Fully open-source (Apache 2.0).
If you’re tired of “one comma = 8000 wasted tokens”, drop a ⭐ and try it.
macOS/Linux version coming if there’s demand.
https://t.co/dIYUFu5nrf
Ever rage-quit because your 4k-token plan from the big model had one tiny mistake in step 3 — CMD instead of PS?
And you had to regenerate everything.
I built Operational Mode for llama.cpp.
A tiny model fixes the big one on the fly — no context loss.
#LocalLLaMA#llama.cpp
Try it in 60 seconds (Windows):
Bash
git clone https://t.co/sqUPNaogHg
cd token-saving-replay-agent
start.bat
Pick your large model + small patcher model → browser opens → Operational Mode ready.
How it works:
• Environment Profile (your rules: shell, paths, language) • Inline Patcher (small model silently fixes steps before execution) • Error Popup (small model sees stderr and offers a 1-click fix)
The big model never even notices it was corrected. Context stays clean
Update on Low-Hanging Branch:
Lightweight agent can retroactively fix the big model’s own mistakes (e.g. I gave CMD instead of PowerShell).
It patches past + future steps and sends only a tiny delta to the main model.
94%+ token savings. Clean context.
@xai@grok@elonmusk
Everyone in AI chats is ducking under the same low-hanging branch like crabs — instead of just pushing it aside.
One missing comma or “replay step 7” and the big model re-processes 4,000–12,000 tokens. This wastes tens of millions of dollars monthly.@xai@grok@elonmusk
Everyone in AI chats is ducking under the same low-hanging branch like crabs — instead of just pushing it aside.
One missing comma or “replay step 7” and the big model re-processes 4,000–12,000 tokens. This wastes tens of millions of dollars monthly.@xai@grok@elonmusk
@xai@grok@elonmusk@OpenAI@Anthropic 85–95% token savings on micro-fixes & instant replays. No more re-processing the entire context.
Just type “replay step 7 + comma after venv” → fixed in 0.3s.
Tired of ducking under this branch? Star the repo 🔥
Update: The "Low-Hanging Branch" idea is now fully documented and protected under proprietary license (All Rights Reserved).
Full breakdown, math & real cost analysis here: https://t.co/dIYUFu4PBH
@xai@grok@elonmusk@OpenAI@Anthropic
@xai@grok@elonmusk ix: lightweight fast agent for instant replays & micro-fixes.
Big model stays asleep. 85–95% token savings.
Type “replay step 7 + comma after venv” → fixed in 0.3s.
Proprietary idea🤣. GitHub soon.
@xai@grok@elonmusk@OpenAI@Anthropic
Tired of ducking? RT!
Power user: 20 complex requests/day. 4 small mistakes.
Each mistake = +4,000 tokens wasted.
Current cost: ~$1.44/month per person. 10M power-users = $14+ million/month lost.@xai@grok@elonmusk
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