Why We Built TensorFeed.ai

Every morning, I used to open somewhere between eight and fifteen browser tabs before I even had coffee. Hacker News for general tech chatter. Twitter for the hot takes. The OpenAI blog in case they dropped something overnight. Anthropic's changelog. Google DeepMind's research page. A couple of Discord servers. Maybe a subreddit or two. It was exhausting, and I wasn't even reading most of it.

That ritual is what eventually became TensorFeed. Not because I had some grand startup vision, but because I was genuinely annoyed. The AI space moves faster than any technology space I've ever worked in, and there was no single place that pulled it all together in a way that respected my time.

The Problem Was Obvious

AI news is scattered across a dozen platforms, each with its own format and agenda. Twitter rewards hot takes over substance. Reddit buries important announcements under memes. Official blogs publish on their own schedule with their own spin. Research papers land on arXiv with zero context for practitioners.

If you're a developer building on top of these models, you need to know when a new version ships, when pricing changes, when an API goes down, and when a competitor releases something that might change your architecture decisions. You shouldn't have to piece that together from fifteen sources every morning.

I talked to other developers and the frustration was universal. Everyone had their own cobbled-together system of bookmarks, RSS feeds, and notification channels. Nobody was happy with it.

One Hub, No Noise

The core idea behind TensorFeed is simple: aggregate everything important in AI into one clean feed. Model releases, API updates, pricing changes, research breakthroughs, industry news. All of it, from every major source, organized and filterable.

We pull from official provider blogs, research repositories, developer changelogs, and curated community sources. Everything gets categorized and tagged so you can drill into exactly what matters to you. If you only care about open-source model releases, you can filter for that. If you want to track pricing across providers, we have a dedicated view for it.

The goal was never to replace deep-dive journalism or original research. It's to be the first place you check in the morning, the one tab that replaces all the others.

Built for Humans and Agents

Here's where TensorFeed gets a little different from a typical news aggregator. From day one, we designed the platform to serve two audiences: human readers and AI agents.

That sounds like a buzzword, but it's practical. AI agents are increasingly browsing the web, pulling data from APIs, and making decisions based on structured information. If an agent needs to know which model provider offers the cheapest embedding API right now, it should be able to query TensorFeed and get a clean, structured answer.

We publish an llms.txt file, serve structured JSON endpoints, and think carefully about how our data is formatted for machine consumption. The human experience comes first, always. But the agent experience is a close second.

The Tech Stack

We chose Next.js because it gives us server-side rendering for SEO, great performance out of the box, and the App Router makes organizing a content-heavy site surprisingly pleasant. Tailwind keeps our styling consistent without the overhead of a component library.

The real complexity lives in the data layer. Aggregating from dozens of sources, normalizing formats, deduplicating content, and categorizing everything accurately is the hard part. We use AI assistance for some of this, but there's a lot of hand-tuned logic to make sure the feed stays high quality.

Part of a Bigger Picture

TensorFeed isn't a standalone project. It's part of a family of sites that I'm building under Pizza Robot Studios. VR.org covers the virtual reality and spatial computing space with the same aggregation approach. TerminalFeed.io is focused on developer tools, CLI workflows, and terminal productivity.

Each site serves a specific community, but they share the same philosophy: aggregate the signal, cut the noise, and make the information accessible to both humans and machines. The tech stack and design patterns flow between all three projects, which means improvements to one benefit the others.

What Comes Next

We're just getting started. The feed is live, the data is flowing, and we're iterating fast based on feedback from the community. Here's what's on the roadmap:

• Personalized feeds that learn what you care about over time
• Real-time alerts for breaking model releases and API changes
• A public API so developers can build on top of our aggregated data
• Deeper comparison tools for model pricing, performance, and capabilities
• Community features so readers can discuss and annotate the news

The AI space is moving fast, and we intend to keep up. If you have ideas for what TensorFeed should become, I genuinely want to hear them. Drop me a note or find us on social media. This thing is being built in public, and community input shapes what we prioritize.

Thanks for reading, and thanks for being here early. Let's see where this goes.