Appunite

Building a product is never easy. But building one on a fragile tech stack that crumbles with the first surge of users? That’s a real nightmare. Elixir was created to avoid exactly this.

Unlike many mainstream languages, Elixir was designed with scalability and concurrency at its core. Running on Erlang’s legendary BEAM VM, it can effortlessly manage millions of processes simultaneously. The result? Fewer servers, lower infrastructure costs, and confidence that your app won’t break when it suddenly takes off.

And there’s more - fault tolerance is built right in. When something goes wrong, the system doesn’t crash. Instead, it recovers on its own and continues running, just like the telecom systems it was originally built for.

Do you use Elixir in your company? Where has it worked best for you? We’d love to hear your experiences.

If you’re new to Elixir, or looking for experts who know how to get the most out of it, check out how we can support your product. Our open-source contributions, conference talks, and the trust of the Elixir community speak for themselves.

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What made you choose Elixir as your main programming language? And how satisfied are you with that choice?

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Conversations about AI often stay on the technical layer - models, libraries, pipelines. Yet the architecture of AI agents itself can turn into a real competitive edge.

At Appunite, we recently chose to build generative AI agents natively in Elixir (our main backend stack) with the Jido Agentic Framework, rather than taking the standard Python route.

The outcome? Stronger integration and reduced operational complexity. For CTOs, this raises a bigger, strategic question:

👉 Should your AI be tightly embedded within your core architecture, or exist separately as its own platform?

You can dive deeper in the full article here: bit.ly/44U2BTW

We’d love to hear your perspective.

Integrating Generative AI into Elixir-based applications by using the Jido agentic framework | Appunite Build AI agents in Elixir using Jido framework - no Python required. Complete guide with LiveView example showing how Elixir's concurrency model outperforms traditional AI stacks.

How do you see the impact of AI revolution on your professional work? Do you see it more as an opportunity, a threat - or does it not make much of a difference to you?

According to Adobe Analytics, the 2025 U.S. holiday e-commerce season is forecast to hit $253.4 billion, up 5.3% YoY. But the real headline? AI-assisted shopping traffic is expected to grow 520% year over year, peaking in the ten days before Thanksgiving.

Here’s how consumers plan to lean on AI tools:
👉 53% may use AI for product research
👉 40% for recommendations, 36% for deal finding, 30% for gift inspiration
👉 Top impact categories: toys, electronics, jewelry, personal care

As AI tools increasingly shape the shopping journey - from discovery to transaction - brands and retailers must be ready to embed intelligent assistance at every touchpoint.

When it comes to building MVPs, Elixir is hard to beat. Running on the BEAM (Erlang VM), it offers exceptional concurrency and fault tolerance perfect for real-time features and scaling under heavy load.

With strong community support and frameworks like Phoenix, your MVP can naturally grow into a robust, production-grade solution.

If your team already works with Elixir, we’d be glad to help you take things further. Learn more →

The Elixir Development Team That José Valim Trusts Trusted by José Valim. The Elixir team that rescues underperforming systems and scales applications to millions of users. Get your free Elixir audit.

At OpenAI’s Dev Day, Sam Altman introduced AgentKit, a toolkit built to accelerate the creation, deployment, and management of AI agents.

It’s not just a framework, but a full stack of components:
✅ Agent Builder - a visual “Canva-style” tool to design logic flows and steps.
✅ ChatKit - an embeddable chat UI to integrate agent conversations into custom apps.
✅ Evals for Agents - built-in performance metrics, trace grading, prompt optimization, and support for external models.
✅ Connector Registry & Admin Controls - secure access to internal and third-party systems with admin oversight.

The real coup? On stage, an OpenAI engineer spun up two working agents from scratch in under eight minutes. As AI moves from “assistants that respond” to “agents that act,” AgentKit signals OpenAI’s bet: making autonomous agent development more accessible, scalable, and enterprise-ready.

Curious how this may change your development stack or product roadmap? Let’s talk 🗣️

What do you think, what’s the best AI chatbot right now from a developer’s perspective? How often do you use it in your professional work?

Google just made a big move: they’re opening up public “real-world” datasets via a new MCP Server (Model Context Protocol) for Data Commons, letting AI/LLM systems fetch structured, factual data (e.g. census numbers, climate stats) through

Why it matters: so much of AI today is trained on messy web data, leading to hallucinations or gaps in knowledge. With MCP + Data Commons, models can be grounded in verified, high-quality sources, essentially giving AI access to a richer, cleaner knowledge base.

If AI agents can pull from “real world data” reliably, do we still need massive, bloated model pretraining on generic web data, or will that become obsolete? 🤔

Microsoft is taking a bold step by integrating Anthropic’s AI models into Copilot - a move that marks a clear shift away from relying exclusively on OpenAI.

Users, especially in business settings, will soon be able to pick either OpenAI’s models or Anthropic’s Claude Opus 4.1 or Claude Sonnet 4. The idea is to offer more flexibility in how AI assistants support complex software work.

This feels like a turning point: rather than one dominant AI “stack,” enterprise users may get to mix & match models based on strengths. It raises big questions about how we define “the best” AI for development and what “vendor lock-in” means in this new era.

So here’s the question:
Will this shift make your life easier, or just more confusing? 🤔