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Day 7: the frontier - open source, personal agents, and what’s next

Today you look ahead... because mastering AI means understanding where it’s going, not just where it is

Open source models: the shift that’s coming

Open source caught up to closed models in ways that seemed impossible two years ago

Kimi K2 from Moonshot AI has over a trillion parameters and beats GPT-5 on major benchmarks while costing roughly 1/10th as much through API access... they just released 2.5 and it’s a beast

DeepSeek V3.2 matches GPT-5 performance with 90% lower training costs and can be self-hosted

The timeline: right now access open source through APIs via OpenRouter... 6-12 months consumer hardware runs capable local models for daily use... 12-24 months open source likely matches or exceeds closed models for most practical tasks

The Operator Sprint prepares you for both worlds: closed models now, open source when the infrastructure catches up

Personal AI agents: the end state

Here’s where things get genuinely weird...

We’re watching the birth of AI assistants that aren’t chatbots in browser tabs, AI that runs on your hardware, connects to every platform you use, remembers everything, and takes action autonomously

Clawdbot is what Siri should have been... an open-source project that runs entirely on your hardware, connects to WhatsApp, Telegram, Slack, Discord, Signal, iMessage, has persistent memory across every conversation, and can read/write files, control browsers, execute scripts, and build its own extensions

The self-modifying part is what matters: ask it to add a feature it doesn’t have, it writes the code, tests it, and hot-loads the changes

2026 is the year of personal agents, the infrastructure exists, the early adopters are already living in this future

Today’s assignment: explore OpenRouter and run the same prompt through 3 open source models, compare outputs to Claude and Gemini, then if you want to see where personal AI is heading check out Clawdbot on GitHub... you don’t need to set it up today, but understanding what’s possible changes how you think about everything you built this week

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The Operator Sprint: why this sequence works

This curriculum follows a deliberate progression and the order matters

Day one gives you the mental model so you’re developing intuition not memorizing tricks

Day two gives you the communication skills that multiply the value of every AI interaction that follows

Day three gives you knowledge infrastructure that eliminates hallucination for your domain

Day four gives you creative tools with immediate commercial applications

Day five gives you the ability to build things that didn’t exist before

Day six gives you automation that works while you sleep

Day seven gives you the map for where all of this is heading

Each day compounds on the previous one... the prompting skills from day two make day four’s image generation better, the context architecture from day two makes day three’s RAG systems more effective, the coding from day five powers day six’s automations

The single highest-leverage move

Build a Claude Project for a task you do repeatedly

Upload relevant documents, write custom instructions that define behavior, and suddenly you have a specialized assistant that saves hours every week

Not hypothetical hours, real hours, the kind you can redirect toward work that matters or reclaim for your life outside work

Resources worth bookmarking

Anthropic Prompt Guide — official documentation with patterns that work

OpenAI Tokenizer — visualize how text becomes tokens, essential for understanding context limits

Andrej Karpathy’s LLM videos — foundational understanding that ages well as tools change

NotebookLM — free RAG without code, working knowledge assistant in under an hour

OpenRouter — unified access to every major model including open source options