AI coding assistants, dev tools, and no-code/app builders.
111 tools · ranked by community votes and reviews
Rank is based on a combination of real, ongoing signals from the AIPick community — not a paid placement or a one-time snapshot.
Rankings update automatically every few hours as new votes and reviews come in, and can change at any time — nobody can pay to move up this list.
AI platform that designs improved proteins for biotech and industrial use.
Cradle uses generative AI to propose protein sequence variants with improved properties, helping biotech and biomanufacturing teams design better enzymes and proteins faster.
Builds foundation AI models that predict and design proteins.
EvolutionaryScale develops large foundation models trained on protein sequences and structures, letting researchers predict protein function and generate new protein designs.
AI research lab building nature-inspired methods like evolving model systems.
Sakana AI researches nature-inspired approaches to AI, including evolutionary methods for combining and improving models, and publishes tools and models from that research.
Experiment tracking and model monitoring platform for ML teams.
Comet ML tracks machine learning experiments, logs metrics and artifacts, and monitors deployed models, giving ML teams a single system of record across the model lifecycle.
AI-assisted data annotation and curation platform for computer vision.
Encord helps teams label, curate, and validate large image and video datasets for computer vision models, using AI to speed up annotation and flag data quality issues.
Open-source tool for visualizing, curating, and debugging computer vision datasets.
Voxel51 makes the open-source FiftyOne tool for visualizing computer vision datasets and model predictions, helping teams find data quality issues and curate better training sets.
Open-source framework for packaging and serving AI models in production.
BentoML lets teams package trained AI models into standardized, deployable services and serve them in production with autoscaling, without building custom serving infrastructure.
Extracts and cleans data from complex documents to feed AI/LLM pipelines.
Unstructured parses PDFs, Word docs, HTML, and other complex file types into clean, structured data ready to feed into LLM pipelines and retrieval-augmented generation systems.
Platform for creating, tokenizing, and co-owning AI agents on-chain.
Virtuals Protocol lets creators launch AI agents with on-chain ownership, allowing communities to co-own and earn from an agent's activity, blending AI agents with crypto tokenization.
Agentic IDE from AWS built around spec-driven AI coding.
Kiro is an AI-powered IDE from AWS that works from structured specs and requirements to generate and maintain code, aiming to keep agentic coding aligned with a defined plan rather than ad hoc prompts.