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Show HN: Precise AI Motion Control for Kling 3.0

Hi HN,Like many of you, I've been experimenting with generative video, but the "camera lottery" was driving me crazy. Even with the best models, getting a consistent 360-degree orbit or a smooth crane shot felt more like luck than engineering.I built AI Motion Control to bring more determinism to the workflow. It's a specialized layer for AI motion control, specifically optimized for the Kling 3.0 architecture.Why this matters: The latest Kling AI motion control update (v3.0)

Show HN: We built AI agents that reduce mortgage processing from 18 days to 3–5

Most mortgage processing delays aren’t due to risk — they’re due to manual workflows.We’ve been working on SimplAI, an AI-driven system designed for banking and financial services, starting with mortgage operations.The problem we kept seeing:15–22 day processing timelinesHeavy manual document handling (500+ pages per loan)Repetitive data entry + verification loopsUnderwriters spending hours on non-decision workSo we built a set of AI agents that handle the operational layer:Document AI (IDP) → c

Show HN: We built a way to try 50 AI models from one API

Hey,We have been working on Qubrid AI — a platform to try and use 50+ AI models (text, vision, audio) from a single API.While building AI apps, we kept running into the same issue: switching between providers, APIs, and formats just to test different models. This slowed down iteration a lot, so we built a simpler way to experiment and compare.You can:- Run different models from one place - Compare outputs side-by-side - Use a unified API instead of multiple integrationsYou can try it here (playg

Show HN: OpenGranola – meeting copilot that searches your notes in real time

link: https://github.com/yazinsai/OpenGranolahey HN, I built OpenGranola — a macOS app that sits next to your calls, transcribes both sides of the conversation locally, and surfaces talking points from your own notes in real time.The idea came from having too many calls where I knew I had the perfect data point or quote somewhere in my notes, but couldn't find it fast enough. I wanted something that would do the retrieval for me, while the conversation is still happening

Ask HN: Are MiniMax Models Scams?

I kept trying to use their M2.5 model and now they released M2.7, but they are TERRIBLE.See this comparison I made:https://aibenchy.com/compare/minimax-minimax-m2-7-medium/minimax-minimax-m2-5-medium/z-ai-glm-5-medium/google-gemini-3-1-flash-lite-preview-medium/Not only that, but M2.5 is #1 on OpenRouter, which is crazy: https://openrouter.ai/rankingsI think the only reason why it is #1 is because it is a scam. In the comparison you can see

Show HN: Xybrid – run LLM and speech locally in your app (no back end, Rust)

Hi HN,We built Xybrid, a Rust library for running LLM + speech pipelines directly inside your app, no server, no daemon, just one binary.We started building it while working on a privacy-focused LLM app with Tauri and realized there wasn’t a straightforward way to embed models directly into shipped applications without relying on a separate server process.Xybrid links into your process like any other library. It supports GGUF / ONNX / CoreML and integrates with Flutter, Swift, Kotlin,

Show HN: Lukan – An open-source agentic workstation in a single Rust binary

Hi HN, I've been building Lukan, an open-source (MIT) agentic workstation that runs entirely as a single Rust binary with zero runtime dependencies. I started this because I wanted a unified workstation optimized for my own productivity. My goal was to build an environment where I could securely remote into my machine from anywhere, seamlessly view and modify local files, and run AI agents or drop into a terminal side-by-side, all integrated with a rich set of built-in tools. Here is what m

Show HN: I got tired of print(x.shape) so I built runtime type hints for Python

As a beginner learning to build ML models, I found it annoying to have to keep printing tensor shapes every other line, having to step through the debugger to check where did I mess up the shapes again.So I built Trickle, it takes the data that flows through your code, caches the types and display them inline (as if you have type annotations).The idea is: "Let types trickle from runtime into your IDE". You get types in Python without having the write them manually.It works by rewriting

Show HN: Open Prompt Hub – Don't share code, share intent

Hey, I’m Mario. After chatting with a colleague about how AI agents are changing dev work, we got stuck on a question: Why share code when prompts can generate it on demand? I wanted to explore this further, so I build "Open Prompt Hub" — think GitHub, but for prompts: https://openprompthub.ioHow it Works:Instead of shipping binaries or source code, you share instructions and specs in form of a prompt. You can take this prompt, paste it into their agent or IDE and watch it bu

Show HN: CLI tool for generating AI images

Hi all, I wanted to show something I've built. Picasso is a simple command-line tool that lets you generate and edit images using popular providers. It wraps OpenAI, Google Gemini, and FLUX (Black Forest Labs) behind one consistent interface, so you can try out different models using the same commands. I'm open to adding other providers if there is demand for it.I created it because juggling multiple AI image APIs is tedious. They each have their own SDKs, parameter names, and quirks.

