Moh. Affan

Backend, DevOps
💻
Rust, Python, Javascript
🌍

[Moh. Affan @affan_20]

Hasilnya: dataset siap training YOLOv26 untuk pelestarian warisan budaya 🇮🇩✨

#ComputerVision #YOLOv26 #SAM #Gemini #KerisDetection #WarisanBudaya #MachineLearning

[Moh. Affan @affan_20]

🗡️ Otomasi pipeline dataset untuk riset tesis deteksi Keris Madura dengan YOLOv26!

🕷️ Crawler → scrape otomatis https://t.co/1UG9Vtwtjh (54 halaman katalog), ekstrak gambar resolusi tinggi + metadata Dapur, Pamor, Tangguh, Luk & Harga via BeautifulSoup

#ComputerVision #YOLOv26

[Moh. Affan @affan_20]

✂️ MobileSAM → klik satu titik di gambar → segmentasi bilah keris presisi tinggi secara zero-shot, langsung generate bounding box + polygon mask

🤖 Gemini AI → baca judul & deskripsi produk → auto-isi metadata Dhapur, Pamor, Tangguh & Luk secara terstruktur (JSON schema)

[Moh. Affan @affan_20]

Classifier ringan untuk merouting query pengguna ke RAG pipeline atau Agent pada sistem chatbot regulasi kepegawaian ASN.

Dikembangkan untuk sistem iASN — BKPSDM Kabupaten Sumenep, Jawa Timur. https://t.co/jagGeJd6di

[Moh. Affan @affan_20]

View my verified achievement from @GoogleCloudTech. https://t.co/uD4mZO6PTq lewat @credly

[Python Programming @PythonPr]

Convolutional Neural Network ( CNN )

[Iran Embassy in Indonesia @IraninIndonesia]

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.

📍 Informasi rekening resmi:
Bank: BRI
Nama Rekening: Embassy of the Islamic Republic of Iran
Nomor Rekening: 020601002438302

#Donasi #KedubesIran #Iran #IndonesiaIran

[Moh. Affan @affan_20]

@effendii_ izinn minta workflow nya mas🙏🏼

[Arifin Effendi @effendii_]

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, “ahh lebay lu, paling hasilnya AI slop”. 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 👇🏻

[The Curut @TheCurut007]

Jadi ikutan mewek denger penuturan mbak ini.😥 Yg diomongin warning semua.

[Matt Dancho (Business Science) @mdancho84]

🚨BREAKING: New Python library for agentic data processing and ETL with AI

Introducing DocETL.

Here's what you need to know:

[Hugging Models @HuggingModels]

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.

[Python Programming @PythonPr]

RAG

[Stocks World @anandchokshi19]

Jensen Huang: AI is a five-layer cake.

[Scholarship for PhD @ScholarshipfPhd]

How to read a research paper

[Prof Lennart Nacke, PhD @acagamic]

Most researchers collect data first, then wonder why nobody reads their papers.

Start with the story.

The data comes after.

[Love Matcha @Pistachiojingga]

Cari kaos pengganti UNIQLO?
You should try to see some of the t-shirt recommendations from these brand with affordable Prices (⁠◔⁠‿⁠◔⁠)

[Tech with Mak @techNmak]

Your Vector RAG Blueprint

A reference for building RAG systems that work.

Here’s a clear 9-step pipeline to build a modern Vector RAG system from scratch.

1./ 𝐈𝐧𝐠𝐞𝐬𝐭 & 𝐏𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐃𝐚𝐭𝐚
➞ 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./ 𝐒𝐩𝐥𝐢𝐭 𝐈𝐧𝐭𝐨 𝐂𝐡𝐮𝐧𝐤𝐬
➞ Use libraries like LangChain or LlamaIndex to break documents into manageable, meaningful pieces, essential for context preservation and optimal retrieval.
➞ Consider various chunking strategies (e.g., fixed-size, semantic, recursive).

3./ 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐞 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠𝐬
➞ 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./ 𝐒𝐭𝐨𝐫𝐞 𝐢𝐧 𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐁 & 𝐈𝐧𝐝𝐞𝐱
➞ Store vectors in specialized vector databases like Pinecone, Weaviate, Qdrant, Milvus - created by Zilliz, or pgvector.
➞ You can also use traditional databases like Elastic MongoDB for document storage, leveraging their vector search capabilities if available and suitable.

5./ 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐞 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧
➞ 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./ 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐞 𝐭𝐡𝐞 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞
➞ 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./ 𝐒𝐞𝐥𝐞𝐜𝐭 𝐋𝐋𝐌𝐬 𝐟𝐨𝐫 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧
➞ 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./ 𝐀𝐝𝐝 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲
➞ 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./ 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞 & 𝐈𝐦𝐩𝐫𝐨𝐯𝐞
➞ 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, it’s your starting point.

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Follow @techNmak 🚀

[Python Programming @PythonPr]

Pandas Essentials

[Fitness Dad @FitnessDadx]

90% of parents don't do this 👇