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From installing Ollama to launching OpenCode with a local model, step by step. The post Build Your Own Local AI Coding Agent with Gemma 4 and OpenCode appeared first on Towards Data Science.
I have been experimenting with multi-agent systems and recently started building a DeepResearch-style agent workflow to understand what goes into a real Agentic AI application. The first version is intentionally simple, but the goal is not just another “LLM + tool call” demo. I’m trying to learn the
In this episode of the Agent Factory, I sat down with Rody Davis, one of Google’s top agentic engineers. We dive into the massive shift from traditional IDEs to agent-first platforms, the reality of code reviews in an AI-driven world, and how to use "skills" to perform at a 100X level. This post gui
Every time I wanted to give my small team some internal tooling - a shared task tracker, a place for documents, a calendar, an assistant that actually knew our files - I ended up wiring together five SaaS subscriptions or a sprawling self-hosted stack: Postgres, Redis, a vector database, a frontend
Every AI agent ships with the same bottleneck: it can only reason over what it can reach. MCP servers dissolve that boundary. They expose tools, resources, and prompts to any compliant client over a JSON-RPC wire format so lean you can implement it in an afternoon. Yet most developers grab a framewo
AI agents are software systems that can perceive their environment, reason through a problem, and take action to complete a goal, all without you clicking a button for every step. If you’re a developer or freelancer trying to understand what everyone in tech is talking about, you’re in the right pla
Here's a pattern I keep seeing: a team builds an AI agent, the demo works, they ship it, and within a few weeks the outputs are unreliable. Someone opens a ticket about hallucinations. Someone else suggests switching to a better model. The model isn't the issue. The data feeding the model is. Multi-
I've been working on a Mac-native agent framework for about a year. One of the hardest problems: making the agent actually remember context across sessions in a way that's useful, not just "here's your last 10 messages." What I ended up with is a knowledge graph — entities (people, projects, tools,
Today, Microsoft Build brought the announcement of Project Solara, a new chip-to-cloud platform for AI agents. With the reveal, Microsoft CEO Satya Nadella shared an interesting quote on the company’s vision for an agent-first computing future. more…
Agents Don't Replace APIs. They Expose How Weak Most APIs Already Are There is a growing narrative that AI agents, often coupled with things like Model Context Protocol, will replace APIs. It is easy to see why that idea has taken hold. Agents can discover tools, reason about which one to call, and
AI agents will need to pay for compute, data, and API calls. The current paradigm of human-managed payment systems breaks down when thousands of autonomous agents need to transact independently. They need wallets — not custodied accounts managed by humans, but autonomous financial infrastructure the
Over the last two years, an entire industry has emerged around AI development within the PHP ecosystem. Where integrating an LLM once looked like a few lines of code calling the OpenAI API, developers today are building full-fledged agent systems: with memory, tools, workflows, observability, and ev
The current wave of AI development is undergoing a massive paradigm shift. We are rapidly moving past simple "prompt wrapper" applications and entering the era of fully autonomous, agentic systems. Yet, if you’ve tried to build an AI agent for a production environment, you’ve likely run into a frust
This is a submission for the Hermes Agent Challenge: Write About Hermes Agent. Most AI assistants wait around like interns who lost the Slack invite. You open a tab. You type a prompt. You explain the same project again. You paste the same links again. Then you spend half the afternoon checking whet
