Atlaso is a memory layer for AI. Connect it once and every AI you use, from Claude Code to Cursor, Codex and ChatGPT, automatically recalls the context that matters: your projects, your decisions, and the way you like to work. No more re-explaining yourself at the start of every session. One shared memory that follows you across every tool, instead of being locked inside one app. Free to start, and backed by original memory research.
Atlaso started as a question I couldn't shake: what would it actually take to give AI a real memory?
Not a bigger context window. Not a notes file stapled onto the side. A real memory foundation layer — one that persists, stays honest, and actually helps across sessions and tools.
That question turned into research. We ran our own memory benchmarks, and our approach held up better than the other memory systems we tested against. A lot of the work was in the unglamorous parts: what should be saved, what should be surfaced, and how to make sure memory orients you instead of quietly making things up.
The reason I cared so much is simple — I was tired of re-explaining myself to every AI I used. I'd tell Claude Code about a project, switch to Cursor, and start over. Then again in Codex. Same context, same decisions, same preferences, over and over.
Atlaso is what came out of that work: one memory layer for the AI tools you already use. Connect it once and your context follows you — global memory for what travels with you, per-project memory for what should stay separate.
My favorite part is Ambient Memory — what we call giving AI a subconscious. Before you type a word, Atlaso surfaces a short orientation from your own memory, so the AI picks up where you actually left off. It orients, it never invents.
It's free to start. I'm Ashish, the founder — I'll be in the comments all day, and I'd genuinely love your feedback: what would make AI memory actually useful to you?
This is awesome, bouncing between all of my AI platforms and pulling context across them is a big pain. Congrats on the launch!
Congrats on the launch Ashish, and the way you’re answering people here is genuinely refreshing. Publishing the benchmark you lose on is not a small thing.
My question comes from outside the dev lane. Memory is scoped global and per project, which is right for one person, but at our company half the context worth remembering belongs to the team, not to me. Brand rules, who owns what, decisions we made and why. Today if I correct something, my AI knows it and my colleague’s still gets the old version. Is a shared team memory layer on the roadmap, with the same supersede logic applied across people rather than tools? That’s the version I could roll out to 36 people instead of just using myself.
The re-explaining tax is real, I switch between Claude Code and other tools daily and re-establishing context every time is the part nobody talks about when they compare AI coding tools. Curious how you handle the per-project vs global split when two projects share similar tech but different conventions.
Does memory compile and bulge the memory file or is there a natural decay built in?
I've built a version of this by hand for a single tool — a memory file with explicit ownership rules so every fact lives in exactly one place, plus an archive for anything superseded. It works, but only because I maintain it myself. Two questions from the maintenance end:
Tidy-up. Mine stays usable because I prune it, and because there's a rule that a new fact contradicting an old one gets flagged to me rather than silently overwriting. As Atlaso's store grows across four tools and hundreds of sessions, what happens when session 40 contradicts session 4 — overwrite, version, or surface the conflict? And does anything decide a memory has gone stale, or does it accumulate forever?
Token cost. If memory is injected into every session, my bill scales with how much I've remembered. Is it selective retrieval or the whole store? Roughly what's the per-session overhead at 500 memories versus 50?
"It orients, it never invents" is the claim I'd want stress tested, and not against a benchmark you ran yourself. The hard case for auto capture isn't invention, it's a decision you reversed three sessions ago still getting injected as settled, and it reads exactly as confident as a correct one. So the real question is how a memory dies. If I can't see what got injected and kill it in one keystroke, I'll turn the whole thing off the first time it confidently reminds me of the wrong thing.
Congrats on the launch!
What sets Atlaso apart from Supermemory, which I'm currently using? Especially interested in how you handle developer-focused workflows across tools like Claude Code and Cursor.
Also, is there an easy way to import or sync existing memory from Supermemory, or do we need to start from a clean slate?
About Atlaso on Product Hunt
“One memory for every AI you use”
Atlaso launched on Product Hunt on August 4th, 2026 and earned 189 upvotes and 25 comments, earning #3 Product of the Day. Atlaso is a memory layer for AI. Connect it once and every AI you use, from Claude Code to Cursor, Codex and ChatGPT, automatically recalls the context that matters: your projects, your decisions, and the way you like to work. No more re-explaining yourself at the start of every session. One shared memory that follows you across every tool, instead of being locked inside one app. Free to start, and backed by original memory research.
Atlaso was featured in Productivity (657.6k followers), Developer Tools (517k followers) and Artificial Intelligence (475.3k followers) on Product Hunt. Together, these topics include over 340.7k products, making this a competitive space to launch in.
Who hunted Atlaso?
Atlaso was hunted by Ashish Khandelwal. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Ashish Khandelwal