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If this is useful, a ❤️ helps others find it. All tests run on an 8-year-old MacBook Air. Waiting 5 seconds for an AI response with no feedback feels broken. Streaming fixes this — tokens appear as they're generated, just like ChatGPT. Here's how to wire Gemini streaming into a Tauri app so the response appears word by word in your UI. Replace :generateContent with :streamGenerateContent: POST /v1
TL;DR I try to keep my eyes on the AI agents. I gave one too much rope once, and the kind of mess it made while I wasn't watching is something I'd rather not retell. Which is why I needed 5 monitors. To run 5 agents in parallel, 5 VSCode windows have to live in one field of view. Physical monitors hit a wall. No desk fits five; even my viewing angle gives out before the desk does. So I strappe
LLMs guess. The EVM executes. This is the fundamental friction at the heart of Web3 AI. Large Language Models are, by design, probabilistic hallucination engines—they are built to be creative. The Ethereum Virtual Machine, on the other hand, is a cold, ruthless, and deterministic state machine. It does exactly what it is told, down to the byte, without remorse. When you bridge a probabilistic brai
🧠 I Built a AI Assistant with Multi-Model Fallback, Voice Chat & a Personal Data Analyst — Here's How What happens when your AI goes down mid-conversation? You lose users. I built Hero's AI to make sure that never happens — and added a whole lot more along the way. Live Demo Have you ever used an AI tool that just... stopped working? Maybe it hit a quota limit, the API went down, or the mod
The math isn't complicated. It's just that nobody runs it until they get the bill. An AI agent handling a 10-turn workflow — reading files, calling tools, revising output — doesn't cost 10x a single query. Because transformer inference processes the entire context on every call, cost compounds with each additional turn. The tenth turn carries everything that preceded it: the original file reads, e
Introduction When building a coding agent, the capability of your base model is only part of the equation. In real production scenarios, what matters just as much is the harness wrapped around that model — the prompt, tools, middleware, memory, execution environment, trace, and evaluation pipeline. This is exactly what the AHE paper addresses: how to make a coding agent's harness continuously ob