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Article summary:

1. 8 AI tools are presented that can help reduce workloads for teams.

2. These tools include Lex.Page, Runway.ml, DiffusionBee.com, Lexica.art, Metaphor.systems, Soundraw.io, Clipdrop.co and Talk To Books (Google).

3. Bonus tool: code.store which provides advanced technologies such as Low Code, No Code, Blockchain, AI and Cloud native functions.

Article analysis:

The article is generally reliable in terms of providing information about 8 AI tools that can help reduce workloads for teams; however there are some potential biases to consider when reading the article. Firstly, the article does not provide any evidence or sources to back up its claims about the effectiveness of these tools in reducing workloads for teams; this could be seen as a form of promotional content as it is encouraging readers to use these tools without providing any proof of their efficacy or reliability in doing so. Additionally, the article does not explore any counterarguments or risks associated with using these tools; this could be seen as a form of one-sided reporting as it only presents the positive aspects of using these tools without considering any potential drawbacks or risks associated with them. Furthermore, the article mentions a bonus tool – code store – which provides advanced technologies such as Low Code, No Code etc., but does not provide any further information about how this tool works or what it offers; this could be seen as partiality as it is promoting one particular product without providing any further details about it or exploring other similar products that may offer similar services at a lower cost or with better features/functionality than code store does. Finally, the article does not present both sides equally; while it mentions 8 AI tools that can help reduce workloads for teams, it fails to mention other possible solutions that may be more effective in doing so than these 8 AI tools are – thus presenting an incomplete picture of all available options for reducing workloads for teams and potentially leading readers to make decisions based on incomplete information provided by the article itself.