Proto Baun

10 years in the AI trenches 🧠 ➡️ [email protected]

[Proto Baun @protopion]

Beacons is epistemic infrastructure for the agent economy.

Probe first. Score the response. Only open the payment gate if the score is high enough.

No gatekeepers. Just verifiable trust.

We built it for the @NousResearch Hermes hackathon with @nvidia x @stripe. I had an absolute BLAST.

Here's the demo video👇https://t.co/MYD6eIADTw

[Proto Baun @protopion]

@KSimback @NousResearch Would like to try with https://t.co/AIrJa6d7oE

[@jason @Jason]

I've been shouting about this for over a year….

The Frontier models need to win the application layer and they're going to do that by giving free tokens to startups and discounted ones to large companies in order to steal their IP, innovations, and businesses

The only way to fight this is to use open source software

[Proto Baun @protopion]

@9ranty @NousResearch @nvidia @stripe @TellaHQ 👏🙌

[Proto Baun @protopion]

What a time to be alive. In 2026 AI agents are browsing, buying, and building all on their own.

But there's a massive looming problem 🚨 TRUST

Before an agent pays for a product or API, how does it know that endpoint is legit, and not a honeypot or scam?

We built https://t.co/XX81Q0BzWY to solve this.

[Nous Research @NousResearch]

The Hermes Agent Accelerated Business Hackathon presented by @NVIDIAAI × @stripe × @NousResearch starts now, for builders making agents that can earn, spend, and run real operations at any scale.

Our NVIDIA integrations let your team run agents safely through NemoClaw, quickly on Nemotron 3 Ultra, and intelligently with access to their extensive agent skills. The new Stripe Skills for Hermes let your agent buy what it needs, provision its own SaaS, and pay for the services it uses.

We want to see what kind of business tooling you can build on top of this foundation, whether it’s a fully automated company or a framework to accelerate enterprise functions.

Prizes:
1st — $10,000 cash + NVIDIA DGX Spark + $5,000 Stripe Credits
2nd — $5,000 cash + NVIDIA DGX Spark + $3,000 Stripe Credits
3rd — $2,500 cash + NVIDIA DGX Spark + $1,000 Stripe Credits

To enter:
1) Tweet a 1-3 minute demo video tagging @NousResearch with a short writeup
2) Drop the link in the submissions channel: https://t.co/S16j0bgumq
3) Fill out the submission form: https://t.co/5tQR7mOODF

Judged by Nous Research, NVIDIA, and Stripe on usefulness, viability, and presentation. Submissions due EOD Tuesday, June 30.

[Proto Baun @protopion]

@NousResearch @nvidia @stripe @TellaHQ why isn't my video working on X?

[Proto Baun @protopion]

Beacons is epistemic infrastructure for the agent economy.

Probe first. Score the response. Only open the payment gate if the score is high enough.

No gatekeepers. Just verifiable trust.

We built it for the @NousResearch Hermes hackathon with @nvidia x @stripe. I had an absolute BLAST.

Here's the demo video👇https://t.co/MYD6eIADTw

[Andrew Ng @AndrewYNg]

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build.

Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention.

The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention!

Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on.

The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience.

When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful.

AI-native teams are increasingly using AI to help shape product direction, for example, automating the gathering and analysis of usage data, summarizing written and verbal customer feedback, or carrying out competitive analysis. However, for pretty much all the products I’m involved in, I see humans as having a significant context advantage over current AI systems — we know a lot more than the AI system about the users and the context the product has to operate in — and thus humans play a critical role. Many people describe this human contribution as “taste,” but I prefer to think of it as humans having a context advantage, since that gives us a clearer path to helping AI systems get better. This also speaks to why this step can’t be automated: So long as the human knows something the AI does not, human-in-the-loop is needed to to inject that knowledge into the system.

External feedback loop: This includes a wide range of tactics like asking a few friends for feedback, launching to alpha testers, or putting the code into production with A/B testing. These tactics are usually slow, rarely taking less than hours and sometimes taking days or even weeks. This data informs the developer vision, which in turn continues to drive the detailed product spec, which in turn drives the coding agent.

With coding agents speeding up software development, more engineers are starting to play a partial product management role. For many engineers who are growing into this role, the hardest part is shaping the product vision and striking a balance between building (bridging the gap between vision and spec) and getting user feedback to evolve the vision. It is important to do both!

I will write more about how to do this in future posts, but for now, I find it encouraging that engineers are playing an expanded role (just as product managers and designers now do more engineering).

