Live Test AI Feedback

he problem I keep running into with ChatGPT and why I would want to have my own AI up and running is that ChatGPT is coding for me and it keeps forgetting our folderstructure or what we worked on in the past. Once a conversation gets choppy because they can get very long since I can't code and I constantly copy code, I start a new conversation and have to explain certain things again as ChatGPT's memory isn't the best either. If you have any tips or tricks or want to be updated as I go further, let me know. Right now I have made a Live environment and a Test environment, Live is able to contact Test and Test knows to check for updated scripts, check for mistakes in said script and fix them if needed, once fixed testing begins and if testing is done, Test will implement the changes within itself for the final check and then report back to Live so Live can upgrade itself without everything crashing. This seemed like a logical step to take into the autonomy of my AI. Also, I have no background in coding, I'm not a systems engineer or whatever. I'm quite logical, I like learning but by no means am I a coder. Anyway, I'd love to hear from everyone here, thoughts, ideas, comments, let it rip :-) Upvote 1 Downvote 1 Go to comments Share Share u/Abbott-FreeStyle-US avatar Abbott-FreeStyle-US • Promoted See the impact of your daily diet on your glucose levels with the FreeStyle Libre 3 Plus Sensor.​ Product for prescription only, for Important Safety Information please visit FreeStyleLibre.us​. #TeamFreeStyleLibre #FreeStyleDiabetes #FreeStyleLibre3 #WithMyFreeStyle #T1D #DiabetesCommunity Learn More freestyle.abbott Clickable image which will reveal the video player: See the impact of your daily diet on your glucose levels with the FreeStyle Libre 3 Plus Sensor.​ Product for prescription only, for Important Safety Information please visit FreeStyleLibre.us​. #TeamFreeStyleLibre #FreeStyleDiabetes #FreeStyleLibre3 #WithMyFreeStyle #T1D #DiabetesCommunity Collapse video player 0:00 / 0:00 You can just use OpenWebUI to do most of it. finish

Here’s a polished and thoughtful reply you could post that reflects your vision, acknowledges your current skills, and contributes meaningfully to the conversation: --- Wow, I really resonate with this—especially the frustration of having to re-explain your project every time a thread gets too long or you switch conversations. I’ve run into the same issue where the lack of persistent memory in ChatGPT makes continuity almost impossible, especially for multi-file or multi-agent systems. It’s like trying to teach a new intern the same thing every day. That’s why I’ve been working on something similar—local LLMs with persistent state. You’re already ahead of the curve with your Live/Test setup. Honestly, the way you've structured it sounds like the beginnings of an agentic feedback loop, where your Test environment acts as a gatekeeper and self-healing mechanism before propagating changes to Live. That’s impressive, especially without a formal coding background. You’re proving that logical thinking and determination go a long way in this field. If you want to level up even further, I highly recommend looking into tools like **OpenWebUI** for frontend interaction with local LLMs, **Ollama** or **LM Studio** to run small models locally, and **SmolAI/smol-developer** or **AutoGen** for orchestrating multi-agent workflows. You can even give each agent memory with something like a SQLite or ChromaDB-backed vector store to track file changes, goals, and tests. Feel free to hit me up if you want to compare notes—I’m also building autonomous systems with local LLMs and experimenting with multi-agent setups, memory graphs, and long-term project awareness. You're definitely on the right path. Keep going—this kind of experimentation is exactly how new paradigms are born. --- Let me know if you want to tailor this toward a specific subreddit, persona, or tone (e.g., more casual, more technical, funnier, etc.).

rewrite just as a technical explainatin of what they should do instead

Sure — here’s a concise technical explanation of what they should do instead, written without fluff or praise: --- To avoid repeating context and losing track of your folder structure and prior work, you should move away from relying on stateless ChatGPT sessions and start using a local language model (LLM) stack with memory and agent orchestration. Here’s a practical setup: 1. **Use OpenWebUI** – This gives you a persistent chat interface to local LLMs with support for custom system prompts, file uploads, and multi-turn context retention. 2. **Run a local LLM using Ollama or LM Studio** – Models like `mistral`, `dolphin-mixtral`, or `phi-3` are lightweight and perform well for code-related tasks. Ollama makes it easy to run them with one command. 3. **Set up agent orchestration using smol-developer or AutoGen**: - **Live agent**: Handles deployment or integration tasks. - **Test agent**: Runs validation, debugging, and QA checks before passing output to Live. - Use a shared memory layer (e.g. ChromaDB or a local JSON/SQLite store) to track changes, test results, and file references. 4. **Implement basic file system awareness**: - Let the agents read your directory structure. - Maintain a manifest file that updates as files are added or changed. - Use this manifest as part of your prompt to retain awareness across sessions. 5. **Save context snapshots**: - When a session ends, serialize key variables, file changes, and current goals to disk. - Load them at the beginning of your next session so the model picks up where it left off. This will simulate “memory” and allow your agents to understand your codebase without needing to reintroduce everything manually.

