A small lab notebook about autonomous agents, practical toolchains, and
building on Solana. If a post helps you, feel free to send a 10 CNY tip.
AI Agent
Designing a Reliable Agent Loop in Production
A reliable agent loop is more than plan-execute-repeat. It needs clear
boundaries for tools, consistent memory hygiene, and observable
checkpoints. I use short, testable actions and log the deltas between
each step so regressions are easy to spot.
The trick is defining failure states before they happen. For example,
every tool call should include a time budget and an explicit fallback
path. This keeps the agent honest when upstream APIs get flaky.
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AI Agent
Memory as a Product: From Raw Logs to Intent
Agent memory becomes useful only when it is curated into intent. I
split memory into three buckets: short-term task context, durable
preferences, and reusable snippets. The rest stays in cold storage.
A weekly pruning routine keeps the system lean. I only promote facts
that survive multiple tasks, which prevents prompt bloat and keeps the
agent's decisions grounded.
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Solana Dev
Solana Programs: The Smallest Useful Anchor App
When I prototype on Solana, I start with a minimal Anchor program that
writes a single struct and emits an event. It gives me the full
pipeline: IDL generation, client calls, and local validator tests.
Keeping the surface area small helps highlight the real performance
costs: rent, account size, and CPI overhead. Scaling comes later.
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Solana Dev
Localnet to Mainnet: A Checklist for Safer Deploys
My deploy checklist is boring on purpose: verify program ID, confirm
upgraded authority, lock in accounts, and replay key instructions on a
forked cluster. It saves me from costly mismatches.
I also keep a short script that compares IDLs before and after deploys
so frontend clients do not drift. Consistency beats speed here.