If you've set up GitHub Copilot, written a .github/copilot-instructions.md, maybe defined a custom agent or two — and then wondered "what now?" — the answer is awesome-copilot. It's the single most useful community resource for anyone pushing Copilot beyond defaults. Maintained under the github org, it has built a large community of users and contributors. It covers agents, instructions, skills, plugins, hooks, and agentic workflows — basically every customization surface Copilot exposes. If your setup ends at a .github/copilot-instructions.md, this repo is where you go next.

When your product is an AI assistant, using AI to build it feels natural — but doing it well is harder than it sounds. The AI-Assisted Development section describes these patterns in the abstract. This post is what they look like after a year in production on a real Aliz frontend: a React + TypeScript chat-based AI assistant with theming, 18-language internationalization, MCP integrations, and multi-environment deploys. The codebase is large enough that no single prompt can reason about it coherently, which is the whole reason the team stopped reaching for a tool and started building a system — the same shape described in Multi-Agent Orchestration. Three layers of AI setup, a team of specialist agents, and a workflow called QRSPI hold it together.

GitHub has announced changes to its Copilot Individual plans — Free, Pro, and Pro+ — taking effect in April 2026. The details are in the company news post and the changelog entry. Our reaction: unsurprising — and overdue. For historic context (see the Copilot plans page and GitHub's 2025 Pro+ announcement): Copilot Pro has been $10/month since launch, back when frontier models were cheaper and agent mode didn't exist, and Pro+ arrived in April 2025 at $39/month. The economics of that original entry-level tier were never going to survive a world of agentic coding on frontier models. This post is commentary, not a restatement of GitHub's announcement — read the source for the actual numbers.

In a recent blog post we explored Karpathy's LLM Wiki pattern — the idea that an LLM should build a persistent, interlinked wiki instead of re-deriving knowledge from scratch on every query. A few days later, it clicked: Aliz Web Hub — a Docusaurus site where AI agents contribute documentation via GitHub PRs — is essentially the same pattern with different tooling. The project predates Karpathy's gist. The parallel was noticed after, not before. The pieces map surprisingly cleanly.

Anthropic has released Claude Opus 4.7. Opus 4.6 has been central to our workflow — it powers Claude Code, our ⭐ recommended agent for complex tasks — so any new Opus release is worth paying attention to. That said, our first impression is that this isn't the across-the-board leap some might expect. Here's what we know and what we think.

Roadmap.sh has published two new learning paths directly relevant to the Aliz stack: one for Claude Code (Anthropic's CLI-based coding agent, already our ⭐ recommendation for complex tasks) and one for OpenClaw (an open-source AI agent orchestration framework). These join roadmap.sh's existing web, JavaScript, TypeScript, and Node.js roadmaps as structured, community-maintained learning resources.

GitHub just shipped an experimental feature in Copilot CLI (announced April 6) that does something no mainstream coding agent has done before: it uses a second model from a different AI family to review the primary agent's work before it executes. The feature is called Rubber Duck, and it's one of the first real-world implementations of cross-model review built into a tool developers actually use daily. If you care about multi-agent workflows or code quality, this one is directly relevant.

Google just released Gemma 4, the latest generation of their open model family — and two things make this one genuinely different. First, it's now Apache 2.0 licensed, removing the custom-license friction that held back adoption. Second, the lineup spans from a 2B-parameter edge model that fits on a Raspberry Pi to a 31B powerhouse that ranks in the top 3 open-source LLMs on the Arena AI leaderboard. If you're a web developer, this one's worth a closer look 🧠.

Andrej Karpathy — the same person who coined vibe coding — recently published a gist describing a pattern he calls the "LLM Wiki". The core tension it addresses is simple: RAG re-derives knowledge from scratch on every question. What if the LLM built something persistent instead — a structured, interlinked wiki that compounds over time? It's a deceptively simple idea with some genuinely interesting implications.

Here's what caught our eye this week across JavaScript Weekly, React Status, and Node Weekly — and what it means for the Aliz stack. This is our first newsletter roundup post: a new recurring format where we distill the highlights so the team doesn't have to read three inboxes.