The Blog Post Release Automation

[Lomanu4]

The Blog Post Release Automation


I made this mistake this week of believing I wanted to automate, using an LLM of course, some parts of the painful podcast release cycle.

Weekly I record episodes of the podcast

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with my awesome co-host. Of course all the episodes are available on our podcast website as well other streaming platforms.

But! Since we're a technical podcast, we decided to make our infrastructure open source (On

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unfortunately), but to go further it also uses

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to publish the

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. There is of course the nasty bit of actually recording the episodes, editing the episodes, and then downloading and formatting them to make them nice.

After that is all done though, it is time to create the episode page and most importantly the cornerstone of ever podcast, an awesome episode image.

So let's get down to it.

Execution


Interestingly enough, the Nova Lite model failed completely attempting to request it to actually build the command I needed to execute the model itself. Not very self-aware, you might say.

However using other models I was able to coax out the following recommendation:

With the episode saved in the transcript.txt file, and the instructions we want to run in the instructions.txt


#!/usr/bin/env node

import { BedrockRuntimeClient, InvokeModelCommand } from '@aws-sdk/client-bedrock-runtime';
import fs from 'fs/promises';
import path from 'path';
import { fileURLToPath } from 'url';

// Resolve file paths
const __dirname = path.dirname(fileURLToPath(import.meta.url));
const instructionsPath = path.resolve(__dirname, 'instructions.txt');
const transcriptPath = path.resolve(__dirname, 'transcript.txt');

// Set up Bedrock client
const client = new BedrockRuntimeClient({
region: 'eu-west-1'
});

(async () => {
try {
// Read both input files
const [instructions, transcript] = await Promise.all([
fs.readFile(instructionsPath, 'utf-8'),
fs.readFile(transcriptPath, 'utf-8')
]);

// Build prompt
const content = `Instructions:\n${instructions}\n\nTranscript:\n---\n${transcript}\n---`;

const payload = {
messages: [{
role: 'user',
content
}],
// Max Token Count and other parameters:

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temperature: 0.7,
top_p: 0.9,
max_tokens: 4096
};

// Invoke the model
const command = new InvokeModelCommand({
modelId: 'amazon.nova-lite-v1:0',
contentType: 'application/json',
accept: 'application/json',
body: JSON.stringify(payload)
});

const response = await client.send(command);

// Decode and print response
const responseBody = await response.body.transformToString();
console.log(' Model response:\n');
console.log(responseBody.output.message.context);
} catch (err) {
console.error(' Failed to invoke model:', err);
process.exit(1);
}
})();

And that's it, we can take the output create a pull request and then release the episode.

Troubleshooting


Of course nothing works the first time, and for us the first issue is


Failed to invoke model: ValidationException: Invocation of model ID amazon.nova-lite-v1:0 with on-demand throughput isn’t supported. Retry your request with the ID or ARN of an inference profile that contains this mode.

Okay turns out there is some magic that it takes to run the Nova model in other regions, so instead of trying to get that to work, we'll switch to the region us-east-1.


Malformed input request: #: required key [messages] not found, please reformat your input and try again.

Hmmm, weird, it turns out there have been some serious changes to the API, in which the documentation is not really up to date. So figuring out the correct parameters is actually a bit of a problem.

But setting the payload as just:


const payload = {
messages: [{
role: 'user',
content: [{
// type: "text",
text: inputText
}]
}]
// Max Token Count and other parameters:

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// temperature: 0.7
// top_p: 0.9,
// max_tokens: 4096
};

Solves most of this problem.

Success!?

The podcast image


The next step is run the generator a second time, but this time use the output from the first step as the input to generate an image relevant to the podcast.

There are a couple of changes that have to be made.

1. We don't need the transcript anymore since we already have a summary.
2. We need to pass an input image, we don't want some random picture we want something that is brand aware.
3. The output will be an image as well.

So instead we'll use the Nova Canvas model: amazon.nova-canvas-v1:0 with the parameters:


const payload = {
'textToImageParams':
{
'text': inputText
},
'taskType':'TEXT_IMAGE',
'imageGenerationConfig': {
'cfgScale': 8,'seed': 42,'quality':'standard','width': 1280,'height': 720,'numberOfImages': 3
}
};
The result


We'll I think pictures are way more expressive than words, so check out the latest episode here on

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to see how exactly well we did!

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