Sunday, October 04, 2026

IaC tool: Alchemy, with Effect (TS)

 Using Effect? Use Alchemy, too! - YouTube by Academind

No matter which kind of (web) application you're building - stop managing infrastructure manually. Embrace Alchemy instead!

This video by Academind demonstrates how using the Alchemy library alongside Effect simplifies infrastructure management directly inside TypeScript web application codebases.

  • Infrastructure as Code (IaC): Developers can replace manual cloud dashboard configuration by defining cloud resources (such as workers or storage buckets) straight in TypeScript.

  • Effect Integration & Type Safety: Built on top of the Effect library, Alchemy provides end-to-end type safety and structured execution rules, making it easier and safer for AI coding agents to generate correct application infrastructure.

  • Unified Application & Infrastructure: Business logic and cloud setup reside in the same stack. Resources like Cloudflare Workers and R2 storage buckets are declared together and linked automatically during deployment.

  • Multi-Provider Support: Alchemy works across multiple hosting providers, including Cloudflare, AWS, and Hetzner, with ongoing support added for additional services.

  • State-Based Deployments: Stacks are updated efficiently by comparing current resource configurations with existing infrastructure states to make precise, incremental updates.



Alchemy Website

What is Alchemy? | Alchemy

Alchemy is Infrastructure as Code built in pure Effect, with Infrastructure as Effects on top. Infrastructure as Code declares, diffs, and deploys cloud resources the way Terraform or Pulumi does. Infrastructure as Effects lets the code that runs on those resources live in the same program, as typed Effects and Layers.


Infrastructure is part of your application

Other IaC tools split an app into two programs, but every feature you build crosses that line. In Alchemy, a Layer owns its infrastructure and service implementation so that features can be decomposed.

Effect TS Ecosystem


Effect is a library for building robust, maintainable, type-safe, and production grade applications in TypeScript. It helps you handle the hard problems at scale: typed errors, dependency injection, structured concurrency, scheduling, tracing, and unified schema validation.


AI book: The Eureka Machine by Richard Socher; AI search you.com

The Leading Web Search APIs for AI | You.com

Recursive.com (AI self improvement)



Richard Socher (born 1983) is a German-born computer scientist focusing on artificial intelligence (AI) research. He is the co-founder and CEO of You.com,[1] an AI search infrastructure company, and co-founder and CEO of Recursive, a company pursuing recursive self-improvement.[2] He is also a co-founder and investment partner of AIX Ventures, an AI venture capital firm.[3] He was a researcher for ImageNet, a visual database. In 2014, Socher co-authored GloVe, an unsupervised learning algorithm that embeds words in multi-dimensional vectors.

The Eureka Machine: Why AI Is the Key to Unlocking a New Era of Scientific Discoveries by AI pioneer Richard Socher (published on eptember 22, 2026) argues that artificial intelligence will fundamentally transform and accelerate the scientific method itself, compressing a century of expected breakthroughs into just the coming decade. [1, 2]
Core Premise
  • Reversing Scientific Sclerosis: While human researchers publish more papers than ever, human expertise has grown narrow and overall scientific progress has slowed down.
  • Full-Stack Transformation: Socher contends that AI connects and transforms every single stage of the scientific pipeline—from forming hypotheses and running experiments to analyzing data and building theories.
  • The Four Pillars: The book outlines foundational knowledge models, unified reality models, simulated worlds, and a "Lab in the Loop" framework for real-world agentic experimentation. [1, 2]
Impact Areas & Real-World Applications
  • Medicine & Biology: Radically speeds up drug discovery timelines and allows subcellular-level simulations of life-forms and organoid research across diverse populations.
  • Neuroscience: Highlights nascent capabilities to translate, read, and record complex human thoughts and dreams.
  • Astronomy & Economics: Enables the rapid analysis of thousands of exoplanets for biosignatures and models complex economies using simulated citizen populations ("sim-citizens"). [1, 2, 3]
The Outlook on Superintelligence
  • Expanding Creativity: Socher remains deeply optimistic, asserting that AI will not replace human creativity, but rather expand the overall "creative possibility space".
  • Self-Improvement: The book projects a future defined by self-improving superintelligence that systematically identifies human blind spots, poses novel questions, and reshapes scientific discovery. [1, 2, 3]

UUID v7: 23x Faster Inserts, PostgreSQL 18

 PostgreSQL 18: 23x Faster Inserts With UUID v7 | Software Engineer, Author, High Performance PostgreSQL for Rails

This article by Andrew Atkinson highlights how migrating database primary keys to UUID v7 in PostgreSQL 18 improved insert performance up to 23x for high-volume tables.

  • Performance Improvements: Multi-row insert queries saw dramatic reductions in execution time:
    • Table A: Reduced execution time from 0.7ms to 0.03ms (23x speedup) at 12,000 calls/min.
    • Table B: Reduced from 0.6ms to 0.07ms (9x speedup).
    • Table C: Reduced from 0.5ms to 0.08ms (6x speedup).

  • Why UUID v7 Performs Better: Unlike UUID v4 (purely random) or UUID v1, UUID v7 is time-ordered (monotonically increasing). This keeps the primary key B-Tree index pages "hot" in the Postgres buffer cache, reducing disk I/O, CPU overhead, and index page splits.

  • Migration Strategy & Challenges:
    • Changing column defaults using ALTER TABLE ... ALTER COLUMN ... SET DEFAULT uuidv7() requires an Access Exclusive Lock, which blocks all reads and writes.
    • To avoid downtime on active tables, the author used short lock_timeout values (e.g., 50–100ms) paired with a PL/pgSQL retry loop with jittered backoffs (50–250ms) to acquire the lock during tiny traffic lulls.

  • Trade-offs: UUID v7 embeds a timestamp in the initial bits, meaning record creation times can be decoded from the ID. If hiding record creation timestamps is a strict security requirement, UUID v4 is still preferred.