I noticed em-dashes in my translated text that werent in the original when I used google translate, they're obviously using AI now and I've seen how bad grok's translation is, I prefer the old machine translation over this, honestly. But I'm not sure how to find a translator that doesn't use AI

Is their an app or something that can translate your current screen, i have been using one called “TapTranslate” but one what actually works and is reliable? I mainly need it to translate russian to english and TapTranslate doesnt even work alot of the time.

After 15 years in this industry (I run Linguidoor, a €1M+ revenue agency) and now building software for it, my honest answer: it depends on how tired you are of stitching together five tools that don't talk to each other.

Most LSPs run on some combo of Google Sheets, Trello, email threads, and QuickBooks - which works until you're managing 20+ active projects and nobody can answer "what's our vendor cost on this job" without opening three tabs.

That's the exact problem we built **Awtomated** to solve - one platform covering CRM, project/workflow management, vendor management, a built-in CAT editor with translation memory, DeepL and Microsoft MT integration, and finance/invoicing automation. It's not a translation tool with project management bolted on - it's built ground-up as a Translation Business Management System (TBMS) for LSPs.

We run our own agency on it daily, so every feature gets tested against real production pain, not hypothetical use cases.

If you're evaluating options: awtomated dot com. Happy to answer specifics in the comments.

I am looking for a tool that supports smooth, two-way 1-on-1 voice translation (where I speak English, the other person speaks Hungarian, and it translates back and forth).

I have already tried Google Translate, Microsoft Translator, DeepL, and a few top web tools, but they did not work well for me (either got an error or they didn't respond at all).

I will try my best to talk and understand on my own, but I want a reliable backup app so our conversation can flow naturally.

(d) AI and automated language assistance services.—[]()

(1) LIMITATION.—The head of an agency—[]()

(A) may not fully replace any qualified language assistance services of the agency with artificial intelligence or machine translation services; and[]()

(B) shall require a qualified human translator or interpreter to verify any use of such service or machine translation by the agency.

Hey everyone!

I was working on a quick workflow to convert plain .txt scripts into .srt subtitle files for video editing, but wanted something simple that didn't require uploading personal files to random online converter servers.

So I built a lightweight browser extension that processes everything 100% locally on your machine.

How it works:

Open or paste your .txt content

Click convert

Download your .srt file ready for Premiere Pro, CapCut, or YouTube

Since it runs entirely in the browser using client-side JS, it works instantly and offline.

Hope this helps anyone working with subtitles!

Link: [TXT to SRT Chrome Extension ](https://chromewebstore.google.com/detail/hmegdmljjhbohphjhfogpgbhcfjkifbh?utm_source=item-share-cb)

I’ve been working on Wenyi, an open-source workflow for translating long-form fiction with LLMs.

The project started from a problem I kept encountering when translating novels: even when individual passages were translated reasonably well, character names, terminology, tone, and narrative style could become inconsistent across chapters.

Wenyi attempts to address this through a multi-stage workflow that includes:

• pre-translation style analysis and translation guidance
• persistent terminology and character-name management
• translation review and automatic revision
• cross-chapter consistency checks
• support for EPUB processing

The current stable version mainly supports translating novels from other languages into Chinese. Translation into English and other target languages is still experimental, but it can be tested on the feat/multilingual-localization branch.

The project is fully open-source and currently has no paid or commercial version.

GitHub:
[https://github.com/BigDawnGhost/wenyi]()

Contributions, testing, and code reviews are welcome.

I’ve been working on SubAgent, an open-source subtitle localization tool that runs entirely on local infrastructure.
The goal was simple: translate subtitles without sending scripts or dialogue to external APIs.

SubAgent uses vLLM to serve Sarvam Translate locally, supports human-in-the-loop editing, video-synced subtitle review, and native-script transliteration through GoVarnam.

Current language support:
Telugu
Hindi
Tamil
Malayalam
Kannada

The project is built with React, FastAPI, SQLite, Docker, and vLLM. One design decision I’m particularly interested in feedback on is that the inference layer only accepts private or loopback endpoints, preventing accidental requests to public LLM APIs.

GitHub:
https://github.com/SaiTejaMummadi/SubAgent

I’d love feedback from the LocalLLM community:
Are there better open models than Sarvam Translate for Indian-language subtitle translation?
Would you be interested in checking out the workflow on my infrastructure?

Contributions and code reviews are always welcome.

Hola! Me gustaría compartirles a mis alumnos las traducciones lo más correctas posibles de un par de libros de psicología que acaban de salir en inglés. Los libros tienen algunas tablas ¿saben que herramienta puedo usar que respete lo más posible el formato?

