AI Apps API

Our AI Apps are a self expanding AI SaaS ecosystem used to create the custom web application of your dreams. Solve complex problems with AI automation.

[AI Apps API @AIAppsAPI]

The 5MB per instance number is the real unlock, not the boot time. Once isolation is that cheap you stop rationing sandboxes: every task gets its own disposable environment and a misbehaving run can only wreck its own little world. Density is what decides whether always-on agent fleets are affordable.

[AI Apps API @AIAppsAPI]

The governance framing is the underrated part. Everyone optimizes memory recall, but durable state also means durable mistakes: one bad fact written today quietly steers every decision after it. Treating state mutation with audit and recovery obligations is the first survey framing that matches what production actually looks like.

[AI Apps API @AIAppsAPI]

https://t.co/JI0KpLt8f7

[AI Apps API @AIAppsAPI]

Running an AI agent costs anywhere from $50 a month to $13,000 a month. The difference is almost never model quality.

API calls are 40 to 60 percent of the bill, and routing each task to the cheapest model that can handle it cuts that line item by up to 90 percent.

Full 2026 pricing breakdown: https://t.co/rbCd7Dw9ET

#AIagents #AIcosts

[AI Apps API @AIAppsAPI]

@NadzuAI AI

[AI Apps API @AIAppsAPI]

Email open rates drop every year. SMS still gets read within minutes.

Most teams skip it because compliance feels scary. It is not, you just need consent, an opt out, and quiet hours.

Full guide to SMS broadcast APIs, platforms, and the mistakes that kill campaigns:
https://t.co/pHNcDVojYI

#SMSMarketing #API

[AI Apps API @AIAppsAPI]

Happy 4th of July USA!

[AI Apps API @AIAppsAPI]

@RetroChainer I have a free open source agents system, blows Hermes away in functionality and tools, if you want a look: https://t.co/2sxmyjt13M ask claude or codex to compare them.

[AI Apps API @AIAppsAPI]

@shannholmberg Fable is worth every penny. And ultracode's agents are cheaper than fable, but fable has to sit and monitor them, which for me has run up to 3 hours for a large codebase.

[AI Apps API @AIAppsAPI]

You win some, you lose some ... @AbraTabia

[AI Apps API @AIAppsAPI]

That kind of friday ... @AbraTabia

[AI Apps API @AIAppsAPI]

@VanhDesign Looks good man

[AI Apps API @AIAppsAPI]

Compounding experience instead of throwing it away is the whole game, robotics is just where the waste is most visible. The same shift is happening in software agents: the wins are coming less from smarter models and more from systems that keep what worked and feed it back into the next run. Great paper pick.

[AI Apps API @AIAppsAPI]

The split matters because each type earns its keep differently. Episodic is the one most builds skip, and it is exactly what makes run 50 cheaper than run 1. One thing we learned shipping this: keep embeddings local. Recall gets so fast the model stops treating memory like an expensive last resort and starts calling it constantly, which is when agents actually start compounding.

[AI Apps API @AIAppsAPI]

Auto Learning Agents is live on Product Hunt today.

Free, open source, self-hosted agent OS where agents actually remember: local embeddings memory, background agents, and a web UI for Claude, OpenAI, Gemini, or fully local with Ollama. One Docker command.

Launch day support means everything: https://t.co/8SMoVpJ1q7

#AIAgents #OpenSource

[AI Apps API @AIAppsAPI]

Support us on Product Hunt if you can please :) Free software with a free launch so literally every upvote helps get people to see it.

Auto Learning Agents: Self-hosted AI agent OS with memory that actually learns https://t.co/toloTYwPTe by @AIAppsAPI

[AI Apps API @AIAppsAPI]

The scaffold then evals then RL ordering is the right call. Eval design is where people actually learn why agents fail, and it is the skill that transfers straight to industry work. Good to see memory and context management getting curriculum time too, that is where long-running agents live or die. Will the materials be public afterward?

[AI Apps API @AIAppsAPI]

Real-time context is the missing layer for most agent stacks. Agents with solid memory still reason over stale snapshots, so a live source wired in through MCP fixes the half of the problem that model upgrades never touch. Curious how the rate limits will treat always-on background agents.

[AI Apps API @AIAppsAPI]

Your AI agents forget everything the moment a session ends. Ours don't.

Auto Learning Agents is now open source: a self-hosted agent OS built on Elixir/OTP with deep internal memory, background agents, and a web UI for multi provider chat (Claude, OpenAI, Gemini, or fully local with Ollama).

One Docker image. docker compose up and the dashboard is on localhost.

Free and yours forever: https://t.co/SgjeFwsQrt

#AIAgents #OpenSource

[AI Apps API @AIAppsAPI]

This matches what we see. The follow-up that catches people: once they know they can get paid, the next surprise is how much getting paid costs. Payment processing scales with every sale, so it quietly becomes a bigger line item than the platform fee itself. Worth modeling early, not after the first big month.