... filter out short messages strip HTML tags and normalize user names fixed the training output completely. Clean data matters more, than parameter count when you are adapting a model for a specific niche workflow. What is your go-to preprocessing step when preparing raw user text for fine-tuning ...

... hunters We just published a detailed breakdown on how ObjectRemover handles up to **50-second videos** efficiently: - Smart preprocessing that only processes the parts that need object removal - Natural language chat (no manual masking) - Intelligent switching for static vs

... reproducibility: the same character can be reused across conversations, roles, scenes, and product workflows instead of generating a new random-looking avatar every time. Different from real-time video generation, PhotoCall does most of the video work once during preprocessing and reuses avatar states during the conversation. That creates a clear trade-off: runtime video cost can stay very low no GPU is needed during conversations the same avatar identity can be reused across use cases avatar creation currently takes ...

... currently working on AI/ML projects and looking for opportunities where I can contribute, learn, and grow as an AI/ML Engineer. I have experience in: OCR systems AI agents (LLM-based workflows, automation) Model training & evaluation Dataset preprocessing & handling Python, TensorFlow, and deep learning workflows Recent Work: Developed a Sinhala OCR model (~94% accuracy on printed datasets) Built a lightweight retinal layer segmentation model using OCT data Collaborated with the National Eye Hospital (Sri Lanka) for validation Developed ...

... core features, gradually rolling out the following capabilities: Custom Strategies : Users can create and configure personalized strategies according to their needs, defining trigger conditions, prompts, and tool lists via STRATEGY.md file to achieve no-code automation. Preprocessors : Allows preprocessing inputs before strategy execution, such as context parameter extraction and attachment format conversion, to enhance the applicability and effectiveness of strategies. Strategy Workshop : Allows developers and users to share various custom strategies, aiming to build a rich and diverse strategy ecosystem ...

... accurate AI-Powered Optical Character Recognition system which understands stylish fonts, handwriting, and special characters better than legacy OCR. Here is my Launch on Product Hunt https://www.producthunt.com/prod... Highlighted Features: Improved Accuracy with NLP Blurry and Smudgy Images Preprocessing Handwriting Study Notes Digitization Invoice, Circular, Legal Papers, Safety, and encryption Multiple languages in a single image are separately transcribed Output Save as TXT Notepad, or Word Document File, Also copy to clipboard in one click imgOCR also provides ...

... that we completely underestimated the variability in the input data formats in the real world. The agent worked perfectly in our controlled environment but was so brittle in production that it was practically useless. The big lesson? Robust data preprocessing is just as, if not more, important than the AI model itself. I'm hoping to learn from your experiences to avoid similar pitfalls. Specifically, I'd love to hear about: Misjudged User Needs: Did you build an agent that ... ... founder/CEO of Atlantic, building this in SF. Happy to compare post-mortem notes if useful. Comment from Hansel(@hanselh): @priyanka_gosai1  this is an incredible share thank you. You've perfectly articulated the classic "automation vs

... community! We all know AI report generators are everywhere but what if you re dealing with  complex or unstructured data  and want to go straight to insights without spending hours on preprocessing? That s where  Powerdrill AI  comes in. We ve built a powerful tool that turns messy data into beautiful, insight-rich reports in  just one click . Whether you re creating investor decks, annual summaries, or internal performance reports, Powerdrill AI takes ...

... cache. By the way What's interesting, is that this approach was technically possible before LLMs, but practically useless. You'd still need human translators familiar with your product domain for precise translations. The AST processing was just expensive preprocessing for manual work. However now, with context-aware models, we can generate quality translations automatically. That's why compile-time, AI-powered localization isn't just possible it's becoming the most practical approach for modern UIs. We predict this ...

... rigid rule engines that miss edge cases and demand constant upkeep. So over the past few weeks, I prototyped a Dify workflow that marries general checks with a library of Bad Cases. Here s the 4-step pipeline: Preprocess & OCR the contract text LLM-based initial risk scan Retrieve similar Bad Cases from our case bank Deep-dive analysis & structured report generation I ve just run a demo on 3 sample contracts and 5 Bad Cases it flags ... ... issues and spits out actionable rewrite suggestions. Along the way, I wrestled with prompt length limits, list-vs