โ๏ธ MobileSAM โ klik satu titik di gambar โ segmentasi bilah keris presisi tinggi secara zero-shot, langsung generate bounding box + polygon mask
๐ค Gemini AI โ baca judul & deskripsi produk โ auto-isi metadata Dhapur, Pamor, Tangguh & Luk secara terstruktur (JSON schema)
Classifier ringan untuk merouting query pengguna ke RAG pipeline atau Agent pada sistem chatbot regulasi kepegawaian ASN.
Dikembangkan untuk sistem iASN โ BKPSDM Kabupaten Sumenep, Jawa Timur. https://t.co/jagGeJd6di
Menindaklanjuti permintaan dari masyarakat Indonesia untuk menyalurkan bantuan kemanusiaan kepada rakyat Iran, Kedutaan Besar Republik Islam Iran di Indonesia telah membuka rekening resmi donasi.
Langkah ini bertujuan untuk memfasilitasi individu, institusi, maupun organisasi yang ingin menunjukkan solidaritas dan dukungan nyata kepada masyarakat Iran.
Seluruh donasi yang diterima akan digunakan untuk mendukung upaya rekonstruksi dan rehabilitasi wilayah-wilayah yang terdampak.
Kedutaan Besar Republik Islam Iran menyampaikan apresiasi setinggi-tingginya atas kepedulian, empati, dan solidaritas yang tulus dari masyarakat Indonesia.
Setiap kontribusi adalah simbol persaudaraan dan nilai kemanusiaan yang luhur.
๐ Informasi rekening resmi:
Bank: BRI
Nama Rekening: Embassy of the Islamic Republic of Iran
Nomor Rekening: 020601002438302
#Donasi #KedubesIran #Iran #IndonesiaIran
GILAAA, GW MINDBLOWN BANGEETT SAMA CLAUDE CODE. SUMPAHH!!!
Gw kira Claude Code x Linear itu udah paling gokil. Ternyataa, kawin silang antara Claude Code, Linear, dan GitHub lebih GG lageee wkwkwk.
Kalau lu masih mikir, โahh lebay lu, paling hasilnya AI slopโ. Kagakkk brooo, itu lu aja yg makenya salah wkwkwk.
Kalau lu bisa kombinasiin si CC sama Linear & GitHub, lu beneran bisa develop software properly kaya enjinir Google sonoo. Asli dahh
Sini gw jelasin cara yg gw pake ๐๐ป
Meet dots.ocr-1.5, a new vision-language model that's changing how we extract text from images. It doesn't just read text, it understands document layouts, tables, and even formulas. This is OCR on steroids, and the community is buzzing about its potential.
Your Vector RAG Blueprint
A reference for building RAG systems that work.
Hereโs a clear 9-step pipeline to build a modern Vector RAG system from scratch.
1./ ๐๐ง๐ ๐๐ฌ๐ญ & ๐๐ซ๐๐ฉ๐ซ๐จ๐๐๐ฌ๐ฌ ๐๐๐ญ๐
โ Start with tools like web scraping libraries/services (e.g., Firecrawl), data connectors (e.g., for databases, APIs), or dedicated ingestion and preprocessing platforms (e.g., Unstructured(.)io) to collect and clean your data before chunking or embedding begins.
2./ ๐๐ฉ๐ฅ๐ข๐ญ ๐๐ง๐ญ๐จ ๐๐ก๐ฎ๐ง๐ค๐ฌ
โ Use libraries like LangChain or LlamaIndex to break documents into manageable, meaningful pieces, essential for context preservation and optimal retrieval.
โ Consider various chunking strategies (e.g., fixed-size, semantic, recursive).
3./ ๐๐๐ง๐๐ซ๐๐ญ๐ ๐๐ฆ๐๐๐๐๐ข๐ง๐ ๐ฌ
โ Transform your chunks into dense vector representations using state-of-the-art embedding models like text-embedding-ada-002, Cohere Embed v3, BGE-M3, or llama-text-embed-v2.
4./ ๐๐ญ๐จ๐ซ๐ ๐ข๐ง ๐๐๐๐ญ๐จ๐ซ ๐๐ & ๐๐ง๐๐๐ฑ
โ Store vectors in specialized vector databases like Pinecone, Weaviate, Qdrant, Milvus - created by Zilliz, or pgvector.
โ You can also use traditional databases like Elastic MongoDB for document storage, leveraging their vector search capabilities if available and suitable.
5./ ๐๐๐ญ๐ซ๐ข๐๐ฏ๐ ๐๐ง๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง
โ Retrieve relevant context using dense vector search (similarity search), sparse retrieval (e.g., BM25, SPLADE), or sophisticated hybrid fusion methods (e.g., RRF, reciprocal rank fusion) via frameworks like LangChain, LlamaIndex, or Haystack. Implement re-ranking (e.g., using bge-reranker or Cohere Rerank) for improved precision.
6./ ๐๐ซ๐๐ก๐๐ฌ๐ญ๐ซ๐๐ญ๐ ๐ญ๐ก๐ ๐๐ข๐ฉ๐๐ฅ๐ข๐ง๐
โ Build your workflow and manage the flow of information between components using orchestration frameworks like LangChain, LlamaIndex, or dedicated workflow automation platforms like n8n or cloud services like Google Cloud Vertex AI Pipelines.
7./ ๐๐๐ฅ๐๐๐ญ ๐๐๐๐ฌ ๐๐จ๐ซ ๐๐๐ง๐๐ซ๐๐ญ๐ข๐จ๐ง
โ Integrate your preferred Large Language Models (LLMs) such as Claude, GPT (e.g., GPT-4o), Gemini, Llama 3, DeepSeek, or Mistral via direct APIs or through AI gateways and routing services like Portkey, Eden, or OpenRouter for consistent access and management.
8./ ๐๐๐ ๐๐๐ฌ๐๐ซ๐ฏ๐๐๐ข๐ฅ๐ข๐ญ๐ฒ
โ Monitor and troubleshoot your RAG system using dedicated observability platforms like Langfuse, PromptLayer, Helicone (YC W23), or Arize AI to track prompt performance, token usage, latency, system health, and model outputs.
9./ ๐๐ฏ๐๐ฅ๐ฎ๐๐ญ๐ & ๐๐ฆ๐ฉ๐ซ๐จ๐ฏ๐
โ Continuously test and refine retrieval and generation outputs using automated evaluation metrics (e.g., faithfulness, answer relevance, context recall/precision), A/B tests, human feedback loops, and fine-tuning (if necessary) for better quality and performance.
This workflow breaks down every stage of a successful Vector RAG pipeline.
Save this guide, itโs your starting point.
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Follow @techNmak ๐