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Parker RexNovember 22, 2024

Did AI Just Get Perfect Memory? (Huge if True - Copy This)

Did AI just get memory? Explore AI memory myths and fresh dev tools: SQL copilots for Postgres/Superbase, VZO for multi-page apps, deploy-ready prompts.

Show Notes

Pushing the envelope on AI tooling, this update covers new SQL copilots, frontend-cloud IDEs, code search acquisitions, memory-focused models, multi-agent workflows, and more. Here are the key takeaways and what to try this week.

SQL copilots for Postgres in Supabase

  • Acknowingged feature: a SQL copilots trained specifically on Postgres that works well even if you’re not using Supabase as your backend.
  • Benefits:
    • Schema-aware suggestions if you’re using Supabase (great for targeted queries).
    • Quick, practical helper for daily SQL tasks and exploration.
  • Actionable takeaway:
    • If you write a lot of SQL, test this Postgres-specific copilot in your environment to speed up query writing and data exploration.

VZO (frontend-cloud style tool) features and implications

  • Multi-page file editor: you can create multiple “pages”/files that the tool writes (SVGs included), giving you a dashboard-like workspace.
  • Deploy integration: five-shot a website, then deploy directly; the tool ties prompts, edits, and deployment together.
  • Live preview and navigation: URL bar, development preview, and a “select and inline edit” workflow (open in new tab, fullscreen, refresh).
  • Standalone and deployable: it’s positioned as the frontend cloud, aiming to let you build, test, and ship by prompting.
  • Grep acquisition: they bought Grep for fast code search, planning a standalone product with API and an integrated search engine.
  • Practical use-cases:
    • Rapid prototyping: generate and deploy UI pieces from prompts without leaving the tool.
    • Quick code discovery: search across repos, prep for forking or contributing more efficiently.
  • Actionable takeaway:
    • If you’re doing front-end work, experiment with VZO’s multi-page workflow and the new code search capabilities to speed up iteration loops.

Code search and repo discovery: Grep and the broader search play

  • Grep integration: fast code search across your stack; faster than traditional GitHub search in some contexts for specific queries.
  • standalone tool and API: designed to let you locate, fetch, and start editing repos quickly.
  • Practical use-cases:
    • Find a repo to fork or contribute to without manually digging through dozens of results.
    • Jump straight into an existing project with a quick search that brings the repo into your workspace.
  • Actionable takeaway:
    • If you regularly explore new codebases, add this search capability to your toolkit to speed up starting new side projects.

Bolt.new and inline editing patterns

  • Bolt.new mentioned as a popular entry point for folks new to coding; somewhat standalone, not deeply integrated for developers yet.
  • Notable detail: code editor components (e.g., CodeMirror) are used in some examples, so you can learn how inline code editing is wired in these tools.
  • Opinion: useful for exploration and getting people excited about code, but less of a day-to-day dev workflow for seasoned builders.
  • Actionable takeaway:
    • If you’re curious how inline editing works in practice, check out Bolt.new’s approach and the CodeMirror integration to understand the building blocks.

Gemini memories and the memory arms race

  • Google Gemini launched “memories” with a larger context window, enabling more persistent context across interactions.
  • Microsoft reportedly claiming near-infinite memory capabilities (in their announcements).
  • Why it matters:
    • Larger context windows and persistent memory can reduce repetition in multi-turn tasks and improve long-running workflows.
  • Actionable takeaway:
    • For long, multi-step tasks (like building a video description pipeline or multi-page prompts), explore memory-enabled models to see if context retention reduces prompt churn and improves consistency.

Other AI copilots and conversational tools

  • Mistral’s LayChat: a competitive model with citations, designed to be strong in a chat/QA flow but still aiming for profitability in deployment.
  • Perplexity added one-click checkout and expanded shopping features.
  • General takeaway:
    • Expect more “context-aware” and chainable assistant experiences that integrate search, pricing, and checkout into AI-assisted workflows.

Windsurf IDE and Cascades by Codium

  • Windsurf IDE (Cascades) pitch: a code-indexing-first IDE that helps you answer questions about your codebase quickly.
  • Codium background: known for indexing and fast code-base queries; aims to accelerate understanding large codebases.
  • Actionable takeaway:
    • If you work with large codebases or monorepos, try Windsurf/Cascades-style workflows to test how quickly you can answer code-base questions and navigate dependencies.

XAI and multi-agent workflows in Verel AI SDK

  • XAI integration in the Verel AI SDK (Verel likely referring to the platform in the talk).
  • Mistal (mistl) approach: one pattern is to tag multiple agents and compose them into a workflow.
  • Human-in-the-loop example: one agent drafts a YouTube title/description, passes output to a blog-post agent, and prompts for your feedback.
  • The big idea:
    • Multi-agent flows without requiring heavy coding could become a standard pattern for end-to-end content generation, data pipelines, or deployment pipelines.
  • Actionable takeaway:
    • Experiment with a small multi-agent chain (e.g., video outline → title/description → blog draft) to see how well the hand-off works and where you want human review points.

Practical takeaways for developers and builders

  • Build fast: leverage the new copilots and multi-page tooling to prototype and ship faster.
  • Search-first workflows: adopt fast code search and repo discovery to reduce friction in starting new projects.
  • Memory-aware workflows: for long-running tasks, experiment with memory-enabled models to maintain context and reduce prompt overhead.
  • Multi-agent workflows: start with simple agent stacks and a human-in-the-loop for quality control; scale up gradually as you validate outputs.

If you want, I can tailor these notes to emphasize a particular topic (e.g., code tooling, memory in AI, or multi-agent workflows) or adjust the level of detail for a deeper dive.