u/Asly97

Question for people running AI agents or AI steps inside no-code workflows (n8n, Make, Zapier, whatever) on real work, not demos.

Over in r/AI_Agents a lot of people keep landing on the same idea: the agent shouldn't be the system, and durable state should live outside the model. One person put it as "Sheets holds the metadata, plain scripts do every repeatable step".

So:

• Where does state live between runs right now? A sheet, Airtable, Postgres, a doc the AI reads first, a vector store, nothing?
• Have you tried an MCP memory server for this? If yes, what happened. If no, what stopped you: didn't know they existed, trust, cost, or not worth the setup?
• What does the stack look like in practice: which tools, how they're wired, how often things run? I'm testing against real workflows, so the boring details are the useful part.

Background since someone will ask: I used to burn hours re-briefing AI tools by hand and I'm building a fix, memory kept in the cloud that every agent and tool reads over MCP. No link, no name. Would you pay for something like that, or is a sheet plus a good prompt enough at your scale?

I'm a solo founder, and I wanted my product to pop up when folks ask ChatGPT or Perplexity stuff in my niche. That needs a ton of specific, actually useful content, and I don't have time to crank out a post every day. So I put together a pipeline where agents handle the heavy lifting and I just pick and approve.

Stack

• Claude (scheduled agent): digs into the question, writes drafts, publishes
• Notion: the content queue, holding the target question, status and live URL
• Ghost: the blog
• Vilix AI: the memory layer, so each run knows what earlier runs wrote and what I approved or turned down
• A second agent that checks rankings every hour

How a day runs

1. Each morning the agent grabs the next question from Notion. Every one is a real question people ask AI tools in my niche.
2. It checks memory for every past article, angle and target question, and tosses anything too close to what's already live.
3. It writes 3 versions with different angles and sends them my way.
4. I read them, pick one, and drop notes if something's off.
5. It applies my notes, posts the article to Ghost, pings IndexNow so search engines grab it quickly, and sends the live URL back to Notion.
6. It stores the run in memory: the version I chose, the reasons the others didn't make it, and my notes, so the next drafts build on that.
7. The ranking agent tracks movement every hour, so upcoming topics drift toward whatever's performing.

What went wrong before

Duplicates. Notion only had the titles, so the agent would keep producing the same article from a slightly different angle just days apart. Storing the full runs in memory and reading them first sorted that out.

What I've picked up about writing for AI answers

• One question per article, answered within the first two sentences
• Actual specifics (exact behavior, limits, versions), not vague filler
• Every article aimed at a question people really type into ChatGPT, not a keyword

Happy to take questions about how it's set up.

Kept a DECISIONS.md in my repo for a few months. Worked great at first. Then one day the agent resurrected a choice I had killed three weeks earlier, and I realized it just... had not opened the file in a while. No error, no complaint. It just stopped checking.

I used to burn maybe an hour a week re-explaining things that were sitting in a file the agent was supposed to read. "Go read the file" turns out to be exactly the step that gets skipped when the context gets long. The damn thing would follow stale instructions instead of the ones I actually wrote down, and I would only notice after the fact.

Since then I have tried a couple of things. A hook that dumps the file into context before every run (worked until the file got long and I watched the tokens evaporate). Telling it to quote one line back to prove it read it (felt like asking my kid whether he brushed his teeth). Honestly, the second one worked better than it had any right to.

Has this happened to you? What actually broke when the agent skipped the read — a stale instruction followed, a dead decision back from the grave? And how do you force it now, if you do? A wrapper that will not start clean until the file opens, injection, some ritual?

Also: has anyone had two agents in the same repo following different versions of the same file? That one cost me an afternoon once, and I still do not fully know how it happened.

Separate curiosity: have you ever tried one of those memory tools that is supposed to keep context across your AI tools? Or does the thought of moving everything into something new just feel like starting over?

Out of curiosity, what are you building with all this, is it your own product, client work, or something else?

Weird one: I do a lot of my thinking out loud with speech-to-text on my phone, and the context always lives on the laptop. Is that just me, or do you lose things moving between phone and laptop too?

