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Codocly Is Not Just a Documentation Tool Anymore — We’re Building an AI-Native Developer Platform When we started building Codocly, the problem looked simple. Developers write code, but documentation is often treated as something they have to do after the real work is finished. You build an API.
We spend a lot of time discussing new AI tools, comparing models, building things, and exploring what’s possible. I do this too. But I want to hear more about what happens after the experimentation. What problem has AI helped you solve, and what difference did that make in your life?
Narrow steps that run on every request, on a model stronger than that step needs, are candidates for a cheaper model. Measured spend then shows where the savings lie. Prove the step on the strongest model, move that one job to a smaller or fine-tuned model, and keep the product metric where it was.
This is a submission for the Sanity Challenge, Path Two: Vibe-Code Something Strange What I Built The Museum of Unfinished Futures is a small museum of made-up inventions from futures that never happened. There is a vending machine that sells extra Mondays.
How I built an open-source WhatsApp claims bot for insurers in the DRC In Kinshasa, nobody fills in an online claim form. If your car is hit by a minibus on Boulevard du 30 Juin, you take photos and send a WhatsApp message to whoever you know at your insurer.
Limit the agent to the conversation that opened the task, keep a citation on every message you use, and search in passes before you summarize. For a project catch-up, Jaime DeLanghe described one keyword spinning off five related searches, then a cluster of interest, then a pared-down set, then the…
This is a submission for the Sanity Challenge, Path Two: Vibe-Code Something Strange. What I Built Veyd is a project workspace for researching documents with a clear path back to the source.
An AI coding agent can inspect your repository, spin up a new endpoint, modify the database schema, wire the frontend, generate unit tests, handle errors, and produce a migration—all in under ten minutes.
From Comics to Code: Building MyZubster One Piece of Evidence at a Time It started with a comic. N4K48 visually represented a journey: a software idea taking shape and imagining its path into the MyZubster metaverse. Those panels were not screenshots of a finished product.
The Problem Managing unstructured consultation notes and inbound client leads often takes hours of manual review. Relying on public cloud AI APIs to parse sensitive notes introduces data privacy risks, potential data leakage, and recurring API costs.
Its test suite passed six out of six. Then I pointed it at thirteen real crash reports from my own server and it got three of eight. I asked Claude to build and ship a commercial product with as little help from me as possible — write the code, write the tests, set up the storefront, publish it.
Most businesses run on disconnected tools: one for accounting, one for customers, one for the online store, one for messaging. Data gets duplicated, processes break between systems, and teams spend hours moving information by hand.
Most small businesses don't fail at AI because the models are weak. They fail because they automate the wrong thing first, or they automate a process that only exists in someone's head.
Businesses that sell technical products answer the same kind of question every day. "What's the accuracy on this range?" "Can it measure through a coating, and how thick?" "Does it come with a calibration certificate?" The answers sit in product datasheets and manuals.
The first week of using a coding agent against your company's API is always the same: paste the docs URL into the prompt, correct the base path, correct the auth header, correct the pagination type, correct the envelope wrapper, repeat in every new session and every new tool.
In this video: 0:00 Do 1M windows end choosing? No 0:19 Prompt engineering is half right 1:25 Two reflexes: polish or dump 2:34 Personas, stuffing and the experiment 5:15 Selection, order and length 8:15 Tools, schemas and examples 10:58 Costs, and when stuffing is fine 12:37 Prompt, context and…
The flagship model of modern deterministic-probabilistic decision engines does not write you a paragraph. It does not tell you a story, apologize for its limitations, or generate a list of caveats. It answers a typed question and hands you back a number.
Disclosure: this post is by the Wanloria team (free, no ads, no affiliate links). It was drafted with AI assistance and fact-checked against the live site, per dev.to's guidelines on AI-assisted posts.
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