@DuarteToaster · X

@Ex3NDR · X

Few days ago Happy had a birthday. Tried to refresh one of the most neglected part of it: our Happy Website: also i built a new harness called “rig”. i started to use at work recently instead of codex/claude code and i liked how it does work. i want to share this with everyone. it is a codex and cause code reimplemented using Pi, but keeping native prompts and expanded a little for uniformity (like added workflows to codex yay!). when something new released that is worthy of porting - i ask Sol to mim for prompts or read sources directly and i can have a feature within an hour. why this is needed? i was constantly blocked by the needing of integrating a lot of models together, which each has their own harness or many doesn’t have any, but i didn’t want to go too far from native and don’t like the idea of extensions - i want experience to be the same everywhere. i think configuration > extensions. more soon

@BobbySBX · X

We just dropped Assay v0.2.6, the open source evaluation engine powering Most registries judge an AI tool by its README. We built Assay to actually run skills, MCP servers, and plugins inside isolated sandboxes with live models to observe what they do before you install them. What is new in v0.2.6: - NVIDIA API Catalog provider: Native support is here, allowing you to use NVIDIA driver and judge models to run behavioral benchmarks and multi-tier audits. - Zero runtime dependencies: Still a self-contained ~240 KB tarball that speaks directly to @nvidia , @AnthropicAI , @OpenAI , @OpenRouter , and local HTTP endpoints without SDK bloat. - Adversarial and static checks: Out of the box verification against prompt injection, tool scope creep, destructive shell commands, and supply chain drift. Every tool on is tested through Assay. That means 4,200+ skills, MCP servers, and plugins are commit-pinned, graded on actual runtime behavior, and rea

@manixh · X

@shawkalent · X

我开发了两个平台，一个是开源平台：

@bashops_dev · X

Building BashOps Radar—repository intelligence for developers. Helping developers identify contribution opportunities that can lead to contracts, paid sprints, and long-term work.

u/12qwww · Reddit

I built a live benchmark to see which AI actually writes the best frontend code Hey everyone! I built OpenVibeEval because I was tired of "vibe-checking" AI-generated frontend code. I wanted to know which model actually produces the most accessible and clean React/Tailwind output. What I built: •A leaderboard of 24 models (Claude, GPT, DeepSeek, etc.) ranked by axe-core accessibility scores. •A Harness Comparator to show how different system prompts change the same model's output. •

@milonspace · X

A specification nobody can find is functionally the same as no specification.

@Galliard232634 · X

Come study with me on

@spec_tacul_ar · X

Specifications for humans and AI.

@fedeponte1 · X

my first saas. i think i built something people actually want and completely failed at the business side. would genuinely love help. openpaper ( writes full, cited research papers from a prompt. research, outline, writing, real citations, export. the honest numbers: - ~2,300 signups - ~1,500 generated a full paper. people actually use it. - ~5 have ever paid. ~$45/mo. activation is great, revenue is basically zero. i've been staring at this for weeks. the paywall is on export (pay ~$12 to download the pdf). but the full cited paper is right there on screen, free, you can read it and copy-paste it. so i'm charging for the gift wrap after handing over the gift. almost nobody even reaches checkout. 76% generate exactly one paper and leave. one-and-done, so subscriptions make no sense. the audience is mostly students and researchers on personal gmail, all over the world, almost none on university or company emails. traffic is x and github. so i genuinely can't tell

mhmt_dmr · HN

MCP Gateway – Turn existing APIs and databases into MCP servers