And the money question, plainly: if something handled the enforcement part for you, made sure every agent actually checked the right file before starting, every run, would you pay for that, or is the DIY wrapper the whole point? I am building something in this direction for myself and trying to figure out whether it is a real problem for other people or if I am just overthinking it.

I had a stupid realization last week: my CLAUDE.md situation had quietly turned into three separate files all claiming to be the source of truth. One in the repo. One in my home directory from months ago. One Cursor generated somewhere in .cursor that I never deleted. All slightly different.

The one that actually got me was an AGENTS.md copy inside one project that had drifted from the real thing. Something like 17KB of rules that were mostly right, except three or four things I changed months ago and never synced over. The agent had been loading that stale copy every single turn and nobody noticed, because stale rules sound perfectly plausible. It wasn't breaking anything loudly. Just following outdated guidance, confidently.

Cleaned it up by hand, obviously. But it got me thinking, how is everyone else handling this? Do you symlink everything to one canonical copy, sync them somehow, or does each project just have its own and you accept the drift? And has a stale copy ever actually burned you, like the agent following an old rule that wasted a real hour?

Also genuinely curious what people here are building with all this, own product or client work or something else entirely? And one more thing: I do a lot of my thinking out loud with speech-to-text on my phone, and the context always lives on my laptop. Is that just me, or do you carry project context on your phone somehow?

And since I'm clearly rebuilding all of this anyway: what if something handled all of it for you, in the cloud over MCP, so every agent and tool got the same context: decisions, failed attempts, conversations, skills, procedures, tasks. You never touch a file again. Would you guys pay for something that fixes this? Or has the thought of paying for a tool to manage all this never even crossed your mind?

My scariest failure was never a crash. Crashes are loud, crashes page you, crashes are almost a relief honestly. It was a dedupe rule that just... stopped firing, and kept running green for weeks.

A working dedupe rule produces zero output. No errors, no logs, nothing to alert on. So success and breakage looked exactly the same, and the first signal came weeks later when I was staring at data that had been quietly piling up the whole time. I used to check my runs from my phone in the morning and everything said green, which made it worse somehow. Cost me a weekend of cleanup and a very awkward conversation.

Anyway, I used to burn real time on this. My ritual was expected-count checks per silent step ("this rule should have matched N rows today"), plus replaying a period against a copy before cutover and diffing outputs. Half of it worked, half of it was vibes. And the quiet fear never fully went away.

So how do you catch the silent ones? Do you write heartbeat rows even on no-op runs? Assert expected counts on steps that should produce zero output? Something completely different? What was the last silent failure that bit you, and how long did it run green before anyone noticed?

Genuinely curious what these jobs are for, too. Own business workflows or client work? Who eats the cost when a silent one breaks, you or the client?

Last one, since I'm sketching something: what if something watched your scheduled jobs for exactly this kind of silent drift, in the cloud over MCP, same context across every agent and tool so decisions, failed attempts, and run history all cross over. You never touch a file again. Would you guys pay for something that fixes this, or does your own heartbeat setup already cover it?

I do my clearest thinking on walks. Or at least I used to tell myself that.

What actually happened: I'd get back from a 45-minute walk with three "decisions" in my head, hit record on my phone to dictate them, and by minute two of the voice note I was already rambling about something else entirely. Play it back the next day and it's a 6-minute voice memo where the actual decision is buried somewhere around the part where I start describing a dog I saw.

Half the time I skipped recording entirely because I knew I'd never listen back. Nobody wants to scrub through their own rambling. So the decisions just died on the walk. The thinking worked great, the capture worked not at all.

I tried phone notes after, which survive (they're text), but they land as "walk thing" with no context and I never connect them back to the project they belonged to.

So honestly: do your walk ideas survive? Do you dictate on walks or commutes, and if you do, what actually works? Transcription, or is the problem deeper than that (the decision evaporates even when you do write it down)?

And out of curiosity, what are you building with all this? Own product, client work, side thing?

Real question underneath it: if something captured the decision properly on the walk and landed it in your project context, no scrubbing, no transcription tax, would you pay for that? Or is "the walk is the thinking" enough and the ideas were never the point?

