u/Single-Cherry8263

Fable will burn tokens fast. Most of that cost comes from routing, not the model itself. so I use it to It plan architecture, debug, Create Skills so opus could execute it. It doesn't grep a codebase, write boilerplate, or drive a browser. Cheaper models handle the volume work.

Here's the setup.

Pin models for subagents

Subagents inherit the main loop's model by default. Spawn one from Fable without specifying a model, and it runs at Fable rates.

Add this to ~/.claude/CLAUDE.md:

## Model & Token Routing

Fable (main loop) is for: architecture, planning, hard debugging, final
review/synthesis, and anything ambiguous. Everything else gets delegated
to cheaper models.

When spawning subagents (Agent tool) or workflow agents, set the model explicitly:
- **sonnet** (default for all exploration and analysis): file/code search,
  exploration fan-outs (use the Explore agent type), codebase analysis and
  summarization, log scanning, data extraction, standard implementation from
  a clear spec, QA runs, doc updates. Do NOT use haiku for exploration — a
  subtly wrong exploration summary taken as given by Fable costs more than
  the tokens saved.
- **opus**: complex implementation, code review passes, tricky refactors
- **omit model (inherit Fable)**: only for design decisions, adversarial
  verification of critical findings, or synthesis across many agent results

Effort: use "low" for mechanical stages, session default for normal work,
"high"+ only for verify/judge/design stages.

Subagents must return conclusions with file:line references — never raw file
contents or dumps. Don't pay Fable rates to re-read what a cheaper model
already read; instruct subagents accordingly in their prompts.

Haiku handles lookup work ("find every caller of X") but not understanding work ("how does auth flow here"). A wrong summary from Haiku looks confident and gets built on. Sonnet is the floor for exploration.

Drop the 1M-context variant

If your config points to claude-fable-5[1m], ask if you actually need whole-codebase context in one session. Every turn re-reads what's accumulated, and the 1M variant lets that pile grow far past what standard Fable would allow.

If subagents keep your main context clean, you rarely need it. Keeping it anyway means running /clear between unrelated tasks instead of letting one session sprawl.

Move heavy plugins out of global settings

Every enabled plugin loads its full skill and agent descriptions into every session, at your main model's cache-read rates. A deploy toolkit with sixty-plus skill entries is dead weight in a session that never touches deployment.

Keep plugins global only if you use them everywhere. Everything else goes in the project's own .claude/settings.json.

Enforce exploration routing, don't just instruct it

Telling Claude to use Sonnet for exploration works until it forgets. Pin the model in an agent definition instead. Create ~/.claude/agents/explorer.md with the model set in the frontmatter.

Fork subagents are the exception. They carry the full conversation, so they always inherit the main-loop model.

Subagents report findings: File:line references only, so Fable never re-reads what Sonnet already checked.
Send well-specified implementation outside Claude: Fable writes the spec, Codex types the diff, Fable reviews it.
Stop maxing effort by default: Run medium or high normally, save the top tier for verify and design stages only.
Route browser automation around Fable: Let a headless-browser tool click through screens, feed Fable the results instead of the screenshots.

i use ai every day, it's part of my job. i feel guilty using it. there isn't enough sustainability content around the internet or maybe the algo don't push it, but it's harming the environment. the energy used to train the data, or run each query, adds up to a lot of litres of water. the water is used to keep the servers cool.

inference, just the everyday use of these models, is now burning more energy than training them ever did, and the user base keeps growing faster than anyone's fixing the footprint.

this isn't unsolvable problem. Companies could build more efficient models and run on cleaner power, they just don't, because it slows them down and costs more.

a few alternatives i've found that are actually built with this in mind:

• mistral ai
• cohere
• liquid ai
• ecogpt

what do you think of sustainable AI, something that uses less of carbon footprint?

anybody attending the 7th july orientation from delhi? would like some company lol

My niece's birthday is next month, and I am looking to get her something useful. She's into soft toys, plushies. she already have multiple of those. what do I gift her that she likes it & also use on daily basis?

I'm looking for something that can help in her growth & can play with it on daily basis as well. her parents insisted on giving her no more toys, as all her last birthday gifts are in cupboards by now.

whats the best birthday gift you've given to a kid. that they genuinely loved? what should I get her?

Organisers needed 413,488 valid signatures by July 1, 2026 to qualify for a statewide referendum. They could collect.

Supporters argue that hyperscale data centers place enormous pressure on local power grids while offering relatively few long-term jobs after construction. Opponents contend that restricting data centers could discourage investment, reduce economic growth, and weaken Ohio's competitiveness in the expanding AI industry.

