F*ck, I wish I'd had this when I started posting. Grok Bot now does the admin side of my content, and it took me 5 prompts. My morning brief lands before I open my laptop, and every brand reply waits for me as a draft. Here's what the 5 prompts do for me: step 1 → paste the prompts below into Grok Bot on Grok 4.7, the morning brief first step 2 → every weekday my brief skips the promos and lists only the emails that need me, brand deals first step 3 → it ends with the 5 X mentions worth answering, read out in a 2-minute voice memo step 4 → the brand prompt goes through my sponsorship emails from the past week and checks each brand online step 5 → it drafts every reply with my rate card, but I still read each one before I send it step 6 → at 8 AM it shows me what's rising in my niche, only what's new since yesterday step 7 → once a script is done, it gives me rough drafts of the thread, the LinkedIn post and the newsletter section step 8 → I saved that as a skill, so next week one line gets me those drafts again The 5 prompts are on the card below. Then read the article below and add 10 more on top.

You can run your own AI on your computer. Most people set it up wrong and get a slow, forgetful chatbot. At GTC 2026 an NVIDIA product manager and an Ollama engineer spent 33 mins on doing it right. 07:38 - quantization and why Q4_K_M is the default 11:04 - picking a model: VRAM, benchmarks, your own tests 19:08 - Claude Code on a local model with Ollama Launch 24:20 - the context limit when running agents locally 28:17 - structured outputs for pulling data out of documents I've seen $500 courses that teach less than this. Watch it first, then read the guide below. It takes you from zero to a working AI on your computer in 10 steps.

LangChain co-founder Harrison Chase: "There's a harness that orchestrates a model and some context." In under 24 minutes, he explains the loop behind tool use and context. That helps you understand what changes when you switch runtimes. I'd put this free talk ahead of many paid courses on agent basics. Watch the talk, then read the article to build products that work with different agent harnesses.

GPT-4 co-author Diogo Almeida: "We are doing a third thing that is optimized for calibrated decision-making" In 18 minutes, he explains why models trained for human approval can be overconfident. You’ll get more out of this video than you would from a $700 course. Watch the talk, then read the guide below to learn what Jev is good at and how to use it.

Andrej Karpathy: "The hottest new programming language is English." In under 40 minutes, he explains how LLMs have revolutionized what we can do in the digital environment and how to use them effectively. This talk contains much more useful info than the $500 course. Watch the full talk, then read the article below to set up a working harness for your agents.

a plain yes-or-no decision inside agent can cost up to 400x more than it has to Harrison Chase, LangChain's CEO, spent 47 mins on what's actually inside an agent's harness 08:27 - what actually counts as a harness 18:14 - the building blocks of an agent 29:51 - why agents need a sandbox 32:37 - sandboxes against prompt injection 41:51 - the mess of evaluating agents watch this first, then read the article below on the model built to handle that decision for a fraction of the price

a plain yes-or-no decision inside agent can cost up to 400x more than it has to Harrison Chase, LangChain's CEO, spent 47 mins on what's actually inside an agent's harness 08:27 - what actually counts as a harness 18:14 - the building blocks of an agent 29:51 - why agents need a sandbox 32:37 - sandboxes against prompt injection 41:51 - the mess of evaluating agents watch this first, then read the article below on the model built to handle that decision for a fraction of the price

Odyssey-3 genuinely surprised me. one world model that powers a humanoid, controls robot arms, drives a car, pilots a drone, plays video games and trains other AIs. the team behind @odysseyml built self-driving cars at Cruise and Wayve and has been on world models since 2023. so when they call task-by-task robot training brute force, I listen. their answer is a physics agent. it learns how the world works by watching it: physics, motion, cause and effect, how people behave. a new machine plugs into that knowledge. they say a few hours of its own data gets it going, with an action head turning the model's understanding into controls. it's basically what LLMs did for knowledge work. one broad model underneath, and every upgrade to it makes everything built on top better. what stuck with me most: it also generates worlds where AI agents can practice, so their mistakes happen in a simulation first. they think world models will end up understanding physics better than we do. big claim. I want to see it hold up when it's public in the coming weeks.

I gave the same prompt to GLM 5.3 Flash, Kimi K3 and DeepSeek v4 Pro and got 3 working apps back. Built all 3 inside @boltdotnew and shipped the best one to a live link. On a normal plan I'd never burn prompts on a comparison like that. Forge is an experimental mode in Bolt that runs open models with up to 50x more usage. You can build and iterate on web apps without worrying about usage limits. One monthly bar, no daily cap. Draft 10 versions if you need to, then switch to Standard or Max when it's time to ship. On Bolt's own build benchmark, the Forge models score 91% of the top paid model. Close enough for drafting. The deal behind it: opt in, and your builds help train open models. It asks every time you switch in. The research preview runs Sept 14 to Oct 14, 2026. Forge is free on Pro plans until Oct 14, and there's a $9/month early-access plan, Bolt Lite, with a waitlist. It's experimental, so duplicate your project before bringing anything serious in. Watch the 3 builds, then try Forge with the link below.

stop spending your evenings fixing the same mistakes your AI agent keeps making a LangChain engineer shows how to make AI check its own work before handing it back to you 08:06 - state, nodes, and edges 10:47 - checking documents and generated answers 11:48 - retrying code after failed tests 37:12 - stopping loops with a step limit 54:46 - tracking the steps an agent takes they shouldn't have given this workshop away for free watch this first, then read my guide below to build a research workflow that checks claims