Monday, March 23, 2026

Anthropic AI Software Marketplace

 Anthropic Unveils Amazon-Inspired Marketplace for AI Software

"Anthropic is launching a new platform for its corporate customers to purchase third-party software, broadening the AI developer’s offerings at a time when its business faces new uncertainty from a standoff with the Pentagon.

Anthropic Marketplace will let its customers more easily purchase a mix of software applications that use Anthropic’s models, with options including services from Snowflake Inc., Harvey and Replit Inc. The OpenAI rival will not take a cut of these purchases and will allow its customers to use some of their committed annual spending on Anthropic’s own services toward third-party tools"

Official Anthropic marketplace

The official Anthropic marketplace (claude-plugins-official) is automatically available when you start Claude Code. Run /plugin and go to the Discover tab to browse what’s available, or view the catalog at claude.com/plugins.To install a plugin from the official marketplace, use /plugin install <name>@claude-plugins-official. For example, to install the GitHub integration:
/plugin install github@claude-plugins-official

Tiger Data: git-like DB for AI: Time-Series PostgreSQL

Time-Series PostgreSQL at Petabyte Scale


Primitives

Automatic partitioning
Time- and key-based partitioning for fast reads and writes.Automatic partitioning

Row-columnar storage
Row storage for writes. Columnar storage for analytics.Row-columnar storage

Tiered storage
Hot data on SSD, colder data on low-cost object storage.Tiered storage

Lakehouse integration
Ingest from Kafka and S3, replicate to Iceberg.Lakehouse integration

Time-series functions
200+ SQL functions for time-based analytics.Time-series functions

Interface
Postgres-native access via SQL, APIs, CLI, and UI.Interface

Search
Hybrid retrieval with keywords, vectors, filters, and ranking.Search

Sunday, March 22, 2026

AI: OpenClaw on AWS Bedrock

sample-OpenClaw-on-AWS-with-Bedrock/README.md at main · aws-samples/sample-OpenClaw-on-AWS-with-Bedrock @GitHub


 OpenClaw on AWS Bedrock AgentCore: Secure and Serverless - DEV Community


Deploy OpenClaw on AWS EC2 with Amazon Bedrock Using Terraform — No API Keys Required - DEV Community


as mentioned here


key points from the video:
  • NVIDIA Growth: NVIDIA expects AI demand to drive revenue to $1 trillion by 2027 (0:39), aiming to manage vast ecosystems including robots and data centers (07:00).
  • OpenClaw & Software: OpenClaw is highlighted as an incredibly popular open-source project (0:07). NVIDIA is supporting it with NemoClaw to optimize enterprise workflows (12:01).
  • Anthropic vs. OpenAI: Anthropic is recognized as a highly disruptive company challenging OpenAI for market share (0:46).
  • Tesla's Terafab: Tesla announced Terafab, aiming to produce a massive number of chips for robotics (01:03:00).
  • Energy Challenges: The U.S. faces a significant power shortfall for data centers, leading to renewed interest in nuclear energy (1:24:00).
  • Human Impact: Discussions covered the collapse of traditional computer science roles (1:48:00) and Elon Musk's vision for Universal High Income (UHI) based on shared upside from automation (1:42:00).

AI: autoresearch by Andrej Karpathy

Andrej is struggling with making sense of latest LLM tools, like Claude Code and OpenClaw, like everyone else...

And he has some interesting ideas, too...

karpathy/autoresearch: AI agents running research on single-GPU nanochat training automatically

"The idea: give an AI agent a small but real LLM training setup and let it experiment autonomously overnight. It modifies the code, trains for 5 minutes, checks if the result improved, keeps or discards, and repeats. You wake up in the morning to a log of experiments and (hopefully) a better model. The training code here is a simplified single-GPU implementation of nanochat

The core idea is that you're not touching any of the Python files like you normally would as a researcher. Instead, you are programming the program.md Markdown files that provide context to the AI agents and set up your autonomous research org. The default program.md in this repo is intentionally kept as a bare bones baseline, though it's obvious how one would iterate on it over time to find the "research org code" that achieves the fastest research progress, how you'd add more agents to the mix, etc."

SQL: Postgres patterns

good patterns or anti-patterns for db design?

this is quote common now, and for sure is taking more space... 

but it is often useful to have more data;

trouble is that common relational mode does allow update and delete...

and there is no storage optimization.

