Client_id and client_secret travel as HTTP request headers, which means they're visible in any intermediary that can inspect headers in transit. Store them in environment variables or a secrets manager (AWS Secrets Manager, HashiCorp Vault, or a CI/CD-native secrets store). They should never appear in source code, version control, or log output. Your application code should read from os.environ["CLIENT_ID"] rather... - Source: dev.to / about 1 month ago

Big thanks to the AWS docs team, the Kamal maintainers, and Hetzner for keeping hosting affordable. Hope this saves you the same headaches I ran into. Now back to building. - Source: dev.to / about 1 month ago

Every modern web app has secrets that need to be shared securely with it. You web application may need to have a environment variable like an API key. That key should be stored in AWS secrets manager, it is a great use case for us to learn about how to give access to instance to the secret. - Source: dev.to / 2 months ago

A well-documented example is Flightcontrol, which deploys application workloads to customers' own AWS accounts using Amazon ECS with either Fargate or EC2 launch types rather than Kubernetes. Fargate is the default path (serverless compute, no node management), while ECS with EC2 is available for teams that need GPU support, Reserved Instance pricing, or custom instance types. All builds run in the customer's AWS... - Source: dev.to / 4 months ago

⚠️ Don't use .env files in production Plaintext .env files on disk have no rotation, no audit trail, and no access control. For production, use AWS Secrets Manager or Systems Manager Parameter Store (SecureString) to manage application secrets and pull them at runtime. The .env approach shown here is suitable for development and tutorials only. You could also set up a blue/green deployment — spin up the new... - Source: dev.to / 5 months ago

The web service supports generating embeddings from OpenAI and Ollama AI models. It also provides a fallback for users without access to AI models running on a remote server through PostgresML. - Source: dev.to / over 1 year ago

That's outside of the database, though. This is more like what I had in mind -- I just found it: https://postgresml.org/. - Source: Hacker News / about 2 years ago

Some excellent tools were created to represent these tasks "naturally" in SQL and even let most of the computation happen inside the database. PostgresML is a great example. It's built above PostgreSQL and provides a set of functions that allow you to train and use machine learning models with SQL. Here's how you can train a classification model for the classic handwritten digit recognition problem:. - Source: dev.to / over 2 years ago

PostgresML | You know Postgres. Now you know machine learning – PostgresML. - Source: dev.to / over 2 years ago

You can swap in almost any open-source model on Huggingface. HuggingFaceH4/zephyr-7b-beta, Gryphe/MythoMax-L2-13b, teknium/OpenHermes-2.5-Mistral-7B and more.If you haven't seen us here before, we're PostgresML, an open-source MLOps platform built on Postgres. We bring ML to the database rather than the other way around. Source: over 2 years ago