real-hacker

"Legacy Privacy Mode disables code storage, so you can't set up a Codebase. Switch to a different privacy mode below to continue." The only other privacy mode is to share data for Cursor to do training.

All your codebases are belong to us.

I thought this kind of experiment (and the Einstein test) can only exist in theory. You basically cannot filter out the dirty data, it is impossible. And any conclusion drawn from the poluted corpus is meaningless.

No. This is not for community plugins, it's for AI generated plugins.

AI hurts ads money. It's a classic Innovators' dillema.

Wake up. The ad revenue model of websites is dying.

deeplearning.ai context hub codream.ai OpenWorker LearnVector

This guy never stops.

The question has to be asked: is it (small models struggling at brownfield) an intelligence problem or a context problem? Even if it is an intelligence problem, is it possible to use a customized harness to achieve the same level of performance as SoTA models? That would be very valuable, I think.

They support Lora, it is something.

In the final episode of 2025, Gilfoyle looks back at the year AI grew up — reasoning models, agents that actually work (most of the time), the rise of vibe coding, benchmark drama, sovereign compute races, and why the real bottleneck now is electricity, not GPUs. Humor, sarcasm, and a healthy dose of “what have we created?” energy.

Every AI chat today is private by default — and massively wasteful. What if AI answers were public, cached, and shared instead? In this episode of _The Gilfoyle Show_, we unpack a deceptively simple idea that challenges privacy, business models, hallucinations, and the very way AI products are designed. Efficient? Dangerous? Inevitable? Let’s talk about the question AI platforms would rather avoid.

Introducing Sensei, an open-source AI-powered learning tool, developed with Antigravity. Sensei generates tutorials with voice explanations and interactive quiz questions based on text materials provided by the user (currently only supporting plain text). It also supports setting the difficulty.

To run it locally, simply follow the instructions in the README. I use Gemini as LLM (requires a Google AI API Key). The TTS service supports Minimax (requires a API Key) or a local model for inference (SuperTonic TTS). The local model needs to be downloaded beforehand from the settings panel (approximately 300MB) and currently only supports English.

When generating a course, you can choose from three difficulty levels (ELI5, General, Professional). The LLM will adapt its language style and content depth based on the selected difficulty. Generated courses can be viewed and replayed in the Library. The text and audio for tutorials are cached after the initial generation, so repeated playback doesn't require API calls. However, determining whether a user's answers to questions are correct still requires calling the LLM API.