２四半期を比較するエンドポイントを追加。 FastAPI Cloudにもデプロイしてみた。 501エラーを懸念していたが、無事にデータを返すことができた。 やはりメタデータAPIによる系列コードの大量取得が問題だった模様。

When I first started building AI AutoCare, I thought I was creating a recommendation system. After learning about AI agents, I realized the recommendation engine is just one piece of a much larger system. #enterpreneur #AIStartup #AIEngineer #AIAgents #FastAPI #Python #RAG #LLM

AI engineering progress report: I made some progress on my GymAI project which I’m building as a way to practice building AI systems. I finished generating a knowledge base of exercises that I personally do in the gym using Claude. Then I created a Pinecone account. Started watching a tutorial on YouTube of a guy building a Medical AI assistant using my same stack (Pinecone, Langchain and FastAPI) So I can see how others do it then implement for my own use case

5/ Deploy, Monitor & Improve Building the agent is only the beginning. Deploy it using frameworks like: → FastAPI → Docker → cloud platforms Then continuously monitor: → accuracy → latency → costs → error rates → task completion → user satisfaction The biggest lesson? Most people build AI prompts. Professional teams build AI systems. Prompts generate answers. Systems generate business results.

FastAPI makes it very easy to build great APIs. When you need to process background tasks, you have a couple of options available: - FastAPI's background tasks - Celery - dramatiq - rq

🐧 Linux Setup for AI/ML Engineers (Complete Checklist) If you're setting up a Linux machine for AI, LLMs, and backend development, this is the stack I'd install. --- 🖥️ 1. Choose a Distro Ubuntu 24.04 LTS (Best overall) Fedora Workstation (Latest packages) Arch Linux (Advanced users) Pop!_OS (NVIDIA-friendly) --- 📦 2. Update the system sudo apt update && sudo apt upgrade -y --- 🐍 3. Python Install: Python 3.12 pip uv Poetry pyenv (optional) --- 💻 4. Development Tools Git GitHub CLI curl wget build-essential tmux htop btop tree unzip jq --- 📝 5. Editors VS Code Cursor Zed Neovim (optional) Extensions: Python Ruff Pylance Docker GitHub Copilot (optional) --- 🤖 6. AI Stack Install: Ollama llama.cpp LM Studio (optional) Open WebUI vLLM Hugging Face CLI --- 🐳 7. Containers Docker Docker Compose Learn: Images Volumes Networks --- 🗄️ 8. Databases PostgreSQL Redis SQLite Optional: Qdrant Chroma Milvus --- ⚡ 9. Backend FastAPI Uvicorn SQLAlchemy Alembic --- 📊 10. ML Install: PyTorch Transformers Datasets Accelerate PEFT TRL Scikit-learn Pandas NumPy Matplotlib --- ☁️ 11. CLI Tools AWS CLI gcloud Azure CLI --- 🔥 12. Terminal Zsh Oh My Zsh Starship fzf zoxide bat eza ripgrep --- 📈 13. Monitoring nvtop nvidia-smi iotop iftop btop --- 🔐 14. Security SSH keys GPG (optional) ufw fail2ban --- 🧠 15. AI APIs Configure: OpenAI Anthropic Google Gemini OpenRouter Groq Together AI Store keys in a .env file. --- 📂 16. Workspace ~/Projects ~/Models ~/Datasets ~/Experiments ~/Scripts ~/Notes ~/Dotfiles --- 🚀 17. Learn Linux Master: Bash grep find sed awk xargs systemctl journalctl chmod ssh rsync cron --- This setup is enough to build AI agents, fine-tune models, run local LLMs, develop FastAPI backends, and work on production AI applications.

