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Hidr4lisk_Tech

Open Source AI Ecosystem Tracker

Created by Federico Furgiuele GitHub Live LinkedIn

English | Español


English

Open source AI, all in one place. Models, releases and news — every 6h, no cookies, no servers.

Modules

# Module Description
00 Digest Auto-generated weekly summary: news count, release count, new models, top items at a glance
01 News Feed Posts from official blogs and community (HuggingFace, Meta, Google, Microsoft, The Gradient, Qwen, r/LocalLLaMA, Simon Willison)
02 Changelog Release timeline for tools (Ollama, llama.cpp, vLLM, Transformers, LocalAI, whisper.cpp, Unsloth, SGLang) and models (via HuggingFace API)
03 Comparator Sortable table: context window, parameters, multimodal support, license, release date
04 Sources Full list of data sources with links and applied filters — includes language disclaimer

How the data pipeline works

  1. scripts/fetch.py runs via GitHub Actions cron (0 */6 * * *)
  2. Fetches RSS feeds, GitHub Releases API and HuggingFace Models API
  3. Merges with existing data (deduplication by URL / release ID)
  4. Writes data/news.json, data/models.json, data/changelog.json
  5. If files changed → commits to main → triggers deploy to GitHub Pages

All data lives as plain JSON in the repo. The browser fetches it directly from the same origin — no external API calls at runtime.

Tech stack

Layer Tool
Frontend Vanilla JS (ES modules)
Styles Tailwind CSS v3 + custom design tokens
Build esbuild (JS) + Tailwind CLI (CSS)
Data Python 3.11, requests, pyyaml, python-dateutil
Deploy GitHub Actions → GitHub Pages
i18n JSON-based, ES/EN toggle, no external library

Data sources

All fetch sources live in data/map.yaml under feeds: (news_rss, tool_repos, hf_orgs) — edit that file to add or remove a source, no code changes needed.

News (RSS) — capped at 12 items per source to keep the feed balanced

  • HuggingFace Blog — huggingface.co/blog/feed.xml
  • Meta Engineering — engineering.fb.com/feed/ (filter: llama, ai, model)
  • Google AI — blog.google/innovation-and-ai/technology/ai/rss/ (filter: gemma, llm, open)
  • Microsoft Research — microsoft.com/en-us/research/feed/ (filter: phi, llm, open)
  • The Gradient — thegradient.pub/rss/
  • Qwen Blog — qwenlm.github.io/blog/index.xml
  • r/LocalLLaMA — reddit.com/r/LocalLLaMA/.rss
  • Simon Willison — simonwillison.net/atom/everything/ (filter: llm, model, local…)

Releases (GitHub API)

  • ollama/ollama · ggml-org/llama.cpp · vllm-project/vllm · huggingface/transformers · mudler/LocalAI · ggml-org/whisper.cpp · unslothai/unsloth · sgl-project/sglang

Models (HuggingFace API)

  • Orgs tracked: meta-llama, mistralai, google, microsoft, EleutherAI, stabilityai, Qwen, deepseek-ai, allenai, nvidia, 01-ai, HuggingFaceTB, ibm-granite, CohereForAI

Run locally

git clone https://github.com/hidr4lisk/tech.git
cd tech

# Fetch data
pip install -r scripts/requirements.txt
python scripts/fetch.py

# Build frontend
npm install
npm run build

# Serve
npx serve .

Contributing to the model map

The model comparator is sourced from data/map.yaml. To add a company or model, open a PR editing that file:

companies:
  - id: your-org
    name: Your Org
    type: lab           # lab | platform | tool | researcher
    description_es: "..."
    description_en: "..."
    models:
      - id: your-model-id
        name: Your Model Name
        params_b: 7
        context_window: 128000
        open_weights: true
        license: apache-2.0
        multimodal: false
        hf_model_id: org/model-id-on-huggingface

Privacy

  • No cookies · no analytics
  • No external requests at runtime (Google Fonts in CSS only)
  • All data served from the same GitHub Pages origin
  • Source is fully auditable

License

MIT


Español

IA open source, todo en un lugar. Modelos, releases y noticias — cada 6h, sin cookies, sin servidores.

