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Data Analytics__data Labeling Training Data__managed Annotation ServicesSoftware Development__coding Assistants Copilots__code Chat Q AData Analytics__data Labeling Training DataSoftware Development__code Generation__full Stack App GenerationSoftware Development__coding Assistants CopilotsSoftware Development__code GenerationData AnalyticsSoftware Development
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Sherpa Coder
✓ verifiedFree
VS Code extension letting developers chat with their own custom OpenAI assistants without leaving the editor.
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Super Annotate
✓ verifiedPaid
Enterprise data-annotation and evaluation platform pairing a labeling tool with a managed expert annotator workforce.
👁 406K/mo
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Angular.dev
✓ verifiedFree
Google's open-source TypeScript framework for building scalable web apps, featuring signals, reactivity and first-party tooling.
👁 1.1M/mo
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Codeflying
✓ verifiedFreemium
Vibe-coding builder creating full-stack apps by chatting with AI.
👁 118K/mo
Pricing
No public pricing
No public pricing
No public pricing
Free: 0$
Basic: 25$
Advanced: 40$
Premium: 200$
Core features
- ✦in-editor chat with OpenAI assistants
- ✦workspace source-code context sharing
- ✦support for custom, user-defined assistants
- ✦secure management of the user's OpenAI account
- ✦Customizable multimodal annotation editors for image, video, text and audio
- ✦Support for RLHF preference data, SFT datasets, RAG and agent evaluation workflows
- ✦Managed expert annotator workforce option
- ✦Data curation, exploration and analytics tools
- ✦Team and project management with SSO on higher tiers
- ✦Integrations with AWS, GCP, Databricks, Snowflake and others
- ✦Signals-based fine-grained reactivity
- ✦Built-in control flow and deferrable views
- ✦Server-side rendering and hydration
- ✦First-party routing, forms and dependency injection
- ✦AI-forward tooling and MCP resources
- ✦In-browser tutorials and playground
- ✦CodeFlying enables full-stack app creation via chat in minutes
Use cases
- →getting coding help without switching out of VS Code
- →using a personalized OpenAI assistant tuned to a project
- →quick in-editor Q&A while writing code
- →Building large-scale labeled datasets to train computer vision or NLP models
- →Running human evaluation and RLHF pipelines for LLM fine-tuning
- →Auditing and scoring AI agent decisions with human review
- →Building scalable single-page apps
- →Enterprise web application development
- →Performance-critical front ends
- →Learning modern web development
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