After 3 years in engineering and 1 year in GTM, I finally mapped the workflow that turns good code into real distribution, demand, and paying customers. That combo taught me one lesson the hard way. Code is a commodity. Distribution is the moat. If your sprints are not synced with GTM signals, you are not building a business, you are burning runway. I keep seeing the same failure modes: Founders build for months, launch, post once on LinkedIn, and wonder why signups flatline. Or they have distribution but the product does not solve a sharp problem. The best teams do Product and GTM in parallel. So I documented the workflow I use as a checklist. The Full-Stack Founder Playbook: 1/ Product research - Problem, ICP, PRD, roadmap (Notion, Linear, Claude) 2/ UX and design - Flows, wireframes, clickable prototype (Figma) 3/ Frontend - Next.js React Tailwind, the full UX 4/ Backend - APIs, auth, payments, email, AI requests (Node.js / FastAPI) 5/ Data layer - Postgres Supabase, object storage (R2) 6/ Auth - Roles, OAuth, magic links (Clerk / Auth0) 7/ Integrations AI - Stripe, Resend, LangChain, Pinecone 8/ Monitoring - PostHog, Sentry, background jobs (Inngest) 9/ Ship - Testing, CI/CD, backups (Playwright, Vercel) 10/ Build distribution before launch - Founder profile landing page CTA (LinkedIn) 11/ Content strategy - Interviews, competitor scans, pillars (Claude) 12/ Content engine - 2-3 posts/week, measure, iterate 13/ Build your TAM - Firmographics, technographics, signals (Apollo.io, Clay) 14/ Score accounts - Tier 1/2/3 15/ Multi-channel outbound - LinkedIn email, systemize follow-ups 16/ Capture buying signals - Signups, engagement, replies, meetings routed into your CRM If you are building a product right now: which step is your biggest bottleneck?

코드 20%를 AI가 짰다, 그것도 레거시에서 라인야후 운영사 LY가 지난달 컨퍼런스에서 꺼낸 숫자가 있다. 대규모 레거시 시스템에서 AI가 작성한 코드 비율이 지난 1년간 20%에 달했다고. 신규 프로젝트도 아니고 30년 넘게 쌓인 레거시에서 이 수치가 나왔다는 게 포인트다. 보통 레거시일수록 AI한테 맡기기 어렵잖아. 맥락도 없고, 문서도 낡았고, 구조도 뒤엉킨 경우가 대부분이니까. 신규 코드베이스에서 20%면 그러려니 하는데, 레거시에서 이 수치가 나왔다는 건 좀 다른 얘기다. 어떻게 했냐면, AI한테 코드를 시키기 전에 '맥락을 먹이는 작업'부터 했다고. 사내 문서를 AI가 이해하기 쉬운 형태로 구조화하고, 서비스 간 관계를 시각화했다고 한다. 코딩 시키기 전에 환경 세팅에 공을 들인 거다. 이게 진짜 핵심인 것 같은데, 대부분은 그냥 AI한테 '이 함수 고쳐줘'만 하거든. 문서 정리는 귀찮으니까 패스하고. 그러면서 결과가 별로라고 하는 거지. 에이전트 빌더도 눈에 들어왔다. AI 전문 엔지니어가 아니어도 하루 만에 AI 에이전트를 만들 수 있다는 내부 도구인데, LY는 이미 쇼핑, 외출, 레시피 같은 22개 영역에서 Agent i라는 이름으로 서비스 중이고 그 뒷단에 이런 인프라를 깔아놨다. 에이전트를 사내 데이터나 API에 연결하고 연결 현황을 추적·관리하는 솔루션도 따로 만들었다고 하더라. '빅테크니까 당연히 잘하지'가 아니라, 도구와 환경부터 다 뜯어고친 결과가 맞다는 느낌. 나한테 바로 쓸 수 있는 레슨을 뽑으면 이렇다. AI한테 코드 짜달라고 하기 전에, 프로젝트 구조를 먼저 정리해두는 게 맞다. README든 주석이든 CLAUDE.md든, AI가 맥락을 잡을 수 있는 문서를 미리 만들어두면 결과물이 달라진다. 퇴근하고 혼자 만지다 보면 진짜 체감하는 부분인데 — 그냥 '이 함수 고쳐줘'보다 '이 프로젝트는 FastAPI 기반이고 이런 흐름이야, 이 함수 고쳐줘'가 결과물 품질이 확실히 다르거든. 맥락 없이 코드 던지면 AI도 그냥 그 함수만 보고 고치니까. LY가 100개 이상 서비스를 운영하는 조직 전체에 한 걸, 나는 사이드 프로젝트 하나에 적용하면 된다는 거다. 기사에서 하나 더 찔린 대목 — '코딩 단계뿐 아니라 기획·설계 단계부터 AI를 활용'이라는 게 있었다. 아직도 AI를 코드 생성기로만 쓰는 사람이 많은데, LY는 이미 그 윗단부터 AI 루프 안에 넣고 있다는 얘기다. 아이디어 구체화나 기능 설계할 때 AI 쓰는 비중이 코딩보다 오히려 더 커지는 게 맞는 것 같기도 하다. 나는 이미 그렇게 되고 있고. AI가 코드를 짠다는 게 아직 어색한 사람도 있을 텐데. 이 기사 보고 드는 생각은, 맡기냐 안 맡기냐보다 어떻게 맡기냐가 이미 경쟁력이 됐다는 거다. 레거시에서 20%가 됐다면, 이미 그 단계는 넘은 거 아닌가. 출처: 이데일리 — LY, 전사 AX 전략 공개…'AI가 코드 20% 작성'

