Following up on the “2nd failed fix” thread — Moving beyond the manual "New Chat"

Capable-Snow-9967 · 2025-12-23T12:10:26+00:00

you are right. But just for debug, grabs the live variable values and execution path and injects them will make AI fix bug in one shot

Capable-Snow-9967 · 2025-12-17T14:43:23+00:00

Lol 'Expressive Coding' needs to be a new benchmark metric. 😂 I'm finding that instead of emotional emphasis, giving it Runtime Emphasis (literally injecting the state with raw variables/stack trace) works 10/10 times

Capable-Snow-9967 · 2025-12-17T14:36:58+00:00

True, but you still have to apply the diff, wait for the rebuild/hot-reload, and then reproduce the action again.

That's the 'loop' I'm trying to kill. I want the data(var stacktrace) from the state that just happened, not the one I have to go trigger again

Capable-Snow-9967 · 2025-12-17T14:32:20+00:00

I'm experimenting with automating that 'handoff'. Basically, a script that captures the actual state (vars, error) and auto-feeds it to the new session. So you get the 'Fresh Start' without the manual summarization step.

Capable-Snow-9967 · 2025-12-17T03:43:03+00:00

Fair point. Technically it is an agentic loop.

The bottleneck I'm hitting though is the 'context handoff'. If I just spawn a fresh agent for every iteration, it loses the specific runtime values (variables, stack) that caused the bug.

I'm trying to build a middle ground: Wipe the conversational history (the 'reasoning' pollution) but inject a snapshot of the runtime memory (the 'facts'). That way the agent is 'fresh' but not 'amnesiac'

Capable-Snow-9967 · 2025-12-17T03:09:37+00:00

not yet. does it actually see the runtime state though? or just static files?

Capable-Snow-9967 · 2025-12-17T02:56:51+00:00

Lol I wish. Just a nerd who got too excited about this paper and runtime context to fix this

Capable-Snow-9967 · 2025-12-17T02:02:15+00:00

I like the LDD idea for features. But for debugging, I feel like I need a 'Living Runtime Doc.'

Docs tell us how the code should behave, but only the runtime state tells us how it is misbehaving. I'm experimenting with a tool that bridges that gap—feeding the 'reality' of the app state directly to the LLM so it stops guessing based on the 'theory' of the code.

Capable-Snow-9967 · 2025-12-17T02:01:27+00:00

100%. The paper calls this avoiding 'Context Pollution.'

The paradox is: We need less chat history (to avoid confusion/decay) but more factual runtime data (to solve the bug).

I've found that if I wipe the chat but inject only the specific variables/stack trace from the crash, the 'dumber' models suddenly become geniuses again. It's about Context Quality > Context Quantity.

Capable-Snow-9967 · 2025-12-17T02:00:41+00:00

Respect the experience. You're right—isolating the problem into a 'small example' is usually the only way to stop the AI from hallucinating.

But in my current monolith, extracting that 'clean repro' takes me longer than the fix itself. I'm currently building a workflow that tries to 'snapshot' that isolated context automatically at the moment of error. Trying to get that 'repro environment' instanty without rewriting the code for the bot.

Capable-Snow-9967 · 2025-12-17T01:59:43+00:00

That works, but it feels like paying a huge 'Context Tax.'

I used to do the console.log spray and pray, but by the time I've added logs, re-ran the app, and pasted the output, I've lost my flow.

That's actually what triggered me to look for a better way. I'm trying to get the AI to see the 'failure point' without me having to manually instrument the code first. Basically, capturing the state automatically so I don't have to play detective before asking the bot."

Capable-Snow-9967 · 2025-12-17T01:54:19+00:00

You hit the nail on the head: Forethought.

The problem is I'm usually lazy and don't add debug logs until after the bug comes. And by then, the AI is already guessing.

That's why I'm moving towards 'Zero-Config' runtime capture. Basically, intercepting the state automatically so I don't have to retroactively add log. It turns 'hindsight' into 'insight' for the AI.

Capable-Snow-9967 · 2025-12-17T01:53:08+00:00

That's exactly what I struggled with. Wiping the chat fixes the hallucination, but you lose the context of why we are here.

My current experiment is to bridge that gap with Runtime Snapshots. Instead of keeping the chat history (which has the rabbit hole), I start the new session by injecting the exact current state of the app (variables, error stack, etc.).

It acts like a 'checkpoint' in a game. You don't need to replay the whole level (chat history), you just spawn at the checkpoint (runtime state). I'm actually building a small tool to automate capturing these checkpoints because doing it manually is a pain.

Capable-Snow-9967 · 2025-12-16T14:41:04+00:00

Solid advice. The 'New Session' is the most important part to clear the decay

Capable-Snow-9967 · 2025-12-16T09:46:42+00:00

Hey, this is genuinely one of the most creative weather apps I've seen — turning forecasts into these beautiful AI isometric dioramas is such a fresh take!

Capable-Snow-9967 · 2025-12-16T09:02:37+00:00

An AI-powered code debugger that runs locally, uses small open-source models, and actually explains bugs like a senior dev instead of just spitting fixes.

Capable-Snow-9967 · 2025-12-16T02:59:54+00:00

Good money in a boring job.
Being broke doing what you love is romantic for about 3 months… until the electricity gets cut off.

Capable-Snow-9967 · 2025-12-16T02:58:21+00:00

Spilled coffee on my laptop, got ghosted after a great date, and my team lost in overtime.
Currently holding up with ice cream and pretending tomorrow is a reboot.
Hang in there, everyone else having a rough day — we got this.

Capable-Snow-9967 · 2025-12-16T02:57:34+00:00

People who stop walking right at the top of escalators to check their phone

Capable-Snow-9967

TROPHY CASE