I used to keep a context file for my coding agent. MEMORY.md, about 200 lines. Solid stuff too, decisions, the why behind weird code, things I kept re-explaining.

And then I noticed I was re-pasting half of it into every new session anyway. Not because the file was bad. Because the agent never read it. I'd write "check MEMORY.md first" in the prompt and three turns later it was making things up about a module it supposedly just read. I got into this dumb ritual of interrogating it: "did you actually read the file? quote me a line." It passed maybe half the time, honestly. The rest I just re-explained. Which is the whole thing the file was supposed to fix.

So I'm sketching something before I build it, and I want holes poked in it. The idea: the memory tools carry their own instructions. The read tool tells the agent, right in the tool description, "check memory at the start of the session, here's how to query it." The write tool says "save back at the end, here's what's worth saving." The reminder lives in the tool call itself, not in my prompt, not in a hooks config I have to maintain. I stop being the nag. And the agent doesn't get to claim it read the brief when it didn't, because the read is part of the call, not a polite suggestion it can skip.

Related thing I keep tripping on: I do half my thinking out loud, speech-to-text, on my phone while walking, and the actual context lives on my laptop. The agent on my laptop knows nothing about the plan I made on my phone. Is that just me, or does the phone-laptop gap bite you too?

Does this sound useful or overbuilt? Would you actually trust it, or is there a part you'd insist on keeping manual? What would you change, and what am I missing?

The money one, asked plainly: if something like this worked and you never had to remind an agent again, would you pay for it over a notes doc you Ctrl+F? Or is the manual reminder just the cost of doing business, fine as-is?

I'm sketching before I build. Tear it apart.

I changed my pricing model about a month ago. New plans, rewrote the billing code, the whole thing. Moved on.

Last week I was asking Claude about something pricing-adjacent and it pushed back, hard, arguing for the OLD pricing. Quoted my own notes doc back at me like it was gospel. Took me an embarrassingly long minute to realize the note was from before the change. The old decision looked exactly as alive as the new one.

I get it, honestly, the tool did what I told it to do: trust the notes. But now I don't know which of my own decisions in there are still true. And that pricing change was a month ago. How many others are just sitting there, looking current?

Anyway I started building myself a little thing to fix this for myself (not selling anything here, I just want to know if this is a real problem or if I'm overthinking it). What if something handled all of this for you, in the cloud over MCP, so every agent and tool got the same context: decisions, failed attempts, conversations, skills, procedures, tasks. You never touch a file again. Old decisions marked dead so they stop looking alive.

So: has this bit any of you? How did you catch it, and what did it cost you (time, shipped the wrong thing, worse)? How do you keep dead decisions from looking alive now, expiry dates, a 'superseded' note, delete-on-sight, something else?

Also curious: when you switch tools, do you get a fresh copy of the same problem (re-brief the new tool on everything, dead stuff included)? And where do you even keep these notes, phone, laptop, somewhere it survives both?

Would you pay for something that fixes this, or is a notes file plus discipline enough for you?

I used to do my best thinking on walks. Phone in pocket, dictating into voice notes, working through a decision out loud until it actually felt settled. Pricing, which feature to cut, whether to rebuild the onboarding screen for the third time. I'd get home feeling like I'd cracked something.

Then I'd sit at the laptop and try to reconstruct what I'd decided and why, and... gone. The voice note would be four minutes of me rambling in circles. The worst part was re-listening and hearing myself say "okay, so the answer is obviously X" with X nowhere in the audio. I started taking walks specifically to think and somehow still did all the real thinking at my desk anyway. Felt dumb, honestly.

Full honesty on what this is: I'm doing customer research for a thing I'm building, not selling anything here. I just need to know if this is a real problem for other solo builders or if my workflow was uniquely bad.

So: where do your away-from-desk decisions actually go? Voice notes, phone notes, just in your head until you get home? And what survives the trip back, the decision itself, or do you basically re-think the whole thing at the desk? I do a lot of thinking out loud with speech-to-text on my phone, and the context always lived on my laptop, so the phone part felt decorative. Is that just me?

The longer idea: what if something handled all of this for you, in the cloud over MCP, so every agent and tool got the same context: decisions, failed attempts, conversations, skills, procedures, tasks. You never touch a file again. Would you guys pay for something that fixes this? Or has the thought of paying for a tool to manage all this never crossed your mind?