After building a few fullstack (Python/FastAPI React PostgreSQL) apps, I've refactored (& continue to make it better) a full-stack Claude Code template, which improves it's performance (basically to a teammate, not an afterthought). Go: 1- clone as template 2- put ur project context in CLAUDE.md 3- & u're good to go. start shipping: 👇

🧠 Hiring: Python Developer - AI/ML - Yalavarthi Software 📍 On-site - Madhapur, Hyderabad | 🕐 3 hours ago - July 06, 2026 Yalavarthi Software is hiring a Python Developer for AI/ML. Perfect for freshers, interns, and 0-1 year experience candidates to build real AI products. 📋 Responsibilities include: - Build REST APIs using Python and FastAPI - Assist in AI/ML model training - Work on LLM and Computer Vision projects - Integrate AI models into applications 🔑 Required Skills: - Python, FastAPI and MongoDB - ML/DL basics, LLMs and Vision Models - NumPy, PyTorch and Scikit-learn - Linux, Docker and Git basics 🛠 Good to Have: - AI/ML Projects, Hugging Face, Fine Tuning, Problem Solving 📩 To apply: Send your resume today via aapthitech.com or call 040 45700932. ⚠️ DYOR! I don’t verify every job. If someone asks to run files or ask for payment 🚩 likely a scam. ❗️ I'm not hiring myself! I just sharing fresh web3/crypto/blockchain roles DAILY for all levels! 💡 For Interns & juniors → t.me/crypto_vazima_english 💼 Mid/senior jobs → t.me/web3_jobs_crypto_vazima #Python #AI #ML #Hiring #Developer #FastAPI #MongoDB #PyTorch #ComputerVision #LLM #Job #Internship #Fresher #Hyderabad

Built a CLI that scaffolds a Turborepo SaaS app in seconds — pick your backend and frontend, and it wires the monorepo for you. You choose Express or FastAPI for the API and Next.js for the web app and get a working monorepo instantly. 🧵 #BuildInPublic #saas

🏠 Built GharIQ — an ML price estimator for Indian homes. Biggest lesson wasn't the model, it was the data: cleaning it (dropping fake datasets trimming outliers) lifted R² from 0.10 → 0.79 Python · scikit-learn · FastAPI · Docker Live 👇 huggingface.co/spaces/akhil0…

My name is Ojeh Agbaje, I am currently open to Software developer role. I’ve built and owned resilient web systems end to end with tools like python/fastapi, node/express and Nextjs, I’m knowledgeable about web3/blockchain technology(cardano network especially).

Claudeのコードスキルがヤバすぎる件。 正直、今のClaude（特にClaude 4 Opus / Sonnet）は、有料級のシニアエンジニアレベルに到達してる。 ・複雑なアーキテクチャ設計から一発で正確に組める ・Next.js、FastAPI、Flutter、LangChain、Supabaseまでモダンスタック完璧 ・リファクタリング指示したら「ここがボトルネックです」と指摘しながら美しいコードに変えてくれる ・エラーログ貼るだけで根本原因特定＋根本解決コードを提案 ・テストコード、ドキュメント、CI/CD設定まで全部作る 特に凄いのは「意図を読み取る力」。 「なんとなくこういう感じで作りたい」を曖昧に伝えても、プロダクション品質のコードを吐き出してくる。 しかも説明が異常に丁寧で、教育的なんですよね。 正直、 「Claudeに設計させて、Cursorで実装して、Claudeにレビューさせる」 この流れだけで、昔のチーム3人分の生産性が出てる。 経理がAIに食われるなら、 初級〜中級エンジニアも確実に食われる。 残るのは「Claudeをどう操るか」と「本当に難しい問題を解ける」上位層だけになる。 Claude Codeの進化はマジで脅威であり、最高の相棒。 もうフリーランスでも月100万超える人が普通に出てくる時代、すぐそこまで来てる。 皆さんも今のうちにClaudeのコード力をガッツリ使い倒しとけ。 これはガチで有料級の情報です。

