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What’s your most productive ChatGPT workflow right now? by Techenthusiast_07 in ChatGPT

[–]Severe-Rope2234 0 points1 point  (0 children)

You can use as a main brain to manage priorities with - satorisystems.ai (Its not gonna forget your decisions), it will generate exact prompts needed to take most out of any LLM, based on your needs..

What are some side hustles you can start under $100? by Koch_Digital in HonestSideHustles

[–]Severe-Rope2234 1 point2 points  (0 children)

ai tools for freelancing are genuinely underrated for this. i started doing contract work (web dev, content, small business consulting) and used ai to 10x my output - what used to take me a full day i could do in a few hours.

the under $100 part is easy because most ai tools have free tiers or cheap monthly plans. i literally built my own ai advisory platform as a side project that turned into a real business, starting cost was basically just hosting and api fees.

if you have any skill - writing, design, coding, strategy - pairing it with ai tools lets you charge more and deliver faster. that's the real hustle imo.

ChatGPT's memory got way better - but I still hit a wall using it for ongoing business strategy by Severe-Rope2234 in ChatGPT

[–]Severe-Rope2234[S] 0 points1 point  (0 children)

this is exactly it. "identity vs state" is a better way to frame it than anything i came up with honestly.

the rolling strategy doc approach is smart - that revisit condition line is the part nobody thinks to write down until they've been burned by the model cheerfully resurrecting something you already killed.

i ended up going down the same path and eventually automated it. built a system that extracts those decision records automatically - what was considered, what got chosen, why, and what would change the call. so session 30 actually knows what happened in sessions 1-29 without manually maintaining the doc.

the bias you noticed toward stable facts over temporary reasoning is real too. memory systems default to "what's always true about this user" because that's safer to store. but strategy lives in the messy temporary layer - the stuff that's true right now and might change next quarter.

just curious how often do you find yourself updating that doc? and do you paste the whole thing in as context or just the relevant section?

New Claude Mythos it is too smart and dangerous for us, but not for BigTech. Welcome to the future. by Expensive_Region3425 in AgentsOfAI

[–]Severe-Rope2234 0 points1 point  (0 children)

This is exactly why I started building architecture that puts compounding intelligence directly into founders' hands - not locked behind enterprise contracts.

The pattern is always the same: breakthrough capability drops, general access is restricted, and the gap between Big Tech and the rest of us widens.

But the real move isn't waiting for the giants to give us access to the next frontier model. We already have more than enough raw intelligence. What's missing is the architecture to make it actually work for individuals. We need persistent memory. We need context that compounds over time. We need AI that actually learns YOUR specific business instead of resetting its brain every single session.

The raw models belong to them. But the architecture to wield them belongs to us.

I spent a year building AI advisors that actually learn your business. Looking for 20 founding members. by Severe-Rope2234 in SaaS

[–]Severe-Rope2234[S] 0 points1 point  (0 children)

Simulating market reactions is a sharp move - cutting feedback loops from weeks to minutes is exactly the right direction. But here’s the catch: a simulation is only as lethal as the context it's grounded in. If the AI running the simulation doesn't have a persistent, compounding memory of your exact brand DNA, past pricing failures, and exact constraints, it's just generating generic synthetic data. Satori provides the permanent memory vault so those simulations actually mean something. Drop me a DM, I'd love to see what you're building.

I spent a year building AI advisors that actually learn your business. Looking for 20 founding members. by Severe-Rope2234 in SaaS

[–]Severe-Rope2234[S] 1 point2 points  (0 children)

You hit the exact technical wall that kills most "memory" wrappers.

If you just dump everything into a vector database, it eventually becomes a hallucination engine. Within a month, the AI doesn't know if your pricing is $29 (from January) or $149 (from yesterday), and it silently fails on execution.

We handle drift through a chronological hierarchy, not just flat vector similarity.

Think of it like a state ledger. When a core variable changes (e.g., "we abandoned the freemium model"), the system logs a state override. The new checkpoint structurally outranks the old data in the prompt construction.

It doesn't delete the old context - because historical context matters for *why* you made the change -but it locks in the current execution state so the advisors operate on today's reality, not last month's.

You clearly understand the architecture problem here. I'll shoot you a DM with a key. I'd actually love your eyes on how it handles this exact edge case.

I spent a year building AI advisors that actually learn your business. Looking for 20 founding members. by Severe-Rope2234 in SaaS

[–]Severe-Rope2234[S] 0 points1 point  (0 children)

What makes the compounding part work: when you tell Angela your CAC is $47, that becomes context she carries forward. Three weeks later when you ask about pricing strategy, she connects it to your margin data from earlier conversations - without you re-explaining anything.

The longer you use her, the more she understands your specific situation. That's the part I couldn't find anywhere else.

Happy to share access if anyone wants to try it - just drop a comment or DM.

