code-yeongyu

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Last 30 days, Oct 4 – Nov 2

GitHub stars1,054
mentions27
017345168Oct 4Oct 18Nov 2
UTC days · repositories code-yeongyu owns6 repositories

Package downloads

Cumulative recorded downloads · since 2025-10-21

npm20.9M
PyPI—
05.2M10.4M15.7M20.9MOct 4Oct 18Nov 2
Oct 28npm: 1/4 packages observed · PyPI: 0/1 packages observed

Across 5 known packages in repositories code-yeongyu owns. Cumulative totals include recorded downloads before the selected range. Missing observations leave gaps; totals may be incomplete. PyPI excludes known mirrors.

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Mentions

27posts
Nov 30 posts
  1. i'm the creator of code-yeongyu/oh-my-openagent (70K+ stars) and i maintain it as a solo builder right now dori manages 100+ agent sessions on my behalf, and i use 200B tokens a week i do like 2000 commits a day lifetime tokens spent: $2.6M+ meet our superagent dori on your - slack - notion - github - whatsapp - imessage - wechat just hand him, let him learn to use it. regardless of what platform / threads you are in, you are talking with one dori persona no need to manage sessions one by one like you do with hermes or openclaw if you are talking with dori, you are talking with dori. that superagent dori. i built dori and have been running it for half a year because i had to

    8 likes · 819 views
  2. Reddit · · r/LocalLLaMA · u/Rozzemak · code-yeongyu/oh-my-openagent

    Qwen 3.8 Flash Next NVFP4 on 64gb RAM and 5090, Basalt. Ditched windows for it. Very hyped for qwen 4 model family now. So, I was asleep on Strata and its specialized forks. Tried basalt and i'm very impressed with the speed/intelligence QFN gives you. Here is my benchmark: preview.redd.it I cannot get it to work fast enough with windows, because W11 eats 1.5GB of VRAM and 6GB of RAM. At that point, experts spill to RAM and CPU gets hit hard... decode/prefill drops by 70% because there is not enough of available RAM anyway. Switched experimentally to cachy just to test... and It's amazing that my AI box can now reach 9K~ prefill. And regarding quality, its just better than qwen 27b at many tasks. Yet to find something it genuinely cannot do. The prefill boost is around 2-3x coming from ninfer and dense 27b model. Cherry on top is, that I can even fit vision adapter to it, and that helps a lot for certain scenarios regarding coding. I usually run some small model on another machine for OCR and VISION as an adapter (sidecar) my harness can use for being to able look at things. Vivid user of omo stack, configured now for locally hosted qwen: code-yeongyu/oh-my-openagent It's a glorified pi harness, but it does so much, and im quite happy with its robustness, anyway, QFN 3.8 rocks, and now im really thrilled if qwen 4 will be any good, because I suspect i may be able to run the new flash model this fast basically. So I hope it will be good :D Im genuinely interested how 2x 5060/5070/5070ti fares. Because anything above 100~ decode and 3k prefill is genuinely usable for work. And it is my experience that 27b dense -> 125BA6 is a very good quality and speed jump. Absolutely love it now. Also, finetunes can be easily converted into basalt format if you need to do it yourself, especially those swift/reverse engineering variants are good. Anyway: CachyOS, 64gb RAM, 32GB VRAM, 5090, 200tps decode, 9k tps prefill, nvfp4 QFN 3.8, 256k context! If you have similar hardware, and are not using at least strata... but more specially basalt, you sleep on performance hard! See ya! PS: Would absolutely love to see your perf benchmarks if you have similar setups edit-- Running config (without vision,.. but it fits nicely): ~/basalt-linux/basalt_config.json ```json    { "server": { "model": "/mnt/g/../basalt/models/Qwen3.8-FN-NVFP4.basalt", "gpu": "0", "host": "127.0.0.1", "port": 8080, "parallel": 1 }, "engine": { "max-context": 262144, "kv": "int8", "prompt-cache": 6, "expert-cache": "auto", "prefill": "auto", "pool-workers": 4, "vram-reserve-mib": 500, "resident-budget-gib": 44 }    } ```    bin/basalt --serve --model /mnt/g/../basalt/models/Qwen3.8-FN-NVFP4.basalt \ --max-context 262144 --kv int8 --prompt-cache 6 --expert-cache auto --prefill auto \ --pool-workers 4 --vram-reserve-mib 500 --resident-budget-gib 44 --grammar on

    47 points · 45 comments
  3. @mohamed_yo32851 @nevermind_turki هذا يفرق وفيه سبعين أداة تانية شوف عنه بتفهمني وسب أجينت ويرتب الشغل بين عدة مودل بحيث يوفر أو حتى ينجز أسرع بالمودل الرخيصة/الخفيفة code-yeongyu/oh-my-openagent

    2 likes · 31 views
  4. Claude's fast mode has a secret name, PENGUIN MODE code-yeongyu/claude-code the endpoint is /api/claude_code_penguin_mode the kill switch is called tengu_penguins_off, still inside the latest build changelogs.core-directive.com Anthropic renamed the button, the penguin is still in the code never been run wtf?? So genny $Penguin - Penguin Mode

    1 like · 37 views
  5. Two words in your prompt and your coding task becomes a parallel graph of AI agents. 🔗 code-yeongyu/oh-my-openagent Credits: code-yeongyu/oh-my-openagent #ohmyopenagent #omo #typescript

    1 like
  6. Claude's fast mode has a secret name, PENGUIN MODE code-yeongyu/claude-code the endpoint is /api/claude_code_penguin_mode the kill switch is called tengu_penguins_off, still inside the latest build changelogs.core-directive.com Anthropic renamed the button, the penguin is still in the code never been run wtf?? So genny $Penguin - Penguin Mode

    11 likes · 2 reposts · 1,325 views
  7. I BONDED TENGU YESTERDAY, AND HAVE FOUND SOMETHING ELSE INSANELY GOOD. Tengu wasn't the only animal hiding inside Claude Code. Claude's fast mode has a secret name, PENGUIN MODE code-yeongyu/claude-code the endpoint is /api/claude_code_penguin_mode the kill switch is called tengu_penguins_off, still inside the latest build changelogs.core-directive.com Anthropic renamed the button, the penguin is still in the code never been run wtf?? So genny $Penguin - Penguin Mode

    4 likes · 1 repost · 392 views
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  1. oh-my-opencodeBest: 2nd by stars, 2,791 stars023

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