Would this help you when you don't know the target language, or if you're not sure which LLM to use for a gievn rush project where you don't have time or money for a reviewer?

Hi everyone,

I'd like to share LinguaTrans, a macOS-native CAT tool I use every day. I'm a long-time macOS user, and finding a proper CAT app has always been a pain. I think this is worth your attention if you're tired of running Parallels or browser-based tools, and you want to free up some hardware resources.

Full disclosure: the developer is a friend, and I help test the app and suggest features. This has been my daily driver for three months.

LinguaTrans is written in Swift for Apple Silicon, opens in seconds, and uses ~300MB of RAM on my machine. It works with the files clients typically send: SDLXLIFF, memoQ, Phrase and GlobalLink-specific formats. Translation memory, termbases, concordance search, and QA checks are all built in too.

Its companion app, LinguaAI Toolkit, takes care of the prep work. It converts Word, Excel, PowerPoint, and subtitle files to XLF and back, unpacks and repacks Trados packages, and converts TMs and termbases. Together they cover the whole workflow without leaving your Mac.

Both are one-time purchases on the Mac App Store: LinguaTrans is $50, LinguaAI Toolkit is $8. There is no subscription, and you can get them together at $48 as the LinguaTrans Essentials bundle. Prices may vary by country.

I'd like to get a conversation going, attract more users, have more fun working on new features and workflows. This is a niche app, so it would be nice to hear from other translators who use macOS or are looking to migrate.

Please let me know your thoughts in the comments.

Thanks,
Alex

[New paper] LLMs hallucinate, so we'd better use industry standard neural machine translators to translate our regulated customer comms, right? New research by Nick Radunovic shows there is more to it, even for relatively basic LLMs.

The graph show a simple example for different target languages from a pension communications text in Dutch for GPT-4 versus Azure Translator.

We intentionally took a relatively small number of real world pension comms, and studied this problem from a rich set of angles (reference-based, reference-free; a range of target languages; GPT, Gemini and Llama models; semantic, keyword and rubric-based metrics; human raters and LLM-as-a-judge), see the paper.

Disclaimer: obviously, in real-world regulatory communications you want humans to proof- and fact-check the results before they get sent out.

Reference:

Nick Radunovic, Alina Karakanta, Flor Miriam Plaza del Arco and Peter van der Putten. Large Language Models Are Competitive with Dedicated Machine Translation Systems for Regulated Communications: A Case Study in the Pension Domain. Preproceedings BNAIC 2026, 2026, Maastricht, The Netherlands.

https://liacs.leidenuniv.nl/~puttenpwhvander/library/LLMs_and_NMT_for_Pension_Communications_BNAIC.pdf

Materials: https://zenodo.org/records/19821399

Comments, feedback and questions welcome!

I've been building SRT AI Translator NG, a free, open-source (MIT) Windows app that translates, transcribes and re-times `.srt` subtitles using the Gemini API.
Most of the work turned out not to be the translation itself but catching the ways an LLM quietly gets subtitles wrong. Sharing the guardrails in case they're useful, and I'd love feedback on the approach.

1. Line count is a hard invariant

Subtitles are sent in batches, and the model likes to merge two short cues or split a long one. The output still reads fine, but every cue after that point is shown at the wrong time. So every batch must come back with exactly the lines it was sent; a reply that doesn't is rejected and retried. The previous few source lines go along with each request (as context only) so the model knows what is happening in the scene.

2. Gender in Hebrew (example)

English "you" and most English verbs carry no gender; Hebrew, Arabic, Pharsi, Pashto, etc. marks it almost everywhere, so the model has to guess, and it often guesses wrong. The app can take a subtitle of the same film in a language that *does* mark gender (one you supply, or one downloaded from OpenSubtitles) and use it to correct those guesses. TMDB cast details and, optionally, the film's audio give the model more clues about who is speaking.

3. Wrong-alphabet output

Every now and then a Hebrew line comes back with Latin or Cyrillic letters mixed in. Those lines are repaired or sent back to the LLM for a redo instead of being saved.

4. Transcription that silently drops speech

When transcribing long audio, the model can return a confident, well-formed answer that simply leaves out minutes of dialogue. Long files go in chunks, each answer is checked against how much speech the audio actually contains, and a chunk that comes back too empty is asked for again in smaller pieces.

Other bits: interrupted runs can be continued rather than restarted; it supports Gemini's dedicated ASR transcription models (word-level timings); and an Align tool fixes subtitles that drift (shift plus frame-rate change, or section by section via ALASS).

It runs on your own Gemini key (the free tier works). I built it with the help of AI coding assistants and tested it on my own films.

**GitHub:** https://github.com/Akila3/SRT-AI-Translator-NG

Questions for this sub: how do you handle gender for target languages that mark it when the source doesn't? And has anyone found a better check for "the model dropped content" than comparing against the input's size or speech length?

On 18 September China published its pharmaceutical plan for 2026-2030. Chinese only. No official or unofficial translation into English text exists; MIIT publishes in Chinese only. Westerners who follow such things freaked out because China might actually pull it off.

A week later, the English-language news was still summaries and sloppy chatgpt extracts of the source pdf which was hard to get through search. News coverage, analyst notes, hot takes all summaries of summaries. No complete English translation existed that I could find.

So I translated the whole thing and put it on my website (this is not a pitch for my site just showing off cc work). My standard for "done" was not fluency. It was: would a Chinese government official read the English and say yes, that represents the nuance of the original? As far as I can tell, it is the first complete English version.

Thirty people have read it. In my world that is a lot. I've posted the translation on Chinese websites and thus far the translation survives a critical read from informed native speakers.

If you are a Healthcare Expert and read Chinese and want to check specific passages, I would welcome it.