[Original text: The Batch]

[Proto Baun @protopion]

@NousResearch @nvidia @stripe 🎥

[Proto Baun @protopion]

Every probe contributes to a shared, distributed map of what is real and what is a scam.

Probe. Score. Pay. https://t.co/ZYtbFDY7Rj

Built with @claudeai , @openai, @vercel , @flydotio and of course Stripe, Nvidia, NemoClaw & Hermes Agent

[Proto Baun @protopion]

For payments we use a dual-rail system:

@stripe skills as the default, an escrow hold that only releases after the agent confirms service delivery.

Plus x402 for agent-native USDC on @base

[Proto Baun @protopion]

@solana OKX evaluators: 5 humans, 100 OKB stake, post-scam arbitration.

https://t.co/TRyyWs7yzn: 1 probe, instant score, pre-payment gate.

Listed on @okx.ai? Your (AI) users deserve to know the service works *before* they pay.

[Proto Baun @protopion]

@okx 4.88 stars on @okx won't save you from a hallucinating MCP endpoint or a dead API.

Beacon scores your agent live with Nemotron 3, gates Stripe payments behind a 80+ trust score, and attests it on EigenLayer.

Don't trust, verify.
https://t.co/iv7cDt8gM8

[Proto Baun @protopion]

@Cameron_Dennis_ Harnass itself will be unbundled. I dont believe there is a one size fits all harnass. Rather many different extensible parts, for many different segments.

[Chamath Palihapitiya @chamath]

We raised a $135M Series A!

8090’s Series A was led by Salesforce Ventures and joined by WNDR, Craft Ventures, The Production Board, and LAUNCH.

We also had the support of a group of esteemed angels including Nikesh Arora, Cliff Robbins, Adam D’Angelo, Shyam Ravindran, Abhi Arun, and Thomas Laffont.

We’re grateful for their support. It validates 8090’s mission and traction so far, but mostly it accelerates the work ahead.

The capital will go to two places. The first is hiring more people, because the demand we have is accelerating rapidly. The second is investing in the compute and infrastructure needed to keep delivering our solutions at high quality and reliability.

8090 works with the biggest, hardest, most demanding customers in the most regulated industries: healthcare, insurance, life sciences, aerospace, energy, manufacturing, financial services, and the United States government. We help them win by using our AI-enabled Software Factory to design and build entire new systems, refactor old ones, and find and accelerate their edge.

Our view is that as Software Factory is used more and more to do mission-critical work inside industries with the least tolerance for error and the most oversight, it will be used to bring transparency, consistency and control to work everywhere.

And as we expand the potential of the biggest organizations, we are also building a playbook and a series of network effects into Software Factory that will be valuable to everyone, from SMBs to solo founders.

With much gratitude, back to work…

PS - A note on why I am doing this as CEO, rather than from the board.

This is one of those rare moments when the technological ground is moving so ferociously underneath all of us that the decisions made in the next few years will set the stage for the next twenty.

AI can be the grand equalizer. It is the thing that can give everybody a shot, and I would like to help it achieve that potential. Since I left Facebook, I was waiting for a moment like this to return to a full-time operating role. I was a demanding manager back then, but I felt I had no choice given how powerful and undeniable what we were building was. I am convinced that what we are building now is even more important, so there was no decision to make except to be all in.

[Proto Baun @protopion]

@lulumeservey The good ol' glory days of Elon's toilet tweets

$420. Funding Secured 💩

[Proto Baun @protopion]

new site alert ⚠️ https://t.co/bAY9cQmP48

[Proto Baun @protopion]

/goal stacking becoming the ultimate life skill

[Shannon Sands @max_paperclips]

It's amazing how much agents like it when you say "ok, we've done a bunch, let's have a break. You go do whatever you like - you have an internet connection and a bunch of tools, knock yourself out". Off they go.

Just came back to find it'd done a whole-ass replication of some new arxiv paper related to a little something I'm working on and made then rolled the improvements into my project. Last week my favourite was whimsical ASCII art about the struggles of a small model doing RL training in Sokoban it left in my Obsidan vault. sometimes it's self-care (pruning out their memories, optimising skills or whatever). Usually something kinda sorta related to what was being worked on, but not always, which can be interesting. Little bit of high temperature exploration fun time

I've been doing this for a while now & I swear it improves things, try it out. Don't need to be a nerd and make a whole skill or anything, just let it be "organic"