Understanding STT: Speech to Text AI for Indian Languages

One of the biggest misconceptions about AI in business is that you can simply “upload a document” and expect flawless answers. In reality, there’s a critical translation step between human-formatted documents and machine-readable structure. Recently, while building an AI agent for closet companies, I was reminded that: AI doesn’t struggle with intelligence — it struggles with ambiguity. Merged cells, visual grids, implied relationships, and inconsistent SOP formatting create friction for machines. The solution isn’t better prompts. It’s better structure. I wrote a short piece on the “human-to-AI translation layer” and how forward-thinking companies can prepare their documentation for the future. Full article here: 👉 https://lnkd.in/erMZTw-c

Sorting through the large number of AI articles that cross my desk. This one provides insights into what Large Language Models are and how they function. It’s clearly written and is a great starting point for those of us non-engineers who need a refresher or a place to start! What Is a Large Language Model, Anyway?

An insightful and interactive website that illustrates the limitations and strengths of AI Large Language Models (LLMs) when applied to geographical perceptions. Backed up by good visuals and theoretical underpinnings. "LLMs do not merely reflect global inequality. They reproduce and amplify it." https://inequalities.ai/

Your sales proposal process is probably eating two hours of your day. You can cut that down to ten minutes using a simple AI stack. Most founders are still manually drafting every single follow-up. Start by feeding AI call notes and your own body language cues into a GPT. Combine that with NotebookLM where your winning proposal structures live. The AI merges the new lead data with the frameworks that actually close. This turns a two-hour manual grind into a ten-minute verification step. New tools are aiming to bring this entire workflow down to sixty seconds. The goal isn't just speed but keeping the human nuance that wins deals. Are you still drafting proposals by hand or have you built an AI stack? Watch the full episode: https://lnkd.in/g4aVXfmx Join us live every Wednesday at 3PM CET: https://lnkd.in/g8j5vin6 Hot takes on Substack: https://lnkd.in/grH4mEYv Have a fab day :) Alexander https://lnkd.in/gdMf7gk7

I personally believe bm25 based search is much more ideal for ai agents than natural language search for code. I built this tool for this. It is obviously much faster than rg/grep because it only has to search an index. https://lnkd.in/dDCJhVyi

Your sales proposal process is probably eating two hours of your day. You can cut that down to ten minutes using a simple AI stack. Most founders are still manually drafting every single follow-up. Start by feeding AI call notes and your own body language cues into a GPT. Combine that with NotebookLM where your winning proposal structures live. The AI merges the new lead data with the frameworks that actually close. This turns a two-hour manual grind into a ten-minute verification step. New tools are aiming to bring this entire workflow down to sixty seconds. The goal isn't just speed but keeping the human nuance that wins deals. Are you still drafting proposals by hand or have you built an AI stack? Watch the full episode: https://lnkd.in/g8j_Gfnw Join us live every Wednesday at 3PM CET: https://lnkd.in/gqAce6-T Hot takes on Substack: https://lnkd.in/ggvdWmbq Have a fab day :) Alexander https://lnkd.in/g778Z8qK

A small AI prompting tip from an unexpected source — my daughter. Large Language Models (LLMs) are fundamentally text systems. They don’t actually “understand” things the way humans do. They work by connecting patterns in text based on frequency and similarity. Images, audio, and video systems work differently — those are usually matched through numeric similarity in signal patterns. Why this matters: My daughter was trying to ask Alexa what melody the Periodic Table of Elements Song is based on. She kept humming the tune, expecting it to recognize it. It couldn’t. So I showed her a different way to ask: “What is the melody for the periodic table of elements song based on?” Because the question was now anchored to text concepts the model knows, the answer came immediately: “Galop Infernal” by Jacques Offenbach (the Can-Can). Same question. Different framing. Completely different result. Lesson: When using LLMs, you’ll often get better results if you frame questions using text references the model was trained onrather than trying to approximate non-text signals like humming, gestures, or vague descriptions. Think in text anchors, not just intent.

Should we still use the classical ASR pipeline in 2026? End-to-end models changed everything. Instead of separate acoustic models, lexicons, and language models. We now train one large neural network to map audio directly to text. Cleaner. Simpler. Powerful. But here’s what I’m realizing while building ASR for a regional language: End-to-end models assume scale. They thrive on: • Massive labeled datasets • Diverse speakers • Clean transcriptions • Strong compute resources Low-resource regional languages rarely have these advantages. And that’s where the classical pipeline starts looking… practical again. With modular ASR: • You can improve the language model separately • You can inject linguistic rules • You can expand the lexicon without retraining everything • You can debug failure points layer by layer End-to-end models are elegant. But modular systems are interpretable. In regional AI, interpretability isn’t just academic, it's operational. When something fails in production, you need to know why. I’m starting to think the future of regional ASR may not be fully end-to-end. It might be a hybrid. What’s your take, elegance or control? #SpeechRecognition #ASR #AppliedAI #LowResourceLanguages #LearningInPublic

AI Hallucinations Explained: Why Mistakes Can Be a Feature AI hallucinations have earned a bad reputation, leaving many users frustrated when large language models (LLMs) confidently deliver inaccurate or bizarre answers. DETAILS: https://lnkd.in/dyBpwAwm

AI just broke the rules of language models. ��⚡ Meet diffusion-native LLMs — where reasoning isn’t linear anymore. Parallel minds. Smarter inference. A new frontier. Read now: https://lnkd.in/d3j2Q3Fm #GenerativeAI #LLMs #AIRevolution