r/GPT3

OpenAI is now on track to generate more than $40 billion in annualized revenue based on its current performance.

That would roughly double its revenue run rate from the end of 2025.

The company’s growth has accelerated in recent months, driven by demand for its AI coding products, ChatGPT subscriptions, and its growing advertising business.

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It helps people discover web and mobile apps from across the internet by showing them one app at a time. People like apps in order to get personalized app recommendations.

If you run a software startup or side project, you can list it for free to get free extra traffic here:https://www.appscout.co/submit/

(Affiliation Disclosure: I'm the sole creator and developer behind AppScout.)

Source: https://www.ft.com/content/53082739-7714-4aae-9816-e55ab423cbee

Mark Zuckerberg just posted a video announcing Muse Glimmer, while at the same time publishing a massive 6,500-word essay explaining his views on the future of AI.

A big part of his argument is that powerful AI should be widely accessible instead of being controlled by a small number of companies or governments.

The timing makes the launch much more interesting. Glimmer feels like one of the first clear examples of Meta actually building around the ideas Zuckerberg laid out in the essay.

I never see many people discussing Hyperagent so I just wanted to post my experience and wondered has anybody else tried it?

Spun up a Hyperagent DevOps agent this morning. Had a few real issues, there always are, right? The actual difference was in the platform giving the agent the ability to correct course, for more than just the session.

The task: TypeScript Fastify gateway for three internal services. OAuth2 + API keys, Zod, rate limiting, structured logs, Vitest suite, Docker image, and CI/CD on tag.

The runtime: 3 hours. Straight.
My active time: Maybbeee, 35 mins?

The agent got the core path right out of the gate. Auth middleware, proxying, rate limits, request ID prop—all clean. The subagent even caught a header-stripping bug on 401 responses that I would've 100% shipped.

About 45 minutes in, the agent tried to pull a specific internal dependency that had been recently deprecated in our registry. Since the agent's knowledge cutoff predates that change, it didn't know the replacement existed. I told the agent that it had been deprecated, normal course correction. What was impressive was how the agent was able to handle that. It didn't just blindly swap the lines. It removed the old dep, searched for an equivalent, updated the relevant import paths to match the new API surface, and kept the tests running. But the real kicker? The agent also added a memory without me saying a word, using Hyperagent's built-in Memory. It logged that specific deprecation into its persistent system. In my experience it’s one thing to give an agent a tool for memory. It’s another to create one and the agent persistently use it without having to request it directly. Hyperagent closes that gap well.

Beyond that one hiccup, everything else was smooth. Wrote the GitHub Actions YAML from scratch, built the multi-stage Dockerfile, and hit a clean 94% test coverage, zero lint errors, and a ~38MB image on the first tag push.

The real math:
My old workflow for this exact spec (scaffold, wire auth, manual curl debugging, hand-write CI, fight registry permissions) takes me a whole day, maybe 4-5 hours. Hyperagent allowed me to manage this from my phone, and I some 35 minutes instead of 4-5 hours.

This run only costed me $66.80. And that $66.80 came from signup credits, so essentially costed me nothing.

I’ve worked with AI since they were made available to the public, and I’m honestly impressed with the platform. If you haven’t checked it out, do it. A couple google searches will get you credits. The links are out there. https://hyperagent.com also had the agent make that cool little photo for the post 😂😂