Localizing AI Platform with Python TypeScript Thrift Protobuf

You don't need LangChain to build AI agents. You need to understand what's inside it. I spent the last month building agents from scratch just Python, Ollama, and the ReAct loop. No frameworks. No abstractions. Nothing I couldn't debug. 6 concepts kept coming back: → Anatomy — the 4 parts of every agent → The loop — how decisions actually happen → Tools — what they really are (not magic) → Memory — the illusion the harness creates → Scaling — what breaks at each stage → Orchestration — when one agent isn't enough I put them in a visual guide. 11 slides. Save it. Open it next time your framework does something weird. Share it with someone building their first agent. This is the reference I wish I had on day 1. 👇 Swipe through. #AIAgents #AIEngineering #LLM #MachineLearning #SoftwareEngineering #Python #BuildInPublic #FirstPrinciples #TechCareers

If you're building with LLMs and getting inconsistent results, check these before changing the model: → Is your system prompt actually specific, or is it just vibes? → Are you measuring outputs systematically, or eyeballing them? → Do you have a fallback for when the model is uncertain? → Are you chunking your context in a way that preserves meaning? I've fixed "bad model" complaints four times this year without changing the model once. The wrapper matters more than the weights. #AI #Python #developer #MachineLearning

If you're building with LLMs and getting inconsistent results, check these before changing the model: → Is your system prompt actually specific, or is it just vibes? → Are you measuring outputs systematically, or eyeballing them? → Do you have a fallback for when the model is uncertain? → Are you chunking your context in a way that preserves meaning? I've fixed "bad model" complaints four times this year without changing the model once. The wrapper matters more than the weights. #AI #Python #developer #MachineLearning

If you're building with LLMs and getting inconsistent results, check these before changing the model: → Is your system prompt actually specific, or is it just vibes? → Are you measuring outputs systematically, or eyeballing them? → Do you have a fallback for when the model is uncertain? → Are you chunking your context in a way that preserves meaning? I've fixed "bad model" complaints four times this year without changing the model once. The wrapper matters more than the weights. #AI #Python #developer #MachineLearning

If you're building with LLMs and getting inconsistent results, check these before changing the model: → Is your system prompt actually specific, or is it just vibes? → Are you measuring outputs systematically, or eyeballing them? → Do you have a fallback for when the model is uncertain? → Are you chunking your context in a way that preserves meaning? I've fixed "bad model" complaints four times this year without changing the model once. The wrapper matters more than the weights. #AI #Python #developer #MachineLearning

If you're building with LLMs and getting inconsistent results, check these before changing the model: → Is your system prompt actually specific, or is it just vibes? → Are you measuring outputs systematically, or eyeballing them? → Do you have a fallback for when the model is uncertain? → Are you chunking your context in a way that preserves meaning? I've fixed "bad model" complaints four times this year without changing the model once. The wrapper matters more than the weights. #AI #Python #developer #MachineLearning

If you're building with LLMs and getting inconsistent results, check these before changing the model: → Is your system prompt actually specific, or is it just vibes? → Are you measuring outputs systematically, or eyeballing them? → Do you have a fallback for when the model is uncertain? → Are you chunking your context in a way that preserves meaning? I've fixed "bad model" complaints four times this year without changing the model once. The wrapper matters more than the weights. #AI #Python #developer #MachineLearning

If you're building with LLMs and getting inconsistent results, check these before changing the model: → Is your system prompt actually specific, or is it just vibes? → Are you measuring outputs systematically, or eyeballing them? → Do you have a fallback for when the model is uncertain? → Are you chunking your context in a way that preserves meaning? I've fixed "bad model" complaints four times this year without changing the model once. The wrapper matters more than the weights. #AI #Python #developer #MachineLearning

In under 2 hours I have built an Python powered Air Hockey game using only Gen AI prompts. What happens when you challenge yourself to build a full game from scratch — without any experience writing a single line of code yourself? I'm finding out. This is my ongoing experiment: leveraging Generative AI as the sole engine of development, with Python as the language under the hood. The short clip above is just a glimpse. The full journey — the wins, the failures, the surprises — is all on YouTube 👇 🎮 Watch the full video: https://lnkd.in/gr2ecC3d #GenerativeAI #BuildInPublic #Python #AIEngineering #GameDev #NoCode

🎬 Clean Your Subtitles with Subtitle Deduplicator! Stop struggling with "scrolling karaoke" effects in auto-generated captions! Subtitle-deduplicator by fr0stb1rd is a lightweight CLI tool to fix repetitive SRT files. Key Features: 🚫 Removes Ghost Entries (≤ 20ms). 🧹 Cleans up Carry-over and overlapping lines. ⚡ Zero dependencies, runs on pure Python. 📉 Reduces file size by ~50% while boosting readability. https://lnkd.in/dtg6-bv5 #OpenSource #Python #AI #Whisper #Transcription #Automation #CLI