ADD blog content

Author: mariaob1201

_build/.doctrees/applications/ag-forecasting-api.doctree

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: ff326529dec566ff85861a347816d852
+config: fe0d8d89ecad092c9e1a52c98b324dc8
 tags: 645f666f9bcd5a90fca523b33c5a78b7

_build/.doctrees/blog/index.doctree

@@ -59,9 +59,16 @@
     <script>const THEBE_JS_URL = "https://unpkg.com/thebe@0.8.2/lib/index.js"; const thebe_selector = ".thebe,.cell"; const thebe_selector_input = "pre"; const thebe_selector_output = ".output, .cell_output"</script>
     <script>DOCUMENTATION_OPTIONS.pagename = 'README';</script>
     <link rel="index" title="Index" href="genindex.html" />
-    <link rel="search" title="Search" href="search.html" />
+    <link rel="search" title="Search" href="search.html" /> 
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
-  <meta name="docsearch:language" content="en"/>
+  <meta name="docsearch:language" content="en"/> 
+<link
+  rel="alternate"
+  type="application/atom+xml"
+  href="blog/atom.xml"
+  title="OpenLambda Blog"
+/>
+  
   </head>
 
 
@@ -175,8 +182,11 @@
     <div class="bd-toc-item navbar-nav active">
         <ul class="nav bd-sidenav">
 <li class="toctree-l1"><a class="reference internal" href="worker.html">Worker</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="blog/index.html">Blog</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="simple">
+</ul>
+</details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="applications/index.html">Applications</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
-<li class="toctree-l2"><a class="reference internal" href="applications/ag-forecasting-api.html">🌾 Agricultural Forecasting API</a></li>
+<li class="toctree-l2"><a class="reference internal" href="applications/ag-forecasting-api.html">Agricultural Forecasting API</a></li>
 </ul>
 </details></li>
 </ul>
@@ -342,8 +352,7 @@ <h2> Contents </h2>
 
 <div id="searchbox"></div>
                 <article class="bd-article">
-                  
-  <section class="tex2jax_ignore mathjax_ignore" id="openlambda-github-pages-myst-markdown-site">
+                   <section class="tex2jax_ignore mathjax_ignore" id="openlambda-github-pages-myst-markdown-site">
 <h1>OpenLambda GitHub Pages — MyST Markdown Site<a class="headerlink" href="#openlambda-github-pages-myst-markdown-site" title="Link to this heading">#</a></h1>
 <p>This repo deploys automatically to GitHub Pages using <a class="reference external" href="https://jupyterbook.org">Jupyter Book</a>
 with <a class="reference external" href="https://myst-parser.readthedocs.io/">MyST Markdown</a> as the technical documentation of Open Lambda.</p>
@@ -408,6 +417,9 @@ <h2>Local Preview<a class="headerlink" href="#local-preview" title="Link to this
     }
     </script>
     <script>kernelName = 'python3'</script>
+<div class="section ablog__blog_comments">
+   
+</div>
 
                 </article>
 

_build/.doctrees/blog/post/2026-05-18-hello-world.doctree

@@ -1,145 +1,11 @@
-# 🌾 Agricultural Forecasting API
+# Agricultural Forecasting API
 
-FastAPI-based service for **crop disease risk forecasting** and **winter rye biomass estimation** using multi-source weather data.  
-Developed by the University of Wisconsin–Madison Data Science Institute.
+FastAPI-based ASGI service for crop disease risk forecasting using multi-source weather data, developed by the University of Wisconsin–Madison Data Science Institute and integrated with Open-Lambda technology.
 