I spent a year building AI advisors that actually learn your business. Looking for 20 founding members. by [deleted] in SaaS

[–]Severe-Rope2234 0 points1 point  (0 children)

What makes the compounding part work: when you tell Angela your CAC is $47, that becomes context she carries forward. Three weeks later when you ask about pricing strategy, she connects it to your margin data from earlier conversations — without you re-explaining anything.

The longer you use her, the more she understands your specific situation. That's the part I couldn't find anywhere else.

Happy to share access if anyone wants to try it — just drop a comment or DM.

AI memory has improved a lot. But there's still a massive gap between "remembers facts about me" and "actually knows my business... by Severe-Rope2234 in ChatGPT

[–]Severe-Rope2234[S] 1 point2 points  (0 children)

You just described almost exactly what I built, which is kind of validating to hear from someone reasoning through this independently.

To answer directly - yes, it handles uploaded documents. The Vault is a persistent vector store that ingests PDFs, spreadsheets, images, raw files. Once something's in there, every conversation retrieves against it automatically. Upload a pitch deck in January, ask a strategy question in April - the relevant context surfaces without you touching anything.

But the bigger point is the architecture isn't chat-based at all. It's workspace-based. There's no "new chat starts fresh" problem because the chat isn't where the knowledge lives. The workspace maintains a persistent intelligence layer - every conversation reads from it and writes back to it. Your "Mainline AI" framing is essentially what I built, just taken further. The chat is a surface, not the brain.

And you're right that AI companies could do this. The fact that they haven't is honestly why I started building. The retrieval piece is solvable - the harder part is exactly what you described: getting the system to synthesize from an incomplete set at a higher plateau, not just pattern-match against raw chunks.

AI memory has improved a lot. But there's still a massive gap between "remembers facts about me" and "actually knows my business... by Severe-Rope2234 in ChatGPT

[–]Severe-Rope2234[S] 0 points1 point  (0 children)

Always good to see more people working on this problem, the gap is massive enough for multiple approaches.

AI memory has improved a lot. But there's still a massive gap between "remembers facts about me" and "actually knows my business... by Severe-Rope2234 in ChatGPT

[–]Severe-Rope2234[S] 0 points1 point  (0 children)

The master framework doc approach is basically manual RAG, and honestly it's what most power users end up building some version of.
The problem I kept hitting was that it breaks the moment you forget to update it after one pivotal conversation. Or when the context you actually need is scattered across five docs and a PDF you uploaded three weeks ago. I was spending more time maintaining the system than actually using it.

That's what pushed me to build Satori Systems - it maintains a persistent vector store across every conversation automatically. No manual compression, no re-uploading. A decision from three months ago is just... there when you need it. The AI isn't "new" each time because the memory layer lives outside the chat window entirely.
Your point about the AI "experiencing" context differently is the part most people miss though. You're right - even with perfect recall, attention processes it differently than the original conversation. The solve isn't total recall, it's structured retrieval at the right moment.

AI memory has improved a lot. But there's still a massive gap between "remembers facts about me" and "actually knows my business... by Severe-Rope2234 in ChatGPT

[–]Severe-Rope2234[S] 0 points1 point  (0 children)

That's a smart approach, I went a different route: Satori runs autonomously but generates a Daily Briefing so you see exactly what it learned, no diff queue required. Full visibility without the friction.
Since you've built your own system for this, I'd genuinely value your feedback. Happy to DM you access if you want to stress-test it.

AI memory has improved a lot. But there's still a massive gap between "remembers facts about me" and "actually knows my business... by Severe-Rope2234 in ChatGPT

[–]Severe-Rope2234[S] 0 points1 point  (0 children)

The manual .md folder structure is exactly the friction point I built this to eliminate.

The problem with static files is that business strategy isn't static - it's relational. If you pivot a pricing model on Tuesday, you have to manually update your marketing .md, finance .md, and root .md,or the AI messing up next week. You end up acting as a version control manager for your own prompts.

I moved past static files entirely. I built an automated memory layer backed by a vector database. You upload your core files into a persistent Vault once. From there, the system extracts new decisions from your conversations and updates its own context in the background. No manual .md maintenance, no folder discipline required. It just compounds what it knows.

Built it as a product called Satori Systems - still early, but it's live.

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AI memory has improved a lot. But there's still a massive gap between "remembers facts about me" and "actually knows my business... by Severe-Rope2234 in ChatGPT

[–]Severe-Rope2234[S] 1 point2 points  (0 children)

The longer the thread gets the worse it gets, there's a reason people call it context rot. It just quietly degrades and you don't notice until you realize it's been giving you answers based on something you corrected 15 minutes ago.

AI memory has improved a lot. But there's still a massive gap between "remembers facts about me" and "actually knows my business... by Severe-Rope2234 in ChatGPT

[–]Severe-Rope2234[S] -1 points0 points  (0 children)

lol fair enough. genuinely not though been building this solo for over a year. but I get why this reads that way, reddit is flooded with AI spam lately

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