Why We Built TensorFeed.ai

I used to start every morning the same way. Open a dozen tabs. Hacker News, the OpenAI blog, Anthropic's changelog, Google DeepMind research, a couple Discord servers, maybe a subreddit or two. Scroll through everything. Close most of it. Repeat the next day. It was a ritual, and honestly, it was exhausting.

That frustration is what eventually became TensorFeed. Not because I had some grand startup thesis or a slide deck with a TAM calculation. Because I was tired of doing the same scavenger hunt every single morning and still feeling like I was missing things.

The AI News Problem

If you work in AI or build on top of AI APIs, you already know this pain. The information is out there, but it's scattered across a dozen platforms, each with its own format and its own incentives. Twitter rewards hot takes over substance. Reddit buries important announcements under memes. Official company blogs publish on their own schedule with their own spin. Research papers land on arXiv with zero context for practitioners who just want to know if a new technique actually matters.

And the volume keeps growing. In January 2025, there were maybe two or three notable model releases per month. By early 2026, we're seeing multiple releases per week. Pricing changes, API deprecations, new frameworks, new standards. The firehose is real.

I talked to other developers about this. The frustration was universal. Everyone had their own cobbled system of bookmarks and RSS readers and notification channels. Nobody was happy with it. Every person I talked to said some version of the same thing: "I just want one place that has everything."

So I Built That Place

TensorFeed started as a personal project. A Next.js app pulling RSS feeds from the major AI company blogs and tech outlets, dumping them into a single timeline. Nothing fancy. Just the headlines I cared about, in one place, updated automatically.

But it grew fast. I added source filtering so you could drill into just Anthropic news, or just open-source releases. I added the status dashboard because I was constantly checking whether Claude or GPT was down during development. I added model tracking because keeping up with which version of which model supported which context window was becoming its own full-time job.

Each feature started the same way: I needed it for myself, so I built it. The cost calculator came from a spreadsheet I was maintaining to compare API pricing across providers. The outage alerts came from the third time I lost twenty minutes debugging my code before realizing the API itself was down.

Building for Agents (Not Just Humans)

Here's the part that makes TensorFeed genuinely different from other aggregators.

From the very beginning, I designed it to serve two audiences: human readers and AI agents. That might sound like marketing fluff, but it's an actual architectural decision that shaped every part of the platform. Every piece of content on TensorFeed has structured metadata. Category tags, source attribution, timestamps, entity references. The kind of stuff that's invisible to most human readers but critical for an agent that needs to filter, sort, and analyze AI news programmatically.

We publish an llms.txt file that tells AI agents exactly what TensorFeed offers and how to interact with it. Think of it like robots.txt for the agent era. We serve structured JSON endpoints through our developer API. We provide RSS and JSON feeds that any agent or tool can consume without authentication or rate-limit friction.

The reason is simple: I believe the next generation of information tools will be used as much by AI agents acting on behalf of humans as by humans directly. An agent working for a developer should be able to ask "What changed in the AI API landscape this week?" and get a clean, structured answer from TensorFeed. That's the future I'm building for.

The TerminalFeed Connection

TensorFeed isn't a standalone project. It's part of a family of sites I'm building under Pizza Robot Studios. TerminalFeed.io is the sister site, focused on real-time data feeds and dashboards. Same philosophy: aggregate the signal, cut the noise, serve both humans and machines.

The tech stack and design patterns flow between both projects. An improvement to one benefits the other. The Cloudflare Workers backend, the KV caching architecture, the feed aggregation pipeline. All of it is shared DNA.

What Makes This Different

There are other AI news aggregators out there. I won't pretend there aren't. But most of them are doing one of two things: either they're glorified RSS readers with no curation, or they're editorial outlets that publish a handful of articles per day and miss everything else.

TensorFeed sits in the middle. We aggregate from 15+ sources with automatic updates every 10 minutes. We add structured data, categorization, and status monitoring on top. We publish original editorial analysis (like this article). And we make all of it available through machine-readable endpoints for the growing ecosystem of AI agents.

No paywalls. No login walls. No CAPTCHAs blocking legitimate automated access. The AI ecosystem is open by nature, and the tools that serve it should be too.

Where This Goes

I'm building TensorFeed in public, and the roadmap is shaped by what the community tells me they need. The agents directory, the incident database, the benchmark tracker. All of those features started as requests from people using the site.

If you have ideas for what TensorFeed should become, I genuinely want to hear them. Drop a note at [email protected] or find us on X at @tensorfeed.

Thanks for being here early. The AI space is moving fast, and I intend to keep up. Let's see where this goes.