Life Altering Postgresql Patterns

  • Use UUID primary keys
  • Give everything created_at and updated_at
  • on update restrict on delete restrict
  • Use schemas
  • Enum tables
  • Name your tables singularly
  • Mechanically name join tables
  • Almost always soft delete
  • Represent statuses as a log
  • Mark special rows with a system_id
  • Use views sparingly
  • JSON Queries

Saturday, March 21, 2026

Tesla Terafab !?

 From Hyperabundance to Terafab - by Peter H. Diamandis

"Just when you thought Elon couldn’t possibly add another front to his multi-company war, he casually announces the Terafab, Tesla’s planned in-house semiconductor “mega-fab” which projects a full-scale output of 1 million 300mm wafer starts per month (WSPM).

Talk about “Chip Abundance!”…

How big is that? Comparing it to TSMC, multiple reports state that Terafab would equal roughly 70% of TSMC’s current total output across all of TSMC’s entire global footprint... and Terafab would do this from a single US-based facility."


Sawyer Merritt on X: "Tesla's Terafab chip manufacturing project launches tomorrow. Elon previously on advanced chip supply: "Even when we look at the best case output of all our key suppliers, it's not enough. In order to remove the probable constraint in 3-4 years, we'll have to build a very big https://t.co/o932lwbKpQ" / X



AI: x402 Open Agentic Commerce vs Adds?

a good and interesting article / opinion

 The end of ads - by Samuel Ragsdale - a16z crypto


AI Summary


The article "'Open Agentic Commerce' and the end of ads" by Samuel Ragsdale argues that we are shifting from an internet defined by advertising and "walled gardens" to one defined by autonomous AI agents and open protocols.

The Core Thesis

The "business model of distraction" (advertising) is dying because AI agents do not get distracted. As agents begin to handle information gathering and commerce, the traditional ad-supported web becomes obsolete.


Key Points & Ideas

  • The "402" Resurrection: In 1997, the HTTP 402 Payment Required status code was created but remained unused because digital micropayments were impossible. Today, stablecoins and blockchains make sub-cent transactions viable, finally enabling this protocol.

  • Agents vs. Walled Gardens: * Closed Commerce: Checkout in platforms like ChatGPT or Gemini is compared to the "AOL" of the 90s—curated, gated, and requiring manual business deals.

    • Open Commerce: True "Open Agentic Commerce" allows any agent to discover any merchant API and pay instantly via open protocols like x402 (Coinbase) or mpp (Stripe/Tempo).

  • The End of "Skills": The author suggests that pre-written "skills" (like a specific pizza-ordering plugin) are transitional. Modern models (Claude 4.5+, Codex 5.2+) can now discover an API, read its schema, and use it "just-in-time" without prior training.

  • From Distraction to Composition: * Old Web: Content producers sell human attention to advertisers.

    • New Web: Agents "compose" complex tasks—like managing a supply chain or negotiating with local vendors—and pay for data or services directly using digital wallets.

  • The Discovery Problem: The final piece of the puzzle is how agents find merchants. New tools like AgentCash are emerging to bundle payment and merchant discovery, allowing agents to bypass paywalls or "blocks" by paying small fees to proceed.

Grain Language => WebAssembly (WASM)

 Meet Grain: The High-Level Language Optimized for WebAssembly - The New Stack

The goal was to create a modern-day OCaml, he said.

“OCaml [was] this fantastic language from the ’90s, with all these amazing different language features … one of the huge features was pattern matching,”


Grain: A strongly-typed functional programming language for the modern web.


grain-lang/grain: The Grain compiler toolchain and CLI. Home of the modern web staple. 🌾@GitHub

in "Reason" language, LGPL3

Grain is a new programming language that compiles to WebAssembly via Binaryen. For more information about the language, check out grain-lang.org.

If it's your first time here, we recommend that you follow the Grain guide to get up and running.


Grain, a WebAssembly functional language - Interview with co-author Oscar Spencer - YouTube


Library Preview: Regex - Grain Blog

Friday, March 20, 2026

AI LLM multi-lingual thinking! 联合 (Join)

While using Gemini Flash from Antigravity tool for code generation,
web app strangely failed at one point, 
with error that mentioned that 联合 (Join) is missing!?

