At Codemancers, we believe every day is an opportunity to grow. This section is where our team shares bite-sized discoveries, technical breakthroughs and fascinating nuggets of wisdom we've stumbled upon in our work.
chat:write scope in bot token scopes, so that our bot can send messages in the channel when it is mentioned.Sign in with Slack we need to request the OpenID scopes—openid, email, and profile.email & profile when we are using the slack openid.connect.userInfo api method.metadata provides additional information about the code. It begins with the character @ followed by either a reference to a compile-time constant or a call to a constant constructor.@Deprecated: This is used to indicate that a feature/method/class is deprecated and should not be used because it will be removed in future versions.@deprecated: This is similar to @Deprecated, with the difference being that a message can be passed with @Deprecated.@override: This is used to indicate that a method is intended to override a method from a superclass.@pragma: This is used to provide additional information to the Dart compiler.groupBy method introduced in ES12 can be used to group elements based on the values returned by the specified callback function. This can be useful when we want to categorize items into distinct groups.const transactions = [
{ account: 'savings', amount: 12, date: '2023-01-01' },
{ account: 'checking', amount: 200, date: '2023-01-01' },
{ account: 'savings', amount: 500, date: '2023-02-01' },
{ account: 'checking', amount: 300, date: '2023-02-01' },
];groupBy to group these transactions by their account type:const groupedTransactions = Object.groupBy(transactions, ({ account }) => account);{
savings: [
{ account: 'savings', amount: 12, date: '2023-01-01' },
{ account: 'savings', amount: 500, date: '2023-02-01' }
],
checking: [
{ account: 'checking', amount: 200, date: '2023-01-01' },
{ account: 'checking', amount: 300, date: '2023-02-01' }
]
}git log --branches --not --remotesShowing page 36 of 83
AI is everywhere but it's unclear which investments will actually move your metrics and which are expensive experiments.
Most AI projects fail at the data layer. Pipelines, quality, access all need work before LLMs can deliver value.
Your engineers are shipping product. They don't have capacity to also become AI specialists with production-grade experience.
Aging, undocumented codebases make AI integration slow, risky, and expensive. They need to move first.