Engineering notes, product thinking, and field reports from building a technical translation engine.
DevSpeak's FREE plan lets you connect your own AI provider key, which removes the hourly request limit that applies to free accounts using DevSpeak's own models. Supported providers include OpenAI, Anthropic, Gemini, OpenRouter, and other OpenAI-compatible APIs that DevSpeak can reach, such as DeepSeek, Mistral, and Cerebras. Creating an account requires no credit card, and the optional 30-day VIBECODER trial requires no payment method and returns to the FREE plan at the end without a charge.
DevSpeak is an AI platform that turns informal requirements into structured technical specifications for any language or framework, across a web app, a VS Code extension, a CLI, and an MCP server. It works in three stages: Optimize, Translate, Refine. Context Projects ground output in project files and connectors under a closed-world rule. Missing details go to an Assumptions & Open Questions section. XML output is validated before delivery, automatic failover reroutes around provider outages, and every version is kept as an immutable snapshot.
Comparison of DevSpeak against a well-prompted general-purpose chatbot for generating technical specifications. Both produce competent output from a single prompt. The difference is that audience is a typed parameter in DevSpeak rather than a sentence inside a prompt, so the same input reliably produces a different document for a Junior Dev than for an SRE. Includes a real side-by-side run and the exact reproducible prompt for the chatbot comparison.
Engineering write-up on building a provider-agnostic LLM gateway: why to define the configuration contract before the abstraction, how a single generic adapter covers most vendors, why a time reserve is required so a slow primary does not starve every retry, why the model allowlist must be enforced server-side, and how reasoning models can return an empty but successful response unless reasoning output is explicitly disabled.
Technical analysis of context in generative and agentic AI, arguing that context is not prompt length but a set of layers distinguished by lifetime and trust level. Covers the distinction between reference data and directives, prompt-injection risk from retrieved material, why retrieval should degrade gracefully instead of blocking a request, and context discipline in data analysis. Documents how DevSpeak injects four context layers — project grounding, account instructions, workspace context, and user memory — into Stage 2 translation and Stage 3 refinement.
DevSpeak is an AI translation engine that turns informal requirements into formal, audience-specific technical specifications in three stages: Optimize polishes raw input; Translate produces the spec for a chosen audience, tech context, format, and tone; Refine revises generated output from feedback. This article maps five documented AI-coding failures — the 2025 Replit database deletion, Samsung's 2023 ChatGPT code leak, package-hallucination slopsquatting, CVE-2025-48757, and the UK AI Security Institute's 2026 agent incident — to under-specified instructions.
Analysis of why software technical specifications fail, arguing the root cause is a translation gap between stakeholders and engineers rather than insufficient documentation effort. Identifies three failure modes: implied context, single-audience writing, and ambiguity that survives review. Proposes audience-specific specification generation as the corrective.