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.
mysql> use your_local_db;
mysql> source your_db_backup_dump.sql;
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git branch -D <branch name>git checkout maingit checkout -b <branch name>git commit -m "Your commit message"git push origin <branch name> -f-f flag in the ‘git push’ command, you force-push the changes to the branch on the remote repository. This erases the previous commits from the branch, giving you a fresh start with your new commit.”
could not connect to server: Operation timed out
\tIs the server running on host "10.220.0.15" and accepting
\tTCP/IP connections on port 5432?10.220.0.15 . In order to see if it is connected or not we need to check the pg_hba.conf file.postgresql directory . In order to get the path we can do ps aux | grep postgres...
user 46455 0.0 0.0 409138608 1248 ?? Ss 13Sep23 0:00.82 postgres: checkpointer
user 46372 0.0 0.0 408998640 2192 ?? S 13Sep23 0:16.79 /opt/homebrew/opt/postgresql@13/bin/postgres -D /opt/homebrew/var/postgresql@13-D flag. So in my case it my path is /opt/homebrew/var/postgresql@13ls and we can find the file called pg_hba.conf . Then we need to run nano pg_hba.confCtrl + O then click Enter and Ctrl +Xhost YOUR_DB_NAME YOUR_USER_NAME 10.220.0.15/32 md5
Gemfile.lock says BUNDLED WITH 2.2.4 , but while doing bundle -v or starting your rails server or rails console you get an error saying"You must use Bundler 2 or greater with this lockfile." . We can resolve that by running gem update --system .
Tab.Group is the parent its wraps all this present inside it.Tab.list in tab list we have defined our tab and we have selected as active tab.Tab.Panels so tabs panels is the parent of all the Tab.Tab.Panel so tab panel is defined under the Tab.Panels Tab.list then we need to paced all 3 Tab.Panel in the same like we define tabs in Tab.List<Tab.Group>
<Tab.List>
<Tab
className={({ selected })} => // apply active class
key="must_pass_unique_key_1"
>
Tab Name 1
</Tab>
</Tab.List>
</Tab.Group>export const getTheResults = async (params) => {
const response = await axios.get(
/api/v1/example.json?${stringify(params)}
);
return response.data;
};param is an object mentioned below , and we are stringifying it before sending the request to backend.stringify from qs npm package.key1=value1&key2=value2&key_3%5B0%5D=value3&key_3%5B1%5D=value4 i.e key1=value1&key2=value2&key_3[0]=value3&key_3[1]=value4key1=value1&key2=value2&key_3[]=value3&key_3[]=value4arrayFormat: "brackets"eg: export const getTheResults = async (params) => {
const response = await axios.get(
/api/v1/example.json?${stringify(params, {arrayFormat: "brackets"})}
);
return response.data;
};
Eg:
Request: /api/v1/calendar.json?date_from=&date_to=&hotel_id[]=1&hotel_id[]=2
def permit_params
params.permit(:date_from, :date_to, hotel_id: []) # hotel_id: [] is important
end\\pset pager off. This command is useful for seeing data from tables with huge jsonb dataundefined value to follow the JSON specification. According to the JSON standard, properties with undefined values are not allowed in JSON objects, these are the allowed JSON values: object, array, number, string, true, false, or null.null or defaults for missing data. On the frontend, ensure data is present before accessing it.// let's say we want to show logos of our client
.logo {
background-image: url('client-logos-sprite.png');
background-repeat: no-repeat;
}
.client1 {
background-position: 0 0;
}
.client2 {
background-position: -100px 0;
}// implementation of login test
const apiEndpointURL = "https://example.com/api/endpoint";
const apiResponse = await page.evaluate(async (url) => {
const response = await fetch(url);
return await response.json()
}, apiEndpointURL)
console.log(apiResponse)class String
def reverse
'Reverse op blocked'
end
end
'Hello'.reverse
>> 'Reverse op blocked'Showing page 22 of 42
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.