python framework, born at Netflix, for building, scaling, and deploying real-world ML, AI, and data science workflows.
Weights & Biases is a widely used MLOps platform for experiment tracking, model management and evaluating AI applications.
No public pricing
No public pricing
No public pricing
- ✦Create presentations, websites, reports, slides, documents, podcasts images & videos
- ✦Conduct deep research on any topic
- ✦Command a virtual computer to browse the web
- ✦Connect and automate with over 2700+ apps
- ✦VRAM/GPU-memory calculator for LLMs
- ✦LLM performance rankings and benchmarks
- ✦Model directory and comparison
- ✦AI/ML courses and learning roadmap
- ✦Calculator API and exportable cost reports
- ✦Engineering blog and guides
- ✦End-to-end agentic and ML observability
- ✦Real-time guardrails (hallucination, PII, jailbreak)
- ✦Continuous evaluations and custom judges
- ✦Root-cause analysis and decision lineage
- ✦AI governance, risk, and compliance controls
- ✦Flexible SaaS, VPC, or on-prem deployment
- ✦Plain-Python workflow orchestration
- ✦Automatic versioning and experiment tracking
- ✦Scale-out compute with GPUs and parallel instances
- ✦One-command deployment to production
- ✦Runs on AWS, Azure, GCP, or Kubernetes
- ✦Event-based triggering of workflows
- ✦Experiment tracking and visualization for ML training runs
- ✦Model and artifact versioning and management
- ✦Hyperparameter optimization tooling
- ✦Collaborative dashboards and reports for ML teams
- ✦LLM application tracing and evaluation tooling
- →Generate a high-conversion landing page for a startup's ebook.
- →Build a web-based image cropper tool with live preview.
- →Create a beginner-friendly PDF cheatsheet for Python.
- →Develop a one-week travel itinerary for a destination like Vietnam.
- →Analyze social media mentions and sentiment for a specific entity.
- →Organize hiring task submissions from emails into a Google Sheets spreadsheet.
- →Clone a pixel-perfect website for design accuracy and responsive web experience.
- →Make a podcast on the rise of SpaceX, make it conversational and include fun facts.
- →Estimating GPU memory before training or inference
- →Comparing and selecting LLMs
- →Learning ML and LLM engineering
- →Modeling production deployment costs
- →Monitoring production AI agents
- →Enforcing safety guardrails on LLM apps
- →Evaluating and debugging model behavior
- →Governance and compliance for enterprise AI
- →Developing and debugging ML pipelines locally
- →Scaling model training to cloud GPUs
- →Deploying experiments to production unchanged
- →Building reactive, event-driven data systems
- →ML engineers tracking and comparing training experiments
- →Research teams versioning datasets and model checkpoints
- →Teams building and evaluating LLM-powered applications
- →Organizations collaborating on machine learning projects