Koyfin VS Stack-ft

• Advanced charting
• Global financials data for 100k+ stocks
• Global analyst estimates & price targets
• Global macroeconomic data
• Global equity screener
• ETF & mutual fund screener
• ETF & Mutual fund data
• 100k+ Company snapshots
• Transcripts, filings, press releases
• Advanced transcript search
• Commodity futures
• Indices
• Bond yields & spreads
• FX data
• Market & macro dashboards
• Custom dashboards
• Dashboard data download
• Custom financial analysis templates
• Portfolio tools
• Model portfolios
• Watchlists
• Custom formulas
• Shareable watchlists
• Live premium news
• Custom news feeds
• Economic & earnings calendars
• Web and mobile platform

• Simplified Fine-Tuning
  Stack-ft provides a streamlined interface for fine-tuning large language models, making the process more accessible to developers and teams who may not have deep expertise in machine learning infrastructure.
• No-Code/Low-Code Approach
  The platform offers a user-friendly, no-code or low-code experience that allows users to fine-tune models without needing to write extensive training scripts or manage complex ML pipelines manually.
• Fast Iteration
  Stack-ft enables quicker experimentation and iteration on fine-tuned models, allowing users to test different datasets, hyperparameters, and configurations with relatively fast turnaround times.
• Cost Efficiency
  By abstracting away infrastructure management and providing optimized training workflows, Stack-ft can help reduce the costs associated with fine-tuning compared to setting up and managing your own GPU infrastructure from scratch.
• Integration with Popular Models
  The platform supports fine-tuning of popular open-source and foundation models, giving users flexibility to work with well-known architectures like LLaMA and other widely adopted LLMs.

Possible disadvantages

• Limited Customization
  As a managed platform, Stack-ft may offer less granular control over training configurations, hyperparameters, and infrastructure compared to running your own fine-tuning pipeline with tools like Hugging Face Transformers or Axolotl directly.
• Vendor Lock-In
  Relying on Stack-ft for fine-tuning workflows can create dependency on their platform, making it harder to migrate to other solutions or self-hosted infrastructure if needs change or the service becomes unavailable.
• Relatively New Platform
  Stack-ft is a relatively newer entrant in the fine-tuning space, which means it may have a smaller community, fewer tutorials, and less battle-tested reliability compared to more established tools and platforms.
• Pricing Transparency Concerns
  Depending on usage patterns, costs can be unclear or may scale in ways that are not immediately obvious, making it harder for teams to accurately budget for fine-tuning projects at scale.
• Limited Documentation and Community Support
  As a newer and more niche platform, Stack-ft may have less comprehensive documentation, fewer community-contributed resources, and slower support response times compared to larger, more established ML platforms.

Koyfin Demo, 2023

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• Resources for equity research and stock reports

  Thanks for the link, that was useful. I found there about koyfin.com which is for free and gives the summary info that I was after. Really spot on. Source: about 4 years ago

• What is a recent estimate of Apph's short position?

  My stock dashboard (koyfin.com) says 15.33% short interest. Which is correct? Also I think that number was around 25% recently so I guess that's come down? Source: almost 5 years ago

• Recommendations for stock investment resources - Xetra

  Personally: For individual stocks: Koyfin.com (good for visualizing US ETFs as well but you won't have access to those anyway). Source: about 5 years ago

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Tracking Stack-ft since Feb 2023.