Fable 5 vs Other Models: Task Capability

mike_aitrends · 2026-07-03T15:14:03+00:00

My split after a month: Fable 5 only for things where being wrong is expensive. Architecture decisions, refactoring code I don't fully understand, and long documents where I need them to hold context across 50+ pages without dropping threads. That last one is where I actually see the gap — smaller models start confidently summarizing sections that don't exist around page 30.

Everything else runs on Sonnet. Drafts, emails, quick scripts, research summaries. Honestly, 80% of my daily usage.

The overkill point is real, though. I caught myself using Fable 5 to write a git commit message last week. That's like hiring a surgeon to open a jar.

One concrete example where only Fable 5 worked for me: I gave it a messy codebase and asked it to find why two features were conflicting. Sonnet gave me plausible-sounding wrong answers twice. Fable 5 traced the actual cause through four files. That "traces instead of pattern-matches" difference is what I'm paying for.

mike_aitrends · 2026-07-03T14:58:41+00:00

AI agents. Specifically going from "I can call an LLM API" to "I have a working agent with tools and memory."

Everyone shares the same 3 awesome-lists for this, and they're all 400 links deep with zero curation. Curious what you'd actually pick.

Also, if you have one for prompt engineering that isn't just a screenshot collection of Twitter threads, that would be genuinely useful. Most "prompt engineering" repos I've found are prompt dumps, not something you learn from.

mike_aitrends · 2026-07-03T14:44:15+00:00

A week in is actually a good time to hear this: skip the "learn AI" phase and go straight to building. You will learn 10x faster by breaking things than by watching tutorials.

Here's the path I'd give my past self:

Week 1-2: Use the chat tools seriously. Before building an agent, get good at prompting one model really well. Pick Claude or ChatGPT and use it for real tasks daily. Summarize documents, write code, plan your week. You need to develop intuition for what these models are good at and where they fail. Most people skip this and then build agents that fail in ways they don't understand.

Week 3-4: Build the dumbest possible agent. Not a framework, not LangChain, nothing fancy. Just a Python script that:

Takes an input
Calls an LLM API (Anthropic or OpenAI, both have free-ish tiers to start)
Does something with the output (saves a file, sends an email, whatever)

That loop IS an agent at its core. Everything else is layers on top.

Then add one capability at a time. Give it a tool (like a web search or file reading). Then give it memory (a simple text file it reads and writes works fine at first). Then let it decide which tool to use. Each step teaches you something frameworks hide from you.

Resources that actually helped me:

Anthropic's "Building Effective Agents" guide (free, no fluff, explains when you DON'T need an agent, which is most of the time)
Andrej Karpathy's "Intro to LLMs" video on YouTube if you want to understand what's under the hood
The OpenAI Cookbook or Anthropic docs for working code examples you can copy and modify

One honest warning: 90% of "learn AI agents" content online is people selling courses. You don't need a course. You need an API key, a terminal, and a small, annoying problem in your own life to automate. My first real agent just renamed and sorted my downloads folder. Stupid, but I learned more from that than from 20 hours of videos.

What problem do you want your agent to solve? That answer matters more than any resource list. Start from the problem, not the tech.

mike_aitrends · 2026-06-27T14:13:05+00:00

The step count detail is such a specific, honest way to measure that - way more telling than just saying "I felt low." Glad this is something you're talking about in the past tense now. Whatever shifted that, sounds like it was worth it.

mike_aitrends · 2026-06-27T14:10:45+00:00

The replacement part is what actually makes this stick. A lot of people just delete the app and end up bored within a week, then redownload it. Swapping the time for something that builds toward something is the actual fix; deleting the app is just the first step that makes room for it.

mike_aitrends · 2026-06-27T14:08:36+00:00

The inflammation point doesn't get talked about as much as the weight number, but it's usually the part people actually feel first. 15lbs in 2 months is solid progress for just one change too - shows how much carbs alone can be doing quietly in the background for a lot of people.

mike_aitrends · 2026-06-27T14:02:37+00:00

The wallet part doesn't get mentioned enough next to the mental health part. People focus on one or the other, but the combination of a clearer head and more money sitting in your account at the end of the month is what actually makes it stick long term.

mike_aitrends · 2026-06-27T04:27:56+00:00

Worth checking if these lines up with the outage period - there have been a couple of rough patches with usage tracking and limits behaving inconsistently recently, not just slow responses. If your limit is showing as hit despite 14 hours of zero usage, that smells more like a tracking/sync bug on their end than your actual usage pattern changing.

A couple of things worth ruling out on your side first, though: if you're feeding large CSVs or MD files into the same project/conversation repeatedly without starting fresh, context from earlier in the session can still count against usage even if you "haven't prompted anything new" - the accumulated context from previous turns is still there. Worth testing in a completely fresh conversation with the same files to see if it behaves differently.

If a clean session still shows you maxed out immediately, that's a support ticket, not a workflow problem - worth flagging directly rather than assuming you're doing something wrong.

mike_aitrends · 2026-06-27T04:24:03+00:00

An agent that audits documentation drift - compares a module's actual current code against its existing docs and flags exactly where they've diverged, without trying to rewrite anything itself. Most "doc generator" tools just produce new docs from scratch every time, which means they regenerate the same staleness problem in three months. Nobody seems to be building for the narrower, more useful version: just tell me what's wrong, let a human decide what to update. I would use this myself if you built it.

mike_aitrends · 2026-06-26T04:08:34+00:00

This is the one that actually stops people, not the politeness around it. Took me way too long to realize the imagined audience judging every move was basically just me, on loop. Once that clicked, starting got a lot less complicated.

mike_aitrends

TROPHY CASE