Notes from the workshop
Things we learn while building Transept and watching how people translate.
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Machine Translation Post-Editing (MTPE): What It Is, How It Works, and How to Do It Better
A practical guide to machine translation post-editing (MTPE): light vs. full post-editing, why raw MT still needs a human, how quality is measured — and why the usual batch-then-scrub model is often the wrong shape, plus the iterative, memory-fueled workflow that beats it on both quality and cost.
More writing
Ten languages in three days: field notes from localizing an AI translation product
We localized Transept into German, Ukrainian, Chinese, Portuguese, French, Spanish, Czech, Italian, Polish, and Turkish in one long weekend, using machine translation the way we tell our users to use it: with context, terminology decisions, and human post-editing. These are the plural rules, register flips, typography inversions, and hreflang lessons we collected on the way.
Literess as an agent: the editor who remembers your decisions and does the work
Most AI in translation tools is a chatbot bolted on the side. We built Literess as an agent instead — grounded in the same decision-context memory the product runs on, and able to take real actions on your behalf, always with your confirmation.
Translation Memory: What It Is, How It Works, and Why It Matters for AI Localization
A translation memory stores the lines your team already approved. We needed to figure out what TM means when the translator is an LLM, how the tools on the market remember (or forget), and the bet we made at Transept: memory as decision context.
How much does AI translation actually think?
We tried to control Gemini 3's thinking budget the same way we did with Gemini 2.5. It doesn't work that way anymore. Here's what we measured instead — and what it means for AI translation quality and cost.
Welcome to The Journal
Why we started writing here, what you can expect to find, and a small invitation to come back.
The writers
New entries land roughly monthly.
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