I've been working with icons and icon systems for thirty years and there have always been tradeoffs and horribly manual processes. I think I've finally gotten a system which is al win. It lets you use SVG icons and icon elements, compose them on the fly, and use them as a design language leveraging CSS variables where needed.
The link is to a blog post that explains the evolution and architecture.
It's written in typescript and can transpile itself.
When it transpiles typescript, type declarations become contracts, and types are available at runtime.
It does all this with a 50-70% overhead (imperceptible in most cases), but there's an unsafe escape hatch (!) => {} functions bypass type checks.
Oh yeah, it does inline WASM and it handles the hard stuff other WASM implementations kind of leave as an exercise -- moving data across. And it support SIMD.
Error propagation is monadic, so functions passed bad arguments just don't execute. Also functions know from whence they came and you can execute code in debug mode and get a trace with your monadic error, so it's very agent friendly.
If you code in tjs natively you can do inline tests of unexported functions at transpile time.
If you transpile ts using tjs it gives you the same capability.
Single pass transpilation does inline tests and generates documentation at the same time.
tjs itself is a TRUE js superset with predicate functions to handle complex types (if your type system is going to be turing complete, own it), full introspection (so it's a true LISP, or what Dylan aspired to be), and safe Eval using a language subset that is deeply async. Again, universal endpoints.
Simple types are declared by example, so:
function greet(name: 'Alice') -> 'Hello, Alice' => `Hello, ${name}`
is not just a declaration of a function that takes a string and returns a string, but the string should look like 'Alice' and if it is 'Alice' the function will return 'Hello, Alice' and this is also an inline test that runs at transpile time.
Every execution atom in the save eval sandbox has a 'gas' cost that also time bounds it, so eval is safe both from the halting problem and type fuzzing, and the only things the code can do is execute capabilities you explicitly give it, and you can give those capabilities the same access tokens the request had. So you get universal endpoints.
I wrote this because I was frustrated by the recent "16 AI Agents build a C compiler" narrative. Building a C compiler is an exercise in implementation; I wanted to see if one dev could use AI for invention.
The result is TJS. It's my attempt to fix what I dislike about TypeScript (erased types) and solve the eval() problem for AI agents. It treats JS like the Lisp it was meant to be: types are real values, autocomplete works by introspection, and execution is strictly gas-metered.
While I was waiting for the night train to Oulu in Helsinki on Friday night, I pondered two problems I have been thinking about for years on the one hand, and for months on the other.
1. How can I build a simple *service-as-a-service* endpoint that pulls data, maybe does a little work on it (e.g. whittling it down, converting XML to JSON, etc.) caches it, and returns it? This seems to require *eval* to be really useful and then it's a whole deployment, code-review thing (for good reason).
2. How can I build an LLM-powered agent that… you get the idea. If service-as-a-service, why not *agent-as-a-service*? I'd already played with lang-chain and found it quite fiddly even for simple things, and this had led me to build a lighter, schema-first alternative to zod.
The epiphany I had was that these are the same question, and the problem was *eval*. So why not make *eval* completely safe?
The result is *agent-99* – a Turing-complete, cost-limited virtual machine that enables "Safe Eval" anywhere (Node, Bun, Deno, and the Browser). The runtime model is so simple it could easily be ported to any of your favorite languages: Python, Rust, Go, Java, Zig… Haskell. It's a perfect fit for Erlang.
Oh and because the underlying language is JSON schema, it's easy to hand it to an agent as a set of tools. So *agent-99* makes it easy to do that, too.
It's the infrastructure for *sky-net* but, you know, type-safe, deeply asynchronous, and without a halting problem.
Most Agent frameworks (like LangChain) rely on heavy "Chain" classes or graph definitions that are hard to inspect, hard to serialize, and hard to run safely on the client.
`agent-99` takes a different approach: *Code is Data.*
1. You define logic using a *Fluent TypeScript Builder* (which feels like writing standard JS).
2. This compiles to a *JSON AST* (Abstract Syntax Tree).
3. The AST is executed by a *Sandboxed VM* (~7kB gzipped core).
Or to put it another way:
1. A program is a function
2. A function is data organized in a syntax tree
3. An agent is a function that takes data and code
`agent-99` provides a *builder* with a fluent-api for creating your language and creating programs, and a *virtual machine* for executing those programs with explicitly provided capabilities.
### Why use a VM?
* *Safety:* It solves the halting problem pragmatically with a "Fuel" (Gas) counter. If the Agent loops forever, it runs out of gas and dies.
* *Security:* It uses *Capability-Based Security*. The VM has zero access to fetch, disk, or DB unless you explicitly inject those capabilities at runtime.
* *Portability:* Because the "code" is just JSON, you can generate an agent on the server, send it to the client, and run it instantly. No build steps, no deployment.
### Batteries Included (But Optional)
While the core is tiny, I wanted a "batteries included" experience for local dev. We built a standard library that lazy-loads:
* *Vectors:* Local embeddings via `@xenova/transformers`.
* *Store:* In-memory Vector Search via `@orama/orama`.
* *LLM:* A bridge to local models (like LM Studio).
### Proof of Concept
I sent the repo to a friend (https://github.com/brainsnorkel). He literally "vibe coded" a full *visual playground* in a couple of hours using the library. You can see the definitions generating the JSON AST in real-time.
JSON Schema is a schema built on JSON and it’s already being used. Using XML would mean converting the XML into JSON schema to define the response from the LLM.
That said, JSON is “language neutral” but also super convenient for JavaScript developers and typically more convenient for most people than XML.
I wrote this library this weekend after realizing that Zod was really not designed for the use-cases I want JSON schemas for: 1) defining response formats for LLMs and 2) as a single source of truth for data structures.
Your timing couldn't be better. I was literally in the process of trying to figure out how the heck the SpeakHere demo actually works -- now maybe I don't have to.
It would be nice to have a little more documentation. How do you set audio sample formats? (I assume you can.)
I can't believe I'm posting something that might be taken as defending Facebook but...
If a site appears to contain malicious content at time X but not at time Y than I would PREFER to be notified that it is a dubious site until the site has earned back trust in some way. Continuing to warn users about a site that historically contained badness seems to me to be a FEATURE.
The link is to a blog post that explains the evolution and architecture.