... Real-time Streaming Both SDKs support native streaming with async iterators. Responses stream in real-time while memory extraction happens in the background. // JavaScript const stream = await chat.process({ content: 'Tell me a story', stream: true }); for await (const chunk of stream) { process.stdout.write(chunk.content

... best of two worlds: Speed: Classical NLP ( spaCy ) for initial NER (10-50x faster than BERT). Reasoning: State-of-the-art LLMs ( AWS Nova Pro v1 ) to validate complex semantic relationships and refine extraction. 3/ Semantic & Adaptive chunking Forget about arbitrarily cutting text every 500 characters. Our algorithm respects natural paragraph boundaries and evaluates semantic coherence . If a fragment loses the thread, the system detects the topic shift via embedding similarity to decide exactly where ... ... Secret sauce": Context preservation This is where NeuroBlock transforms the data. We don't just store vectors; we build a Contextual Knowledge Graph in Neo4j . We create `NEXT_CHUNK

... Hello, I ve been working on a passion project I called Sapling (use.sapling.mobi) on and off since 2016. I have spent the last 12 months improving it because I kept seeing small charities lose huge chunks of their donations to complex platforms that take high fees or guilt-trip donors into "tipping" the software. It felt wrong, so I decided to build a cleaner, simpler alternative

... Awards for AI Dictation and now we re moving to the next category: AI Automation. This one is for the tools that actually do work for you clearing chores, running workflows in the background, or quietly taking over a chunk of your week without turning into another dashboard you have to babysit. We re digging into the AI Automation category based on traction and reviews, not hype. So we d love your help: Drop your favorite AI automation tools ... ... community templates save hours of setup time, and the AI workflow builder is genuinely useful for complex automations. Make (formerly Integromat) - When you need granular control over data transformations, Make shines. The scenario editor is more powerful than most alternatives

... still a mess of spaghetti code. So we built Raptor Data to be the "Stripe for RAG Ingestion." What is Raptor Data? Raptor is a developer-first API that turns unstructured documents (PDF, DOCX) into version-controlled, AI-ready chunks. We don't just "parse" files. We treat them like code repositories. The "Git-Like" Magic (How we save you 90%) When you upload a file to Raptor Data, we use Structure-Aware Chunking

... design and intended purpose. Making sense of voice AI latency metrics When evaluating model speed, you'll often encounter "TTFB" (Time To First Byte): This measures how long it takes from sending your text request to receiving the first chunk of audio data back (essentially, how quickly you hear the voice start speaking). This metric is crucial for real-time applications because it directly impacts the responsiveness users experience. For context, human conversation typically has response delays under 200ms ... ... working on one of the voice models we ve tested for this post or one we haven t we d love to explore an integration. Comparing today s leading text-to-speech voice models Beyond the real-time VS

... anything radical; it was just using a tool to save time. Little did I know I was participating in a budding movement. As AI coding assistants improved, my vibing only increased. I started letting GitHub Copilot auto-complete whole chunks of code while I casually tabbed through its suggestions. I d begin writing a comment like // function to shuffle a deck of cards and before I could finish my thought, Copilot would offer to generate the entire function ... ... best practices for vibe coding sanely: Break Problems into Bite-Sized Pieces: Don t ask the AI to build your entire app in one go; that s a recipe for disappointment. Instead, split the work into small chunks

... happy to let you know that Chunk v1.2.0 is now live . Here s what s waiting for you: Keyboard Shortcut: Pick your own key combo to open and close the main panel. Head over to Settings Shortcuts and set it up in a way that fits your flow. 12-Hour Clock Mode: You now have a choice between 11:00 AM 1:00 PM or 11:00 13:00 in the clock settings. Pick whichever makes more sense ... ... System Theme: Chunk

... Chunkers! I hope you're all staying productive and laser focused We have some great news!! In the coming days we will be releasing Chunk

... What s New We just launched a native integration between LangChain and Tensorlake! Now you can pass unstructured documents to a LangGraph agent and trust that parsing, chunking, and field-level accuracy are handled by Tensorlake s document engine no hacky pipelines required. Why it matters Many LangChain projects break down when document structure is inconsistent, or field extraction needs to be accurate and explainable. Tensorlake s integration provides: Reliable document ingestion Schema-driven field extraction Native support ... ... pipelines Built-in SDK + Playground for fast iteration It s now super simple to use documents (like SEC filings, contracts, or invoices) as context in LLM apps with structured outputs and markdown chunks

... Text2Note now processes whole books (like The Metamorphosis ) in chunks for detailed, comprehensive notes, all in the background with no clicks needed. Shared context between chunks

... world for cents, and it was delightful. In February 2025, Microsoft announced it was closing Skype down, and on May 5 it officially stopped operations. I was really sad when I heard the news with Skype, a huge chunk of my life disappeared. It felt the same way as if, one day, nobody would be playing on Call of Duty 2 servers anymore (that hasn t happened yet, right?). Anyway, here I ve compiled a list ... ... alternatives

... LLMs! Anyone else building cool stuff like PDF Q&A or custom bots with RAG, but finding the context retrieval step... frustrating? Most AI app data stacks these days use vector search (Pinecone, Weaviate, etc.) to grab text chunks for the LLM. But sometimes it feels like it finds stuff that's keyword-similar while totally missing the actual point the user asked for. Leads to those slightly weak or "confidently wrong" LLM answers. So, what's your single ... ... biggest headache right now in getting truly relevant context for your RAG pipeline? Is it nailing the chunking

... personally look at packages and am okay downloading things from internet devs but I'm curious what folks here think? Would distribution hurt download chances? Awareness: This is pretty much the same imo other than App Store search vs SEO but curious if anyone has had negative/positive experience regarding discoverability either through App Store or self-distribution Payments : I would probably use something like @Superwall but am curious what the best way to introduce a " free for 7 days then ... ... clean a/b test b/c the apps are different but also an option. curious what you decide to do. good luck with the launch, mate Comment from Tania Bell(@taniabell): one more thing to throw into the mix - build the chunk