The takeaway from Fable 5 being BANNED by the government: GET GOOD AT LOCAL MODELS SO YOU HAVE 100% CONTROL. My entire weekend was going to be building my craziest ideas with Fable 5. That's now cancelled. So instead of building with Fable this weekend, I've decided I'll go deep on local models: 1. Start with the runtime. Download Ollama or LM Studio first. This is the thing that actually runs models on your machine. 2. Match the model to your hardware. A model's size is measured in billions of parameters (7B, 32B, 70B). Bigger is smarter but needs more memory. Rule of thumb: a 7B model runs on almost any laptop, a 32B needs a good Mac with 32GB RAM, a 70B needs serious hardware like a DGX Spark or a maxed-out Mac Studio. 3. Know which model for which job. Qwen 3 is the best all-around choice for most tasks. DeepSeek for reasoning and coding. Gemma 4 when you need something tiny that runs on a phone. Llama when you want the biggest community and the most fine-tunes. 4. Quantization. You can shrink a model to run on weaker hardware with barely any quality loss. Look for versions labeled Q4 or Q5. This is how a model that "needs" a server runs on your laptop. Learning this one concept changes everything. 5. Connect it to your agent. Point Hermes or your agent stack at a local model. 6. Context window is your real constraint locally. Cloud models give you huge context for free. Local models make you pay for it in memory. A bigger context window eats RAM fast. Keep your sessions tight and your prompts lean or your machine chokes. 7. Learn to give local models tools. A smaller local model with web search, file access, and code execution beats a giant model with none. The capability gap closes fast when you wire up the right tools. The model is the engine but the tools are the wheels. 8. Fine-tuning is more accessible than you think. You don't need this on day one, but know it exists. You can take an open model and train it on your own data so it gets good at your specific domain. I'll probably do a breakdown at some point on this @startupideaspod if people are into it. The lesson from this ban is basically don't build your entire workflow on something that can disappear with a single letter. Own part of your stack. Local models are insurance. It reminds me when people realized they don't own social media accounts. And then you saw people build email lists etc. I remember running a startup and my biggest traffic source was organic FB. All of a sudden, algo changed, and I lost 99% of my traffic. Same sorta moment (but bigger) for AI. This is a wake up call.

Massive output uptick due to agentic AI. Complete flat adoption.

As I wrote this, I saw X go into meltdown over tokens. You've seen the headlines: “Uber blows yearly AI budget in just one quarter.” “Meta employee burns 281 billion tokens in April.” But, the problem isn't spending. Spending works. Since 2023, the top quartile of our AI spenders doubled their revenue. The bottom quartile? Flat. It's blind spending. We don’t know which spend worked. A sales team has qualified leads. A support team has resolved conversations. These are units you can measure against. All a token tells you is the meter ran, not whether the work was worth it or not. Finance says, “half the budget,” engineering says, “double it” and you don’t know who’s right because there is no shared language of value. There’s no attribution, and no attribution means no allocation. For example, right now, all work, no matter the size or shape, defaults to frontier models. But meeting summaries and calendar updates don’t require GPT-5.5 Pro. In isolation this seems trivial, but re-route just 10% of a $10M AI bill from frontier to GPT-4 level intelligence you’ve saved nearly one million dollars. This sounds like a made-up stat — it’s not. It truly is that much cheaper. This is the future of finance: not blindly rubber-stamping or rejecting AI spend, but allocating it with the same rigor companies apply to headcount.

Fascinating results Anthropic running away with it right now So many people want to start their own company Google over OpenAI Vercel, Linear, Every, PostHog overperforming A great list if you're trying to figure out where to go work 👇

I’ve never really used Apple Shortcuts on Mac because triggering them never felt natural enough in my workflow. So I built a free app that lets you launch Apple Shortcuts, apps and websites on your Mac with a quick tap on the device that’s probably sitting next to you anyway. It’s called choclift, and it’s probably the sweetest app you’ll try today.

Building an app where couples can send widgets to each other’s home screens to share messages, moments, and reminders so you’re always just a glance away <3

you can now build apps in AI Studio that directly interact with Google Workspace spin up tools that pull data from Google Sheets, organize your Google Drive, or process team documents, all without leaving AI Studio

Introducing the world’s first AI CMO. The teams winning now aren't the ones with the best product. They’re the ones flooding the algorithm. Fastlane deploys social media accounts, generates viral content, and posts for you autonomously. Today, we kill marketing:

We're taking startup ideas from @startupideaspod / @ideabrowser and bringing them to life with a team of agents: Deep research on the problem space → Product prototype → Cinematic landing page → Billboard in a real location Ideas like: 🏡 Hyper-local market reports for real estate pros 💻 Lead gen that scans GitHub for buying signals 📱 Mobile app studio built for SMB budgets Get $1k in inference to run these agents for yourself below ⬇️

Idea: An anonymous “vote to end meeting” button on Teams where if 50% of people press it, the meeting ends immediately.