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I renewed my investigation this weekend, into whether there is an autonomous strategy that averages more than one million points per game. An experienced human player can score a million points with long-term strategic thinking, but the best automated strategies I could come up with are currently averaging around 300k-400k points. One of the easiest ways to contribute to advancing this research is to compete on the global leaderboard and submit your human strategy - https://drop7.dev/compete.

This project has also been an enlightening experiment into using agents as research collaborators and evidence collectors.

Repository: https://github.com/keshavsaharia/drop7-bench

I’d especially appreciate feedback from people who are knowledgeable in game theory, stochastic planning, learned value functions, or heavy-tailed evaluation. How would you approach long-horizon action ranking here? I’d also love to hear whether the research makes sense to someone encountering the project for the first time.

https://www.kimi.com/code/docs/en/kimi-code/models.html

https://www.macrumors.com/2026/10/23/apple-music-has-a-hidde...

Swurf allows you to swipe through the real web one page after another. It learns what you like and doesn’t show you the same page twice. The idea came naturally: there’s a seemingly infinite amount of high quality blog posts, random topic rabbit holes, and trending or cool sites that exist which the average person simply will never load on their browser. Swurf solves that by allowing you to rapidly traverse a constantly growing and learning feed of sites that it thinks you’d want to view. Quickly sift through endless pages and find things you like - effortlessly.

One of the core drivers for making this project was the desire for real content. The project started as a simple tool for myself to find new and interesting things to learn and read about. Hours spent scrolling on my phone - while it still has its place - is increasingly losing its sense of reward. It’s genuinely hard to put into words exactly how swurfin’ feels but it’s almost refreshing in a sense. I encourage you to try it out and provide feedback. At this time there are no accounts and you can jump in and start swurfin’ immediately. There is a three dot menu button on the bottom right which allows you to submit links, save links, control adult content, and search similar topics. One of the things that’s great about Swurf is that its almost like a group browsing session (though entirely private) where everyone else who is spending time hunting down great reads or solid sites is helping massage the feed for you and you for them - all within your own set of interests.

Enjoy and please ask any questions you have as theres plenty of room to grow.

Sharing a thought experiment I put together. The premise: what might declarative programming feel like for the physical world?

We’re already familiar with this paradigm in software land (eg SQL etc where we declare what we want and the execution engine figures out how).

I didn’t have access to an actuator so I mocked up the concept in software. The way it works is you demonstrate the effect and the system figures out how to achieve it. So say you want to ring a bell and you have a crank. The system will find a way to connect the two. If you add obstacles it figures out how to work around them (runs a constraint solver runs behind the scenes).

I think what’s interesting about this is being able to think about the problem at a higher level of abstraction, with the system figuring out a way to achieve the intended effect.

Anyways, curious to read reactions, feedback, and interesting ideas on how to extend this!

Couldn't OpenAI or Anthropic make a lot more money by just starting companies themselves that leverage the models and building their own products?

Is selling tokens really the best strategy?

Especially with commodification and prices declining wouldn't that indicate there is more money to be made on the application-side of the models rather than the utility side

I was pretty terrible at singing, now I’d say I’m bad but not terrible. This is because I’ve been using a singing app (won’t name it here) that scores me on my pitch and timing throughout the song so that I can improve.

Lately however, the app I’ve been using has flooded their song list with songs that aren’t originals. I didn’t like this at all as for me, it’s easier to try to improve when you’re singing along to the original.

So, I asked an agent (Claude Code) if it could write some code to listen to Spotify and also listen to me and see if it could detect both pitches and score me. It impressed me by writing some Python and showing me my pitch in the terminal!

It couldn’t go all the way though and detect the singer’s pitch in real time.

So I found the next best setup and built OnKey, a Mac app that needs to listen to the song once (it doesn’t save it) and then when it’s learnt the song (takes a couple of minutes), you can sing along to the pitch track that it’s learnt. OnKey is free to use and open source under the MIT license.

It can listen to Spotify, Apple Music, and YouTube playing in your browser with the right permissions.

During the build I found that someone had done something similar in Nightingale, but this requires a library of songs exported to disk and who has this in 2026 (maybe some of you ;) - but we need to help the normies too).

You can check out a demo video on the site of me singing (warning given) here: https://singonkey.app

The app sends out to find the song info (short Shazam fingerprint, and looks up song details), lyrics (from LRCLIB), and checks for updates.

The app never sends the music (it doesn’t save this to disk) and your voice. Everything you’d expect to stay private does.

It’s still weak in a few places:

- when the song’s backing is noisy it’s hard to pick the pitch track out

- sometimes it gets a note an octave off

- it’s limited to Apple Silicon Macs, M1 or later, and only macOS 14.2 up because extra drivers aren’t required past this point

- the scoring is built for beginners like me so it’s fairly easy on you

Would love for people to try it out and let me know what works and what doesn’t for them!