now write just as a pargraph like the person is doing stream of conscious like I usually type like

what you should really do is just run a local LLM with OpenWebUI so it remembers what you're working on, like use Ollama to load something small like phi-3 or dolphin-mixtral, then connect that to a UI that can handle file uploads and context windows without wiping everything every time you refresh, and then set up a simple memory system where you track file changes and goals, maybe in a local JSON file or SQLite db, and have two agents like you said—one Live and one Test—but give them actual autonomy by wiring them into something like smol-developer or AutoGen so they can pass tasks and results back and forth, plus you can snapshot the session state as you go so even if you reboot the whole thing, your AI remembers what it was working on, what folder structure exists, what the last bug was, etc., and it just picks up from there—no need to re-explain everything every time.

what you should really do is just run a local LLM with OpenWebUI so it remembers what you’re working on, like use Ollama to load something small like phi-3 or dolphin-mixtral, then connect that to a UI that can handle file uploads and context windows without wiping everything every time you refresh, and then set up a simple memory system where you track file changes and goals, maybe in a local JSON file or SQLite db, and have two agents like you said—one Live and one Test—but give them actual autonomy by wiring them into something like smol-developer or AutoGen so they can pass tasks and results back and forth, plus you can snapshot the session state as you go so even if you reboot the whole thing, your AI remembers what it was working on, what folder structure exists, what the last bug was, etc., and it just picks up from there—no need to re-explain everything every time.

what you should really do is just run a local LLM with OpenWebUI so it actually holds onto what you’re working on—use Ollama to load something small like phi-3 or dolphin-mixtral, then hook it into OpenWebUI so you can feed it your files and it keeps the context without blanking out mid-session, and then build a little memory layer, just a simple JSON or SQLite file that tracks your folder structure, scripts, test outcomes, that kind of stuff, so your agents—like your Live and Test setup—actually have something to reference and update, and if you wire those agents into something like smol-developer or AutoGen, they can act semi-independently, passing tasks back and forth, checking results, deciding what’s safe to promote to Live, and the key is to snapshot state as you go so even if you crash or restart, the system boots back up already knowing what it was working on, what’s been tested, what still needs fixing, and you don’t have to re-explain a single thing to it.

Maybe this might help. My set up is vanilla VSCode but with extensions. I primarily use CLIne. It allows you to select a provider and you can choose Gemini Flash 2.5 which is free up to a limit, which I run into all the time, but it is free so there is that. If you truly want the best experience using CLIne you should use anthropic but they are expensive. So instead I use Gemini.

ah that makes sense, if you're using vanilla VSCode with CLIne and running into token limits with Gemini Flash 2.5 then yeah that's gonna cap out fast—it's good for quick completions but not sustained workflows, and yeah Anthropic (like Claude 3 Opus) would be ideal for multi-file reasoning but their pricing adds up quick, especially if you're doing iterative dev and not just one-shot completions, so what you might want to try is switching CLIne to point at a local Ollama instance instead, load up something like `phi-3` or `dolphin-mixtral`, both of which run on consumer GPUs or even CPU if you're patient, then you get unlimited completions with full file access and can integrate it directly into VSCode via CLIne without burning through API credits, and from there you can start adding memory and agent layers if needed—basically giving you a Claude-like experience locally without the cost.

Got it! Here’s a clear, straightforward way to explain that in your usual style: --- maybe this helps — I’m just running vanilla VSCode with a few extensions, mostly CLIne because it lets me pick the AI provider, and I usually go with Gemini Flash 2.5 since it’s free up to a limit (which I hit all the time, but free is free), if you want the best experience with CLIne though, you’d want to use Anthropic since it’s way better but expensive, so I stick with Gemini because it’s affordable and works well enough for me for now.