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The JWT signature verification failed. Check the signing key and the algorithm.
Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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🧠 Generative AI Tools & Platforms (2025)

Author: Tarek Masryo Β· Kaggle
License: CC BY 4.0 (Attribution)

A clean, structured catalog of 113 Generative AI tools and platforms released up to 2025.

Each row represents one tool/platform and includes:

  • Vendor/maintainer
  • Canonical category and primary modality
  • API availability and status
  • Open-source indicator
  • Release year + derived timeline features
  • Modality flags and breadth count

Designed for ecosystem mapping, benchmarking, and analytics workflows.


πŸ“¦ What’s inside

Canonical file: data/genai_tools_platforms_2025.csv
Unit of analysis: 1 row = 1 tool/platform
Rows: 113
Columns: 22

Column groups

  • Core info: tool_name, company, website, source_domain
  • Taxonomy: category_canonical, modality_canonical
  • Access: open_source, api_available, api_status
  • Timeline: release_year, years_since_release
  • Modality flags: mod_text, mod_image, mod_video, mod_audio, mod_code, mod_design, mod_infra, mod_productivity, mod_safety, mod_multimodal
  • Derived: modality_count (sum of supported modality flags)

πŸ“š Data dictionary

Column Type Description
tool_name string Tool/platform name (intended unique key)
company string Vendor/maintainer
website string Official homepage (full URL)
source_domain string Extracted domain for grouping (derived from website)
category_canonical string Standardized use-case family (e.g., LLMs, Image Gen, Video Gen, RAG)
modality_canonical string Primary capability type (e.g., text, image, video, audio, code, multimodal)
open_source int (0/1) 1 if marked open-source, else 0
api_available int (0/1) 1 if a public API is available, else 0
api_status string API status (api or unavailable)
release_year int First public release/launch year
years_since_release int Years since release (computed as 2025 - release_year)
mod_text int (0/1) Supports text modality
mod_image int (0/1) Supports image modality
mod_video int (0/1) Supports video modality
mod_audio int (0/1) Supports audio modality
mod_code int (0/1) Supports code modality
mod_design int (0/1) Supports design modality
mod_infra int (0/1) Supports infra/dev tooling modality
mod_productivity int (0/1) Supports productivity modality
mod_safety int (0/1) Supports safety/moderation modality
mod_multimodal int (0/1) Marked as multimodal (as provided in the dataset)
modality_count int Sum of supported modality flags

πŸ› οΈ How to use

Load with Datasets (recommended)

from datasets import load_dataset, get_dataset_config_names

repo_id = "tarekmasryo/genai-tools-platforms-data"

print("Configs:", get_dataset_config_names(repo_id))

ds = load_dataset(repo_id)  # loads the default config
df = ds["train"].to_pandas()

print(df.shape)
print(df.head(3))

Quick checks

# API coverage rate
api_rate = df["api_available"].mean()
print("API availability:", round(float(api_rate), 2))

# Category distribution
print(df["category_canonical"].value_counts().head(10))

# Modality breadth
print(df["modality_count"].describe())

🧭 Suggested use cases

  • Ecosystem maps and dashboards for the GenAI landscape
  • Benchmark API and open-source coverage across categories
  • Trend views by release year and timeline analytics
  • Cluster tools by capability breadth and modality mix
  • Simple recommendation baselines (rule-based or ML-based)

πŸ“ Notes

  • This is a curated catalog compiled from public sources (official websites, docs, product pages).
  • Coverage is best-effort and may not be exhaustive.
  • Fields like open_source and api_available reflect publicly advertised status at collection time.

🧾 Citation

Tarek Masryo. (2025). Generative AI Tools & Platforms (2025).


πŸ“œ License

CC BY 4.0 (Attribution) β€” free to share and adapt with attribution.

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