My AI Couldn’t See My Files — I Built a Zero-Dependency MCP Server
. The functions had grown too long and the variable names made no sense anymore. Every time I wanted feedback on a file, I stopped, opened the chat, copied the whole thing in, and waited. Then went back to the editor, applied the change, opened the next file, and did it again. At some point […]
The Fundamental Choice in Reinforcement Learning: On‑Policy vs. Off‑Policy
is often introduced through a long list of algorithms. SARSA, Q-learning, PPO, DQN, SAC etc. Each name seems to point to a different method, a different trick, or a different mathematical formulation. But many of these algorithms are built around a much simpler question: Should an agent learn only from the behavior it is currently […]
Automate Writing Your LLM Prompts
Image created by Serj Smorodinsky, co-author of Building LLM Applications with DSPy we’ve probably all had the experience of getting responses that weren’t quite what we wanted. Usually we’ll try rewording the prompts a few times until we get something reasonable. We sometimes have to be more clear, more precise, give examples, describe why we […]
How to Fine-Tune an SLM for Emotion Recognition
Introduction models (SLMs) fine-tuned for sentiment classification infer sentiment as a single score, capturing the overall emotional tone of the text. For many use cases, the positive-negative classification does not tell the full story a company needs. Emotion recognition models go further, decomposing sentiment into emotion classes (“anger”, “approval”, “disappointment”, etc.) and assigning probabilities to […]
Building Semantic Search with Transformers.js and Sentence Embeddings
In this article, you will learn how sentence embeddings work and how to build a fully client-side semantic search engine using Transformers.js, with no server, no API key, and no backend infrastructure required. Topics we will cover include: How sentence embeddings and cosine similarity form the foundation of semantic search. How to generate and cache […]
How C3 AI agents will automate predictive maintenance for Shell
Shell will use agents from C3 AI to shift from basic anomaly detection towards fully-automated predictive maintenance. The global energy giant is building on their current use of the C3 AI Reliability Suite, which already keeps tabs on more than 30,000 crucial pieces of equipment across upstream and downstream operations. Shell now intends to lean […]
NVIDIA AI Releases Dynamo Snapshot: A CRIU-Based Fast Startup System for AI Inference on Kubernetes
In production inference deployments, demand fluctuates over time, requiring inference replicas to scale elastically. Cold-starting inference workloads on Kubernetes can take several minutes. During that time, GPUs are allocated but idle, generating no tokens and serving no requests. ‘Cold start’ means the full sequence a model server must complete before serving any request: pulling the […]
Perplexity AI Introduces Hybrid Local-Server Inference Orchestrator for Personal Computer: Automatic On-Device and Cloud Task Routing
Perplexity AI announced what it calls the first hybrid local-server inference orchestrator at Computex 2026. The system is designed to automatically route AI tasks between a user’s local device and cloud-based frontier models without requiring the user to decide in advance. The feature is expected come to Perplexity Computer in July 2026. What is Hybrid […]
Microsoft Fara Tutorial: Run a Browser-Use Agent in Google Colab with a Mock OpenAI-Compatible Endpoint
mock_server_code = r”’ from fastapi import FastAPI, Request import time app = FastAPI() STATE = {“calls”: 0} @app.post(“/v1/chat/completions”) async def chat_completions(request: Request): payload = await request.json() STATE[“calls”] += 1 model_name = payload.get(“model”, “mock-fara-7b”) if STATE[“calls”] == 1: content = ( “I will open a stable public test page so the browser-control loop can be demonstrated.\n” […]
15 Best Vibe Coding Tools in 2026 Compared: Pricing, Features, and Best Fit
AI-first development is changing how software gets built. A new approach called “vibe coding” sits at the center of that shift. Developers describe what they want in plain language. An AI agent turns that description into working software. The term was coined by Andrej Karpathy. It captures a move away from line-by-line coding toward natural-language […]