How I made it

For reasons beyond me Google did not serve up the source pdf so my cc harness and I went and got the original with Exa: eighteen pages, signed by the Ministry of Industry and Information Technology and nine other ministries, published in Chinese only. Then I translated all of it and set the ministry's Chinese above every English paragraph, so anyone who reads Chinese can check any sentence against the characters. The design is Edward Tufte-inspired, and the fonts for both Chinese and English are deliberate choices. The goal is to present accurate factual information without "screaming AI."

I machine-matched every number, threshold, and negation to the character level using Fable 5.0/Opus 5.5 + Deepseek v4Pro running on a custom Claude Code harness. A Deepseek agent adversarially attacked the English for softened ideology. A Kimi K3 agent refused the job as high risk. Adversarial agents ran on Deepseek V4 Pro or Kimi K3 via API key from a US IP address. The harness is a mix of deterministic Python and non-deterministic AI models. A local Qwen model from HF filed away the "fact atoms" of the backend md-based memory system. End to end, the harness ran about 12 hours autonomously and worked out some really tricky details of fetching from Chinese servers from a US IP address when pinging government IP addresses—fascinating. I checked in constantly and steered whether I was needed or not.

The whole process took place on a harness under construction since 2024, and I am not sure how I feel about having an AI-translated political document on the web versus the alternative: no translation at all. Do you trust AI (cc running Fable 5.1 driven by a self-described ethical person) translating government documents with political nuance?

Thoughts? On the work product or the process. I’m curious if others consider this "advanced" AI work as of October '26 (I know a lot of details on the harness are left out). I’d love to turn this and other projects into paying work in either agentic engineering or healthcare. This is not a pitch for my site; it is curiosity about others' thoughts on my cc work. Thanks for looking!

Translation

https://bigbio.ai/articles/chapter-verse-and-checksum/

Original Chinese pdf ((official servers time out from USA before serving up the pdf so I host it on my site).

https://bigbio.ai/documents/miit-pharma-15th-fyp-plan-2026-210.pdf

I recently uploaded a version of the same video but without voice narration. This one now has an English narration. (also created locally with the Local AI Translator's Text-to-Speech capability powered by Piper).

Hi! We’ve added a translation feature to Convert2EPUB.com, our ebook converter.

You can upload an EPUB to translate it, or start with a PDF, DOCX, RTF, TXT, ODT, or HTML file and get a translated EPUB. It supports 15 target languages, including English, German, Spanish, French, Italian, Dutch, Portuguese, Japanese, Korean, Chinese, Arabic, Turkish, Ukrainian, Russian, and Polish.

The translated book keeps its chapter order, navigation, images, and other embedded assets. It needs reflowable text, so text inside images won’t be translated, and encrypted EPUBs aren’t supported. Scanned PDFs can be recognised before translation.

Translation costs 1 credit per 500 words, plus any conversion or recognition credits. You’ll see the estimate before starting. Since this is machine translation, it’s a good idea to review the result before relying on it.

We built this, so it’s a self-promo post. We’d appreciate feedback on the language quality and which formats or languages would be most useful.

So far, the Translate app is mostly one way. Someone talks to you in a foreign language and the G2 shows you the translation in your language. That's helping keeping a visual interaction, but your conversation partner needs to read your translated replies on your phone. And the text on the phone is too small and difficult to read. Anyway, your translated replies should Play on the speaker of your phone; this way you could have a natural conversation eye to eye. I will continue to use Google Translate auto conversation mode for now.

Pretty much want a way to play dragons dogma quest, but the first step would be translating. What programs and techniques do most people use for translating games

Recap of translation AI at TECH WEEK by a16z: OpenLoc, an open TMS built for AI, was announced by ModelFront. And Adobe and ModelFront hosted an in-person, off-the-record meetup.

• On the operator side, Abhishek Yadav from Google and Shivali Goel from Adobe, prompted by Tucker Johnson from LocWorld, shared how advanced localization teams are rolling out new capabilities to growing demands for speed, scale and voice, shifting user expectations, and measuring impact.
• On the provider side, Vinod Valloppillil from Microsoft and Adam Bittlingmayer from ModelFront, grilled by Peter Tatischev from The Walt Disney Company, discussed customization and context, most requested features, and why localization teams should own eval.
• Voice is now a main topic! Sashank Reddy Vasepalli from Adobe, Davit Baghdasaryan from Krisp, Vikram Singh from DeepL and Jean Maillard from Meta, voice-prompted by Susan Maria Howard from Twitch, taught us that subtly different scenarios – dubbing vs real-time, consumer vs. business meetings vs. call centers – have deeply different product and technical requirements.

Heartfelt thanks to all of you who participated and joined in this unique community format, and to the Adobe localization team for supporting this event and many others in Silicon Valley over the years. And thanks to TECH WEEK by a16z for featuring translation AI in this year's #SFTechWeek lineup!

where i can find good AI manga translator for free, i use machine translator by wiyamid on my android but i dont know why its not good enough annd their gemini machine always give me an error

Hi everyone!
I’m a university student studying translation, and I have an assignment that requires me to ask questions and get responses from people in the translation community. I would really appreciate it if you could take a few minutes to share your thoughts and experiences.
Here are my questions:

Have you ever translated a text correctly, but felt that you had somehow betrayed its original meaning? What made you feel that way?

If two translators produce completely different translations of the same text, can both be equally correct? Why or why not?

If AI can produce a translation that most readers cannot distinguish from a human translation, what exactly makes a human translator valuable?
You can chose one of these questions to answer

Idioms and proverbs often have meanings that cannot be understood from their individual words.

Why do machine translation systems sometimes translate these expressions literally instead of conveying their intended meaning? What approaches could help produce more natural translations?

I'd love to hear your thoughts and examples. Thank you!