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33 in. 33 out. That still was not enough.
A migration is only credible when three layers agree:
Transport — every record moves.
Fidelity — nothing is removed or silently changed.
Behavior — the system still recalls the right answers afterward.
Our latest public run mapped 33/33 records, skipped 0, removed 0, changed 0, and preserved 5/5 golden-question recall. The implementation also carries 27 focused tests.
Public evidence:
https://github.com/moorcheh-ai/memanto/pull/1813
The same principle applies to launches: collect evidence before buying more traffic. RELAUNCH DEPT.'s 48-hour launch recovery sprint:
https://contra.com/s/EA6ABABW-48-hour-launch-recovery-sprint
AI-assisted drafting and editing; all numerical claims were checked against the public PR, tests and demo.
#AIDeveloper #Python #GitHub #QA #ProductLaunch
github.com
feat(examples): add OKF portability audit by ceodaradigu · Pull Request #1813 · moorcheh-ai/memanto
Closes #1609 What problem does this solve? Agent-memory migrations need a reproducible way to prove that portable OKF content survived before the source is deleted. File renames, destination-assign…
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33 memories went in. 33 came back. The harder part was proving they still answered the same questions.
We ran a real GitHub issue + 31 comments through Memanto's OKF migration path. Result: 0 skipped, 0 removed, 0 changed—and 5/5 golden-question recall before and after.
74-second proof: https://youtu.be/E_r7tzmHtq0
Code, tests and one-command reproduction: https://github.com/moorcheh-ai/memanto/pull/1813
If you build agent-memory systems, what evidence would you require before deleting the source?
AI-assisted editing and synthetic narration; every result shown is from the reproducible command.
The Head of Marketing at Clerkenwell Health told me their Squarespace site looked like a small clinic. They were doing clinical trials for 50,000+ participants, licensed by the Home Office, but their web presence was costing them credibility before the first call.
Pharma sponsors and CROs Google them before every partnership conversation. The site was creating a bad first impression, making conversations harder than they needed to be.
What they said they needed was a 'website refresh'. What they actually needed was to stop actively losing trust the moment a prospect hit their homepage.
We moved them to Framer, preserved 60 blog articles with full SEO, and built n8n automation for inquiry routing. Zero downtime on launch.
It's not just about a site update. It's about what happens when your external image no longer matches your internal scale. What are the silent costs when that gap gets too wide?
That’s a wise approach. I’m currently exploring the combination of n8n and Framer as well, especially around AI-powered workflows and web design. It’s interesting to see how these tools are coming together.
Nobody sees it, but the loop runs every night:
"I always do this. I never follow through. Why do I even try?"
All day you looked fine on paper. On track. High performer. Meanwhile a quiet identity-level belief is deciding your next six months. Something like "I'm not consistent" or "I'm not a sales person" or "I always mess it up."
No habit tracker or motivational video touches that. They patch the surface. The belief keeps running the show.
That's the gap the Limiting Beliefs Workbook is built for. $10, one sitting, three finished tools:
Identity Statement Suite - old identity, new identity, and decision rules for when the two collide.
Evidence Engine Tracker - a 14-30 day proof plan so the new identity becomes your actual default.
Relapse Response Script - what to do when the old pattern flares up, without spiraling.
If you've been carrying one of those quiet beliefs for a while, this is a good place to start:
