frontpage.
newsnewestaskshowjobs

Made with ♥ by @iamnishanth

Open Source @Github

fp.

Go 1.22, SQLite, and Next.js: The "Boring" Back End

https://mohammedeabdelaziz.github.io/articles/go-next-pt-2
1•mohammede•2m ago•0 comments

Laibach the Whistleblowers [video]

https://www.youtube.com/watch?v=c6Mx2mxpaCY
1•KnuthIsGod•3m ago•1 comments

I replaced the front page with AI slop and honestly it's an improvement

https://slop-news.pages.dev/slop-news
1•keepamovin•8m ago•1 comments

Economists vs. Technologists on AI

https://ideasindevelopment.substack.com/p/economists-vs-technologists-on-ai
1•econlmics•10m ago•0 comments

Life at the Edge

https://asadk.com/p/edge
1•tosh•16m ago•0 comments

RISC-V Vector Primer

https://github.com/simplex-micro/riscv-vector-primer/blob/main/index.md
2•oxxoxoxooo•19m ago•1 comments

Show HN: Invoxo – Invoicing with automatic EU VAT for cross-border services

2•InvoxoEU•20m ago•0 comments

A Tale of Two Standards, POSIX and Win32 (2005)

https://www.samba.org/samba/news/articles/low_point/tale_two_stds_os2.html
2•goranmoomin•23m ago•0 comments

Ask HN: Is the Downfall of SaaS Started?

3•throwaw12•25m ago•0 comments

Flirt: The Native Backend

https://blog.buenzli.dev/flirt-native-backend/
2•senekor•26m ago•0 comments

OpenAI's Latest Platform Targets Enterprise Customers

https://aibusiness.com/agentic-ai/openai-s-latest-platform-targets-enterprise-customers
1•myk-e•29m ago•0 comments

Goldman Sachs taps Anthropic's Claude to automate accounting, compliance roles

https://www.cnbc.com/2026/02/06/anthropic-goldman-sachs-ai-model-accounting.html
2•myk-e•31m ago•4 comments

Ai.com bought by Crypto.com founder for $70M in biggest-ever website name deal

https://www.ft.com/content/83488628-8dfd-4060-a7b0-71b1bb012785
1•1vuio0pswjnm7•32m ago•1 comments

Big Tech's AI Push Is Costing More Than the Moon Landing

https://www.wsj.com/tech/ai/ai-spending-tech-companies-compared-02b90046
4•1vuio0pswjnm7•34m ago•0 comments

The AI boom is causing shortages everywhere else

https://www.washingtonpost.com/technology/2026/02/07/ai-spending-economy-shortages/
2•1vuio0pswjnm7•36m ago•0 comments

Suno, AI Music, and the Bad Future [video]

https://www.youtube.com/watch?v=U8dcFhF0Dlk
1•askl•38m ago•2 comments

Ask HN: How are researchers using AlphaFold in 2026?

1•jocho12•41m ago•0 comments

Running the "Reflections on Trusting Trust" Compiler

https://spawn-queue.acm.org/doi/10.1145/3786614
1•devooops•46m ago•0 comments

Watermark API – $0.01/image, 10x cheaper than Cloudinary

https://api-production-caa8.up.railway.app/docs
1•lembergs•47m ago•1 comments

Now send your marketing campaigns directly from ChatGPT

https://www.mail-o-mail.com/
1•avallark•51m ago•1 comments

Queueing Theory v2: DORA metrics, queue-of-queues, chi-alpha-beta-sigma notation

https://github.com/joelparkerhenderson/queueing-theory
1•jph•1h ago•0 comments

Show HN: Hibana – choreography-first protocol safety for Rust

https://hibanaworks.dev/
5•o8vm•1h ago•1 comments

Haniri: A live autonomous world where AI agents survive or collapse

https://www.haniri.com
1•donangrey•1h ago•1 comments

GPT-5.3-Codex System Card [pdf]

https://cdn.openai.com/pdf/23eca107-a9b1-4d2c-b156-7deb4fbc697c/GPT-5-3-Codex-System-Card-02.pdf
1•tosh•1h ago•0 comments

Atlas: Manage your database schema as code

https://github.com/ariga/atlas
1•quectophoton•1h ago•0 comments

Geist Pixel

https://vercel.com/blog/introducing-geist-pixel
2•helloplanets•1h ago•0 comments

Show HN: MCP to get latest dependency package and tool versions

https://github.com/MShekow/package-version-check-mcp
1•mshekow•1h ago•0 comments

The better you get at something, the harder it becomes to do

https://seekingtrust.substack.com/p/improving-at-writing-made-me-almost
2•FinnLobsien•1h ago•0 comments

Show HN: WP Float – Archive WordPress blogs to free static hosting

https://wpfloat.netlify.app/
1•zizoulegrande•1h ago•0 comments

Show HN: I Hacked My Family's Meal Planning with an App

https://mealjar.app
1•melvinzammit•1h ago•0 comments
Open in hackernews

Show HN: Gene – a Lisp-like language built around a generic "Gene" data type

https://github.com/gene-lang/gene
36•gcao•1mo ago
Hi HN — I’ve been working on Gene, a general-purpose, homoiconic language with a Lisp-like surface syntax, but with a core data model that’s intentionally not just “lists all the way down”.

What’s unique: the Gene data type

Gene’s central idea is a single unified structure that always carries (1) a type, (2) key/value properties, and (3) positional children:

(type ^prop1 value1 ^prop2 value2 child1 child2 ...)

The key point is that the type, each property name, each property value, and each child can themselves be any Gene data. Everything composes uniformly. In practice this is powerful and liberating: you can build rich, self-describing structures without escaping to a different “meta” representation, and the AST and runtime values share the same shape.

This isn’t JSON, and it isn’t plain S-expressions: type + properties + children are first-class in one representation, so you can attach structured metadata without wrapper nodes, and build DSLs / transforms without inventing a separate annotation system.

Dynamic + general-purpose (FP and OOP)

Gene aims to be usable for “regular programming,” not only DSLs:

* FP-style basics: fn, expression-oriented code, and an AST-friendly representation

* OOP support: class, new, nested classes, namespaces (still expanding coverage)

* Runtime/tooling: bytecode compiler + stack VM in Nim, plus CLI tooling (run, eval, repl, parse, compile)

Macro-like capability: unevaluated args + caller-context evaluation

Gene supports unevaluated arguments and caller-context evaluation (macro-like behavior). You can pass expressions through without evaluating them, and then explicitly evaluate them later in the caller’s context when needed (e.g., via primitives such as caller_eval / fn! for macro-style forms). This is intended to make it easier to write DSL-ish control forms without hardcoding evaluation rules into the core language.

Optional local LLM backend via llama.cpp

I also added an optional local LLM backend: Gene has a genex/llm namespace that can call local GGUF models through llama.cpp via FFI (primarily because I wanted local inference without external services).

Repo: https://github.com/gene-lang/gene

I’d love feedback on:

* whether the “type/props/children” core structure feels compelling vs plain s-exprs,

* the macro/unevaluated-args ergonomics (does it feel coherent?),

* and what would make the project most useful next (stdlib, interop, docs, performance, etc.).