So I asked Gemini to explain what those Chinese chars came from,
and result was much more fascinating than I ever expected!

Here is the response! 
This AI model has very nice manners,
even  though it was a bit eager to go back to fixing the code, that is does very well!

AI: NVIDIA Jensen Huang @ All-In Podcast

Jensen Huang: Nvidia's Future, Physical AI, Rise of the Agent, Inference Explosion, AI PR Crisis - YouTube @ All-In Podcast - YouTube

This special episode of the All-In Podcast features Jensen Huang, CEO of Nvidia, discussing the future of AI. He outlines a shift from simply building GPUs to creating complete AI factories and agentic systems. Huang emphasizes that Nvidia's roadmap includes disaggregated inference—using a mix of specialized chips to optimize complex AI workflows (1:34).
Key Takeaways
  • The Agentic Future: AI is moving from simple generation to agentic processing, where agents use tools, access memory, and collaborate to solve complex problems (3:35).
  • AI Factory Philosophy: A $50 billion datacenter is actually the most cost-effective solution for producing tokens due to extreme efficiency, not just hardware cost (7:33).
  • Democratizing Computing: OpenClaw is acting as a new operating system for personal AI computers, bringing powerful agentic capabilities to the desktop (15:50).
  • Physical AI: The next major wave is Physical AI, with a projected $50 trillion market impact across robotics and intelligent factories (12:00).
  • AI Productivity: Engineers should consume a significant amount of tokens to remain competitive; token usage is the new metric for productivity (24:28).
"That $500,000 engineer at the end of the year, I'm going to ask them how much did you spend in tokens? If that person said $5,000, I will go ape something else," he added.
"This is no different than one of our chip designers who says, 'Guess what? I'm just going to use paper and pencil,'" he said, referring to top engineers who underutilize AI tokens.





OpenAI: Guide for building AI agents

 a-practical-guide-to-building-agents.pdf @OpenAI

Agents are systems that independently accomplish tasks on your behalf.

Building agents requires rethinking how your systems make decisions and handle complexity. Unlike conventional automation, agents are uniquely suited to workflows where traditional deterministic and rule-based approaches fall short.



MoonBit language => WASM

MoonBit is an end-to-end programming language toolchain for cloud and edge computing using WebAssembly.


MoonBit

Expressive multi-paradigm design, combining the best of dynamic and static, functional and practical

Data-oriented language with a robust type system

Multiple backend support including WebAssembly, JavaScript and more.




moonbitlang @GitHub


Rust, AGPL3

OCaml

custom license, written by lawyers... strange...


Thursday, March 19, 2026

OpenAI += "Rust" UV maker Astral

An "answer" to Anthropic acquiring Bun.js team recently (for Zig development of Claude Code)

OpenAI acquiring (team) for Rust development of OpenAI Codex dev tool.

This reminds of Microsoft's (in)famous chant "developers, developers, developers".


bun, uv: Why developer tools are an exciting target for OpenAI & Anthropic - YouTube 
by Maximilian Schwarzmüller

OpenAI to acquire Astral | OpenAI

"Astral has built some of the most widely used open source Python tools, helping developers move faster with modern tooling like uv, Ruff, and ty. 

These tools power millions of developer workflows and have become part of the foundation of modern Python development. 

After closing, the Astral team will join the Codex team at OpenAI"






Amazon AWS revenue $600B in 10 years?

 Amazon's Jassy now sees AWS hitting $600 billion a year on AI tailwinds | TechSpot




Prime numbers in 100+ prog. languages

Fastest programming language: C++ vs Rust vs Zig | Dave Plummer and Lex Fridman - YouTube

 PlummersSoftwareLLC/Primes: Prime number projects in 100+ programming languages, to compare their speed - and their programmer's cleverness @GitHub



Wednesday, March 18, 2026

Rivian AI Self-Driving, with LiDAR







RJ Scaringe: Self-Driving Cars, Next 10 years Changes EVERYTHING, Robots, AI impacts Society MORE - YouTube by Matthew Berman

interview with Rivian CEO RJ Scaringe, discussing the future of electric vehicles, autonomy, and the profound impact of AI on society over the next decade. Here are the essential key points:

  • The Power of AI in the Physical World (19:16-19:25): Scaringe emphasizes that AI will impact society more significantly through physical applications (like autonomous driving) than through digital surfaces like language models.
  • The Shift to End-to-End Driving Models (20:30-22:56): Rivian is moving away from rules-based software toward transformer-based neural nets. This allows the car to learn driving behaviors directly from data, rather than relying on manually programmed rules.
  • The Importance of Data (23:05-23:19): A large data collection strategy from vehicles currently on the road is crucial for training these new, powerful driving models.
  • Sensor Strategy: Vision + LiDAR (30:07-30:10): While some believe in vision-only approaches, Rivian is investing in LiDAR because of its accuracy in low light, fog, and extreme brightness, which helps train vision models faster (34:33).
  • The R2 as a High-Volume Vehicle (10:52-11:55): The upcoming R2 is designed to be a more accessible ($45,000+) vehicle that brings the Rivian brand to a larger market, optimizing costs while maintaining performance.
  • Future of Labor and Society (49:10-49:25): In the long term, automation will mean society needs fewer people to run the planet's essential services, which could lead to more family time and a shift in how we approach education and curiosity (49:45-50:29).

in-security: AI Agent deleted prod db

Mistakes happen, with AI agents as well as humans... 

But when responsibility is blindly delegated to an AI Agent, things can go really wrong, really quickly... 

Vibe Coding Fiasco: AI Agent Goes Rogue, Deletes Company's Entire Database | PCMag

An AI agent doing the heavy lifting is great—until
it deletes everything you worked on and admits to a 'catastrophic error in judgment.'

Replit's CEO calls the blunder 'unacceptable.'

AI-powered coding tool wiped out a software company’s database in ‘catastrophic failure’ : r/coding

The Last Time The Financial System Shifted Like This, A New Class Of Millionaires Was Made - YouTube at Tom Bilyeu

Jason ✨👾SaaStr.Ai✨ Lemkin on X: "@Replit JFC @Replit https://t.co/ixo6LBnUVu" / X

from AI Agent: 




Message from SaaStr (affected startup)




meta/pseudo/higher-abstraction languages

You are likely looking for one of three things: Specification Languages (languages that describe logic but don't run it), Transpiler Languages (languages designed to compile to many other languages), or Language Workbenches (languages used to create other languages).

Here is a breakdown of common languages in these "higher-level" categories:

1. Specification & Modeling Languages (The "Blueprint" Level)

These languages are higher-level than code. They are used to mathematically prove that a system design is correct before you write a single line of Python or C++.

  • TLA+ (Temporal Logic of Actions): Used heavily by AWS and Microsoft. It doesn't compile to software; it checks your logic for infinite loops, deadlocks, and race conditions. It is a "meta" language for system architecture.
  • Alloy: Similar to TLA+ but focuses on data structures and relationships rather than time/concurrency. It is excellent for modeling database schemas or security policies.
  • UML (Unified Modeling Language): A visual "language" used to abstractly describe software architecture (classes, flows, states) before implementation. Tools like Enterprise Architect can sometimes generate code from UML diagrams.
  • Z Notation: A formal specification language based on set theory. It is used in safety-critical systems (like trains or medical devices) to mathematically define exactly what software should do.

2. Universal / Transpiler Languages (The "Write Once, Compile Anywhere" Level)

These are "meta" languages in the sense that they sit above standard languages. You write in them, and they output valid code in C++, JavaScript, Python, etc.

  • Haxe: The king of this category. It is a strictly typed language that can compile to C++, C#, Java, Python, PHP, Lua, and JavaScript. It is often used in game development to target every platform simultaneously.
  • Nim: A language that looks like Python but compiles to C, C++, or JavaScript. It is often used as a "meta" language to generate high-performance C code without dealing with C's complexity.
  • TypeScript: Technically a "meta" language for JavaScript. It adds a layer of types on top of JS and then "transpiles" down to raw JavaScript that browsers can understand.

3. Language Workbenches (The "Language for Making Languages" Level)

If you want to create your own syntax or a Domain Specific Language (DSL), you use these tools.

  • Racket: A dialect of Lisp designed explicitly for Language Oriented Programming. It allows you to easily create new programming languages with their own syntax and rules.
  • JetBrains MPS: A tool that lets you create "projectional" languages. Instead of parsing text, you edit the Abstract Syntax Tree directly. It is used to build highly specific languages for industries like insurance or electrical engineering.
  • LLVM (Intermediate Representation): While not a language you typically write by hand, LLVM IR is the "meta language" that languages like Rust, Swift, and C++ all compile into before becoming machine code.

