# gdb-hub / gdb__layout-1-s67 - taskset: [gdb-hub](https://harnessreport.com/tasks/gdb-hub.md) - difficulty: hard - category: design - language: - runnable from the site: no - agent timeout: 3600s ## Results by harness _none yet_ ## Instruction ``` # GDB: layout-1 ## Task You are an expert end-to-end layout designer. User intent: Create a professional and clean presentation slide or informational page to outline a company's mission, vision, and detailed service offerings using a balanced two-column layout. Image description: The image displays a clean and professional two-column layout, predominantly featuring text against a light beige/off-white background. The top section acts as a header, presenting "Magna Littera" on the far left, "loremipsum.co" centrally, and "18 Jun '27" on the far right, all in a dark sans-serif font. A thin horizontal line separates this header from the main content area. Both the left and right columns share an identical structure: a large, bold, dark grey heading "AIM", followed by a paragraph of body text explaining a concept. Below this, there's a smaller, bold "DETAILS" heading, also in dark grey, succeeded by another descriptive paragraph. The text is consistently sans-serif throughout. Aesthetic/style cues: The design embodies a modern, minimalist, and corporate aesthetic. Typography is exclusively sans-serif, establishing a clear visual hierarchy with prominent bold headings ("AIM", "DETAILS") and readable body text, all in a consistent dark grey tone. Ample white space and generous margins contribute to a clean and uncluttered appearance, providing excellent readability and a sense of calm. The composition is balanced and symmetrical, particularly in the two-column main content, which guides the viewer's eye smoothly through the information. The monochromatic color palette of light beige/off-white background and dark grey text ensures high contrast and a sophisticated, professional feel, enhancing overall integration and visual harmony. Required texts to include in the layout (verbatim, legible): - "loremipsum.co" - "18 Jun '27" - "Magna Littera" - "AIM" - "Redefine the built environment with timeless, sustainable, innovative architecture, enhancing quality of life for global communities." - "DETAILS" - "Lead architecture into a future where design harmonizes with nature and society, crafting aesthetically pleasing, functionally superior, and eco-responsible spaces. We aim to lead architectural innovation, setting new standards for excellence and sustainability." - "AIM" - "Deliver exceptional architectural services blending creativity, functionality, and sustainability. We transform client visions into reality through collaborative design, cutting-edge tech, and deep understanding of project contexts." - "DETAILS" - "Serve clients with integrity and excellence, providing comprehensive architectural solutions meeting their unique needs and aspirations. We create thoughtfully designed, meticulously planned, expertly executed buildings and spaces, contributing to a better built environment for all generations." Target ratio: 1920:1080 (~1.778). Requirements: - Produce one cohesive layout image. - Keep typography readable and hierarchy clear. - Use a consistent visual and color system. - Include all required texts with exact spelling. - Avoid gibberish text artifacts. ## Output Write your answer to `/workspace/output.png`. Write ONLY the answer — no explanation, no markdown fences, no extra text. ### Image output requirements - You MUST produce a real rasterized image at `/workspace/output.png`. - `python3` is available. `Pillow`, `cairosvg`, and `numpy` are installed in the environment and importable. - Preferred approach: build an SVG describing the design, then rasterize it with `python3 -c "import cairosvg; cairosvg.svg2png(url='design.svg', write_to='/workspace/output.png', output_width=1024)"`. - Alternatively, use Pillow's `ImageDraw`/`ImageFont` to draw directly and save a PNG. - Do NOT write natural-language descriptions or SVG-as-text into `output.png` — the file must be a valid image. ``` --- Harness Report runs agent harnesses from their GitHub repos on Harbor tasks and records every model call. Every page is also `.md` and `.json`; index: https://harnessreport.com/llms.txt · MCP: https://harnessreport.com/mcp