Módulos

# Módulo Descripción
00 Digest Resumen semanal auto-generado: cantidad de noticias, releases, nuevos modelos y los items más recientes
01 Noticias Posts de blogs oficiales y comunidad (HuggingFace, Meta, Google, Microsoft, The Gradient, Qwen, r/LocalLLaMA, Simon Willison)
02 Changelog Timeline de releases de herramientas (Ollama, llama.cpp, vLLM, Transformers, LocalAI, whisper.cpp, Unsloth, SGLang) y modelos (via HuggingFace API)
03 Comparador Tabla ordenable: contexto, parámetros, soporte multimodal, licencia, fecha de release
04 Fuentes Lista completa de fuentes con links y filtros aplicados — incluye disclaimer de idioma

Cómo funciona el pipeline de datos

  1. scripts/fetch.py corre via GitHub Actions cron (0 */6 * * *)
  2. Consume feeds RSS, la API de GitHub Releases y la API de HuggingFace Models
  3. Fusiona con datos existentes (deduplicación por URL / ID de release)
  4. Escribe data/news.json, data/models.json, data/changelog.json
  5. Si los archivos cambiaron → hace commit a main → dispara el deploy a GitHub Pages

Todos los datos viven como JSON plano en el repo. El browser los consume directamente desde el mismo origen — sin llamadas a APIs externas en runtime.

Stack tecnológico

Capa Herramienta
Frontend Vanilla JS (ES modules)
Estilos Tailwind CSS v3 + design tokens propios
Build esbuild (JS) + Tailwind CLI (CSS)
Datos Python 3.11, requests, pyyaml, python-dateutil
Deploy GitHub Actions → GitHub Pages
i18n JSON-based, toggle ES/EN, sin librería externa

Fuentes de datos

Noticias (RSS)

  • HuggingFace Blog — huggingface.co/blog/feed.xml
  • Meta Engineering — engineering.fb.com/feed/ (filtro: llama, ai, model)
  • Google Research — blog.research.google/feeds/posts/default (filtro: gemma, llm, open)
  • Microsoft Research — microsoft.com/en-us/research/feed/ (filtro: phi, llm, open)
  • The Gradient — thegradient.pub/rss/

Releases (API de GitHub)

  • ollama/ollama · ggerganov/llama.cpp · vllm-project/vllm · huggingface/transformers · mudler/LocalAI

Modelos (API de HuggingFace)

  • Orgs rastreadas: meta-llama, mistralai, google, microsoft, EleutherAI, stabilityai, Qwen, deepseek-ai

Correr localmente

git clone https://github.com/hidr4lisk/tech.git
cd tech

# Obtener datos
pip install -r scripts/requirements.txt
python scripts/fetch.py

# Compilar frontend
npm install
npm run build

# Servir
npx serve .

Contribuir al mapa de modelos

El comparador se alimenta de data/map.yaml. Para agregar una empresa o modelo, abrí un PR editando ese archivo:

companies:
  - id: tu-org
    name: Tu Org
    type: lab           # lab | platform | tool | researcher
    description_es: "..."
    description_en: "..."
    models:
      - id: id-del-modelo
        name: Nombre del Modelo
        params_b: 7
        context_window: 128000
        open_weights: true
        license: apache-2.0
        multimodal: false
        hf_model_id: org/model-id-en-huggingface

Privacidad

  • Sin cookies · sin analytics
  • Sin requests externos en runtime (Google Fonts solo en CSS)
  • Todos los datos servidos desde el mismo origen de GitHub Pages
  • Código completamente auditable

Licencia

MIT

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Open source AI, all in one place. Models, releases and news — every 6h, no cookies, no servers

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