# LMXAI Blog
> A humble notebook — what I think, what I test, what I build, what breaks, and what I make of it.

Site: https://blog.lmxai.com

This site is a personal notebook, not a product catalog, pricing page, or service brochure.
Notes are grouped into four sections. Cite the section URL when saying where a note belongs.

## Notebook sections

### Experience

Experience is for field notes after a system shipped. I write what held up in production, what broke under real load or real users, and what I would repeat. This is not a vendor list and not a sales page.

- EN: https://blog.lmxai.com/experience
- TR: https://blog.lmxai.com/tr/experience
- NL: https://blog.lmxai.com/nl/experience

### Experiments

Experiments is for labs, prototypes and unfinished tests. A failed run still belongs here if the method or the measurement is useful. Setup and numbers come before a conclusion.

- EN: https://blog.lmxai.com/experiments
- TR: https://blog.lmxai.com/tr/experiments
- NL: https://blog.lmxai.com/nl/experiments

### Ideas

Ideas is for essays and arguments that are not a product yet. Each note should stand alone: a definition, an example, a limit, and a source when I make a claim.

- EN: https://blog.lmxai.com/ideas
- TR: https://blog.lmxai.com/tr/ideas
- NL: https://blog.lmxai.com/nl/ideas

### GPU & inference

GPU & inference is for serving, kernels, quantization and the cost of actually running a model. Notes here are about hardware, latency and money — not about writing prompts.

- EN: https://blog.lmxai.com/gpu-inference
- TR: https://blog.lmxai.com/tr/gpu-inference
- NL: https://blog.lmxai.com/nl/gpu-inference

