any cs person can go from zero to deeply knowledgeable in llms and ai in ~2 years, top to bottom the elite don't want you to know this
LLMS
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Weekly ML LLMs AI Agents Tutorials and Open Source Repositories
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If you're interested in ML, LLMs, and AI agents and want to receive weekly tutorials and open-source repos, subscribe to AI Engineering (it's free):
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Open Weight Models and On-Device AI Privacy Benefits
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Releasing the open weight models to the research community and developers in general is not a bad thing though. On the topic of on-device. It can be interesting for a lot contexts though, which is why Apple does/tried it. One is privacy (I don’t think most people want third
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OpenAI Evals Team Publishes Benchmarks Showing Claude Superiority
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I find it unimaginably based that the OAI Evals team keeps making benchmarks finding that Claude is better and publishing it anyway. they are 3 for 3 this year in acknowledging specifically how much Claude is better at tasks OAI care about. there is no sarcasm here folks. this
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100+ Free AI Agents and RAG Systems Tutorials
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100+ free step-by-step tutorials with code covering: AI Agents RAG Systems Voice AI Agents MCP AI Agents Multi-agent Teams Autonomous Game Playing Agents P.S: Don't forget to subscribe for FREE to access future tutorials.
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Awesome LLM Apps Reaches 70k Stars: 100+ Free AI Agents
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Awesome LLM Apps just crossed 70k+ stars. 100+ AI Agents and RAG apps with step-by-step instructions. 100% free and Opensource.
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Build Your Own LLM: Practical ML Learning Roadmap
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> tired of "learn ml in 5 years" energy? want the roadmap from: > "what's a token?" to
> "i built + shipped my own LLM"? phase 0: build from zero, no theory jail > math + code sanity check: > > if matrix multiplication scares you, hit 3Blue1Brown's linear algebra series
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GPT-5 Release: User Expectations for Advanced Thinking Models
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Going to sleep now, I would be very disappointed if it is just regular GPT-5 slop, if it is the thinking model with a bit of special flair, then I might be impressed. Fingers crossed, looking forward to waking up tomorrow
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GGUF Quantization: 350M Model Uses Just 380MB
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Really tiny. We released GGUF quants, so with 8-bit precision and 4k context the 350M will use ~380 MB and the 1.2B ~1.4 GB (to give you a rough estimate).