Anomage | Gnoma | (🧙‍♂️,🧙‍♂️) | KGeN

PERPLEXITY ENTERPRISE PRO GRATISS!!! Nih ada salah satu AI yang lumayan bagus, namanya Perplexity bagi-bagi Perplexity Enterprise Pro nya gratis. Harga awalnya $34, nih dikasih cuma-cuma 3 bulan buat lu!! Step by stepnya : - Buat akun Bank Jago - Di Bank Jago itu kalian bisa buat CC, nah kalian harus buat dulu, gausah deposit jg bisa - Nextnya masuk ke visa.co.id/in_id/visa-com… - Klik tukarkan/redeem now - Masukkin 9 angka pertama kartu Bank Jago kalian - Nanti ada kode redeem, di copy codenya - Terus masuk ke Perplexity, cari plan terus redeem deh kodenya... Semoga bermanfaat ya...

semoga gw kecanduan sholat sampe mati

Become a Claude Certified Architect Here is the complete resource list in one place: Link to join: anthropic.skilljar.com/claude-certifi… Training courses: anthropic.skilljar.com (13 free courses) Cookbook: github.com/anthropics/ant… Exam Guide: share.google/0eqIbebzRMUt8K… Practice questions: claudecertifications.com (free) MCP documentation: modelcontextprotocol.io (free) API documentation: docs.anthropic.com (free) Partner Network: anthropic.com/partners (free to join) Personal Playbook someone created after the exam: drive.google.com/file/d/1luC0rn…

Cara garap Tempo @tempo buat org awam sekalipun gw tutorin detail disini @wha_am kalo mau join colek dia aja @xAvellesch Bikin walletnya disini : wallet.tempo.xyz Cari code akses buat walletnya dan aktivasi dapet 2$USDC Base lalu ke : tempo.xyz/SKILL.md (buat agent) Integrasiin ke frameworknya : mpp.dev Last Text micropaymentnya ke : agents.prospectbutcher.shop/llms.txt ato bisa bikin apps sendiri hehe beli kue,takjil ato apapun dah yang murah murah buat banyakin txnya Karena masi banyak yang belum paham atau mulai bikin Ai Agent jadi gw ringkas bisa run di local pake PC/Laptop. Alpha ini gratis pokoknya ama gw ga ada yg bayar, mau claim garapan wallet yg ke drain, ambil stakingan juga insyaAllah gw bantu secara GRATIS!!!

AWS x OpenClaw FREE CREDIT $200 ‼️ AWS memberikan kredit sebesar $200 selama 6bln khusus new user. Tutorial : 🔹masuk ke site : aws.amazon.com/free/ 🔹Create Account 🔹setelah itu, masuk ke Console, cari bagian "Explore AWS". 🔹Klik "Launch an instance using EC2" ⭐Untuk settingnya : Name : (bebas, apa aja!) OS: Ubuntu 24.04 Instance type: m7i-flex.large 🔹Klik "Create key pair", beri nama, pilih "ED25519", dan key file format .pem, trus save. 🔹Atur login SSH jika pake WSL: nano ~/.ssh/config 🔹jika pake Powershell Windows: notepad $env:USERPROFILE\.ssh\config 🔹Jalankan salah satu di terminal/CMD Config ‼️Catatan : Untuk pengguna WSL jalankan: chmod 600 ~/.ssh/Openclaw.pem Jalankan SSH dengan mengetik: ssh (nama profil kamu)

🚨 DOHE is Hiring! ( Global ) 👀 Hy Moots! Banyak lowongan buat anak IT & UI/UX Designer dengan kesempatan kerja remote di Singapore 🇸🇬, Korea Selatan 🇰🇷, atau England 🇬🇧. 💻 Posisi tech & design sedang dibuka — peluang buat kamu yang ingin karir global tanpa harus pindah negara. 🔎 Cek detail posisi & cara apply di sini: t.ly/gLqPN Tag temanmu yang lagi cari kerja di bidang IT / UIUX 👇 #LowonganKerja #UIUXDesigner #TechJobs #RemoteJobs #Inggris

Allah can allow you to get married even when you least expect it. Even if you think you’re not good enough. Even if you think you are not wealthy enough. Even if you think no one would want to marry you because of some situation you’re in. Even if you think you are not pretty or educated enough. Even if you think who will marry you because you wear Niqab. Allah says “Kun” and miracles happen, the most impossible things happen. He says “Be” and it is.

