10 posts tagged with "ai"

Most of the "agentic AI" I've shipped so far has one uncomfortable dependency baked in: a network call to somebody else's cloud. Every tool call, every reasoning step, every retry — all of it round-trips to an API. So when Meta dropped Muse Glimmer this week, a 30-billion-parameter agentic model that runs locally on a single GPU under an Apache 2.0 license, it caught my attention for reasons that have nothing to do with benchmark charts.

Let me walk through what it is, how they pulled it off, and where I think it actually fits.

If you use AI coding agents on a real repo, you've probably noticed they keep making the same avoidable mistakes — running the wrong test command, touching files they shouldn't, inventing a table name that doesn't exist. AGENTS.md is the fix, and it's quietly becoming a cross-tool standard. But there's a catch most people miss: a lazily written AGENTS.md can actually make your agent worse. So it's worth doing properly.

Here's a frustration anyone who works with AI agents knows well: every conversation starts from zero. The model doesn't remember how your team does code review, what your design system rules are, or the workflow you painstakingly explained yesterday. And the usual fix — stuffing all of that into one giant system prompt — makes things worse as it grows, because a bloated prompt dilutes the model's attention. SKILL.md is a genuinely clever answer to this, and I think it's one of the more important ideas in agent tooling right now.

A post by Günter Richter got me thinking: everyone's rushing to vibe code, but almost nobody talks about the skills that decide whether you succeed or just generate an expensive mess. He's right that the tools don't replace fundamentals — they raise the stakes on them. So here's my own version: the three skills I'd want anyone to have before they let an AI write their software.

Most AI coding tools still assume you're sitting in the driver's seat, hands on the keyboard, with the AI whispering suggestions. Google Antigravity starts from a different premise: what if the AI is the one doing the work, and your job is to direct and verify it? It's an agentic development platform, and the framing — the editor as mission control rather than a text buffer — is the interesting part.

The "which AI coding agent is best" debate never really ends, and honestly it shouldn't — the answer depends far too much on how you work. I've spent time with all three of the big ones lately: Anthropic's Claude Code, OpenAI's Codex, and Google's Gemini Code Assist. Rather than pretend one wins outright, here's how I actually think about the trade-offs.

Every week there's a new tool promising to turn a sentence into a working app. I've lost count. The problem with most "best vibe coding tools" lists is that they rank everything against each other as if they're the same product — they're not. A tool built for a non-technical founder shipping an MVP is solving a completely different problem than one built for an engineer refactoring a 50k-line codebase.

So instead of a leaderboard, here's how I'd actually choose, grouped by what you're trying to do.

I rolled my eyes the first time I heard the phrase "vibe coding." It sounded like something invented to sell a course. Then I actually spent a few weekends doing it — describing what I wanted in plain English, letting an LLM write the code, running it, fixing what broke — and I had to admit the eye-roll was premature. There's something real here. There's also a trap, and most of the hype skips right over it.

So here's my honest take, from someone who ships backend and blockchain code for a living.

Artificial Intelligence is evolving rapidly, with models becoming smarter, more capable, and increasingly conversational. But how do these models remember what you told them earlier? How do they maintain context over long interactions or recall relevant details from past sessions? This is where the Model Context Protocol (MCP) comes into play.

In this article, we’ll break down: • What is MCP? • Why MCP is important • How MCP works • Technical structure of a context • Real-world use cases • Limitations and privacy considerations

DeepSeek-R1 is a powerful language model that you can run on your local machine using Ollama, which simplifies downloading, running, and interacting with LLMs. This guide will walk you through setting up DeepSeek-R1 and making API calls to use it in your applications.

Why Run DeepSeek-R1 Locally?​

Running DeepSeek-R1 on your own system provides several benefits:
✅ Privacy & Security – Your data stays on your device.
✅ Faster Responses – No network latency from API calls.
✅ Offline Access – Work without an internet connection.
✅ No API Costs – Avoid paying for cloud-based LLM services.
✅ Customization – Fine-tune and modify model settings as needed.