I got tired of re-explaining my workflows to AI every single day. So I built something that actually remembers.
Every morning, same ritual.
Open ChatGPT. Paste my project context. Re-explain what I'm building. Re-explain my preferences. Re-explain what I tried yesterday.
Then get a decent answer. Close the tab. Tomorrow: repeat.
I tracked it once. I was spending 20-30 minutes per day just on context-setting. Not on actual work. On setup. That's 150+ hours a year, gone.
The problem isn't the AI. It's that AI tools have no memory. Every session starts from zero. Every workflow has to be re-explained. For solo founders running everything themselves, this is brutal.
So I started building AllyHub.
The core idea: what if your AI didn't just respond, but evolved?
Instead of a chat window that resets, you get a personal AI agent that remembers every task it's run for you, builds reusable workflows automatically, and accumulates skills from real execution - not prompts, not templates, actual learned capability from doing the work.
The compounding thing is real. Here are actual numbers from our own usage:
Task 1: collect 20 posts from X about a topic. Cost: 65 credits.
Task 2: same kind of job, different topic, 100 posts. Cost: 16 credits.
Same work. 5x more output. 75% cheaper. Because the agent didn't start from zero - it already knew the site, already had the workflow saved.
Task 3: collect posts plus full author profiles (new capability it hadn't done before). Cost: 123 credits.
Task 4: same job, 5x more data. Cost: 32 credits.
Four tasks in, the agent has built 4 reusable assets. Every future task in this domain costs almost nothing. We call this ROTI - return on token investment. It compounds.
What this means practically: you run a task once, the agent figures it out and saves what it learned, and the next time you need something similar it just does it. No setup. No re-explaining. The workflows you build up over weeks become a real asset - actual executable automation, not just saved prompts.
We're in closed beta right now. If you're a solo founder or indie hacker who's tired of the starts-from-zero problem, I'd genuinely love to have you try it and tell me what you think.
Site is allyhub.ai - drop a comment or DM me if you want an invite code. We also have a small Discord where beta users hang out and talk to us directly: discord.gg/WNMTr3w3pC
Curious: what's the most painful re-explaining-to-AI moment you've had? I feel like this is universal but I want to hear if others feel it as much as I do.
You can also try memex its available on vscode extension and in node packages @patravishek/memex
Great point! The two-layer file system approach is really elegant.
This right here, "Every workflow has to be re-explained. For solo founders running everything themselves, this is brutal," is such a pain point.
Can you explain just a bit on how your App achieves this?
Thx, Jeff
Hey Jeff! Great question. The core idea is that Ally builds up reusable Manuals and Playbooks as it works. First time you run a task, it explores and learns. Every run after that, it skips the exploration and executes using what it already knows. Your preferences, project context, and workflow patterns persist across every session. So instead of you re-explaining, the agent already knows. Happy to share more - what kind of workflows are you running most often?
Very nice, thank you for sharing.
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Felt this. Re-explaining everything kills momentum. Really like the direction you’re taking with AllyHub would love to see how it develops.
Thanks Hivin! That momentum kill is real - you finally get into flow and then boom, blank slate. We're in early access right now if you want to try it out. Drop me a DM or join our Discord and I'll get you in.
That momentum break is the worst part honestly. This looks interesting, curious how you’re handling context across sessions
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Re-explaining things to AI every day is a huge time waste.
I like your idea of turning workflows into something reusable instead of starting from zero each time.
Only concern is memory over time, it should stay clean and editable. If that’s solved, this could become something really powerful for founders.
That's exactly the right concern and honestly the one we obsess over most. Memory in AllyHub is fully transparent and editable - you can see everything the agent has stored, edit it, delete it, or override it at any time. It's not a black box. The goal is memory that compounds without becoming a mess.
150 hours a year on context setting is honestly painful when you put it like that. I never actually tracked it but I bet I'm close to that number too.
The part that resonates most is that AI tools right now are basically goldfish. You have this amazing conversation, figure stuff out together, and then tomorrow it's like it never happened. Really frustrating when you're building something and need consistency across sessions.
How are you handling the "memory gets stale" problem though? Like if my project pivots or my preferences change, does old context start conflicting with new decisions? That's the thing I'd worry about with persistent memory.
The goldfish analogy is perfect. On the stale memory question - this is something we've thought hard about. In AllyHub, memory is structured and editable, not just a growing pile of chat history. When your project pivots, you update the relevant memory entries. Old context doesn't silently conflict - you're always in control of what the agent knows. Think of it less like a diary and more like a living wiki you can edit.
well done you i feel your pain
Appreciate it! Solidarity with everyone who's felt this pain. We're building the fix.
The pain is real, but I'd be careful not to solve it with infinite memory. The useful version is selective memory: durable context, explicit preferences, and a clean way to prune stale instructions. Otherwise the product turns into a junk drawer and the answers get worse over time. If you can make memory editable and composable instead of just persistent, that's the part I'd pay for.