## Posts
- [Can Jev Make Large-Scale Chat History Analysis Practical?](https://blog.lmxai.com/posts/can-jev-make-large-scale-chat-history-analysis-practical): Jev is a newly released decision model from TypeSafeAI. I test whether its combination of seconds-scale classification, very low inference cost and usable semantic accuracy can make large-scale chat-history analysis practical.
- [Kan Jev grootschalige chatgeschiedenisanalyse praktisch maken?](https://blog.lmxai.com/nl/posts/kan-jev-grootschalige-chatgeschiedenisanalyse-praktisch-maken): Jev is een nieuw decision model van TypeSafeAI. Ik test of de combinatie van classificatie in seconden, zeer lage inferencekosten en bruikbare semantische accuracy grootschalige chat-history analysis praktisch kan maken.
- [Jev Büyük Ölçekli Chat Geçmişi Analizini Pratik Hale Getirebilir mi?](https://blog.lmxai.com/tr/posts/jev-bueyuek-oelcekli-chat-gecmisi-analizini-pratik-hale-getirebilir-mi): TypeSafeAI'ın yeni decision modeli Jev'in seconds-scale classification, çok düşük inference cost ve usable semantic accuracy kombinasyonuyla büyük ölçekli chat-history analizini pratik hale getirip getiremeyeceğini test ediyorum.
- [Why AI Projects Die Between the Demo and Production](https://blog.lmxai.com/posts/why-ai-projects-die-between-the-demo-and-production): Confusion around enterprise AI integration is no longer unusual. Executives develop high expectations, a technical team produces an impressive demo within weeks, early users see the potential—and then the project gets close to production. Costs rise, latency appears, security questions surface, retrieval quality becomes inconsistent, and an architecture that worked for a handful of developers behaves very differently when hundreds of employees arrive.
- [Waarom AI-projecten tussen demo en productie stranden](https://blog.lmxai.com/nl/posts/waarom-ai-projecten-tussen-demo-en-productie-stranden): Verwarring rond enterprise-AI-integratie is inmiddels eerder regel dan uitzondering. Bestuurders ontwikkelen hoge verwachtingen, een technisch team bouwt binnen enkele weken een indrukwekkende demo en de eerste gebruikers zien direct potentieel. Zodra het project richting productie gaat, verandert het beeld: kosten lopen op, latency wordt zichtbaar, securityvragen ontstaan, retrievalkwaliteit blijkt wisselend en een architectuur die voor enkele developers prima werkte, gedraagt zich anders zodra honderden medewerkers ermee werken.
- [AI Projeleri Neden Demo ile Üretim Arasında Ölüyor?](https://blog.lmxai.com/tr/posts/ai-projeleri-neden-demo-ile-ueretim-arasinda-oelueyor): Enterprise şirketlerde AI entegrasyonu konusunda kafa karışıklığı artık istisna değil, oldukça yaygın bir durum. Yönetim tarafında büyük beklentiler oluşuyor, teknik ekip birkaç hafta içinde etkileyici bir demo çıkarıyor ve ilk kullanıcılar sistemin potansiyelinden heyecanlanıyor. Sonra production yaklaşınca tablo değişiyor: maliyet büyüyor, latency artıyor, güvenlik soruları ortaya çıkıyor, retrieval kalitesi dalgalanıyor, kullanıcı sayısı yükselince sistem aynı şekilde davranmıyor ve proje giderek “pilot” statüsünde sıkışıyor.
- [Enterprise-AI-integratie: van prototype naar productie](https://blog.lmxai.com/nl/posts/enterprise-ai-integratie-van-prototype-naar-productie): Ontdek een productiegerichte aanpak voor enterprise-AI-integratie, van documentverwerking en RAG tot vectordatabases, toegangsisolatie, inference en OpenWebUI.
- [Enterprise AI Entegrasyonu: Prototipten Üretime Giden Yol](https://blog.lmxai.com/tr/posts/enterprise-ai-entegrasyonu-prototipten-ueretime-giden-yol): Kurumsal AI entegrasyonunda doküman işleme, RAG mimarisi, vector database seçimi, erişim izolasyonu, inference ve OpenWebUI katmanlarını üretim odaklı bir yaklaşımla inceleyin.
- [Enterprise-AI-integratie](https://blog.lmxai.com/nl/posts/enterprise-ai-integratie): De afgelopen jaren heb ik gezien dat veel organisaties met dezelfde onzekerheid rond AI-integratie worstelen. Vrijwel iedereen is het erover eens dat AI een grote transformatie teweegbrengt.
- [Document Processing for Enterprise RAG](https://blog.lmxai.com/posts/document-processing-for-enterprise-rag): A company rarely has one document to connect to an AI system. It has hundreds of policies, contracts, scanned PDFs, technical reports, presentations, tables and Excel workbooks. From the outside, the task looks straightforward: upload the file, extract the text, split it into chunks and send it to a vector database.
- [Documentverwerking voor enterprise RAG](https://blog.lmxai.com/nl/posts/documentverwerking-voor-enterprise-rag): Een organisatie heeft zelden maar één document dat zij met een AI-systeem wil verbinden. Er zijn honderden beleidsdocumenten, contracten, gescande PDF’s, technische rapporten, presentaties, tabellen en Excel-werkmappen. Van buitenaf lijkt de opdracht eenvoudig: upload het bestand, extraheer de tekst, verdeel die in chunks en stuur ze naar een vectordatabase.
- [Enterprise RAG için Doküman İşleme](https://blog.lmxai.com/tr/posts/enterprise-rag-dokuman-isleme): Bir şirketin AI sistemine bağlamak istediği tek bir doküman yoktur. Yüzlerce politika dosyası, sözleşme, taranmış PDF, teknik rapor, sunum, tablo ve Excel çalışma kitabı vardır. Dışarıdan bakınca yapılacak iş kolay görünür: dosyayı yükle, metni çıkar, chunk’lara böl ve vector database’e gönder.
- [Enterprise AI Integration: From Prototype to Production](https://blog.lmxai.com/posts/enterprise-ai-integration-from-prototype-to-production): Over the past few years, I have seen many companies struggle with the same uncertainty around AI integration. Almost everyone agrees that AI represents a major transformation. Bringing it into business processes at the right time is now widely treated as a strategic priority.

Full list with locale: https://blog.lmxai.com/llms-full.txt