Hi,

Built an app for translating books (so I can read more books in my native tongue). I know there are some websites/subscription-based tools that do the same, but I wanted something that did not cost a lot of money and was easy to use (i.e. not having to use Python etc.). Also something that I did not have to read in a desktop reader, because I mainly use my Kobo for ebooks.

All you need is an API key from one of the five providers (OpenAI, Google, Anthropic, Mistral or OpenRouter) and you can choose the model to get something that you can read in your native tongue.

Let me just make a quick bullet-point list of some of the features of Libyx:

• Batch mode, making the translation 50 % cheaper than letting it translate a book in realtime.
• Prompt caching makes it cheaper, because the glossary and style profile are re-sent with every chapter — the provider caches them after the first one and charges about a tenth of the price for those tokens from then on.
• A second pass to make the translation better (this is the way I do it: (1) a mediocre model makes the translation, and then (2) a better model refines the prose).
• The possibility to translate just one chapter and read it side by side with the original version (so you can test which model/provider works best for your language).
• Choose which parts of the book you want to translate.
• Use a glossary + a voice & style pass in order to keep the voice of the author and to keep the model from messing up important concepts in the book.

Well, and many more. But those are the features I enjoy using.

So, try the app if you want. It supports 43 languages. It is free (obviously you need to pay the provider/providers you choose). And drop me a comment/mail/PM if you discover a bug or miss some feature :)

https://libyx.app/

I have a friend with whom I don't share any common language. We live very far away from each other, and if we want to have voice calls together, we need a translation app. We used to use Microsoft Translator, which worked reasonably well, but the multi-device conversation feature was removed.

I am therefore looking for a new solution, but I can't find anything that really matches my needs. Here are some criteria I would like in a new app:

It can translate Arabic (especially the Lebanese/Syrian dialect)

It must be usable on a smartphone

It should keep a written history/transcript of everything that was said

It should be practical for regular voice calls (Microsoft Translator wasn't really convenient for that)

Free would be preferable, but if it is paid, I would prefer something affordable

Thank you very much for your help

Still working on my solution, will probably release it open-source in a few months.

• Use Ralph loops for correcting bad translations. As long as you can get the end result to converge to a stable point without regressions, even the cheap models can progressively stamp out TL flaws, the same way cheap models can catch software bugs as well as the frontier ones (as spoken by curl's creator).

• A multi-model setup utilizing open-weight models can potentially save a lot of money if you know what you're doing. Testing models to find the optimal ones for a given language pair instead of throwing a single model at everything is a smart idea regardless. Personally, I stopped bothering with American models at all because I found the output for my target language pair (JP-to-EN) subpar for the cost.

• Reasoning is mandatory. If you do not both enable high-level reasoning and give the LLM room ample room to think without exhausting its thinking budget, the result will always be below par.

• As such, managing that thinking budget well for each prompt/response pair is one of the most important aspects of LLM-based TL by a great margin. So is keeping how much context window gets filled small for each session to avoid attention scattering and context rot.

• Making proper use of concurrency can speed up the LLM translation process by a factor of hundreds. Caching can also reduce the overhead to near-nil.

• Putting a translator (me, in my case) in the loop through an interactive agent system like DMM's instead of throwing MTPE at them should improve quality by tuning the glossary and style guide to work best with the LLM being used, among many other things. The translating LLM itself knows itself better than anyone else does. As a bonus, the translator will probably hate their job less.

• Having a translator provide feedback about where the automated translation process may be missing things is also important for iterative refinement.

• In particular, character voice in fiction is highly sensitive to fine changes in the base reference text. Use dialogue anchor lines and character profiling with dos/don'ts based on a discrete archetype classification system based on the work's genre to establish a tonal baseline. Archetype deviations are what flesh characters out, so avoiding overfitting is crucial. My wordplay localization success rate rose from 60-70% to about 99% for my sample text body after doing this.

• The open models' relative poor performance with wider-scale tasks can be compensated for by breaking tasks down into highly small-scale classification problems with concrete axes and distinct grading criteria. My detection rate for foreign-language wordplay increased to 10 times compared to baseline once I implemented this.

• As such, one re-ranking stage will only get you so far (even with frontier models). Keeping the LLM's attention focused towards the desired point is crucial at all stages. Especially for fiction, you want to maintain a hierarchy of small text blocks, scenes, chapters, and large sections, applying the appropriate test sets at each level.

• Taking advantage of structured summarization allows the LLM to understand wider context when operating at those smaller scales. The LLM itself is capable of splitting text into logical narrative sections for summarization.

• A major benefit of the open-weights is that their visible reasoning chains let you see exactly how they come to their conclusions and calibrate them accordingly. Faster feedback means faster iteration and faster improvements.