A five-minute walkthrough that packages a Task, a Massive connector and memory folders into one qbash MCP server, adds it to Grok Bot, and calls it for stock data and a drafted post.

A qbash Task matches company mentions from call transcripts to database records with Jev and OpenAI Decisions. Jev scored 93% and OpenAI Decisions 80% with the full catalog in every prompt. A Double Metaphone shortlist of up to 10 candidates brought both to 83% at about a ninth of the estimated cost.

A qbash Task where Claude drafts a product launch email, Jev answers 12 review questions about it, and regex checks enforce the house style. Failed drafts go back for another round. The logs from two runs show where a decision model helped.

A short walkthrough of TypeSafe's Jev decision model in qbash. Enable Jev on the Models page, create an inline Jev prompt with Pick one, Score, and Yes or no questions, and run it over four support tickets from a Python Task.

Mail merge for fillable PDFs. A Task reads one PDF form and a Google Sheet of 20 customers, fills a copy of the form for each row, and zips the results into a single download.

How we test our connectors with qbash Tasks. One prompt to the builder produces a script that exercises every tool on the upcoming LinkedIn integration, cleans up after itself, records each response, and reports 46 passes, five failures, and two skips across 53 tests.

The AI Ticker section of our newsletter starts with two Tasks. One scans model subreddits every day and stores a summary of each new thread in a memory folder. The other ranks a week of those summaries, writes the ticker, and runs a copy review, one single-purpose prompt at a time.

Two MCP servers put qbash inside Codex: one exposes Tasks as callable tools, the other exposes memory folders. One prompt then drafts our weekly newsletter from 14 days of commits, a week of blog posts, and 24 hours of AI news, in the house style the newsletter archive supplies.

The objection to test-driven development was always the cost of writing tests by hand. The AI editor writes them, runs them, and reads the failures. This walkthrough builds a square root task tests-first, extends it to take a parameter, then breaks it to watch the suite catch the regression.

Triaging our busiest Slack channel into Asana tickets was a Monday morning job. This Task reads a week of Slack threads, checks two Asana sections and merged GitHub PRs for what already shipped, and files one Asana ticket with a subtask per theme.

Last week's model test suite had eight runners, a judge, a change detector, and a six-phase script. This version has one model, two inline prompts, three tests, and 24 lines of Python, so every step of the Task is visible.

Fable 5.1 and GPT-6 Astra arrived a day apart. This build is a qbash Task that runs 15 business tests across 8 models every Monday, scores them with a judge prompt, and diffs the results against last week.

Long-bond yields are at 19-year highs. This Task searches the web for remarks from 20 Fed officials, classifies each one as hawkish, dovish, or neutral with a confidence score, and writes the new rows to Airtable.

Alex turns on Searchable by AI for a folder of AI governance PDFs, exposes vector search through a single Task, and asks Claude a question that takes several searches to answer.

A checkout fails after the card is charged. Checkpointing in the Tasks SDK records which steps finished so a retry skips them, and every step runs exactly once.

Integration calls in Aisle Tasks are standard Python API calls, not agent tool invocations. Credentials stay out of the context window and tokens go toward the work.

An Airtable base of deals, one Task with four modes, mounted on an MCP server. The sales team updates the pipeline by chatting, without edit access to the table.

Deploy a Task as an MCP server and call it from ChatGPT, Claude, or any client that supports the protocol. Same Task, same behavior, every client.

Mail merge for PowerPoint decks. One template, many rows of data, and a zip of finished decks ready to download.

The most copied lead-gen workflow on n8n scrapes Google Maps and ends in a spreadsheet. Here's the same job rebuilt as an Aisle Task with Google Maps, Apollo.io, HubSpot, and Slack.

How to set up feature and spec memories in Claude Code that survive across sessions and carry across everyone on your team, instead of re-explaining the same architecture every morning.

The AI Task Builder takes a spec and compiles a working Task from it: script, prompts, and wiring, generated end to end.

A look at how moving from agentic reasoning to a deterministic Python task in Aisle can cut token consumption by up to 50x, with no loss in what the automation actually does.

Use an Aisle task to create a deterministic AI automation that summarizes all new commits to a project and delivers them to your team.

Connect an OpenSearch instance to an Aisle project and query your server logs in plain language using AI.

How to build a shared, multi-model prompt library for your team in Aisle - with variables, per-prompt model settings, versioning, sharing, and prompts that run across ChatGPT, Claude, Gemini, Grok, and more.

Three ways to build the same automation on Aisle - a two-prompt workflow, a single-prompt workflow, and a native Python Task - and what each one cost in tokens, effort, and maintenance.

In this video we show using the Playgrounds feature to compare the responses of different models - such as Opus 4.6 and GPT 5.4 - along with different prompt settings.

Use the Aisle memories feature to create an on the fly, searchable AI RAG database that extends past usual context limits. Upload hundreds of documents including PDF, PPT, DOC and then chat with your data.

In this tutorial, Mitch takes us through the Aisle Projects feature - how to bundle prompts, workflows, MCP connectors, and knowledge bases into a single agent-run workspace.

How much time do you spend on prompt structure? Mitchell takes us through a quick demo of the Aisle Prompt Builder, which takes a natural language task description and fleshes it out into the Aisle preferred prompt structure, complete with variables and output structure.

How to build a prompt in Aisle from scratch. Mitch walks through the Prompts feature: setting up a prompt, configuring structured outputs, and deploying it to your team.

How to write effective AI prompts using prompt engineering best practices. Step-by-step guide: anatomy, variables, model settings, and deployment from scratch to production in Aisle.

How to summarize a YouTube video with AI and extract YouTube transcripts. Build one workflow, run it on any video. Step-by-step with Aisle and Supadata.

How to fork conversations in Aisle chat. Explore multiple AI responses without losing context. Version control for conversations.

The original Aisle Platform Demo from April 2025 - taking early users through multi model chat (ChatGPT, Claude), setting up reusable AI prompts and creating chained AI workflows.