morgan

@pulse__ai

[Jianna Liu @jianna_liu]

As products get faster to build, go-to-market becomes the advantage.

The teams that experiment, iterate, and discover new ways to sell will win.

Today, we're launching Cardinal - the AI Revenue Platform where companies like Deepgram, Giga, and Mintlify design how they sell.

Tell Cardinal the play:
→ "If someone gets promoted, send them a cake."
→ "If a prospect publishes a new engineering blog, email them with my take."
→ "Follow up with people who used my product in the first week but dropped off."

Design how you sell.

[Tempo @tempo]

@sid_mnk @Pulse__AI 🔥

[Machine Payments Protocol @mpp]

Your agent needs data it can actually work with.

Pulse handles that; turning complex documents like financial statements, contracts, and claims into clean markdown and structured data for agents.

With MPP, agents can access and use Pulse via API. No accounts needed:

[Tempo @tempo]

Pulse turns complex documents such as financial statements, contracts, and claims into clean markdown and structured data for agents.

With MPP, your agent can use Pulse as soon as a task requires it; payment and access to the data are handled through the API and settled on Tempo

[sid @sid_mnk]

Agentic payments are now live on @Pulse__AI .
An agent can point at our API with no account, no API key, and no signup, and pay for extraction on its own, one call at a time.

It runs on the Machine Payments Protocol (MPP), settling inline over HTTP in USDC, in the same request that asks for the work. The agent sends an extraction request, Pulse answers 402 Payment Required with the priced terms, the agent pays and retries with proof, and the result comes back. Two round trips, no invoice to reconcile.

The 402 has sat reserved since the early days of HTTP, waiting for a caller that could pay on its own. It’s live on Pulse today, more here:
https://t.co/k405lJw2jq

[morgan @morgancfung]

@echen @Pulse__AI 👏👏

[echen @echen]

@Pulse__AI ran GDP.pdf through their document stack.

GDP.pdf measures whether AI can handle the messy, high-stakes documents enterprises run on.

Pulse kept the models and the grader fixed and changed only the evidence layer - feeding each model a cleaner, structured version of the PDF.

The result was +10 points in whole-task accuracy.

One of the things they noticed: models often fail because a footnote gets dropped, a value comes from the wrong row, or a number loses its unit along the way.

The public set is open to anyone working on document AI. Check it out!

https://t.co/Fejh9gF2P4

[Pulse @Pulse__AI]

Every document extraction system can read the text on a form, yet almost none of them can read the pen, so the checkmarks, circles, underlines, and strikethroughs people use to record decisions get treated as noise and quietly ignored.

Today Pulse is launching Selection Mark, a model trained to read them, available now through a single parameter in the extraction API.

More: https://t.co/eXT0HBCTAI

[morgan @morgancfung]

@defnotbeka https://t.co/HX3RaKP3Q9

[morgan @morgancfung]

@no_side666 https://t.co/HX3RaKP3Q9

[The Daily Californian @dailycal]

From Friday to Sunday, energy drinks, mattresses, computers and tech start-ups littered the floor and tables at the Palace of Fine Arts in San Francisco, where nearly 3,000 “hackers” competed in the 12th annual Cal Hacks, a three-day hackathon
https://t.co/tS8A3VFMMe

[Sam Poder @sam_poder]

Cal Hacks 12.0 was insane! I'm super proud of the whole team for pulling it off :D

We had 3.3k hackers and 700 projects were shipped - that's double last year's Cal Hacks!

Plus we did it all at the Palace of Fine Arts w/ great food, a silent disco, and beautiful designs.