I'm a solo founder. I paid $416 on Fiverr for a 60–90 sec sponsored segment. The gig advertised a "10M" YouTube network and showed case studies with 2,000–4,400 signups.

What happened:

• The video went on a channel with about 1.5M subs.
• About 2.7K views and 96 comments. Not one comment has a single like. They came in bursts, and some are word-for-word copies from different accounts.
• Zero signups.
• The seller said the video would only go live after I left a review, so I reviewed before seeing any results.

I opened a ticket with the evidence. The first reply was a template about "more time to review." The second offered a 60% refund because I "completed the order and left a raving review," and I've asked for a supervisor.

Has anyone gotten a full refund after a review they were pushed into? Is 60% the best I'll get, or is it worth escalating further? I'll post the full breakdown with screenshots depending on how this ends.

I have run both daily for real work, mostly a Next.js app and backend scripts. Here is the honest version.

Where Codex wins Straight codegen speed and code review. It catches things I would have shipped. If the task is well-defined, it just goes.

Where Claude Code wins Fuzzy tasks. Give it a vague goal and let it plan, explore the repo, run things, iterate. It recovers from its own mistakes better than anything else I have used.

The part nobody talks about Both of them have total amnesia between sessions. Context limit hit, tool switched mid-task, next morning, you are re-explaining your project like it is day one. I lost more time to that than I ever lost picking the wrong tool.

CLAUDE.md and AGENTS.md help with the static stuff. For the living context, what I decided, what broke, where I left off, I keep one shared memory all my tools read. I use Vilix AI for that, it is an MCP server so Claude, Codex and Cursor pull from the same store. Honest caveat: the model decides when to check memory and models are lazy, so I nudge it with "check memory first" when it forgets. Not magic, still beats re-explaining three times a day.

Verdict Pick whichever feels better in your editor and stop overthinking it. The tool was never the bottleneck. The amnesia was.

Free plan and a 7-day Pro trial with no credit card at https://vilix.ai if you want to try the memory setup.

This is the setup I wish someone had handed me on day one. One shared memory across every AI coding tool, over MCP. Takes about ten minutes.

1. Create your Vilix AI account at https://vilix.ai Free plan to start, 7-day Pro trial with no credit card if you want the full thing.

2. Connect each AI client to the same account Claude Code, Codex, Cursor, OpenClaw, whatever you run. Each client connects separately to your one Vilix AI account, either with OAuth or with an API key as a Bearer header to https://api.vilix.ai/mcp (that is the route for headless agents). One approval does not configure the others, so do each tool once.

3. Enable the memory tools in each client Two tools matter. get_context pulls the relevant saved context into the conversation. save_turn stores the exchange. Retrieval is semantic, it finds what you meant, not just what you typed, and it is recency-aware, so the newest version of a memory is what the AI sees.

What actually gets stored The full exchanges your clients send, with source labels so you can see which tool saved what. Plus derived memories, your projects and tasks, personal rules, and reusable agent skills. Everything is visible and editable from any connected AI or from the dashboard at app.vilix.ai.

The honest caveats The model decides when to call the memory tools, and models are lazy. If it is not checking, tell it: "check Vilix AI for context first." Connecting a client does not import its old chat history, it starts fresh from there. And when two tools save conflicting info, last write wins, so correct things in one place and move on.

The exit door Export everything in a portable format anytime, delete individual memories or wipe the whole account instantly. Your data is isolated per user, never sold, never trained on.

That is the whole setup. Plan in Claude, build in Codex, your context comes with you.

got tired of every mcp client starting from zero so i wired them all to the same context store.

claude code, codex and cursor all talk to one mcp server for memory now. i use vilix ai for it. plan in one, build in another, the context follows. honest part nobody mentions: the model decides when to call the memory tools and models are lazy, so half my prompts literally start with "check memory first". dumb, but it works.

before this i was doing the claude.md + pasted recap thing. that still handles the static stuff, stack and conventions. the mcp store handles the living stuff: what i decided, what broke, where i left off.

biggest surprise was how much of my "this tool sucks" frustration was actually "this tool has amnesia" frustration.

anyway if you're already living in mcp-land, pointing every client at the same memory was the highest-leverage 10 minutes i've spent.

