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Our first product is a device that reduces the physical cost of carrying heavy loads over distance, allowing people to move farther, carry more, and arrive less fatigued.

The company is early but already operating in the field, with hardware on real users and a direct feedback loop in the most demanding conditions imaginable.

We are backed by Y Combinator and a group of leading deep-technology investors.

We recently moved to the Bay Area to grow the team and are hiring three principal engineers (we're in stealth for the time being, but hopefully not for too much longer!).

$200-250K + 1-2% equity + full benefits, onsite in Palo Alto.

These senior roles each own their domain with full authority, no inherited design decisions, and no one between them and the problem. What you build will go on real users within weeks.

- Mechanical (Principal Engineer): own the mechanical platform, including structures, joints, actuator integration, ruggedness, and field serviceability. You have designed hardware for robots, humanoids, or other high-performance actuated systems.

- Firmware (Principal Engineer): own the embedded stack, including real-time BLDC motor control, sensor integration (IMUs, encoders, current sensing), OTA, and debug tooling. You have written real-time firmware for physical robots or autonomous systems.

- Software (Principal Engineer): own the software infrastructure, including device-to-cloud telemetry, test and log databases, data pipelines, and user applications. You have built software for hardware fleets and the people who use them.

Please send a note to chrome.braindance@gmail.com that shows what you've built. A resume works, but a quick description of a real project is better. Photos, videos, and links are welcome.

We look forward to meeting you!

We match SOTA extraction quality while being 20x cheaper. Cleaning 1 billion webpages costs $7,900 with Pulpie versus $159,000 with Dripper, the current leading extractor.

The gains come from architecture. Today's leading extractors are decoders that generate output one token at a time. Each step reads the full model from memory to produce a single token. Conversely, Pulpie models are encoders. They run one forward pass over the full input HTML and label each block as boilerplate or content. As a result, Pulpie is compute-bound while decoders are memory-bound. Cheaper GPUs have relatively more compute than memory bandwidth. This makes Pulpie easy to run optimally.

Here's Pulpie and Dripper cleaning the same pages side by side: https://www.youtube.com/watch?v=ibd-tIiQECo. You can try a side-by-side comparison yourself: https://huggingface.co/spaces/feyninc/pulpie

Our motivation for Pulpie came from building a deep research harness. Every search API returns noisy content that contains ads, nav elements, and sidebars. In one instance, an ad for "Gemini on Pixel" slipped into our search results, got passed into LLM context, and ended up in the final answer served to the user. Pretty embarrassing moment for us but it helped us realize how bad data kills model intelligence. We built Pulpie to get clean data for cheap.

All models are open source on Hugging Face. You can read about our training process and how to use Pulpie here: https://usefeyn.com/blog/pulpie-pareto-optimal-models-for-cl...

Happy to answer any questions!