 ---
 
 ## Overview
 
-The API provides geospatial agricultural intelligence for Wisconsin, combining weather data with validated agronomic models.
-
-### Key Features
-- 🌽 Crop disease risk forecasting (corn & soybean)
-- 🌱 Winter rye biomass estimation
-- 🌦 Multi-source weather integration (IBM EIS, WiscoNet, NOAA)
-- 📍 Coordinate and station-based queries
-- 🗺 GeoJSON outputs for GIS applications
-- ⚡ Async batch processing for multi-station analysis
-
----
-
-## System Architecture
-
-The system is structured into four main layers:
-
-- **API Layer (FastAPI)**  
-  Handles incoming requests and routing (`/ibm`, `/wisconet_g`, `/models`)
-
-- **Data Layer**  
-  - IBM EIS: high-resolution global weather API  
-  - WiscoNet: Wisconsin mesonet station network  
-
-- **Processing Layer**  
-  Weather normalization, unit conversion, GDD calculation, rolling features
-
-- **Model Layer**  
-  Disease risk models and winter rye biomass model
-
----
-
-## Core Modules
-
-- Weather ingestion (IBM + WiscoNet)
-- Disease risk modeling
-- Winter rye biomass estimation
-- Async pipeline orchestration
-- GeoJSON response formatting
-
----
-
-## API Endpoints
-
-### IBM Forecasting (Coordinates)
-`GET /v2/ag_models_wrappers/ibm`
-
-Returns disease risk + biomass using IBM weather data.
-
----
-
-### WiscoNet Forecasting (Stations)
-`GET /v2/ag_models_wrappers/wisconet_g`
-
-Returns station-based time-series disease risk and biomass.
-
----
-
-### Model Metadata
-`GET /v2/ag_models_wrappers/models`
-
-Returns available disease and biomass models.
-
----
-
-## Disease Models
-
-- **Tarspot (corn)** – humidity and temperature-based risk
-- **Gray Leaf Spot (corn)** – temperature + dew point model
-- **Frogeye Leaf Spot (soybean)** – GDD + rainfall model
-- **White Mold (soybean)** – precipitation and soil moisture model
-
----
-
-## Winter Rye Biomass Model
-
-Predicts dry biomass (lb/acre) using:
-
-- Growing Degree Days (0°C base)
-- Planting date (day-of-year)
-- Fall precipitation
-- Logistic growth curve
-
-### Outputs
-- Biomass (lb/acre)
-- Color class (gray / yellow / green)
-- Interpretation message
-
----
-
-## Data Sources
-
-### IBM Environmental Intelligence Suite (EIS)
-- High-resolution global weather data
-- Hourly forecasts and historical data
-- Requires authentication
-
-### WiscoNet
-- Wisconsin mesonet (~100 stations)
-- Daily weather observations
-- Public API access
-
----
-
-## Response Format
-
-All outputs are returned as **GeoJSON FeatureCollections**, including:
-
-- Weather variables
-- Disease risk scores
-- Biomass predictions
-- Station metadata
-
----
-
-## Performance Features
-
-- Async multi-station processing
-- Cached weather and station data (6h–7d TTL)
-- Parallel risk computation
-- Optimized data aggregation pipeline
-
----
-
-## Setup
-
-```bash
-git clone <repo>
-cd ag_forecasting_api
-
-python -m venv .venv
-source .venv/bin/activate
-
-pip install -r requirements.txt
-
-uvicorn app:app --reload
-```
+The API provides geospatial agricultural intelligence for Wisconsin, combining weather data with validated crop disease forecasting models.
+The tool is now entering its next phase through integration with OpenLambda technology.
+For reference, please check the documentation [here](https://github.com/UW-Madison-DSI/ag_forecasting_api.git).

_build/.doctrees/environment.pickle

@@ -0,0 +1,11 @@
+# OpenLambda Blog
+
+   News, releases, and technical deep-dives from the OpenLambda project.
+
+```{postlist} 10
+   :format: "{title} — {author}, {date}"
+   :date: "%B %d, %Y"
+   :list-style: circle
+   :excerpts:
+   :sort:
+```

_build/.doctrees/index.doctree

@@ -0,0 +1,13 @@
+---
+blogpost: true
+date: 2026-05-18
+author: open-lambda
+category: Releases
+tags: announcement, openlambda
+language: en
+---
+
+# Hello, OpenLambda Blog
+
+Welcome to the new OpenLambda blog. We'll use this space to share release
+notes, performance experiments, and design discussions.