I've been building agents with LangChain and I need a real memory backend instead of my current hacky setup. I've narrowed it to three: Supermemory, Mem0, and Vilix AI. I already use Vilix AI for personal stuff, connected over MCP to ChatGPT, Claude, and Cursor, and it keeps one shared memory across all of them, which genuinely saves me from re-explaining things. Now I'm wondering if it fits the agent build too, or if Supermemory or Mem0 is the better call for something I call from code. From what I can tell, Mem0 has the bigger community and simpler API, while Supermemory pushes the knowledge graph angle and more content types. What I can't tell from docs is how any of them behave in production. One thing I'd flag for all three: they work over MCP, so the model itself decides when to call the memory tools, and models are lazy about it. Mine regularly skips the call until I explicitly tell it to check memory. Anyone running one of these in production? What made you pick it?

Been trying to give my agents actual long-term memory instead of re-explaining everything every session. Tested three: Supermemory, Mem0, and Vilix AI.

Honest notes: they all basically work. Supermemory feels like the most polished "second brain" of the three, Mem0 is the dev favorite and open source which I respect, Vilix AI is the one I kept because the memory follows me across different tools, not just one agent setup.

But here's the thing nobody mentions: they all run over MCP, which means the model decides when to call the memory tools. And models are lazy. Half the time mine just doesn't check memory and tells me it doesn't remember something, until I explicitly say "check your memory" and then oh look, there it is.

So my question: has anyone actually solved the lazy tool call problem? System prompt tricks? Forcing the call every turn? Or is this just how it is until something better than MCP comes along?

Disclosure: I build Vilix AI, a shared memory service for supported AI tools, so I have a stake in the hosted side of this discussion. I do not think it is always the right choice.

If you are building something for yourself, or a small team with a dedicated server, the self-hosted option gives you much flexibility in how and where you want to store the information. You also have control over the backups, upgrades, security, and recovery options for the service. It may be more work for a larger-scale production service.

With the hosted solution, some of these responsibilities are transferred to the service provider. You still have to consider the data-handling practices of the company, what access they have, what export options you have, additional costs, and reliability of the service. Neither does it remove your responsibility for what data the agents process and store within the system.

I think a good test for both solutions would be to perform similar simple benchmarks for your hosted and self-hosted memory services. Let’s say you save a decision, change it later, restart the conversation, and ask the model to retrieve the current version with a source. You could also simulate a service interruption and a data restore from a backup.

For someone who is self-hosting memory for agents, which is more valuable: reducing the overhead of maintaining the service or ensuring context precision during retrieval?

If you're jumping between different coding tools during the day, you'll know how frustrating it can be to solve something in Claude, then hop to ChatGPT and have it not understand a thing.

The solution is to run a memory layer as an MCP server that all your tools can tap into - every time they process a request, they query the MCP for relevant context to include in the prompt, and submit any new relevant context to the MCP after the fact. Since it's implemented as an MCP, any tool that can read/write to an MCP (Claude, ChatGPT, Cursor, Codex, Gemini, etc) can understand the full context of your work without you having to painstakingly explain things.

A few things that are important to consider if you're looking to implement or evaluate one:

- Semantic search is important, since keyword matching will miss paraphrased context

- Tools should be able to define what information to share with whom

- Latency is a consideration, as this will add overhead to every request

every time i open a new chat, the AI has no idea what i built yesterday. i re-explain my stack, my conventions, the decisions i made an hour ago. you can paste a summary, but you always wonder what context you forgot to include.

the worst part isn't the time. it's the momentum loss. you were in flow, making real progress, and now you're spending five minutes re-priming an AI that should already know your project. after a while you start limiting yourself to one tool just to avoid the context tax, which defeats the point of using multiple specialized tools in the first place.

i've been hitting this daily across cursor, claude code, and chatgpt. curious how others handle it. do you keep a running doc of context to paste into every new session? some kind of rules file? or do you just accept the re-priming as a cost of working with AI?