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AI coding tools have come a long way. I use them daily and they've made me more productive.
But a lot of us don't work on new repos. We work on codebases that have been around for years. Functions 3000+ lines long. Code from 2015 still running in production.
When you point AI at that kind of code, it struggles. The context fills up. It loses the bigger picture. Suggests a fix that works in isolation but breaks something three files away.
The tools work great on fresh projects. On older codebases, the experience is very different.
The context memory problem? Nobody has solved it yet. Once the conversation gets long, the AI loses track. Basically type and hope.
If you're on legacy code and AI isn't the magic wand it seems in demos you're not doing it wrong.
What's your experience using AI on older codebases?
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I’ve been working on a small skill called Manual Orchestrator to make longer projects with AI agents easier to manage.
The problem started with a familiar situation: one chat would accumulate plans, implementation details, corrections, and test results. By the time I was ready for the next phase, I wanted a fresh conversation without losing the decisions we’d already made.
Manual Orchestrator gives that process some structure. One chat acts as the orchestrator: it breaks the work into stages and writes a prompt for the next one. I run that prompt in a new session, then bring the result back. The orchestrator reviews it against the stage’s criteria before preparing the next prompt. If something needs fixing, I go back to the same worker session.
The skill also keeps a record in the project with the current stage, decisions, evidence, and next steps. That makes it easier to pause, resume, or hand the work to another session.
The handoff is manual. I choose when to open a session and carry the result back. The orchestrator and worker sessions can also use different agent CLIs, with the prompt adapted to the capabilities available.
It’s open source and still a work in progress. I’m using it to learn what helps in practice and where the workflow needs to improve. Installation has been checked for Codex, Claude Code, and Gemini CLI, but the full workflow hasn’t been tested across every CLI or version.
If you try it, I’d be interested in hearing what works and where it gets in your way.
Opus Clipping vs. Real Retention edit
Opus Clip is great for quick cuts. But AI doesn't know your audience's psychology it can't feel which exact sentence will hold attention or drive sign-ups.
If you have hours to waste testing AI clips, it’s fine. But for creators and businesses who want real retention, standard AI cuts just don't work.
We took the exact same podcast section and edited it properly at StoryNexis.
Note: Opus wasn't given the full podcast we fed it the exact same high-potential clip we cut to make it a fair test.
Be honest which one actually holds your attention longer? Opus or StoryNexis? Drop your vote below 👇
5 votes
Ends in 21h
Real retention edit. Ofcourse. It feels human! Great to have exact transitions where needed. I dont think any AI can do that.
I didn't agree with you fully because there are ways to remove the ai slope, I think a human skill + ai is a killer combo 💀...
But in short AI has given us the speed of 10x even sometimes its 100x, so the thing you have to learn is the basics process:
(Input -> Process -> Output)
and then you have to guide the ai and it will slay it off like a pro🔥
Trust me bros its true 🙌
Manual work is always better than ai slop you can use ai for some clips but full video made with ai is unprofessional and a waste of money and resources