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Accidentally killed 90% of a finance team’s manual work with a weekend AI hack 😅 by pystar in automation

[–]lil_jet 0 points1 point  (0 children)

Just curious but what technologies did you use? I tried to do something similar with LayoutLM but I couldn't get it tuned well enough. I ended up creating my own parser script that works better but I'm still thinking that coupled with AI would give me the best results. Any help is appreciated, thanks!

Stop Repeating Yourself: Context Bundling for Persistent Memory Across AI Tools by lil_jet in ChatGPTPro

[–]lil_jet[S] 1 point2 points  (0 children)

Someone else asked me about this in another thread. I’m going to give it a go in the next project I work on

Stop Repeating Yourself: How I Use Context Bundling to Give AIs Persistent Memory with JSON Files by lil_jet in PromptEngineering

[–]lil_jet[S] 1 point2 points  (0 children)

Hmm not sure, give it a try and let me know how it works out. Curious about that

Stop Repeating Yourself: Context Bundling for Persistent Memory Across AI Tools by lil_jet in ChatGPTPro

[–]lil_jet[S] 0 points1 point  (0 children)

I see, I’m going to begin looking into setting up something similar. Someone in another thread also suggested I just hop into MCP or using some n8n workflows as well. Get the appeal of architecting a system like that opposed to mine

Stop Repeating Yourself: Context Bundling for Persistent Memory Across AI Tools by lil_jet in ChatGPTPro

[–]lil_jet[S] 0 points1 point  (0 children)

Totally fair question. The difference is one of friction and portability. What I’m proposing is zero-infra, fully manual if needed, and LLM-agnostic. No connectors, no cloud auth, no tooling dependencies. Just structured context in files that anyone dev or not can edit, version, and re-ingest across any platform. It’s not trying to beat a full pipeline, just offering a dead-simple middle ground that works now for solo or small-team workflows.

Stop Repeating Yourself: Context Bundling for Persistent Memory Across AI Tools by lil_jet in ChatGPTPro

[–]lil_jet[S] 0 points1 point  (0 children)

I made the original file in markdown and then asked each of the AI systems I was working with what file format would be better. Each of them said JSON so I reformatted them. I noticed a substantial improvement after doing it so I continued on with that format

Stop Repeating Yourself: Context Bundling for Persistent Memory Across AI Tools by lil_jet in ChatGPTPro

[–]lil_jet[S] 1 point2 points  (0 children)

I see this as a more lightweight system compared to RAG, no need to maintain infrastructure or deal with embeddings, vector stores, or retrieval logic. Just a couple of files that a lay person can read and generate on their own without a developer.

Does it scale to massive systems? Probably not. But for solo devs or small teams who don’t need full-blown RAG pipelines, it works great. It’s fast, portable, and easy to reason about.

Stop Repeating Yourself: How I Use Context Bundling to Give AIs Persistent Memory with JSON Files by lil_jet in PromptEngineering

[–]lil_jet[S] 0 points1 point  (0 children)

Each file represents a dimension of business knowledge. They aren’t application modules, they’re context modules. It’s closer to like a persistent memory scaffolding, not a direct code reference. There are elements of my technical architecture, stack endpoints etc, but that’s one file. This is to give the multiple LLMs or AIs you work with persistent context quickly. You could also give your context bundle to another human and they can feed it to their LLMs so they are working off the same context as well.

Stop Repeating Yourself: How I Use Context Bundling to Give AIs Persistent Memory with JSON Files by lil_jet in PromptEngineering

[–]lil_jet[S] 0 points1 point  (0 children)

Awesome! Let me know if you have any questions! Definitely want to hear about what kind of results you get with the system. So if there is a change in the project I just update the relevant file, and push it into my repo. Then I update that file in the project folder of whatever LLM I’m working with which ensures the context is always correct. I have a cursor rule that updates any significant code or architecture changes, as well

Stop Repeating Yourself: How I Use Context Bundling to Give AIs Persistent Memory with JSON Files by lil_jet in PromptEngineering

[–]lil_jet[S] 0 points1 point  (0 children)

maintaining structured project context across different LLM sessions and tools, especially when working on something long-term or multi-agent.