Muhammadiyah resmi keluarkan fatwa kripto. HALAL: Investasi kripto (spot) Airdrop Store of value Utility token HARAM: Futures Leverage trading Short selling Meme coin Lending berbunga Sebagai alat bayar Intinya: asetnya boleh, tapi cara mainnya banyak yang haram.

yoo nemu trik keren lagi di claude.ai untuk belajar suatu topik tertentu jadi gue lagi deep dive ke High Performance Computing sama Parallel Computing, terus iseng minta bikinin roadmap dan dijadiin artifact di claude - dan YEAH fitur artefak ini agak laen emang 🫶

🐍 Python Libraries - Difficulty to Learn 🔥 • 🌐 Requests — 🟢 Easy • 📊 Pandas — 🟢 Easy • 🔢 NumPy — 🟢 Easy • 📈 Matplotlib — 🟢 Easy • 🕸️ BeautifulSoup — 🟢 Easy • ⚡ FastAPI — 🟡 Easy–Medium • 🧩 Pydantic — 🟡 Easy–Medium • 📦 Pytest — 🟡 Easy–Medium • 🗄️ SQLAlchemy — 🟠 Medium • 🤖 Scikit-Learn — 🟠 Medium • 🔥 PyTorch — 🟠 Medium • 🧠 TensorFlow — 🟠 Medium • 📉 Statsmodels — 🟠 Medium • 🧬 Dask — 🔴 Hard • ⚡ Ray — 🔴 Hard • 🔗 LangChain — 🟠 Medium • 🧠 LangGraph — 🟣 Very Hard • ⚙️ Build Your Own Python Framework — ☠️ Extreme

Meet the Indonesian sentiment specialist. This RoBERTa model is fine-tuned specifically for Indonesian text classification. It's a game-changer for understanding sentiment in Southeast Asia's largest digital market.

Part 2 is here🔥 A system design guide to deploy MLOps and Agentic AI systems on AWS 📖 🔗 open.substack.com/pub/kmeanskara… This blog continues from Part 1 of designing the stock-agent-ops architecture. It mainly covers: 1. Deploying 5 Docker images (FastAPI backend, UI, Prometheus/Grafana, Redis, Qdrant) on AWS infrastructure 2. Setting up AWS CLI, IAM roles, and Terraform 3. Creating 2 EC2 instances, ECR, and spinning up a 2-node EKS (Kubernetes) cluster 4. Replacing AWS Bedrock with Ollama for LLM calls 5. Writing Terraform scripts for single-command infrastructure deployment 6. Syncing the flow with GitHub Actions CI/CD 7. Deleting everything from AWS with a single Terraform command And much more. This is Part 2 of the MLOps series. I split it into local and cloud sections. Here’s Part 1 (recommended): open.substack.com/pub/kmeanskara… Visit the repository with all code and docs: github.com/karan842/stock… I’ll write more about MLOps, multi-GPU deployment, agents, and guides for the “boring” ML tasks people often avoid. Read the article! Subscribe: kmeanskaran.substack.com

HAHAHHAHAHA AING BARU SAJA MENEMUKAN CHEAT CODE BUAT ORANG YANG SUKA KETAUAN NULIS PAKE AI! Trik ini bakal improve banget penulisan AI-mu Simak Threadnya!

akhirnya setelah 24 jam lebih ngulik dengan AI agent dan @openclaw akhirnya gw udah bisa deploy super AI agent gw sendiri. nama: shichi. setup: VPS + Openclaw + Minimax 2.5 + Clawhub role: futures trader skills: 17+ and update soon emang gila, tapi gw pengen ai gw ngehasilin duit buat gw, gaperlu langsung bagus gapapa yang penting dia bisa jalan terus scanning market cari peluang 24/7 dan belajar terus. gw set dia pakai heartbeat jadi bisa call LLM-nya terus menerus selama 15-30 menit sekali buat research market. buat platform tradingnya gw pake binance karena API keynya paling stabil. kalian udah pada nyobain openclaw belom?

Bertahun-tahun belajar bhs Inggris, aku baru tau ini pas kuliah: Setelah preposisi (in, on, at, for, about, before, after, dll) hanya bisa diikuti oleh kata benda (noun), kata ganti (pronoun), atau gerund (verb-ing). Kata kerja setelah preposisi, bentuknya harus gerund (verb + -ing), bukan to infinitive (to+verb).  Contohnya: Noun/Noun Phrase: She is talking about the movie. Pronoun: This gift is for him. Gerund (Verb-ing): After eating, I went to bed. Contoh lain dlm bentuk kolokasi: interested in working, afraid of  being, thank you for coming.  Notes: Jika "to" bertindak sebagai infinitive (bagian dari kata kerja dasar), bukan sebagai preposisi, maka setelahnya adalah Verb 1. Tapi, jika "to" adalah preposisi (misal: look forward to, accustomed to, object to), harus diikuti gerund (-ing).  #studytwt #langtwt #LearnEnglish

