🚨 BREAKING: You can run Claude Code completely free now.
No API bills.
No rate limits.
No data leaving your device.
Just Claude Code running locally fast, private, and 100% yours. Here’s how to set up Claude Code on your own machine (free + fully private)
For guide: Local AI Coding Setup: Free Claude-Like Agent (Ollama + VS Code)
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Follow me @s_mohinii so that i can DM you
Rethinking how we build for the agentic era. Stop writing agent skills like human docs—it wastes tokens and invites hallucinations.
Here's a distilled set of core best practices (<5 min read) to help us write better skills.
Give it a read: https://t.co/6ep4VXg2zJ
I'm Boris and I created Claude Code. Lots of people have asked how I use Claude Code, so I wanted to show off my setup a bit.
My setup might be surprisingly vanilla! Claude Code works great out of the box, so I personally don't customize it much. There is no one correct way to use Claude Code: we intentionally build it in a way that you can use it, customize it, and hack it however you like. Each person on the Claude Code team uses it very differently.
So, here goes.
will post a recap of my talk soon — tldr: agent-native is the future and windsurf if the only IDE to do that
thanks @swyx and team for inviting us this past week 🙂
here's the live stream chapter for those interested: https://t.co/LxhRkY15Tp
If you, like many, think relying just on `cat` command's output is enough to be sure about the integrity of a bash file. Think twice, you could get hacked. Read below 👇
New 3h31m video on YouTube:
"Deep Dive into LLMs like ChatGPT"
This is a general audience deep dive into the Large Language Model (LLM) AI technology that powers ChatGPT and related products. It is covers the full training stack of how the models are developed, along with mental models of how to think about their "psychology", and how to get the best use them in practical applications.
We cover all the major stages:
1. pretraining: data, tokenization, Transformer neural network I/O and internals, inference, GPT-2 training example, Llama 3.1 base inference examples
2. supervised finetuning: conversations data, "LLM Psychology": hallucinations, tool use, knowledge/working memory, knowledge of self, models need tokens to think, spelling, jagged intelligence
3. reinforcement learning: practice makes perfect, DeepSeek-R1, AlphaGo, RLHF.
I designed this video for the "general audience" track of my videos, which I believe are accessible to most people, even without technical background. It should give you an intuitive understanding of the full training pipeline of LLMs like ChatGPT, with many examples along the way, and maybe some ways of thinking around current capabilities, where we are, and what's coming.
(Also, I have one "Intro to LLMs" video already from ~year ago, but that is just a re-recording of a random talk, so I wanted to loop around and do a lot more comprehensive version of this topic. They can still be combined, as the talk goes a lot deeper into other topics, e.g. LLM OS and LLM Security)
Hope it's fun & useful!
https://t.co/75mXcUBI8L
My formula for personal success:
Put in honest work.
Live below my means and save as much as I can.
Do something every day that moves me a little closer to my dream.
Make my dream a priority.
Have a no asshole policy; surround myself only with good people... avoid all that weakens me.
Always keep my word.
Speak the truth.
Maintain a thirst for knowledge.
Live with passion and purpose, and advance each day with the intention of creating value in the world.
That's what I came up with off the top of my head while sitting here in a dentist chair, bored waiting to get wokerd on.
introducing Open NotebookLM
turn any PDF 📄 into a personalized podcast 🎧 in no time.
the best part? it was all built in a single afternoon using open-source AI ✨.
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