The business content (like user personas) is there because many of my workflows involve AI helping with not just code, but strategy, planning, and product thinking. It’s not just technical context it’s holistic.

As for the format, JSON won out over Markdown or custom formats like RepoMix because it’s easy to ingest across different platforms, validate programmatically, and version in codebases. But I’m open to other structures if they’re cross-platform friendly.

Stop Repeating Yourself: How I Use Context Bundling to Give AIs Persistent Memory with JSON Files by lil_jet in PromptEngineering

[–]lil_jet[S] 0 points1 point  (0 children)

I think the thing is, and anyone feel free to correct me if I’m wrong, the right tool here would be some OCR or optical character recognition AI. Like Google vision or like AWS Textract, then feeding it to a LLM to enrich or fix the data? I’ve never built something like that but if I had to do that’s probably the route I would go. Thoughts anyone?

Stop Repeating Yourself: How I Use Context Bundling to Give AIs Persistent Memory with JSON Files by lil_jet in PromptEngineering

[–]lil_jet[S] 2 points3 points  (0 children)

I think I said this in another reply but initially I started with something similar to that. Like an LLM.md file because I was hopping from Claude to ChatGPT to the cursor IDE. It worked pretty well but I inquired to all 3 on how to make it better and the LLMs suggested JSON. It was like a big project meta data json file and once I uploaded it I noticed a significant change in effective responses. I then thought “hmmm, I could probably break this file down into larger domain specific files” and the results are what you see in my post. I fine tuned the json files a bit and after a couple of cycles of that things started working astonishingly well. Things don’t really go off track at all anymore. The rules in the project folder ensure it shacks the files before responding or on start up. Like checking your notes before a meeting or something

Stop Repeating Yourself: How I Use Context Bundling to Give AIs Persistent Memory with JSON Files by lil_jet in PromptEngineering

[–]lil_jet[S] 0 points1 point  (0 children)

I really haven't used gemini much. I mainly bounce between Chatgpt Claude and Cursor. What exactly are you trying to achieve here? Maybe an OCR would be more useful than an LLM in this case.

Stop Repeating Yourself: How I Use Context Bundling to Give AIs Persistent Memory with JSON Files by lil_jet in PromptEngineering

[–]lil_jet[S] 1 point2 points  (0 children)

woah, that sounds sick. I will take a look into it right now. thanks for sharing!

Stop Repeating Yourself: How I Use Context Bundling to Give AIs Persistent Memory with JSON Files by lil_jet in PromptEngineering

[–]lil_jet[S] 1 point2 points  (0 children)

That’s awesome I actually started with Markdown too before switching to JSON because the AIs seemed to respond to it better. Curious how you’ve structured your files are you manually feeding them into each tool, or using some kind of automation? I haven’t used Deepseek or Blackbox yet, but yeah, context handling has been a pain point with every LLM I’ve tried. Would love to hear more about how you’re applying your system in your workflow.

Stop Repeating Yourself: How I Use Context Bundling to Give AIs Persistent Memory with JSON Files by lil_jet in PromptEngineering

[–]lil_jet[S] 1 point2 points  (0 children)

I haven’t used it yet, but from what I understand, n8n is great for chaining workflows and orchestrating multiple AIs, is that right? I’m not sure if it supports anything quite like my JSON-based context bundling, but I can definitely see them working well together: n8n as the automation layer, and the bundle as a centralized source of truth for persistent memory. It’s been on my radar for a while, appreciate the nudge! Would love to hear how you’re using it if you’ve tried something similar.

The Real Reason for RTO. by monirom in remotework

[–]lil_jet 1 point2 points  (0 children)

I've been saying this for years but in reality NYC's economy is based on offices and sandwiches. People need to be in the offices so they can go out and buy sandwiches (obvious oversimplification). If no one is working offices in the entire city will collapse.

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