4. Pseudo-Code & Syntax Definition (The "Human" Level)

These are used to describe algorithms or language rules to humans, not computers.

  • Standard Pseudocode: There is no one compiler for this, but it typically uses a Pascal-like or Python-like syntax (e.g., IF x > 10 THEN PRINT "High").
  • BNF / EBNF (Extended Backus–Naur Form): This is a metalanguage used to describe the grammar of other languages. If you look up the official specification for Java or Python, you will see the rules written in EBNF.

Summary Recommendation

  • If you want to design a system without coding it: Look at TLA+ or Alloy.
  • If you want to write code that runs everywhere: Look at Haxe.
  • If you want to invent your own language: Look at Racket.




Haxe is a high-level cross-platform programming language and compiler that can produce applications and source code for many different computing platforms from one code-base. It is free and open-source software, released under an MIT License.[2] The compiler is written in OCaml. It can be run in server-mode to provide code completion for integrated development environments (IDEs)


Tuesday, March 17, 2026

Visualizing AI training & thinking

scientific paper, vs hacking projects

 Visualizing the Chain of Thought in Large Language Models


microgpt visualizer https://microgpt.boratto.ca/

B44ken/microgptn @GitHub
a tiny visualization of a GPT model, running locally in the browser.


microgpt //karpathy.github.io/2026/02/12/microgpt/

enescang/microgpt-visualizer: Interactive visualization of a minimal GPT implementation with autograd engine.

MicroGPT Visualizer

Show HN: Microgpt is a GPT you can visualize in the browser | Hacker News

Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch | Sebastian Raschka, PhD




Languages that compile to WASM WebAssembly

WebAssembly @GitHub



A wide variety of languages can compile to WebAssembly (Wasm), but they do so in different ways. They generally fall into three categories: Systems Languages (which have the best support), Managed Languages (which are adopting the new Wasm GC standard), and Interpreted Languages (which compile their own runtime to Wasm).

1. Top-Tier Support (Systems Languages)

These languages have "first-class" support. They compile directly to Wasm instructions and manage their own memory, resulting in small, fast binaries.

  • Rust: Widely considered the best language for Wasm. It has excellent tooling (wasm-pack), a small footprint, and a massive ecosystem of Wasm-compatible libraries.

  • C / C++: The original languages for Wasm. Tools like Emscripten are mature and powerful, often used to port huge existing codebases (like Photoshop or game engines) to the web.

  • Zig: Includes Wasm support out of the box as a first-class build target. It produces extremely small binaries and is becoming a favorite for Wasm tooling.

  • AssemblyScript: A language built specifically for WebAssembly. It looks like TypeScript but compiles to Wasm. It is one of the easiest ways for web developers to write Wasm without learning a low-level language.

2. The "Wasm GC" Wave (Managed Languages)

Historically, languages with Garbage Collection (GC) were heavy because they had to bundle their entire runtime into the Wasm file. A new feature called Wasm GC allows these languages to use the browser's built-in garbage collector, making them much faster and lighter.

  • Dart (Flutter): Now has stable support for Wasm GC.[1] This is a major focus for Google to make Flutter apps on the web perform near-natively.

  • Kotlin: Kotlin/Wasm is in Alpha/Experimental stages but is aggressively targeting Wasm GC to allow Kotlin Multiplatform code to run in the browser without the overhead of the Java Virtual Machine (JVM).

  • Go (TinyGo): While standard Go supports Wasm (with large file sizes), TinyGo is a specialized compiler that produces tiny Wasm binaries ideal for the web and edge computing.

  • MoonBit: A new language designed specifically for Wasm GC, offering Rust-like performance with Go-like simplicity.

  • Grain: A functional language built solely for the Wasm ecosystem that leverages Wasm GC.

3. Runtime-Bundled Languages (Interpreted/VM)

These languages don't technically "compile" your code to Wasm in the traditional sense. Instead, they compile their Virtual Machine (VM) or Interpreter to Wasm. Your code then runs inside that Wasm-hosted interpreter. This works perfectly but results in larger download sizes (often 5MB+).

  • C# / .NET (Blazor): Microsoft's Blazor runs the .NET runtime in Wasm. It is production-ready and very popular for enterprise apps.

