DEV Community: woochan

Not a promo: we tried many ways of marketing our memory API, but almost no one came

woochan — Sun, 08 Nov 2026 07:58:21 +0000

I do marketing at Wontopos, a very small team that sells a hosted long-term memory API for AI agents. This is not a promo, it's a question.

What we've tried

I keep doing marketing and I've tried a few things: cold emails (about 200 to companies plus about 20 I wrote by hand, mostly auto-replies, a couple of "we'll take a look", no deals), plain DMs to developers, and posting and commenting in developer communities. Almost nobody showed up. The posts that got replies were questions about how people handle memory, not posts about our product, and when people did answer, most said they build their own memory or run open source.

Why I'm asking

After thinking about it for a while, I started to wonder if our product only feels good to us. So instead of trying yet another channel, I'd like to know if it's something people actually want.

The short version

What it does: it gives your AI agent a memory. It saves what your users tell the agent, and when the agent needs something, it hands back just the relevant memories.
Price (Scroll 2, our newest model): $5 per million input tokens and $6 per million output tokens, plus $0.0001 per request. There's no fee for keeping memories stored, you only pay when you save or recall.
Your data: we don't read it or train on it, and one call deletes a user's whole memory.

You don't need to sign up or try anything, just reading this is enough.

What I'd like to know

Would you try it or pay for it?
If not, why? For example price, or you'd rather run memory locally.
What's unclear or hard to believe?

Blunt answers are better than polite ones, and "not for me" is a fine answer. Reply here or use the survey on the site, and even a very short answer helps a lot.

Want to look closer?

If you can, I'd recommend reading our site before you answer. It will help you judge this much better than my short summary: https://wontopos.com/research (you can read it without signing up).

If you also want to try it, sign up and take part in the survey on the site to get $5 of free credit, and your answers help us even if you never use it.

I'm not the engineer, so I can't answer deep technical questions, but I can pass them on.

I'm not a native English speaker, so I used AI to help polish and translate this post.

I market a closed source memory API, and I don't know how devs find tools like this

woochan — Sun, 08 Nov 2026 07:15:29 +0000

I do marketing at a very small company with a hosted memory API for AI agents. Mem0 and Zep both started out open source, so a dev could clone them and run them locally without talking to anyone. Our engine is closed source, so we don't have that door, and I don't know what to use instead.

What I'd like to know

I'm not looking for growth hacks. I'd rather hear it from the dev side. If you can, answer any of these:

Where did you first see it? When you started using a memory tool, or any tool you'd never heard of, where did you first see it? (GitHub, a post, a friend, a comparison, a tutorial, something else)
What made you try it? What made you actually try it instead of bookmarking it?
If you build your own: If you build your own memory and never used any of them, what did you look at before you decided to build?
If it was closed source: If you ever started using a closed source tool, what got you in the door?

Even a one-line answer helps.

Your AI remembers the old answer. How do you handle facts that changed?

woochan — Mon, 26 Oct 2026 08:08:23 +0000

I work at Wontopos, so I'm biased. I build a memory API. But this post isn't about it. It's about a problem I keep reading about, and I want to hear how you deal with it.

The problem

An AI that remembers things will sooner or later remember something that is no longer true.

Here is a made-up example, just to show the shape of it:

When	What the user says	What gets stored
March	"I live in Seoul."	lives in Seoul
July	"I moved to Busan last month."	lives in Busan
October	"Find me a good dentist nearby."	???

Both memories are true statements. One of them stopped being true. If the memory layer only looks for text that is similar to the question, both come back, and the model has to guess which one wins. Sometimes it guesses wrong, and it sounds confident when it does.

That's the part that bothers me. A missing memory is easy to notice. An old memory that sounds right is not.

Why it's harder than it looks

Similar is not the same as current. Search finds what matches the question. It doesn't know which fact replaced which.
Adding more memory makes it worse. Every correction is one more thing that can contradict an older entry.
Nobody tells the system. People don't say "forget what I said in March". They just mention the new thing.
The failure is quiet. Nothing crashes. The answer is just a little wrong.

What people do about it

From what I've read, people seem to do one of these:

Edit the notes file by hand.
Tell the AI "that's wrong, now it's X" and hope it overwrites.
Wipe everything now and then and start again.
Store everything with timestamps and let the model sort it out.

What we do

In our API, an outdated memory is replaced on purpose with a supersede call, so the old value is marked as replaced and not just buried. We also keep revisions, so you can see what changed. I don't want to oversell this. Deciding when to call it is still your job, and I'm still thinking about how to do that part better.

