AI BriefingAI is Glue
Key point
LLMs act as a new kind of 'glue,' bridging the gaps between different interfaces and tools.
Details
In the past, Perl, Python, and Shell scripts served as glue code, connecting disparate systems. Now, LLMs have taken over that role, powerfully binding together mismatched interfaces such as human language, CLI, API, Webhook, and databases.
The reason LLM-based glue is effective is that it can handle the irregular data formats each interface has. The strength of LLM shines in the process of reading a Webhook's JSON payload to extract the necessary fields, converting them into the format required by a CLI, or parsing result values to pass on to the next step.
A real-world workflow example through the partnership with Snyk is as follows:
- Snyk sends a vulnerability alert via Webhook
- LLM extracts severity and file path
- LLM generates a patch based on the code context
- Tests are run via CLI and a PR is created with GitHub CLI
Rather than introducing new tools, the key is to optimize workflows by connecting the CLI, API, test runners, and other tools you already trust with LLM.
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