As you all may know open code go has cut their limits by up to 500%.
Just wondering what people are switching to?
Me - command code + Nvidia nim
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3-STACK TOKEN AUDIT: MemPalace + Headroom + RTK
Stack installed: July 21, 2026
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COMPONENT STATUS
1. HEADROOM (Context Compression) ✅ RUNNING
- Proxy: 127.0.0.1:8789, healthy, v0.32.1
- Backend: OpenAI-compatible (OpenCode Go)
- Compression savings: $1.89 (377K tokens saved)
- Compression ratio: 81.7% of input compressed
- 55 compression events since install
2. MEMPALACE (Long-term Memory) ✅ RUNNING
- Replaces built-in MEMORY.md injection
- ChromaDB vector store: 952KB
- Facts DB: 35.6KB (dg10_facts.md)
- Old memory injection: 8,283 bytes/turn (~2,070 tokens)
- New: only relevant facts on-demand via semantic search
- Savings: ~2K tokens/turn eliminated from system prompt
3. RTK (Terminal Output Rewrite) ⚠️ MINIMAL IMPACT
- Installed: v0.43.0
- Only 3 requests processed, 1,573 tokens saved
- Not being heavily utilized
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TOKEN SAVINGS — THE NUMBERS
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BEFORE STACK (all sessions before July 21):
Sessions: 2,209
Avg input/session: 3,596,053 tokens
Total input: 7.94B tokens
Total output: 28.6M tokens
Cost (if DeepSeek): $1,132.77
AFTER STACK (July 21 onward):
Sessions: 31
Avg input/session: 563,720 tokens
Total input: 17.5M tokens
Total output: 373K tokens
Cost (if DeepSeek): $2.77
PER-SESSION REDUCTION:
Input tokens: -84.3% (3.6M → 564K per session)
Output tokens: -6.9% (12.9K → 12.1K per session)
Cost per session: -82.6% ($0.51 → $0.09)
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WHAT EACH COMPONENT ACTUALLY SAVED
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HEADROOM — Direct measured savings:
- 377,118 tokens compressed away from LLM input
- $1.89 saved at DeepSeek pricing
- 89.66% compression ratio in best session
- Every turn's tool output gets shrunk before hitting the API
MEMPALACE — Indirect but large:
- Old: 8,283 bytes (~2,070 tokens) injected EVERY turn as system prompt
- New: semantic search returns only relevant facts (~200-500 tokens)
- Savings: ~1,500-1,800 tokens per turn × avg 30 turns/session
- = ~45K-54K tokens saved per session
- This is the biggest single contributor to the 84% input drop
RTK — Negligible:
- 1,573 tokens saved total
- Terminal output rewriting isn't hitting enough volume to matter
- The heavy lifting is done by Headroom compressing the same output later
HIDDEN WINNERS (not part of the 3-stack but set up same day):
- show_reasoning: false — stopped storing reasoning traces as messages
- agent.max_turns: 50 (was 180) — shorter sessions = less accumulated context
- These two alone account for a significant chunk of the 84% drop
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COST IMPACT (OpenCode Go flat-rate)
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You're on OpenCode Go at $10/month flat. The token savings don't
directly save dollars on that plan — but they mean:
1. You can run 6-7x more sessions before hitting rate limits
2. Sessions stay fast (smaller context = faster inference)
3. If you ever move off flat-rate, you'd save ~$1,100/month
at DeepSeek pricing for the same workload
4. Headroom alone is on track for ~$2/week savings at current usage
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VERDICT
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The 3-stack is saving you 84% input tokens per session.
The MVP is MemPalace (kills system prompt bloat) + Headroom
(compresses tool output). RTK is barely used — not pulling
its weight.
The hidden heroes are show_reasoning:false + max_turns:50,
which were set up alongside the stack and contribute heavily.
I am using opencode go mimo 2.5 model with hermes agent nd i am gettig this weird error.. i just recharged the account yesterday and this error on newly topped account is very weird, anyone else facing same?