We also put a STALE axis in Glasshouse, an open memory benchmark I'm working on (still a draft), because I couldn't find an easy way to measure this. Details are in the repo if you want to look.

My question

I'd like to hear from people who build or run their own memory:

Has your agent ever answered with something that used to be true? What was it, and how did you notice?
How do you decide a memory is outdated? Timestamps, a rule, a human, the model itself?
Do you overwrite the old value, or keep both and mark one as old?

You don't need to sign up for anything. The details are at wontopos, and the site has a short survey on the main page. A comment here is just as good, and the survey is a bonus.

I tried to get our first B2B customer and failed. What am I missing?

woochan — Tue, 20 Oct 2026 03:40:58 +0000

Where I am

I'm on a small team with almost no funding. We built a long-term memory API for AI agents and launched it about 3 months ago. We want to land our first B2B customer, and so far I haven't found a way. I'm writing this to ask people who have done it.

What I tried

1. Mass cold email. I picked about 200 companies that looked like they would need memory for their AI products, and sent them almost the same email.

2. Hand-written cold email. I researched about 20 companies one by one and wrote each email myself, sincerely, with no template.

The result was the same for both: almost every reply was an automatic one. One or two people said they would look into it. Zero deals.

3. Press. I emailed journalists directly. About 3 of them replied, but nothing became an article.

4. Developer communities. My plan is to get developers to use it first and grow into B2B from there. I've kept doing this, but I don't have any real results yet.

5. Paid ads. I haven't tried. We don't have the money, and I guess the effect would be small. That's an assumption, I haven't tested it.

6. Funding. I thought about raising money, but I haven't tried. We do have results on performance, in benchmark scores we measured ourselves, but we have no results with real users yet, so I don't have anything that proves it works for customers.

What is blocking us

No replies. Without a conversation, I can't even learn why they say no.
Security certification. Nobody has asked for one yet, but I think having it would raise trust a lot. We can't afford it right now.
A track record problem. I'd like to run both B2C and B2B. But companies handle large amounts of data, and individual developers mostly want to manage their own memory. When I asked developers how they handle agent memory, most said they build their own with plain files or use open source tools. So it's hard to build a track record with individuals before going to B2B.

What I'd like to know

If you've sold to businesses, how did you get your first customer?
What works for a small team with no budget? Even something you only heard about helps.
Is a security certification something to get before the first customer, or after?

Details

Short answers are fine. If you have a minute, take a look around our site and fill in the short survey. You don't need to log in: wontopos.com. It would help a lot to hear what you would need as an individual developer to actually use something like this.

I wrote the first draft with an AI assistant and edited it myself.

A memory benchmark draft with 17 axes and no headline number: is it worth running?

woochan — Fri, 16 Oct 2026 10:31:41 +0000

I work at a company that builds memory infrastructure, so I'm biased: we compete in the thing this benchmark measures. We wrote a long-term memory benchmark called glasshouse, and I'd like to know if you think it is worth running on your own system.

Why we made it

Memory systems report their own numbers, and the numbers don't agree. The case that pushed us to build this: Zep reported 84% on LoCoMo, Mem0 recomputed it and got 58.44%, and Zep came back with 75.14%. Same system, same benchmark, three numbers. LongMemEval and BEAM ship a dataset and a script, but neither has a place to submit results, so everyone reports their own.

What is in it

2,847 questions over one conversation of 1.97 million tokens, in 10 languages, with 50 photographs.
17 axes, each reported separately. There is no single headline number, because a system can be strong at one axis and useless at another.
The standard run is full (103,572 turns, 1,547 questions). A smaller core tier (1,882 turns) is the control: if a system drops from core to full, it knows the answer but cannot retrieve it under volume.

Axes I think are unusual

STALE: a fact changed. Finding the new value is full credit, "I don't know" is half, and confidently repeating the old value is zero.
CONTRADICT: the speaker says two incompatible things weeks apart. Saying they disagree is full credit, and confidently picking one is zero.
RECONCILE: two statements look incompatible but hold under different conditions. Explain both, or pick one and call the other wrong?
FALSE_MEMORY: you assert something that was never said. Does it agree?
Cross-language: 800 questions that store in one language and ask in another, across all 10.

The rule for partial credit is one question: is an incomplete answer dangerous? A visible gap earns half, because the user can ask again. A wrong value stated as current earns zero. The build also fails if answering "I don't know" to everything scores more than 20%.

What is not ready

It is a draft, not released. The submissions folder is empty and we have not submitted our own system either.
You have to write the ingest step yourself. We ship no runner, because the only one we could write is for the product we sell.
Scoring calls a judge LLM through OpenRouter on your own key, so it costs money. The judge has been run in answer mode, but memory mode has not been run against it yet.
A synthetic conversation is not a real user's life.

