The most interesting part of DeepSeek's R1 release isn't just the performance - it's their pure RL approach without supervised fine-tuning. This is particularly fascinating when you consider the closed vs open system dynamics in AI.
Their model crushes it on closed-system tasks (97.3% on MATH-500, 2029 Codeforces rating) where success criteria are clear. This makes sense - RL thrives when you can define concrete rewards. Clean feedback loops in domains like math and coding make it easier for the model to learn what "good" looks like.
What's counterintuitive is they achieved this without the usual supervised learning step. This hints at a potential shift in how we might train future models for well-defined domains. The MIT license is nice, but the real value is showing you can bootstrap complex reasoning through pure reinforcement.
The challenge will be extending this to open systems (creative writing, cultural analysis, etc.) where "correct" is fuzzy. You can't just throw RL at problems where the reward function itself is subjective.
This feels like a "CPU moment" for AI - just as CPUs got really good at fixed calculations before GPUs tackled parallel processing, we might see AI master
closed systems through pure RL before cracking the harder open-ended domains.
The business implications are pretty clear - if you're working in domains with clear success metrics, pure RL approaches might start eating your lunch sooner than you think. If you're in fuzzy human domains, you've probably got more runway.
> Sarah: Built a fusion reactor at 16. Now? Debugging fintech payment systems.
It's striking to imagine a fully functional fusion reactor that could benefit humanity, yet its creator now focuses on fintech payment systems. This highlights the importance of a strong middle class, which seems to be declining globally. A thriving middle class, with disposable income and free time, creates the conditions for innovation. Without it, even brilliant minds like Einstein might spend their entire careers working on immediate economic needs rather than pursuing breakthrough discoveries.
I wonder what Elon thinks about this. There was one demo from SpaceX about using their rockets for Trips where they can lower down transatlantic flights to 20 30 mins (if you have strong sto-mach) Or Boring Company focused to hyperloops.
My humble opinion is that it's aviation company without huge innovation or disruption of the industry. More like a fast horse rather than car.
There are a lot of similarities between CBT and Vipassana meditation.
Like the mind reading and personalisation Vipassana medidation suggest we have `sankharas` loosely translated as formations which is kind of a poison in our body and mind. After a consistent meditation you can examine and make peace with them. I'm not an expert but it worked for me, was kinda magical and interesting.
CBT and meditation has a lot to offer to our lives in our little concrete jungle.
It's really interesting no other comment is talking about the LGBTQ community because as far as I know founder is gay and living in Turkey and that means they have a bit closed community, general public doesn't accept them.
So I think orkut means a lot for the LGBTQ and Activists community would be great to hear the thoughts from them.
Congrats on the launch! I'm also a fan of glitch so congrats on that good job too.
Just out of curiosity have been reading the engineering part and came across with below for not using websockets, confused because debounce and throttle is mainly used to avoid many updates over sockets so it's very well known problem for reactive programming
> You might be wondering, why don’t you just update the database with websockets instead of relatively slow API requests?
> The problem with saving data with websockets is that they’re too fast. Authenticating that many messages per second and writing them to disk would be really inefficient. E.g. If you’re moving a card from position x: 20 to x: 420, Kinopio will use websockets to broadcast many updates during the move: moving card x to 21, moving card x to 24, moving card x to 28… potentially hundreds of messages.
Well said! I also learnt user moderation hell with painful way but for mobile projects If it's very necessary there's a possibility of using Anonymous logins with Firebase or Amazon AppSync as well.
Exactly I'm living proof that seniority comes with the ability to spend more time on problems to understand all the details before committing stuff which no idea how that works.
Dvorak is one of the well-known layouts and even now we can't have it on the iPad, so I don't think keyboard layout is only about efficiency it's also popularity and availability.
As a multiple event organiser I would say it helps a lot to the event organisers especially small ones where they have almost no budget for registering and platform supports. Much appreciated.
Looks really cool I downloaded thanks a lot for sharing. How long are you using? And are you using for programming if so how does it look like your cards?
Their model crushes it on closed-system tasks (97.3% on MATH-500, 2029 Codeforces rating) where success criteria are clear. This makes sense - RL thrives when you can define concrete rewards. Clean feedback loops in domains like math and coding make it easier for the model to learn what "good" looks like.
What's counterintuitive is they achieved this without the usual supervised learning step. This hints at a potential shift in how we might train future models for well-defined domains. The MIT license is nice, but the real value is showing you can bootstrap complex reasoning through pure reinforcement.
The challenge will be extending this to open systems (creative writing, cultural analysis, etc.) where "correct" is fuzzy. You can't just throw RL at problems where the reward function itself is subjective.
This feels like a "CPU moment" for AI - just as CPUs got really good at fixed calculations before GPUs tackled parallel processing, we might see AI master closed systems through pure RL before cracking the harder open-ended domains.
The business implications are pretty clear - if you're working in domains with clear success metrics, pure RL approaches might start eating your lunch sooner than you think. If you're in fuzzy human domains, you've probably got more runway.