Error: HTTP 400: Error from provider (Console Go): Upstream request failed
Provider details
HTTP 400: Error from provider (Console Go): Upstream request failed
Copy
01:09 PM↑to response
Done
MiMo V2.5MiniMax M3MiniMax M2.7MiniMax M2.5Kimi K2.7 CodeKimi K2.6Kimi K2.5GLM-5.2GLM-5.1GLM-5DeepSeek V4 ProDeepSeek V4 FlashQwen3.7 MaxQwen3.7 PlusQwen3.6 PlusQwen3.5 PlusMiMo V2 ProMiMo V2 OmniMiMo V2.5 ProMiMo V2.5Kimi K3Hy3 PreviewGrok 4.5Deepseek ChatDeepseek ReasonerGemini 3.1 Flash LiteGemini 3.5 FlashGemini 3 PRO PreviewGemini 3.1 Flash Lite PreviewGemini 2.5 Flash LiteGemini 2.5 PROGemini 3 Flash PreviewGemini Flash LatestGemini 2.5 FlashGemini Flash Lite LatestGemini 3.1 PRO PreviewLlama 3.3 70B VersatileLlama 3.1 8B InstantLlama 4 Scout 17B 16E InstructQwen3 32BGPT OSS 120bGPT OSS Safeguard 20BGPT OSS 20BOpen Mistral NemoOpen Mixtral 8x7bMistral Small 2506Magistral SmallDevstral Small 2507Labs Devstral Small 2512Mistral Medium LatestDevstral Medium LatestOpen Mixtral 8x22bCodestral LatestMinistral 8B LatestMistral Medium 2604Mistral Large 2512Mistral Large LatestMagistral Medium LatestNemotron 3 Ultra 550b A55bNemotron 3 Super 120b A12bNemotron 3 Nano Omni 30B A3B ReasoningGLM 5.2Kimi K2.6Minimax M3YI LargeDracarys Llama 3.1 70B InstructFuyu 8BJamba 1.5 Large InstructSEA Lion 7B InstructBGE M3Starcoder2 15BSeed OSS 36B InstructDbrx Instructanthropic/claude-fable-5anthropic/claude-opus-4.8anthropic/claude-opus-4.8-fastanthropic/claude-sonnet-5anthropic/claude-haiku-4.5openai/gpt-5.6-solopenai/gpt-5.6-sol-proopenai/gpt-5.6-terraopenai/gpt-5.6-terra-proopenai/gpt-5.6-lunaopenai/gpt-5.6-luna-proopenai/gpt-5.5openai/gpt-5.5-proopenai/gpt-5.4-minigoogle/gemini-3.1-pro-previewdefault
Medium
7
Context window
7% used (93% left)
70.7k / 1.0M tokens used
Auto-compress at 744.0k (74%)
Cache: 86% hit (294.3k read / 0 write)
FilesArtifacts 4Todos
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Hey guys so I am not a professional warzone player, I would rather prefer calling it hobby. I hve been playing cod mobile snceit was launched, 32 times legendary there.
But I recently got my new workstation setup and it's one hell of powerful beast for any game literally at 2k no 4k, but y monitor is also 2k. So I thought why not shift to warzone.
So today was my first game, I directly entered warzone br with full confidence and guess what. Got killed with 0 kills. I wasn't angry or annoyed because I knew is just the beginning, first time pc gaming, first time controller, testing buttons righ there and figured out A is my best friend. I was able to avoid enemies only.
So I wated to have suggestions on how to improve and buil muscle memory faster..
I've been building a local AI workstation over the last few days and wanted to share the final configuration because one optimization completely changed the result.
llama-server ^
--no-mmap ^
-m "Qwen_Qwen3.6-35B-A3B-IQ4_NL.gguf" ^
-ngl all ^
--n-cpu-moe 25 ^
--flash-attn on ^
-c 65536 ^
--parallel 1 ^
--host 127.0.0.1 ^
--port 18080
Initially I was getting
Everything worked, but it wasn't practical because this is my main development workstation.
While benchmarking I noticed llama.cpp printing this warning:
>
I tested it expecting maybe a small performance improvement.
Instead...
RAM usage dropped from roughly 98% to ~71–72% while generation speed stayed around 51–53 tokens/sec.
That one flag completely changed the usability of the machine.
I honestly didn't expect a 35B MoE model with 64K context to be this usable on a 12GB RTX 3060.
It's now responsive enough that I'm using it as the reasoning engine for my own Hermes ( i have given hermes cloud glm 5.2 for cmplex task but 90% local ai) while still keeping VS Code, browser, terminal and my normal development workflow open.
Hopefully this helps anyone trying to squeeze more out of similar hardware.
Has anyone else seen the same behavior with --no-mmap on recent llama.cpp builds?
Sleep 5
Asleep Forever 😂
I have been using this golang catch all email verification tool but it's missing on accuracy... 4 to 5 out of 100 are not accurate verification. I do have port 25 open..
Any suggestions? Been in love with rust recently because of its performance.
Is there any catch all email verification library in golang?? I did tried one it works well but misses on like 4 to 5 percent emails.
Hey guys so I have considerably big project that I handle with hermies it was it was all good in the starting when the project was a small ever now the project has gone considerably big and every time I ask her means to make some change you know what it does, breaks something. So I was wondering have you come with the solution to manage big Complex project efficiently. I am using mimo 2.5 here, but I do brainstorm and system design decisions with Claude and hermes mostly is the implementer. I am Also using opencode task delegation for any coding tasks. But still......