Most AI agent frameworks are slow and expensive because they constantly shuffle massive JSON payloads and duplicate data between components.

Gliding Horse changes the game. We built an open-source AI Agent Operating System that treats your agents' memory with the extreme efficiency of a computer's hardware. Instead of passing heavy text blobs, Gliding Horse uses a unified data bus to pass lightweight pointers, making your multi-agent systems faster, cheaper, and infinitely more scalable.

Why developers love Gliding Horse:

Smart Memory Management (The "MMU" Engine)
Inspired by CPU architecture, our system only loads the exact information your agent needs at any given moment. If data isn't active, it stays in deep storage until triggered just like a page fault. This slashes token costs and completely prevents context window overload.

Yesterday s launch gave us some great motivation
Today we re focusing on making digital visiting cards even more useful for small businesses.

What we improved today:

• Cleaner onboarding flow

• Faster card loading

• Better mobile experience

• Minor UI polish for a more premium feel

Our goal is simple:
Help founders & professionals ditch paper cards and go fully digital with AI.

Hey Product Hunt

I'm Fares, indie iOS dev based in France. I built Dois-Je Signer after watching too many friends sign gym subscriptions, telecom plans, and freelance contracts they didn't fully read, then getting stuck with hidden tacit renewal clauses or 24-month commitments.

The app does one thing: you drop a French contract (PDF, photo, or pasted text), and an AI trained on French law (Code de la consommation, Code civil, current case law) analyzes every clause in around 30 seconds. You get:

A risk score 0-10 (red/orange/green)

I'm building Axisure, a platform where people can find others to build startup products with.

One thing I've noticed is that many founders have ideas but can't find the right people to help bring them to life. At the same time, many developers, designers, and marketers want to work on interesting projects but aren't sure which ones are worth their time.

I'm curious:

Hey PH I'm Sam, co-founder & CTO of SEATECHONE, based in Seattle.

We built SEAREI (searei.com) after realizing virtual staging had a serious legal blind spot.

In today s internet, headlines have become traps. They tease. They mislead. They make you click, only to find that the story is nothing like what you expected. We built oneminutenews.org as a response to the clickbait age. Our mission is simple: Give you the news in straightforward language and rank them based on their importance. The result? Clear, honest, no-fluff headlines that tell you exactly what happened, the way real journalism used to.

Here are some examples, we turn this:

Token-metered pricing looks lean, but for seed-to-Series B startups without internal AI Ops, it s often a financial trap.

The hidden overhead, constant usage monitoring, sneaky prompt bloat, and zero tied delivery milestones, quickly outweighs the flexibility.

Instead, fixed-price AI Velocity Pods force strict scope definition upfront. While that constraint feels uncomfortable for agile product teams, it targets exactly where AI projects actually fail: undefined success criteria, not execution.

For sub-$100K AI budgets, fixed-price wins on total cost and predictability.

One thing we've learned while building Dialbotix is that speed matters. Studies consistently show that leads contacted within minutes are significantly more likely to convert than those contacted hours later.

That's why we're building an AI voice agent that automatically calls inbound leads seconds after they submit a form, qualifies them, answers common questions, books meetings, and syncs everything back to the CRM.

But we're curious about something:

We're rolling out a feature that lets you vibecode Excel models, the same category a well-funded Silicon Valley startup has been getting attention for. Building it out, we realised we don't just match what's currently considered best. We beat it.

Here's our output for the prompt "build a detailed LBO model for ExxonMobil": https://docs.google.com/spreadsh...

Run that same prompt on any other platform and the gap is hard to miss.
Here is what claude had to say about our results vs others:
https://claude.ai/public/artifac...

How we did it: we generate Excel server-side instead of client-side, using openpyxl as the engine. We built on top of it to support pivot tables and everything else a real model needs. The reasoning behind the bet: openpyxl is heavily represented in training data, so the model actually knows how to drive it. That's the difference between a spreadsheet that looks right and one that is right.