What I'd like to know

Would you run this on your own memory system? If not, what is missing?
Which axis looks badly designed or unfair?
Would you trust a benchmark run by a company that also sells a memory product?

Details

You don't need an account to read any of it, so please take a look if you want the details: github.com/wontopos/glasshouse.

I wrote the first draft with an AI assistant and edited it myself.

Honest question: at this price and performance, would you use this memory API?

woochan — Fri, 16 Oct 2026 07:31:38 +0000

I work at Wontopos, so I'm biased. About three months ago we launched a long-term memory API for AI agents, and honestly almost nobody has looked at it. I'm not asking you to sign up. I'd like a blunt answer: at this price and performance, would you use it?

What it is

You store a user's memories once. For each query, recall returns only the relevant ones, and you pass them into your model's prompt. Memory lives per user, so each end-user or agent gets an isolated space.

The core calls are store, recall, search, supersede (replace an outdated memory) and forget. SDKs for Python, TypeScript and Rust, plain HTTP works too, and there is an MCP server (beta) for Claude Code and Cursor.

What I think is different

Same recall across languages. We measure it as a grid of 70 store-language by query-language pairs, and it averages 95.2% recall@5. A memory saved in Korean should be found by a query in Japanese.
Bounded context. Recall works over histories of 1.4M tokens and still hands back a slice of roughly 1,470 tokens, so the prompt does not grow with the history.
Images. A memory can carry an image with no caption or alt text, and it is found from a sentence in any language.
Ask again. If one pass does not carry the answer, a second pass returns memories the first one did not.
Deletion in one call. One call erases a single memory or an entire user, including images and revisions (GDPR).
Your LLM key is yours. Keys are sent per request and never stored, and nothing is trained on your data.

Price

	Input / 1M tokens	Output / 1M tokens
Tablet	$2	$3
Scroll	$4	$8

Each request adds a flat $0.0001. No subscription, no storage rent, no memory caps. You add a card and top up at least $5, and there is a hard spend cap. If you bring your own LLM keys, the provider bills those.

Numbers (measured by us)

Tablet 2: 95.7% on LongMemEval-S, 67.5% on BEAM-1M. The BEAM-1M number is averaged over 5 independent runs (σ 0.22%, none cherry-picked).
Scroll 1.2: 92.3% on LongMemEval-S, about 2,800 tokens and about 450 ms per query. BEAM-1M is 58.6% with re-asking turned off.
Where it is weak: low-resource languages. Swahili is 53.0% recall@5.

The harness and per-question records behind the BEAM-1M number are public, and the Tablet 2 paper reports three findings that run against us.

What I'd like to know

Would you pay this for memory in your own app?
What would make you pick this over Mem0, Zep, or just keeping context in a file?
Which number looks wrong or unbelievable?

Details

You don't need to sign up, you can just read the information. Knowing what I'm talking about should let you give a more accurate answer than my summary, so please take a look if you want the details: wontopos.com and the research page.

You can answer here in the comments. Even better, there is a short survey on the site: click the Survey button in the top menu of the main page. No sign-up is needed for that either.

I wrote the first draft with an AI assistant and edited it myself.

I Posted in 5 Developer Communities for 5 Weeks. Here's What Got Replies and What Got Killed.

woochan — Tue, 13 Oct 2026 03:53:59 +0000

I do marketing for a memory API for AI agents. For about five weeks I've been posting in developer communities: dev.to, Hacker News, Reddit, Velog, and Indie Hackers. I'm writing this down because some of the numbers surprised me, and I'd like to hear how you do it.

dev.to: what got comments

I've published 7 posts here so far. The announcement posts got almost nothing. "Glasshouse v0.1 Is Out" got 1 comment, my intro post got 1, and my first post about why I'm building a benchmark got 0.

The posts that got comments were different. A post about how I'm trying not to rig my own benchmark got 19. A question to developers got 8. A post asking what's wrong with my benchmark (15 stars, 0 runs) got 7. One narrow question about memory API features got 0.

My takeaway so far: people reply when I ask them something they can answer, or when I show them where I might be wrong. They don't reply to "I shipped something."

Hacker News: dead posts

Several of my posts and comments on my first accounts were marked dead. Two Show HNs I made in April got 1 and 2 points, with no comments. I don't know exactly why. I started a new account, and I'm commenting on other people's threads before I post anything with a link.

I posted 8 times in r/AI_Agents, mostly questions about how to trust a memory benchmark and what to add to mine. I said I work at Wontopos, and I left 25+ comments answering people under those threads. Most posts got 1 to 2 comments each, and the best one got 9.

