🧠 𝐀𝐈 𝐚𝐩𝐩𝐬 𝐟𝐚𝐢𝐥 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞 𝐦𝐞𝐦𝐨𝐫𝐲 𝐢𝐬 𝐛𝐚𝐝

🧠 𝐀𝐈 𝐚𝐩𝐩𝐬 𝐝𝐨𝐧’𝐭 𝐟𝐚𝐢𝐥 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞 𝐦𝐨𝐝𝐞𝐥 𝐢𝐬 𝐛𝐚𝐝.
𝐓𝐡𝐞𝐲 𝐟𝐚𝐢𝐥 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞 𝐦𝐞𝐦𝐨𝐫𝐲 𝐢𝐬.

As more teams ship AI assistants, one quiet problem keeps showing up:

➡️ 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧𝐬 𝐠𝐞𝐭 𝐥𝐨𝐧𝐠𝐞𝐫
➡️ 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐤𝐞𝐞𝐩𝐬 𝐠𝐞𝐭𝐭𝐢𝐧𝐠 𝐫𝐞-𝐬𝐞𝐧𝐭
➡️ 𝐂𝐨𝐬𝐭𝐬 𝐞𝐱𝐩𝐥𝐨𝐝𝐞 — 𝐚𝐧𝐝 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐝𝐫𝐨𝐩𝐬

Above we've together the comparison below to show how the main “memory” approaches stack up — and when each one actually makes sense.

What stood out:

🔹 𝐌𝐨𝐬𝐭 𝐭𝐨𝐨𝐥𝐬 𝐣𝐮𝐬𝐭 𝐬𝐭𝐨𝐫𝐞 𝐦𝐞𝐬𝐬𝐚𝐠𝐞𝐬
🔹 𝐅𝐞𝐰 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐦𝐚𝐧𝐚𝐠𝐞 𝐰𝐡𝐚𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 𝐢𝐧𝐬𝐢𝐝𝐞 𝐭𝐡𝐞𝐦 — facts, preferences, user state, continuity, and cost control.

That’s the gap @Mnexium AI focuses on:

👉 𝐚 𝐩𝐞𝐫𝐬𝐢𝐬𝐭𝐞𝐧𝐭, 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐦𝐞𝐦𝐨𝐫𝐲 𝐥𝐚𝐲𝐞𝐫 that follows the user across sessions and even across models — without bolting together vector DBs, pipelines, and manual logic.

I’d love to hear from people building AI products:

👉 𝐖𝐡𝐢𝐜𝐡 𝐦𝐞𝐦𝐨𝐫𝐲 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐚𝐫𝐞 𝐲𝐨𝐮 𝐮𝐬𝐢𝐧𝐠 𝐭𝐨𝐝𝐚𝐲?

👉 𝐖𝐡𝐚𝐭’𝐬 𝐛𝐞𝐞𝐧 𝐡𝐚𝐫𝐝𝐞𝐬𝐭 — cost, complexity, or reliability?