A quick overview of the @aisle_sh Tasks feature - build deterministic AI workflows in Python!

In the four months since Sonnet 4.6 shipped, the price of running a top-tier Claude model has gone from $3/$15 to $10/$50 per million tokens. That's more than triple. Top-tier model releases are arriving faster and costing more, but for most business uses (content generation, summarization, extraction, RAG, classification), Sonnet 4.6 already handles the job, and has since February. You could have picked Sonnet 4.6 four months ago, ignored every release since, and today you'd be running a model that handles those same tasks at a price that hasn't moved.

Let's get Peter Molyneux to hype the new Fable model it's only fitting. No doubt it will cure cancer and world peace within 2 weeks.

Done a few CTO trainings and the biggest topic for the last 2-3 months was token spend. They are not idiots and expect the subsidies to wind down at some point making the reality even harsher.

Maybe all of those holes Mythos fixed came from Opus #iykyk

Bring back my trusty mate Sonnet 3.5. Peak times.

Tasks are really worth checking out. So easy to throw out a script connecting a few prompts integrations and then share it with the team.

This would be a hit

Opus 4.8 is already live on @aisle_sh . Compare it to older models using our Playgrounds feature or just start chatting and prompting! aisle.sh/blog/claude-opus-48…

Re-posting my reasoning through MCP from Sept 2025. I still largely feel the same way. Building custom MCP servers you start to really feel flakiness - and a lot of the time with agents I end up just writing direct API proxies. aisle.sh/blog/mcp-distributi…

If you needed any more proof Claude is a senior dev now..:

"Do not write this to be quoted" has to be one of my bigger secrets to prompts that write solid prose. Lots of other rules but this kills the similar sentence structure and cadence in its tracks.

Compare different prompts, models and settings with the @aisle_sh Playgrounds feature. Quick overview video attached.

Spent the last few weeks talking to mid-market and enterprise teams about why their AI rollouts are stuck. It's never the model. Every team built something that works in a demo. And then it hits the approval stage. The IC who built the prototype can't ship a change without a compliance review, and the iteration speed that made it good in the first place dies in a Jira queue. The two teams I talked to who actually shipped stopped treating governance as a separate step. They put it inside the tool the work happens in. One place, one audit trail, one shipping cycle.

Picking #3 first gets you a keynote slot. Doing #1 and #2 first is what compounds. aisle.sh/blog/the-order-is-t…

Another day another Opus outage. If you ever wanted a case study on why we build reliable, retryable flows vs single shot agents.... @aisle_sh