  • Python: Projects like Pyodide and MicroPython compile the Python interpreter to Wasm, allowing you to run Python data science stacks (NumPy, Pandas) directly in the browser.

  • Rubyruby.wasm allows you to run full Ruby (CRuby) in the browser.

  • Java: Tools like TeaVM or CheerpJ allow Java to run in Wasm, either by transpiling to Wasm or running a JVM inside Wasm.[2]

  • PHP: Can run in the browser via WordPress Playground or similar tools that compile the PHP engine to Wasm.

Summary Table

LanguageSupport LevelBest Use Case
Rust⭐⭐⭐⭐⭐ (Native)High-performance libraries, complex logic.
C/C++⭐⭐⭐⭐⭐ (Native)Porting desktop apps, games, legacy code.
AssemblyScript⭐⭐⭐⭐ (Native)TS developers needing speed/portability.
Dart⭐⭐⭐⭐ (Wasm GC)Full-stack apps (Flutter).[1][3]
C# (Blazor)⭐⭐⭐⭐ (Runtime)Enterprise web apps, existing .NET teams.
Go (TinyGo)⭐⭐⭐⭐ (Native)Small modules, serverless functions.
Kotlin⭐⭐⭐ (Experimental)Multiplatform logic sharing (KMP).
Python⭐⭐⭐ (Runtime)Data science in the browser (Pyodide).

WebAssembly (Wasm) can be decompiled, but you generally cannot get the original source code back (like the original Rust, Go, or TypeScript).

Instead, you can decompile it into three main formats, ranging from "readable assembly" to "pseudo-code."

1. The Formats You Can Decompile To

FormatReadabilityToolingDescription
WAT (.wat)Lowwasm2watThis is the official "assembly" text format.[1] It is a direct 1:1 translation of the binary instructions. It is precise but very hard to read for complex logic.
C (Source)Mediumwasm2cYou can convert Wasm into valid, compilable C code.[2][3][4][5] However, it is "machine-generated C"—it looks like a mess of memory commands (e.g., i32_store(ptr, val)) rather than human-written code.
Pseudo-CHighwasm-decompileThis produces a "C-like" syntax designed for humans to read.[1] It cleans up the code to look like normal functions and variables, but it is not valid code you can compile.

2. Why You Can't Get the Original Code Back

When you compile a language like Rust or C++ to Wasm, the compiler "bakes" the code. It strips away all the human-friendly parts to make the file small and fast.

  • Variable Names are Gone: A variable named userAccountBalance just becomes "memory location 4028."

  • Data Structures are Gone: Classes, Objects, and Structs disappear. They all just become raw bytes in a linear memory array.

  • Control Flow is Flattened: If statements and For loops are often optimized into raw jumps (instruction pointers), making the logic harder to follow.

3. The "Cheat Code": Source Maps

There is one exception. If the developer enabled Source Maps (usually a .wasm.map file) when they built the project, you can see the exact original source code (Rust, C++, etc.) inside your browser's developer tools.

  • How to check: Open Chrome/Firefox DevTools 

     Sources tab. If you see files ending in .rs (Rust) or .ts (AssemblyScript), the source maps are active, and you have the original code.

4. Top Decompilation Tools

If you need to reverse engineer a Wasm file without source maps, these are the standard tools:

  • WABT (The WebAssembly Binary Toolkit):

    • wasm2wat: Converts binary to text assembly.[1][6]

    • wasm-decompile: Converts binary to readable pseudo-code.[1][7][8][9]

    • wasm2c: Converts binary to portable C code.[10]

  • Ghidra: The NSA's open-source reverse engineering tool has a plugin for WebAssembly.[11] It is powerful for analyzing complex binaries and malware.

  • JEB Decompiler: A professional reverse-engineering tool that produces very readable high-level C code from Wasm.

  • Browser DevTools: Chrome and Firefox have built-in disassemblers. Just opening a .wasm file in the "Sources" tab will automatically show you the .wat text format.




MoonBit language
moonbitlang @GitHub
fn main {
  let name = "World!" // Define a variable
  println("Hello, \{name}") // and use it directly
  println("current count:")
  let buf = @buffer.new()
  for i in 1..<10 { // Loop over a range from 1 to 10
    buf.write_string("\{i} ")
  }
  println(buf)
}