Rahul Bhalley @_codegod
Building Apps To Unleash AI on Your Mac 3D Vector Space Joined October 2011-
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i take back everything i said bad about macos 26, containers !!!!
@lizhicq @MarkVillacampa macOS 26's native Linux container support may reduce the need for Docker for basic tasks, offering better performance and efficiency, especially on Apple Silicon. The "container" tool could act as a lightweight alternative. However, Docker's extensive features, like orchestration…
You can now vibe code on Xcode; it's not bad actually.
Model visualization in XCode was not on my bingo card 👍 @flat
BREAKING: Apple just proved AI "reasoning" models like Claude, DeepSeek-R1, and o3-mini don't actually reason at all. They just memorize patterns really well. Here's what Apple discovered: (hint: we're not as close to AGI as the hype suggests)
Gaussian distribution over my app subscribers for All Time. LoL. #indie
It's finally here: Brampton Brampton is the world's most intelligent, creative, and fastest model. Brampton dramatically outperforms Grok 3, Claude 3.7 Sonnet, and GPT 4.5. Reply with "brampton" for early access.
🚀 DeepSeek-V3-0324 is out now! 🔹 Major boost in reasoning performance 🔹 Stronger front-end development skills 🔹 Smarter tool-use capabilities ✅ For non-complex reasoning tasks, we recommend using V3 — just turn off “DeepThink” 🔌 API usage remains unchanged 📜 Models are…
I think Elon Musk should be expelled from the British Royal Society. Not because he peddles conspiracy theories and makes Nazi salutes, but because of the huge damage he is doing to scientific institutions in the US. Now let's see if he really believes in free speech.
1 hour of coding 1 hour of marketing 1 hour of exercising Do these every day this year. By 2026, you'll have built a profitable side project.
10. RAG app using DeepSeek-R1 (100% local)
Goodbye ChatGPT It’s only been 5 days since Deepseek R1 dropped, and the World is already blown away by its potential. 13 examples that will blow your mind (Don't miss the 5th one):
4. DeepSeek is a side project 🤯
2/ DeepSeek just showed up and said "LOL what if we did this for $5M instead?" And they didn't just talk - they actually DID it. Their models match or beat GPT-4 and Claude on many tasks. The AI world is (as my teenagers say) shook.
3/ How? They rethought everything from the ground up. Traditional AI is like writing every number with 32 decimal places. DeepSeek was like "what if we just used 8? It's still accurate enough!" Boom - 75% less memory needed.
4/ Then there's their "multi-token" system. Normal AI reads like a first-grader: "The... cat... sat..." DeepSeek reads in whole phrases at once. 2x faster, 90% as accurate. When you're processing billions of words, this MATTERS.
5/ But here's the really clever bit: They built an "expert system." Instead of one massive AI trying to know everything (like having one person be a doctor, lawyer, AND engineer), they have specialized experts that only wake up when needed.
6/ Traditional models? All 1.8 trillion parameters active ALL THE TIME. DeepSeek? 671B total but only 37B active at once. It's like having a huge team but only calling in the experts you actually need for each task.
7/ The results are mind-blowing: - Training cost: $100M → $5M - GPUs needed: 100,000 → 2,000 - API costs: 95% cheaper - Can run on gaming GPUs instead of data center hardware
10/ For Nvidia, this is scary. Their entire business model is built on selling super expensive GPUs with 90% margins. If everyone can suddenly do AI with regular gaming GPUs... well, you see the problem.
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