nlp

Have you ever wanted your own Jarvis? A voice assistant that listens, thinks, and speaks back - all running privately on your own hardware? Here's how to build one with Whisper.cpp, Ollama, and Kokoro TTS. No cloud, no wake-word fees, no data leaving your machine. Prerequisites Hardware: Any modern computer with a microphone Software: Python 3.10+, Ollama installed Time: ~30 minutes setup Install…

Want a ChatGPT-like experience where your AI can search the web, read your files, query databases, and run code? Open WebUI + MCP makes it possible - all running locally on your hardware. The Model Context Protocol (MCP) is an open standard that lets AI connect to external tools. Open WebUI supports MCP natively, turning your local Ollama setup into a tool-equipped AI assistant. Prerequisites GPU…

The TL;DR If you need to spin up a local, privacy-first AI agent that can query your own internal documents without sending data to third-party APIs, this guide covers the exact architecture using TypeScript, Python, and Ollama. Time to complete: ~15 minutes. Prerequisites: Python 3.10+ or Node.js installed, basic familiarity with embeddings. The Problem: API Costs & Data Privacy When building pr…

For about a year I used AI to code like a slot machine. Paste a wall of code. Type "fix it" or "make this better." Pull the lever. Sometimes I won. Usually I got a confident answer that solved the thing I asked about and quietly broke two things I didn't. I assumed the problem was the model. So I switched models. Same slot machine, nicer graphics. The problem wasn't the model. It was that I was g…

Hey DEV Community! I recently participated in a hackathon and built Samiksha AI , a universal review and comment analyzer designed to turn messy customer feedback into structured business intelligence. The Problem Generic star ratings on e-commerce platforms don't give business owners the full picture. Rule-based sentiment tools often fail to catch sarcasm, background noise (like shipping complai…

Without these, Claude will be confidently wrong. The post 4 Lines You Should Include in Your Claude Skill appeared first on Towards Data Science .

We wanted to know when LLM is guessing versus when it actually knows the answer. LLM models expose logprobs - after every word they generate, you can request the top alternative tokens and their probabilities. Low entropy means the model was certain, high means it was guessing. I tested two models using 0.0 temperature and 3 prompts: "what is 2+2", "the opposite of hot is", "once upon a time." Th…

I recently took on a side project that needed to tap into multiple AI models – GPT-4 for complex reasoning, Claude for creative writing, and a local Llama 2 for quick drafts. My naive plan was to just call each API directly from my Python backend. Three days later, I had a tangled mess of authentication headers, inconsistent rate limits, and error handling that looked like a love letter to try/ex…

"Chat with your PDF / your notes / your docs" is everywhere. Today we build it from scratch and you'll see it's just three moves : retrieve, then generate — with one prompt trick that stops the hallucinations. This is Day 46 of TechFromZero. Yesterday (Day 45) we built the retrieval half with pgvector. Today we add the answer half and host it on Supabase. RAG in one line Find the relevant chunks …

I'm a test automation engineer with ~12 years in Java/Selenium/Playwright. Last year I went back on the job market — and I was reminded how genuinely bad the job-search loop is. You open LinkedIn/Indeed/Naukri, scroll 200 listings, and maybe 5 are actually a fit. Then for each of those 5 you re-tailor your resume, guess at the ATS keywords, and fire it into the void. The filtering work — "is this…

Ask Claude this, today, with no setup: "What did user 4421 do in our app yesterday?" You will get an answer. It will be confident, specific, and completely made up. Claude has no access to your production database. So it does what a language model does when it has no facts: it pattern-matches what an answer should look like and hands you fiction in a calm voice. That is the whole problem. Not tha…

If you're building AI Agents with Pydantic AI, understanding Capabilities is invaluable - it's the recommended way to add modular, reusable features to your agents. This tutorial is part of my ongoing Pydantic AI series on YouTube , where I build a full no-code AI agent platform from scratch. What is a Capability? A capability in Pydantic AI is a modular unit of behavior that can be passed to an …

This article was originally published on BuildZn . Everyone talks about agentic AI, but nobody explains how to stop these things from just making stuff up or skipping crucial steps. I spent weeks wrestling claude-3-opus-20240229 in FarahGPT, and it consistently fumbled complex multi-tool workflows. The official docs give you the basics, but building a bulletproof agent that provides verifiable ev…

Three giants of AI set for mammoth IPOs – but questions linger over whether there is enough investor money to go around

Don't trust large context windows I recently watched a video that put a name on something I'd been feeling. The author splits an LLM's context window into two zones. There's the smart zone, where the model is sharp, and the dumb zone, where attention drops off and the model starts forgetting what you told it five minutes ago. The cutoff sits somewhere around 100k tokens. It doesn't matter how big…

I've been building a personal AI assistant for my developer blog – you know, one of those floating chat widgets that answers questions about my projects. The idea was simple: feed in my content, hook it up to an AI API, and let visitors chat with it. But my first implementation was a disaster. Visitors would type a question, see the spinner spin for ten seconds, and then get the entire response d…

Type "animals that live in the ocean" into a normal search box and it hunts for the words animals , live , ocean . An article titled "Blue whale" that never uses any of those words? Missed. Today we fix that. We'll build a search engine that matches on meaning , so "animals that live in the ocean" surfaces Blue whale and Coral reef — no shared keywords required. The whole thing is a few hundred l…

When I open a link, say on Hacker News, and I see a blog post or a GitHub README obviously written by AI, I feel a few things. I feel offended, because it’s like I’ve been tricked, like the author thinks I’m a rube who won’t notice or mind. I feel sad at how common this experience is, how many people are happy to dump their sewage on the commons and sign their name on it. And I feel contempt for …

Two months ago I started building voice agents for small service businesses. dental clinics and HVAC companies that lose real money every time a call goes to voicemail. I'm doing it solo, alongside a day job, which means every wrong turn costs me a weekend I don't get back. Here's what actually went wrong, what I'd tell myself on day one, and the parts of the stack that held up. The stack, briefl…

On-device only A local speech model, running on-device. No servers, no accounts, and nothing you record ever leaves your Mac. Trace captures your microphone and system audio, transcribes locally on your Mac, and hands you a markdown transcript with flagged moments inline. No accounts. No cloud. No meeting bots. Requires macOS 14 or later and Apple silicon. One keystroke brings Trace up over any a…