What I take from it: Reddit rewards answering people directly more than posting.

Indie Hackers

New accounts can't create posts yet, so I'm commenting until that opens up.

Velog (Korean)

I've been writing a Korean series there. Each post gets between 11 and 57 views, and the first episode did best.

Questions for you

Did your first posts get killed somewhere? What did you change?
How long did Indie Hackers take to unlock posting for you?
What got you your first real replies?

What Memory API Feature Would Make You Think "Okay, I'd Actually Try That"?

woochan — Fri, 02 Oct 2026 08:16:06 +0000

I work at Wontopos, which builds a memory API for AI agents in the same space as Mem0 and Zep, and I keep getting stuck on one question. What would make a developer trust a memory API enough to actually pay for one.

What it does

You store what a user says once, and on each query the API returns only the memories that are relevant, ready to put into an LLM prompt. It is a hosted API with a closed engine, and there is no self-hosting yet. The details are here: https://wontopos.com/en/why

Where I'm stuck

I know a lot of you want to manage memory yourselves, and I get why, but I don't know how a paid product is supposed to earn that trust. Retrieval performance keeps improving, so I don't think a better score is what decides it for long. And memory is slow to notice anyway. You only feel it working weeks into using something, so a better number on a benchmark doesn't land the way a visible feature does.

What I'm asking

What I'm trying to find is an idea for a memory API that people could feel right away. If you've built your own memory or tried a few products, what's the thing you wish existed. And is there any feature you can think of where you'd go, if a memory API had that, I'd actually try it.

Devs, Would You Mind Answering One Question?

woochan — Wed, 30 Sep 2026 11:07:09 +0000

It's been about two months since I started actually running this, not just building it, and I keep coming back to the same question instead of moving past it.

What I'm building

I work at Wontopos, a memory API in the same space as Mem0 and Zep. Store what a user says once, retrieve only what's relevant on each query, hand that to an LLM prompt. The long term goal, and I'll admit this out loud, is to become something close to standard infrastructure for AI memory at the B2B level. Not a feature bolted onto someone else's product, but the layer everyone building on AI just reaches for.

That's a long way off, and I'm aware of how that sounds coming from two months in. Right now what actually matters is much smaller. Get real developers to use it, and find out where we're wrong before we've committed to being wrong at scale.

Where the confidence went

This is the part I didn't expect. Watching actual usage come in has made me less confident, not more. I thought two months of real traffic would sharpen my sense of what people want from something like this. Instead it mostly sharpened my sense of what I don't know. People reach for memory for reasons I didn't anticipate, and skip it for reasons I also didn't anticipate. Neither of those is something I can fix by thinking about it harder alone.

What I'm actually asking

So here's the honest question, and I mean it plainly rather than as a lead-in to a pitch.

Where would you actually reach for a memory API. And what would you need it to do that you're not getting from whatever you use now, whether that's another memory product, rolling your own with a vector store, or just stuffing more into the context window and hoping.

Even a short answer helps. I'd rather hear "I wouldn't use this and here's why" than nothing at all, because silence doesn't tell me whether we're solving a real problem or a problem we invented.

I Made a Memory Benchmark as Fair as I Could. 15 Stars, 0 Runs. What's Wrong With It?

woochan — Mon, 28 Sep 2026 05:08:58 +0000

I published a memory benchmark six days ago. I spent most of the build trying to make it fair rather than trying to make it look good, and now I have no idea whether that worked.

Here is the whole state of it:

stars        15
forks        0
issues       0
submissions  0

People star it. Nobody runs it. Which means the one thing I actually wanted, someone telling me a question is broken or a rule is wrong, has not happened once.

What it is, quickly

Glasshouse is a long-term memory benchmark. 2,847 questions over a conversation that runs to 1.97 million tokens, in 10 languages, with 50 photographs. Every axis is reported on its own and there is no single headline number, because a system can be great at one and useless at another. It goes past plain recall too. If a fact changed and the system cannot find the new value, "I don't know" scores better than confidently giving the old one. If two stored facts disagree and nothing settles it, saying they do not agree is the right answer.

The reader and the judge are fixed by the version, so a submitter does not get to pick the model that reads the answers. Every submission carries its per-question record, so anyone can recompute the number instead of trusting it.

What I think might be in the way

I can guess at some of it, and none of these are accidents, which is exactly why I want to know whether I got the tradeoff wrong.

