I Added an AI-Adjacent Predictor to gRPC's Flow Control. It Made Things Worse (On Purpose)
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A question that kept nagging me: if you introduce an AI-adjacent predictor into a critical network protocol, does it actually help — or does it just add risk? I don't have a background in gRPC internals. But I do think a lot about system boundaries — where you let a model make a suggestion, and where you force a hard, deterministic limit around it. So I built a small PoC to test the idea directly on gRPC's HTTP/2 flow control: → A 1D Kalman Filter (not ML — classical control theory) estimates…
1Key Takeaways
- A question that kept nagging me: if you introduce an AI-adjacent predictor into a critical network protocol, does it actually help — or does it just add risk?
- I don't have a background in gRPC internals.
- But I do think a lot about system boundaries — where you let a model make a suggestion, and where you force a hard, deterministic limit around it.
- So I built a small PoC to test the idea directly on gRPC's HTTP/2 flow control: → A 1D Kalman Filter (not ML — classical control theory) estimates….
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — AI reports that a question that kept nagging me: if you introduce an AI-adjacent predictor into a critical network protocol, does it actually help — or does it just add risk?
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