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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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Coder
✓ verifiedFreemium
Self-hosted cloud development environments and AI-agent governance, letting enterprises run coding agents on their own infrastructure.
👁 208K/mo♥ 41
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Watsonx.data
✓ verifiedFree trial
IBM's open, hybrid data lakehouse that connects, governs and optimizes enterprise data to make it AI-ready across clouds and on-premises.
Pricing
No public pricing
Historical Data Pack: $49.9
Base Plan: $14.9/month
Advanced Plan: $24.9/month
Enterprise Plan: $34.9/month
No public pricing
Community: $0 (open-source, self-hosted, unlimited workspaces)
Free trial available
No public pricing
Free trial available
Core features
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
- ✦Commits and Pull Requests Dashboard
- ✦Advanced Developer Skills Analysis
- ✦Strategic Investment Balance Monitoring
- ✦Collaborative Developers Map
- ✦Benchmarking Comparison with Other Teams
- ✦Smart Notifications
- ✦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
- ✦Self-hosted workspaces with desktop and web IDEs
- ✦Coder Agents run coding agents on isolated infrastructure
- ✦AI Governance gateway for LLM usage control
- ✦SSO (OpenID Connect) and role/group sync
- ✦Audit logging and resource quotas
- ✦Multi-organization access controls
- ✦High availability and workspace proxies
- ✦Open hybrid data lakehouse
- ✦Connects data across clouds and on-prem
- ✦Governance, lineage and access controls
- ✦Business-context enrichment
- ✦AI-ready data for analytics and models
Use cases
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Visualize historical graphs of code evolution
- →Assess development team performance using RSI and EMA
- →Understand developer skills and identify areas for improvement
- →Categorize commits by type (fixes, refactoring, etc.) to analyze investment balance
- →Identify individual and collective contributors within the team
- →Compare team performance with industry benchmarks
- →Receive weekly and monthly reports with AI-extracted insights
- →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
- →Standardize developer environments
- →Run AI coding agents securely on-prem
- →Enforce governance and compliance
- →Cut VDI costs
- →Speed up developer onboarding
- →Unifying fragmented enterprise data
- →Governing data for AI workloads
- →Moving AI pilots to production
- →Powering analytics with trusted data
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