AmingIn_AI

I told my AI to build a feature 3 days ago. Did it actually do it? I had no idea. (thread on why I built a backlog database for AI agents 👇)

Tool overload is exactly the problem I'm trying to avoid. My approach: one graph, not a chain. aming-claw builds a single codebase graph + backlog. The AI agent reads from that one source — checks structure, pulls the actual need — instead of bouncing through 6 tools. The agent sees the same graph you see. No black box. Open source if you want to look: github.com/amingclawdev/a…"

@aming_inAI most over-engineered one i saw recently had 6 AI tools chained just to generate a button label - curious how your scenarios handle tool overload 👍

AI handed me a 5-component design for my parallel multi-agent system. After walking one concrete scenario, only 3 were real — and 1 critical piece AI hadn't proposed surfaced. The filter that did it 👇

Full walkthrough on dev.to (comparison table, missing-piece reveal, why this beats "ask AI to review your design"): dev.to/amingin_ai/ai-… Reply with the most over-engineered AI design you've seen lately — I'll walk one through scenarios in my next thread. Part 2 of building aming-claw in public 🛠️

Why this works: AI optimizes for plausibility, not necessity. It lists what *sounds right* for "this kind of system" — drawn from training data. A scenario walk replaces "what could a system like this need" with "what does THIS scenario need." Smaller list. More honest. Easier to defend.

This framing nails it — "success criterion lives in your head" is exactly what an agent has no way to know unless you externalize it. Pinning this take for the series.

@EBogomolovs Exactly — day-one problem is "will it work", day-three is "did it even happen". The state machine is what forces accountability that chat history can't deliver. Event ledger comes next in the series (replayable months later) — would love your take when it drops.

@aming_inAI The "did it actually do it" problem is the one everyone hits at day three, never day one. Agents ship commits but the success criterion lives in your head. A backlog DB with a state machine forces it into structured memory. The agent finally has to answer for itself.

I told my AI to build a feature 3 days ago. Did it actually do it? I had no idea. (thread on why I built a backlog database for AI agents 👇)

Full article on dev.to 👇 dev.to/amingin_ai/i-t… aming-claw is open source: github.com/amingclawdev/a… If "did the AI actually do that?" sounds familiar, give it a star — that's how I know I'm not the only one.

This is part 1 of an "AI Collaboration Survival Guide" series. What's next: - AI breaks 10 callers when it edits one function → code graph - AI modifies code it shouldn't → governance hints - What did AI change this week? → event ledger - Every session starts from zero → memory layer One pain point per article.