Don't drink it. don't lick it. hold it up. just blow, don't throw it. not at me. don't lick it off the floor.

I've been saying these phrases like 100 times a day. it gets exhausting for a parent to repeat on a daily basis. I know that this is normal but its still tiring right.

what's the thing for you? what makes you exhausted after taking care of your baby on a daily basis.

Running agents in parallel only works if each one knows when to stop.

Before starting any long-running task, I lock a definition of done the agent can score itself against. Without that, the agent either over-runs or stops too early and you can't tell which.

The targets I actually use:

• Model or eval work: the agent changes the approach, runs the eval, keeps the change only if the score moved. Same pattern as Karpathy's autoresearch.
• Web app or UI: Playwright loads the page and screenshots it. Pass if it still does the thing.
• Backend or refactor: failing tests first, then green, stays green.
• Speed or flakiness: p95 or a benchmark. Change and measure until the target is under the line.
• Data cleanup: row count. Loop until zero rows fail validation.

With the target locked, the prompt tells the agent to loop. Change something, measure against the target, keep or revert, go again. The stop conditions matter most: metric hits target, no improvement after a few tries, out of ideas, or blocked and needs a call.

For notifications I give the agent a Model Context Protocol server path plus /notify. In practice that routes to Slack because that's where everything else already is. The agent sends status updates and decision pings as normal messages. My reply becomes the next input the loop runs on.

The loops run in the cloud. A four-hour task can't live on my laptop. I use my own client as the orchestrator and fan work out to cloud agents from there.

Once a loop is running I start another. Usually three or so, sometimes five, and those are only the long ones. Shorter agents often run at the same time on a pull request, a quick investigation, something that won't take hours. If things get quiet I add one. If three are waiting on a decision I stop and go review.

The prompt template I use:

Task: <the long thing>

/loop until <metric / tests / eval / QA check> hits <target>. Treat it as the source of truth and don't change it while you run.

Stop early if it stalls for several tries or you run out of ideas. If you're blocked or unsure, stop and ask.

/notify on start, on anything surprising, and when you're done or stuck. Ping me when you need a decision.

The definition of done has to exist before the agent touches anything. The looping mechanic is secondary. Get the target wrong and the loop runs in the wrong direction.

We're the generation that got social media at 13 with zero guidance. We watched it happen to us in real time. it came with multiple other things like comparison spirals, body image stuff, anxiety that came from nowhere and turned out to have a very obvious source.

Now I have a kid. And I genuinely don't know what I'm going to do. Part of me wants a hard rule: no social media until 16, no exceptions.

what's your plan? what will you do when your 12-year-old asks for it & everyone around him/her has it.

Our company brain wasscattered across slack, gong, hubspot. we added persistent memory across our agent stack at single grain and three weeks in, memory became the bottleneck. it ate 40% of the context window. agents had more information but pulled the wrong things at the wrong time.

the fix was a retrieval layer between all that context and the work agents need to do.

the broken version:

Call transcript
     ↓
Random notes
     ↓
Human remembers
     ↓
Agent starts cold
     ↓
Human re-explains

the human becomes the router for everything. that doesn't scale.

the version that works:

Calls ─────┐
CRM ───────┼──► Retrieval ─► Agent ─► Work
SOPs ──────┤        ▲          │
Slack ─────┘        │          ▼
             Human correction ─► Rule

memory is raw material. retrieval is the operating layer. calls, crm, sops, slack all feed in. the agent pulls what's relevant for the task. human corrections turn into rules.

six layers make this work:

┌──────────────┐
│  Execution   │ agents, workflows
├──────────────┤
│  Feedback    │ corrections → rules
├──────────────┤
│  Permission  │ who can use what
├──────────────┤
│ Source Truth │ what to trust
├──────────────┤
│  Retrieval   │ right context now
├──────────────┤
│   Capture    │ calls, CRM, SOPs
└──────────────┘

it record meetings, dump docs into a vector database, and call it a brain. that's a storage unit. retrieval is where it starts working. an agent drafting outbound doesn't need company history, it needs icp, objections, brand voice, and current campaign goal. source truth is which source wins when two docs disagree. skip that and agents format their hallucinations well. permissions keep the marketing agent out of client financials. feedback loops make the system compound, each correction updates a rule.

we ran 2,862 gong call transcripts through this. one ingestion pass across 15 calls produced 390 insights, 470 facts, and 125 frameworks. a single call becomes an objection library, a sales training input, a positioning signal, a content source, a crm risk flag. the point is converting scattered company data into reusable operating intelligence.