Why is everyone so skeptical of AI memory tools? Fair question. Here are real answers.

Last week I presented Cortex — a semantic memory for AI assistants I've been building for two years — on Reddit. The response was brutal: "Obsidian works fine." "A text file is enough." "Thanks for inventing RAG for the millionth time."

Instead of deleting the post, I answered everyone. And I realized the skepticism isn't stupidity — it's precious information. This article is the structured version of those answers, including the uncomfortable ones.

(Transparency note: I build memory tools for AIs, and I use AIs daily to multiply what one person working alone can do — including help writing this article. The architecture, the patent filing, the benchmarks and two years of late nights are mine.)

Why the distrust is justified

Three reasons, all well founded.

Saturation. A new "memory tool" ships every week. Almost all do the same thing: save conversations into a vector database, fish them back out. Distrust is an immune response, and it works.

The promises. "Infinite memory," "your second brain," "never forgets anything." Slogans nobody can measure, so nobody believes them anymore.

The black boxes. You hand your data to a system that won't tell you what it saves, why, or what it will answer tomorrow. An AI that doesn't remember is a tool. An AI that remembers badly — picking at random between contradictory memories — is worse than useless.

Cortex was born from exactly these three problems. Not "how do I save everything?" but: how do I decide what deserves to be remembered? And what happens when memories contradict each other?

"Why not Obsidian?"

Because they solve two different problems that look like the same problem.

Obsidian is memory for you, maintained by you: you write, organize, link. It's excellent at that. Cortex is memory for the AI, maintained by the system. Four concrete differences:

• Who decides what gets in. In a vault, everything you write gets stored. In Cortex, every write passes a quality gate that in production rejects ~80% of proposals as redundant. A vault has no concept of "this note doesn't deserve to exist."
• Contradictions. In a vault, the March note and today's note coexist in silence. Cortex detects and tracks them.
• Where it works. A vault lives on your desktop. Cortex is identical from the web, your phone, Claude and ChatGPT.
• Maintenance. A useful vault is a garden that needs pruning. Cortex prunes itself.

The honest concession, same one I made on Reddit: if you're disciplined, desktop-only and single-model, a good vault covers most of the need. For free. Obsidian archives documents. Cortex governs memories.

"A memory.md file is enough"

At first, it is — we've all been there. Then it grows: no deduplication, no contradiction detection, and it gets pasted whole into every conversation's context — and context costs money. A memory file is a sticky note: perfect as long as the project fits on a sticky note.

"What about Mem0, Zep, Graphiti?"

That's the right comparison class, and they're solid projects. If you can self-host and want full control, use them. The differences I claim: Cortex is hosted with zero setup (works from your phone, no infrastructure), the write-time quality gate is the core of the design rather than an option, and conflicts are first-class objects.

"It's just RAG with marketing"

The retrieval side is RAG, no argument: embeddings, semantic search, context handed to the model. But classic RAG is a read-only pipeline over an archive someone else has to keep clean: it indexes everything, and answer quality depends entirely on archive quality.

Cortex steps in where RAG stops:

1. Writing is governed — novelty filter, facts extracted as typed claims, deduplication at write time.
2. Memory has a lifecycle — consolidation over time, updates linked to the record they supersede, a database that refines itself instead of bloating.
3. An epistemic layer — tracked contradictions, a coherence score, citable sources.

RAG retrieves from an archive. Cortex curates the archive. If that's still "just RAG" to you, fine: it's RAG with a bouncer.

The best question Reddit asked: "When two memories contradict each other, who decides which one wins?"

The system doesn't choose in silence — that's a design decision. Search returns both memories, flagged with a warning, reliability scores and dates. The synthesis layer prefers the most recent but declares that the older one has been superseded, citing both. And the conflict remains an object with a status: open or resolved. You can list them; you can resolve them yourself.

One rule: the system may have an opinion about who wins, but it never has the right to hide the disagreement.

The uncomfortable number

On the LoCoMo benchmark (1,986 questions over long conversations), Cortex scores ~69% with the filter off and ~56% with the filter on. We published both numbers ourselves.

Why publish the worse one? Because the filter isn't free: 13 points is the measured price of a memory that rejects 80% of writes instead of accumulating everything. LoCoMo rewards total recall of conversational trivia; a memory that filters trivia loses points on trivia questions, by design. Meanwhile adversarial questions stay at 95%, and storage drops by 30%.

Filter off tells you the retrieval engine is at state-of-the-art level. Filter on is a different objective: governed memory, not maximum recall. Choose what you need — but choose it with the numbers in front of you, not the slogans.

The quick ones

Open source? No — the engine is closed, covered by a patent application filed May 2026. The connector, docs and examples are on GitHub, and Cortex is on the official MCP registry. I'd rather say it to your face than dance around it.

Do I need an account to try it? No: add the remote MCP connector to your client and the trial space creates itself on first sign-in. 30 days, no credit card.

Why pay when open source is free? If you can self-host, genuinely, do it. Cortex is for those who can't or don't want to.

Who's behind it? One person: independent researcher, founder of SKYNETLAB, Bergamo, Italy. Two years of work, no team, no funding.

What I actually learned from getting roasted

Skepticism toward AI memory tools is rational — the category earned it. The only sane response is verifiability: publish the write path, publish all the numbers (including the ones that hurt), and keep disagreements visible instead of swallowing them.

Every answer here is written down, with the numbers, on the FAQ page. Brutal feedback welcome — it's the most useful gift you can give. I learned that this very week.