HANDS-ON DEMO: CODING WITH GLM 4.7 FOR AI-IN-THE-LOOP DEVELOPMENT
A community video shows using GLM 4.7 to write and iterate on code, highlighting a practical generate-run-fix loop and the importance of grounding the model wit...
A community video shows using GLM 4.7 to write and iterate on code, highlighting a practical generate-run-fix loop and the importance of grounding the model with project context. While there are no official release notes in the source, the workflow demonstrates how to use an LLM as a coding assistant for everyday tasks without heavy agent frameworks.
It shows a low-friction pattern to add LLMs to day-to-day coding without changing your stack.
Grounding prompts with repo and task context remains the difference between helpful and noisy outputs.
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terminal
Run a small bake-off where GLM 4.7 generates an API handler, unit tests, and a SQL query fix, then measure review diffs, runtime correctness, and edit distance to final code.
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terminal
Evaluate latency and context limits by prompting with real repo snippets (e.g., service configs, schema files) and verify reproducibility via fixed prompts and seeds.