𝘾𝘾𝙎 𝙒𝙊𝙍𝙇𝘿
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🎁 Canva.com 3 Month pro 👾
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https://www.canva.com/brand/join?token=4X1b70i0QYEKqovIiQxtLw&referrer=team-invite🚫 As this promo may get expire anytime so i posted it before the tools method 🎉 Now i will post about the tools method 💎
🎁Time for A banger method 👾
Free ai video generation tools will Ai models included
I want Max reacts 😡. otherwise no method 😭
And Share our channel guys 😭. i am sharing a lots of new values daily .
🎁 Xiaomi MiMo V2.5 — Now 100% FREE on B.AI 👾
🎉Xiaomi's flagship open-source model, MiMo V2.5, is now completely free on B.AI — no strings attached!💎
📣 Why MiMo V2.5 is a beast:⭐️ ☑️310B sparse MoE power ✅ Massive 1M token context ☑️Text, image, video & audio in one ✅Built for coding, agents & analysis
💰Was premium. Now it's $0. 🆓📌Step-by-Step Guide🚀
🌐 For Web Users:🔍 ☑️Go to : 🔗CCSWORLD ✅Select MiMo V2.5 from the model dropdown ☑️Start chatting for free!
⚙️ For Developers (API)🛡 ☑️ Open the B.AI API Dashboard ✅Choose "Official" → "Full Access" ☑️Select MiMo V2.5 as your model ✅Start building at $0 cost!🎉 Bonus: DeepSeek V4 Flash, Hy3, DeepSeek V4 Flash Vision Exp are FREE right now too! 🔥 ⚠️Don't Forget to leave a reaction 😭
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💎it's basically an open-source alternative to Slack and Discord, but built on Nostr so you're not locked into one company's servers☄️
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🎁Free $20 credits for GLM 5.3 in Autoclaw.z.ai 👾
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🎁you can now use kimi-k3 and 50+ AI models for FREE with no credit card 👾
💎AIHubMix has opened a free model catalog with models like:🎉 ☑️Gemini 3.7 Flash ✅Gemini 3.5 Flash-Lite ☑️Gemini 3.6 Flash ✅Coding Kimi K3 ☑️GPT-OSS 20B ✅NVIDIA Nemotron Nano 9B v2 ☑️NVIDIA Nemotron Nano 12B V2 VL ✅Ling 3.0 Flash ☑️North Mini Code
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🔀 Introducing RankFuse
I just published my first open-source Python library — a hybrid retrieval engine for RAG pipelines.
Most RAG apps search with dense embeddings only, which are great at semantic similarity but miss exact keyword matches (try searching for an error code or product ID in a pure vector search — it often fails). RankFuse fixes this by combining:
• Dense search (embeddings)
• Sparse search (BM25 keyword matching)
• Reciprocal Rank Fusion to merge both rankings
• Optional reranking (local cross-encoder or Gemini LLM-judge) for a final precision pass
Two method calls and you're done:
from rankfuse import Retriever, RetrieverConfig retriever = Retriever(config) retriever.ingest(documents) results = retriever.search("your query", top_k=5) pip install rankfuseA few things I cared about while building this: → Fully local, no hosting — runs on file-based ChromaDB, BYOK for embeddings/LLM → Lightweight by default (~150MB install, GPU-heavy cross-encoder is opt-in) → A real, reproducible benchmark — no cherry-picked numbers. I documented exactly where hybrid search helps and where it doesn't, with the full diagnostic writeup in the repo
GitHub: https://github.com/GauravFrr/RankFuse
PyPI: https://pypi.org/project/rankfuse/Feedback, issues, and PRs welcome 🙌
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