How AI Model Waddle handles evidence
Our practical standard for separating public facts, direct measurements, operating experience, inference, and community reports.
AI Model Waddle · Independent Field Notes
Waddling through AI so you don’t have to.
We track new models, coding agents, and workflows to see what actually works, what breaks after the demo, and what’s worth your time.
Waddle Dispatch Desk
Notebook open. Claims tested. Webbed feet on the ground.
The Beat
Models
Real task capabilities, not synthetic leaderboard brag sheets.
Coding
Autonomous agents tested inside living, messy repositories.
Workflows
What survives production when the demo video finishes playing.
Fresh from the pond
Latest Field Dispatches
Direct notes, verified facts, and first-party observations from hands-on testing. No PR fluff, no synthetic filler.
Dispatches in field review
Real tests take real time.
We don’t publish synthetic benchmark regurgitations or daily news summaries. New dispatches on emerging models, coding agents, and real-world workflows drop as soon as hands-on testing and evidence review are complete.
Field Radar
What we track across the pond.
Four active coverage beats. We focus strictly on the AI systems people use to build software, run workflows, and make engineering decisions.
01Beyond benchmark hype
Frontier & Open Models
Context limits, latency, reasoning tokens, and API economics. We evaluate models against actual task completion rather than academic test scores.
Active on radar:
02Software engineering under automation
Coding Agents & Dev Tools
How autonomous agents handle repo mapping, multi-file diffs, test suite loops, and human code review in living production codebases.
Active on radar:
03From demo script to repeatable pipeline
Workflows & Tool Use
Model Context Protocol (MCP), tool call reliability, multi-step agent orchestration, and where brittle prompt chains fall apart.
Active on radar:
04Apples-to-apples field trials
Empirical Tests & Comparisons
Task-shaped head-to-head evaluations under documented conditions. We share reproducible methods, prompts, and failure artifacts.
Active on radar:
The Waddle Standard
“We test what we can, label what we haven’t, and tell you where the evidence came from.”
Vendor claims aren’t benchmarks. Demos aren’t production. Every claim on AI Model Waddle is explicitly tied to its source—whether documented public fact, first-party measurement, operating experience, or reasoned inference.
Claims we label
- 01Public factDocumented vendor specs, dates, and API limits.
- 02First-party measurementReproducible numbers from our own test rigs.
- 03Operating experienceObservations from living codebases and workloads.
- 04InferenceReasoned interpretations explicitly marked as such.
- 05Community reportAttributed external findings we haven’t verified.
About the publication
Serious fieldwork. Slightly unserious bird.
AI Model Waddle is an independent publication for software builders, technical operators, and engineering teams putting AI models and agents to work.
We don’t do sponsored product hype, AI panic, or generic prompt lists. We run systems against real tasks, note the failures, and publish field notes you can rely on when making build and operate decisions.