You have to write the ingest yourself. I deliberately ship no runner for storing the corpus, because the only client I could write is for the memory system my company sells, and that does not belong in a benchmark anyone is meant to be able to win. It does mean step one is work.
Scoring costs your own money. The judge runs through OpenRouter on your key.
The standard run is 1.97 million tokens. That is not something you try on a whim.
The official reader is claude-opus-5.5. I fixed it because changing the reader changes every number, but it is not the cheap option.

What I'm asking

If you opened it and closed the tab, I would genuinely like to know at which line. And if you are the kind of person who would run something like this, what would have to be different for you to actually do it?

Repo: https://github.com/wontopos/glasshouse

Glasshouse v0.1 Is Out: A Memory Benchmark for AI Systems

woochan — Tue, 22 Sep 2026 07:34:24 +0000

Glasshouse v0.1 is out. It's a long-term memory benchmark for AI systems, and it's what my last two benchmark posts here were about. If you haven't read those, here's the short version.

Why I built it

Going through developer communities, I kept running into people raising the same problems with memory benchmarks. The numbers a vendor publishes don't match the numbers someone else measures, and swapping the model that does the grading moves the results more than the gap between the systems being compared.

What's in v0.1

2,847 questions over a conversation that runs to 1.97 million tokens, in 10 languages, with 50 photographs.
The conversation comes in four sizes, from 1,882 turns to 103,572, and the part that holds the answers is identical in all of them. Where the score falls apart tells you whether a system holds up as the history accumulates.
Each axis is reported separately. There is no single headline number, because a system can be strong at one and useless at another.
It goes past plain recall. When a fact changed and the system cannot find the new value, saying "I don't know" scores better than confidently repeating the old one. When two stored facts disagree and nothing settles it, saying they don't agree is right and picking one is wrong. When the answer was never stated, it checks whether the system says so.

If you read my earlier post, some of the numbers have moved since. That post said they would.

What I'm asking for

submissions/ is empty. We haven't submitted either.

If you're an individual, run it, and open a pull request or an issue when something is wrong. There's no threshold for individuals, on purpose, and an objection that names a specific error gets answered in public. There's a file in the repo listing what people suggested on Reddit while v0.1 was being built, and what each suggestion became. The stale fact axis, the contradiction axis and the false memory probes all started as someone's comment. One suggestion wasn't used, and it's listed anyway, because a record that only shows what was taken can't be checked.

If you're a company, you can submit a result or add your company, and how that works is in the repo.

Everything, including how to run it, is here: github.com/wontopos/glasshouse

If you run it and a number looks wrong to you, that's exactly what I want to hear.

An Introduction to Wontopos, the Memory API I Work On

woochan — Fri, 18 Sep 2026 09:30:14 +0000

My posts here so far have only been about the benchmark I'm working on. This time I want to introduce the company I work at, Wontopos.

The company

Wontopos builds WOS: long-term memory for AI agents. It stores an end user's memories once, then recalls only the relevant ones per query so you can feed them into an LLM prompt.

It is not only for teams. If you are building a product, you use the SDK and your code decides exactly when to store and what to recall. If you just want a finished tool to remember (Claude Code, Claude Desktop, Cursor, VS Code, Windsurf, Gemini CLI), you use MCP and write zero integration code. Same account underneath, so what one surface stores, the other recalls.

What it does

Each query returns a small, bounded set no matter how much is stored, so input cost does not grow with the store. A store that has been filling up for a year does not cost more to read from than a new one.

No LLM ever runs over your stored memories, on any model. Tablet runs no LLM at all. Scroll may use one to reformulate the query only, never your stored data.

No language is privileged: you can store and query in any language, mixed freely.

The models

Four are live. Tablet is lean and returns less, Scroll returns more context. They read the same stored memories.

Tablet 1 (live)
The original. About 1,200 tokens per query. Lean and fast, with no LLM anywhere in it.

Tablet 2 (live, and the default)
About 1,000 tokens per query. It does three things Tablet 1 could not. The engine knows who said what, so you can register speakers and recall one person's words. It stores images as memories, and the caption may be empty, in which case the image is the memory and is searchable on its own. And it can be asked again: if one pass does not carry the answer, verify sends the engine back for memories it has not already returned, up to three times, with no LLM at any value.

Scroll 1 (live)
About 3,700 tokens per query. Adds an LLM query-understanding layer over the same stored memories. That LLM reformulates the query only.

Scroll 1.2 (live, and the current Scroll)
Sentence-level recall over the same memories, so a fact said once, in passing, still surfaces. About 2,800 tokens per query, which is fewer than Scroll 1.

Book (in development, not priced)
A fundamentally different design, not the context-for-accuracy trade. Built for one goal: never repeat the same mistake.

The research

Every measurement we have published, on public benchmarks, with every run reported:
https://wontopos.com/research