The Rise and Fall of the Cloud – Again with Tom Lyon

Tom Lyon begins by suggesting that if cloud computing is defined as outsourcing data processing to a company that owns the equipment, then the concept is nearly a hundred years old. He traces its origins to the 1930s, when IBM established service bureaus where clients could bring data to be processed using punch cards and tabulating machines, an expensive service akin to modern cloud offerings. This early period, marked by the Great Depression, saw basic arithmetic being outsourced, with computi

Launch HN: Captain (YC W26) – Automated RAG for Files

Hi HN, we’re Lewis and Edgar, building Captain to simplify unstructured data search (https://runcaptain.com). Captain automates the building and maintenance of file-based RAG pipelines. It indexes cloud storage like S3 and GCS, plus SaaS sources like Google Drive. There’s a quick walkthrough at https://youtu.be/EIQkwAsIPmc.We also put up this demo site called “Ask PG’s Essays” which lets you ask/search the corpus of pg’s essays, to get a feel for how it works: https

Show HN: Oxyde – Pydantic-native async ORM with a Rust core

Hi HN! I built Oxyde because I was tired of duplicating my models.If you use FastAPI, you know the drill. You define Pydantic models for your API, then define separate ORM models for your database, then write converters between them. SQLModel tries to fix this but it's still SQLAlchemy underneath. Tortoise gives you a nice Django-style API but its own model system. Django ORM is great but welded to the framework.I wanted something simple: your Pydantic model IS your database model. One clas

Show HN: Context Gateway – Compress agent context before it hits the LLM

We built an open-source proxy that sits between coding agents (Claude Code, OpenClaw, etc.) and the LLM, compressing tool outputs before they enter the context window.Demo: https://www.youtube.com/watch?v=-vFZ6MPrwjw#t=9s.Motivation: Agents are terrible at managing context. A single file read or grep can dump thousands of tokens into the window, most of it noise. This isn't just expensive — it actively degrades quality. Long-context benchmarks consistently show steep accuracy

Future After the AI Revolution

Current AI revolution is building larger models, using feedback to fine-tune, building agents around them and such.I was thinking what will be the next revolution. We will have a true leap forward.We will have self-aware beings among us. John von Neumann architecture will be done for good. There will be zero software as a consequence. ( All in learning models ). Even biology is not there actually (DNA is a lot like John von Neumann than we would think), so this is a very tall claim.We may potent

Show HN: Vibe-budget – CLI to estimate LLM costs before you start vibe coding

I built vibe-budget because I kept burning tokens without knowing the cost upfront. You describe your project in plain English (or Spanish), and it detects the tasks involved, estimates token usage, and compares real-time prices across 85+ models via OpenRouter.Example: vibe-budget plan ecommerce with stripe oauth and supabaseIt detects 4 tasks, estimates ~497k tokens, and shows you the cheapest, best quality-price, and premium model options side by side.It also has a scan command — point

Show HN: Agents shouldn't operate software–they should coordinate commitments

I've been working on Covenant Layer — an open protocol and framework for shifting AI agent systems from tool orchestration to outcome coordination.The core idea: agents shouldn't operate software step by step. They should publish objectives, compare competing provider offers, accept the best one under policy, and let providers fulfill outcomes with evidence and settlement.Why this matters: we're still building agents as "software operators" — better interns that click th

Launch HN: Chamber (YC W26) – An AI Teammate for GPU Infrastructure

Hey HN, we're Jie Shen, Charles, Andreas, and Shaocheng. We built Chamber (https://usechamber.io), an AI agent that manages GPU infrastructure for you. You talk to it wherever your team already works and it handles things like provisioning clusters, diagnosing failed jobs, managing workloads. Demo: https://www.youtube.com/watch?v=xdqh2C_hif4We all worked on GPU infrastructure at Amazon. Between us we've spent years on this problem — monitoring GPU fleets, debug

Show HN: LocalAgent v0.5.0, a local-first Rust agent runtime

LocalAgent is a local-first agent runtime in Rust focused on tool calling, trust and approval gates, replayable runs, and benchmark-gated coding workflows.A lot of the recent v0.5.0 work was about hardening coding-task behavior, improving validation and completion behavior, and reducing the ways evals can be gamed.One thing that stood out during that work was OmniCoder-9B Q8_0. I care less about “looks good in a demo” and more about whether a small model still holds up under real repo tasks, exp

Show HN: Open Prompt Hub – Don't share code, share intent

Hey, I’m Mario. After chatting with a colleague about how AI agents are changing dev work, we got stuck on a question: Why share code when prompts can generate it on demand?I wanted to explore this further, so I build "Open Prompt Hub" — think GitHub, but for prompts: https://openprompthub.ioHow it Works:Instead of shipping binaries or source code, you share instructions and specs in form of a prompt. You can take this prompt, paste it into their agent or IDE and watch it bui