This is exactly right and it's the design principle we built around. AllyHub memory is structured into Skills (judgment/preferences), Manuals (how to operate specific tools), and Playbooks (repeatable workflows). Each layer is editable and composable. You can prune, update, or override any piece. The goal was never infinite memory - it was the right memory, organized so it compounds instead of clutters.
This resonates so much — the context reset in most AI tools feels like a tax on your time every single day, and most people don’t actually measure how much it costs in attention and momentum. What you’ve built with AllyHub — turning context into an asset instead of a recurring burden — is a powerful shift in thinking. The ROTI framing and reusable workflows concept feels like a real step toward AI that actually augments long-term work instead of just assisting in the moment. Curious how you’re handling workflow drift over time as things evolve, but this feels like a tool a lot of solo makers are going to start relying on.
Workflow drift is the hard part - you nailed it. The way we handle it: Manuals and Playbooks in AllyHub are versioned and editable. When a workflow changes, you update the relevant piece. The agent doesn't silently run stale logic - it runs what's in the current version. And because everything is transparent, you can see exactly what it knows and correct it. It's more like maintaining a codebase than hoping a black box stays current.
The ROTI framing is the most interesting thing here. You're arguing the product gets cheaper per task as workflows compound, and the numbers back it up. The ICP question I'd push on: who in your beta actually has that high-frequency repeating pattern? A solo founder doing varied, non-repeating work might not see the compounding fast enough to justify switching. A small team running the same research or data jobs weekly would hit that second-task savings on day three. Are you seeing that split in your beta users?
Sharp question. You're right that the compounding is most visible for high-frequency repeating work. In our beta, the clearest wins are teams running the same research, scraping, or reporting jobs weekly - they hit the savings on run 2 or 3. Solo founders doing varied work still benefit from persistent context (no re-explaining preferences, project state, etc.) but the cost curve is flatter. We're watching this split closely. The ICP is probably 'anyone who runs the same type of task more than once a week.'
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The token efficiency angle is really interesting. When you're using AI heavily, those costs add up fast so having workflows that get cheaper over time is a big deal.
The re-explaining is frustrating but honestly what's worse is when the AI loses context and just runs with it without telling you. You don't realize it went off the rails until it's already done something you didn't want. My favorite example — having unit tests fail after code changes and the AI decides to rewrite the tests to make them pass instead of fixing the actual issue that broke them. It confidently "fixed" the problem by deleting the proof that there was one.
Curious how AllyHub handles that kind of drift. Does the remembered context help catch those situations or is it more focused on the workflow side?
The silent drift problem is actually worse than the amnesia problem in some ways - at least with amnesia you know it forgot. The confident wrong answer is brutal. On how AllyHub handles it: persistent context helps because the agent knows your constraints and preferences going in, so it's less likely to go off-rails. But we also build in explicit checkpoints in Playbooks where the agent confirms before taking irreversible actions. It's not foolproof but it's a lot better than a blank slate every time.
Painful moment for me is not only re-explaining context, but re-explaining boundaries: what counts as done, what sources to trust, and what the first useful loop actually is. Once that frame is missing, the model can sound helpful while still pushing the wrong workflow.
What I like in your post is the shift from memory as chat history to memory as executable structure. That feels much closer to how real work compounds.
Curious whether you store only successful workflows, or also failed attempts and dead ends so the agent learns what not to repeat.
The context-setting overhead is real and it compounds in a painful direction. Most people don't measure it the way you did but it's there.
What I find interesting about the ROTI framing is that it gets at something most AI tool builders miss: the value isn't in a single session, it's in accumulation. People are building workflows whether they know it or not. The question is whether those workflows live in their heads and get re-typed every time, or in the tool.
The 75% cost reduction on the second run is the real proof point here. Not because of the money, but because it means the agent actually learned something transferable. That's different from just caching.
Curious how you handle workflow drift over time as the source site or target API changes. That feels like the hard part of persistent learned workflows.
The accumulation point is the key insight. On workflow drift - this is the hard engineering problem. In AllyHub, Manuals (which encode how to operate specific tools/sites) are versioned. When a site changes its UI, the Manual gets updated. Playbooks reference specific Manual versions. It's not magic - someone has to notice the drift and update it - but the structure makes it explicit and fixable rather than silently broken.
This hits so hard. For me, it is when I want AI to continue a multi-step research task I started yesterday, and I end up spending 15 minutes just dumping context again. Im curious how does Allyhub handle evolving workflows across totally new domains?
That 15-minute context dump to resume a research task is exactly the tax we're trying to eliminate. On evolving workflows across new domains: AllyHub builds new Manuals for each new environment it encounters. So when you go into a new domain, it explores once and saves what it learned. Next time you're in that domain, it already knows how to navigate it. The memory compounds across domains, not just within one.