Benchmarking Models on Procedural Geometry: An Animated DNA Double Helix Third in a small series testing how AI models handle real builds from a cold spec. Tetris tested game logic. The solar system tested 3D math and interaction. This one tests procedural geometry and continuous animation — and it was built mobile-first, designed for a portrait phone screen before desktop. The brief: an animated 3D DNA double helix you can rotate and zoom, spinning continuously to show its twist. The hard part isn't drawing a spiral — it's the parametric math underneath. Two backbone strands have to wind around a shared axis offset by half a turn, with base-pair "rungs" connecting the correct point on each strand at every step. Get the math right and it instantly reads as DNA; get it wrong and you get a tangle. There's very little room to fake it. Built with Cline in VS Code paired with MiMo-V2.5-Pro via OpenRouter — same setup as the other two. https://www.columbiawork.com/mimo25_dna.html
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Benchmarking Models on a Visual Challenge: An Interactive 3D Solar System Tetris tests game logic. This one tests something different â 3D math, rendering, and animation. The brief: build an interactive, animated solar system you can orbit and zoom, with the Sun, all eight planets, Saturn's rings, a moon tracking Earth as Earth circles the Sun, a starfield, and visible orbit paths â correct relative motion throughout, smooth on both desktop and mobile. One spec, handed to the model cold, with the freedom to pick its own rendering approach. Like the Tetris build, this came together with Cline in VS Code paired with MiMo-V2.5-Pro via OpenRouter. The trickier requirements here are the ones that separate a model that understands a 3D scene from one that just spins dots in a circle: nested orbits (the moon has to follow Earth, not the Sun), a draggable camera that doesn't flip or trap itself, and pinch-zoom that works on a phone without scrolling the page. Getting those right in one pass is the real test. https://www.columbiawork.com/mimo25_solar.html
Visit site →Benchmarking Local + Hosted Models with a Classic: Building Tetris I've been running a small experiment to compare how different AI models handle a real coding task from scratch â not a toy snippet, but a complete, playable HTML5 Tetris with collision, rotation, line clears, scoring, level progression, and touch controls, all specced out in a single build document and handed to each model cold. This build came together using Cline (the open-source agentic coding extension for VS Code) paired with MiMo-V2.5-Pro, Xiaomi's reasoning-and-code model, routed through OpenRouter. The interesting part wasn't just the model â it was the harness. Earlier attempts with a different setup kept stalling on the file-editing step, where the model would write solid code but fumble applying it cleanly. Swapping the agent harness to Cline, same class of task, and it read the spec file itself and wrote the whole game in one pass. The lesson worth keeping: for agentic builds, the harness matters as much as the model behind it. Total cost for the run: about five cents.
Visit site →Three iterations on the Precoa Portland office wireless migration runbook, each grounded in real data rather than assumption. v1.1 incorporated findings from a full read-only Meraki Dashboard API audit (18 devices, 2 organizations, 8 open alerts, 6 wireless zones uncovered). v1.2 replaced the original ASCII topology with two proper SVG engineering diagrams âÃÂàa physical/Layer-2 cable-path view and a logical/Layer-3 trust-boundary view. After uploading a sanitized SonicWall tech support report (2.6 MB, 58k lines, secret-scanned before analysis), the design got an honest reframe: deploy guest wireless first as a low-risk Phase 1 (~45 min maintenance window, blast radius limited to visitor Wi-Fi), defer corp wireless and AWS/Azure VPN updates to Phase 2. Discovered along the way: a retired SAN segment on X4 still passing 290M+ packets, three unused legacy wireless zones, and the default public SNMP community string still configured. All findings rolled into post-project hardening backlog.
The most useful thing AI tools built today wasn't code ? it was eliminating friction from a workflow. Every manual step in publishing content to a website (edit file, commit, push, open PR, approve, merge) is now a single button. The insight: AI is most valuable not when it replaces thinking, but when it removes the tedious mechanical steps between thinking and publishing. The faster you can go from idea to live, the more you actually ship.
Built a browser-based admin console that publishes directly to GitHub via API ? no terminal, no file uploads, no copy-paste workflow. The tool writes new Dispatch entries into the site's HTML, commits to a staging branch, and auto-creates a pull request for review, all from a form behind Cloudflare SSO. Debugging the publish pipeline surfaced a classic infrastructure gotcha: two duplicate GitHub Actions workflow files from an earlier failed Azure setup, causing one check to always fail on every PR. Cleanup was straightforward once the root cause was clear ? delete the orphaned workflow file and secret, confirm no ghost resources in Azure or Cloudflare DNS. The full loop now works from any browser on any device: write, publish, review preview, merge, live in 45 seconds.
Built and deployed a full three-subdomain stack across two days using AI tools ? day one was the public journal site and a live stock portfolio dashboard pulling real-time market data via Finnhub, day two was an admin console that publishes directly to GitHub via API without touching a file manually. The admin tool calls GitHub from the browser, injects new Dispatch entries into the site, commits to staging, and auto-creates a PR for review ? all behind Cloudflare SSO. Total infrastructure cost: $0/month. Right tool, right job: Claude for architecture and code, fal.ai for imagery, v0.dev for UI layout, Cloudflare for security, GitHub Actions for deployment.
Needed a cinematic landscape image for a website. SVG and CSS couldn't cut it. Found fal.ai ? basically OpenRouter but for image models. Ran the same prompt across three models simultaneously: nano-banana-2, grok-imagine, and gpt-image-1.5. Total cost: free. Result in minutes, dramatically better than anything code could generate.
Built a live stock portfolio dashboard from scratch ? 10 positions, real-time Finnhub quotes, smart market-hours refresh logic that freezes prices after close. Zero prior front-end design experience. Wired a free API key directly into the browser behind Cloudflare SSO instead of routing through an Azure Function. Simplest architecture wins.
Deployed a full Azure Static Web Apps stack with Cloudflare Zero Trust SSO in a single session. Google OAuth, custom domain, GitHub Actions CI/CD pipeline, DNSSEC. Azure Easy Auth was a dead end ? it always routes through Microsoft AAD regardless of provider. Cloudflare Access at the edge is the right answer.
v0.dev is the right tool for UI layout and component generation ? it has real design sensibility. But it outputs Next.js/TypeScript by default, which is overkill for a static site. Always ask it to convert to a single self-contained HTML file before pulling the code. Saves an hour of untangling build pipelines.
Azure Function App creation failed on a free trial subscription ? both Consumption and Flex Consumption plans are quota-blocked. The fix isn't to fight it, it's to rethink the architecture. Calling Finnhub directly from the browser behind SSO is simpler, faster, and free. The Function App was solving a problem the SSO already solved.
Claude is better at wiring and logic than visual design. SVG landscape art, CSS gradient mountains, polygon trees ? it tries hard but the output doesn't match what a real design tool produces. Know the tool's limits: use AI for architecture, APIs, and code. Use purpose-built image models for anything that needs to look good.