I vibe-coded an MLOps + Agentic AI project with AWS deployment 😎. IN JUST 13 STEPS! 🌟 Project. stock-agent-ops: stock report for the next 7 days, with sentiment analysis and top news. Here's how I built both local and AWS versions step-by-step: 1. Problem statement: - Take stock ticker as input - Generate financial dashboard with sentiment (bearish–bullish–neutral) - Predict next week open and close prices - Show top news articles 2. EDA and feature engineering: - Used yahoo-finance API for stock data - Performed EDA to guide feature engineering - Created time-series and stock movement features - Built LSTM model for forecasting 3. ML pipeline design: - Implemented transfer learning: parent model (S&P 500 index) → child models (AAPL, NVDA, etc.) - Trained and evaluated parent model for cold start - Stored evaluation metrics and weights - Used parent weights to train child models for any US stock → saved ~40% compute and time - Stored weights for inference - Built inference pipeline to forecast next 7 days - Saved forecast open/close prices to Redis by date 4. ML backend: - FastAPI inference pipeline - Separate endpoints for parent/child training and inference - Redis for storing forecasted values (keyed by ticker) - Async request handling - Rate limiting for training and prediction - Extra endpoints: check Redis records, system status, clear cache/weights, training status, etc. 5. Agentic AI: - Agents fetch forecast data from Redis and analyze it - 4 agents via LangGraph: performance metrics, market expert (news), Bloomberg-style report generator, critic - Stored reports in Qdrant for semantic caching - Used Ollama models (gpt-oss + nomic-embed-text) - Dockerized FastAPI backend 6. MLOps integration: - Feast as feature store (mainly for inference) - MLflow + DagsHub for training experiments (R² score, MSE) - Dockerized ML backend - Bash scripts for pipeline execution - Docker Compose for local apps 7. Frontend: - Simple Streamlit UI: ticker input + report display + progress bar - Dockerized UI 8. Observability: - Prometheus + Grafana for backend monitoring (RAM, disk, network) - Tracked API calls, training/prediction counts, cache hits - Admin Streamlit dashboard: health, audits, parent cold-start training, last N logs - Dockerized dashboard 9. Kubernetes + Terraform: - K8s manifests for all services (3 core images + Prometheus, Grafana, Redis, Qdrant) - Configured ingress, load balancer, etc. - Generated Terraform for AWS deployment 10. AWS deployment: - Switched local Ollama to AWS Bedrock - Set up IAM roles and aws-cli - Pushed 3 core images to ECR → deployed on EKS (2 clusters) - Launched t3.xlarge EC2 instance - Fully Terraform-managed 11. CI/CD: - GitHub Actions workflow for Terraform apply and deployment logging (separate AWS branch) 12. Publicity: - Posted every major update from scratch - Published Substack article (10k+ views) - Uploaded YouTube video with system design explanation - Shared regularly on LinkedIn and X 13. Documentation: - In-depth system design docs (local + AWS) This single full-stack ML project taught me more than 1000 certificates combined. I used ChatGPT for Q&A and system design, Aider for coding and README/documentation. It helped me learn how to design ML systems using available tools with minimal pain. GitHub: github.com/karan842/stock… YouTube: youtu.be/pqDu1H2uFl8 Substack: kmeanskaran.substack.com/p/part-1-desig… System Design: github.com/karan842/stock… AWS Design: github.com/karan842/stock… Next: advanced AI agents and distributed LLMs. Keep learning ;)

Tips puasa lancar tanpa drama lapar & lesu KURMA + PISANG Bahan : ▪︎ 3 butir kurma ▪︎ 1 buah pisang matang ▪︎ 150 ml air / susu Cara buat : ▪︎ Masukkan semua bahan ke blender ▪︎ Blender hingga halus ▪︎ Tanpa gula tambahan

Selepas Subuh, doa yang gak pernah absen aku baca. "Innā fataḥnā laka fatḥan mubīnā". (11X) Pelan aku melangkah, yakin Allah sedang membuka jalan. Bismillah.

amalan keterima kerja kemarin waktu kena layoff: 1. shalat taubat tiap malem 2. dilanjut shalat tahajud 3. abis itu shalat tasbih dilanjut shalat hajat 4. abis itu dilanjut shalat qabliyah shubuh 5. terus lanjut sholat shubuh + baca al waqiah sama ayat 1000 dinar 6. running pas pagi 7. mandi dan sebelum berangkat kerja shalat dhuha. 8. puasa senin - kamis subhanallah, langsung kena offering letter 3 perusahaan dan 1 hrd di perusahaan lain langsung ngehubungi aku karena ada rekomendasi😭

Research papers you must read for AI Engineer interviews - 1. Attention is all you need (Transformers) 2. LoRA (Low rank adaption) 3. PEFT ( Parameter Efficient Fine Tuning) 4. VIT (Vision Transformers) 5. VAE (Variational Auto Encoder) 6. GANs ( Generative Adversarial Networks) 7. BERT ( Bidirectional Encoder Representation from Transformers) 8. Diffusion Models (Stable Diffusion) 9. RAG (Retrieval Augment Generation) 10. GPT (Generative Pre-trained Transformers)