I rejected my AI's $600 refund. It came back asking for $580. That's Deskmate — a multi-agent support system that knows when to ask permission. A ticket comes in and a team of agents handles it: • Triage → classifies category, urgency, risk • Retrieval → pulls the relevant policy passages (BM25 over the KB) • Resolver → decides the action drafts the reply, with policy citations Then the part that matters: risky actions (refunds >$50, cancellations, deletions) hit a LangGraph interrupt — the graph PAUSES mid-run and the ticket waits in an approval inbox until a human decides. Safe stuff flows straight through: password reset auto-resolves in 2 seconds. A $600 refund stops and asks. The $580 moment: I rejected the refund with a reason ("billing shows $580, not $600"). The resolver read my correction, revised to $580, stayed flagged risky, and paused again for re-approval. Propose → reject → revise → re-approve → execute — every step human reason in the audit trail. The flex: the pause is durable. Kill the backend, restart it — the paused ticket is still there, still approvable (SQLite checkpointer persisting graph state). There's a test script in the repo that proves it. Autonomous agents that can spend money are a liability. Agents that pause for human judgment are a product. Stack: LangGraph (interrupts checkpointing) · FastAPI · React/TS · provider-swappable LLM (local Ollama dev → gpt-4o-mini, one env var) code github.com/im-manideep/AskFi… #MultiAgent #LangGraph

Just deployed a Customer Churn Prediction API 🔮 Built with Python FastAPI Scikit-learn Github : github.com/LamsalYTz/Profess… Open to freelance ML projects 👇 #Python #MachineLearning #FastAPI

I'm passionate about building scalable AI-powered products and backend systems. If you're hiring or can refer me, I'd sincerely appreciate your support. DMs are open. #OpenToWork #AIEngineer #SoftwareEngineer #Python #LLM #GenAI #FastAPI #Hiring #TechJobs

Open to Work. I'm actively seeking Full-Time AI Engineer / Software Engineer roles. harshrajj.dev ~1 year of hands-on experience building AI applications with Python and js FastAPI • LangChain • LangGraph • RAG • LLMs • Next.js • PostgreSQL • AWS ...

🚨Everyone wants to become an AI Engineer But very few know there are multiple AI career paths. Pick the one that matches what you actually want to build 🤖 AI Engineer ➡️ Build AI applications with LLMs, RAG, and APIs. Skills: Python • LLMs • RAG • FastAPI • Docker • Cloud 🤖 AI Agent Engineer ➡️ Design and build autonomous AI systems that can plan, take actions, and interact with tools. Skills: Python • MCP • LangGraph • Tool Calling • RAG • Multi-Agent Systems 🔒 AI Security Engineer ➡️ Protect AI systems from attacks and secure LLM applications. Skills: Python • Cybersecurity • OWASP LLM • Guardrails • IAM • AI Red Teaming 🖥️ Local AI Engineer ➡️ Run powerful AI completely offline. Skills: Ollama • llama.cpp • GGUF • CUDA • Local RAG • Docker ✨ GenAI Engineer ➡️ Build AI copilots, chatbots, and content generation products. Skills: LLMs • Prompt Engineering • Embeddings • RAG • APIs • Fine-tuning ⚙️ MLOps Engineer ➡️ Deploy, monitor, and scale machine learning models. Skills: MLflow • Docker • Kubernetes • CI/CD • Monitoring • Cloud 🏗️ AI Infrastructure Engineer ➡️ Build the platforms that serve AI models at scale. Skills: GPUs • CUDA • vLLM • Kubernetes • Ray • Distributed Inference 🔄 AI Automation Engineer ➡️ Replace repetitive business work with AI workflows. Skills: Python • APIs • n8n • MCP • OCR • AI Agents 🧠 AI Research Engineer ➡️ Train and improve the next generation of AI models. Skills: PyTorch • Transformers • Math • Distributed Training • RLHF • CUDA 🚀 AI Product Engineer ➡️ Turn AI ideas into products people actually use. Skills: Python • FastAPI • LLMs • UI • Analytics • Cloud The AI industry is no longer one job title. Choose your lane. Master it. Then build relentlessly. Which one are you aiming for?