VisuaLeaf
Visualeaf is a MongoDB GUI I’ve been building over the past year. Stack is Electron + Angular + Spring Boot. There’s a live playground on the site if you want to try it without installing or putting in your connection (I provided one). The goal was to combine a visual workflow with the depth needed for real development work. Most existing MongoDB tools tend to optimize for either beginners or power users, but not both in the same interface. Core features: Query builder that supports full MongoDB query expressiveness + being able to drag and drop elements from the collection to the query builder Form based aggregation builder with synchronized JSON view Schema visualization and generation tools GridFS viewer with MP4 streaming support (streaming mp4 was pretty tricky ) IDE style split panels and multiple workspaces Import/export transformations (mask/edit fields during export ) Tree view ( finding a way to expand recursively thousands of nodes was a challenge) Table view (I had to build my own take on AG Grid focusing on optimizing horizontal and virtual scrolling to get it to scroll smoothly on thousands of rows and columns) A lot of the work ended up being performance engineering. It currently loads ~500MB of data into the UI in about 5 seconds on an M1 MacBook. And can even easily display over 20k documents of an average size (12kb) . Here’s a walkthrough of all its features:: https://www.youtube.com/watch?v=WNzvDlbpGTk Happy to answer questions! Thank you so much!
Filling PDF forms with AI using client-side tool calling
Hey HN! I built SimplePDF Copilot: an AI assistant that can interact with the PDF editor. It fills fields, answers questions, focuses on a specific field, adds fields, deletes pages, and so on. It's built on top of SimplePDF that I started 7 years ago, pioneering privacy-respecting client-side pdf editing, now used monthly by 200k+ people. As for the privacy model: the PDF itself never leaves the browser. Parsing, rendering, and field detection all run client-side. The text the model needs (and your messages) goes to whatever LLM you point at. By default that's our demo proxy (DeepSeek V4 Flash, rate-capped), but you can BYOK and point it at any cloud provider, or go fully local (I've been testing with LM Studio). Unlike the existing "Chat with PDF" tools that only retrieve the text/OCR layer, Copilot can act on the PDF: filling fields, adding fields (detected client-side using CommonForms by Joe Barrow [1], jbarrow on HN with some post-processing heuristics I added on top), focusing on fields, deleting pages, and so on. I built this because SimplePDF is mostly used by healthcare customers where document privacy is paramount, and I wanted an AI experience that didn't require shipping PII to a third party. Stack is pretty standard: - Tanstack Start - AI SDK from Vercel - Tailwind (I personally prefer CSS modules, I'm old-school but the goal since I open source it, I figured that Tailwind would be a better fit) The more interesting part is the client-side tool calling: events are passed back and forth via iframe postMessage. If you're not familiar with "tool calling" and "client-side tool calling", a quick primer: Tool calling is what LLMs use to take actions. When Claude runs grep or ls, or hits an MCP server, those are tool calls. Client-side tool calling means the intent to call a tool comes from the LLM, but the execution happens in the browser. That matters for: speed, you can't go faster than client-to-client operations and also gives you the ability to limit the data you expose to the LLM. For the demo I do feed the content of the document to the LLM, but that connection could be severed as simply as removing the tool that exposes the content data. The demo is fully open source, available on Github [2] and the demo is the same as the link of this post [3] What's not open source is SimplePDF itself (loaded as the iframe). I could talk on and on about this, let me know if you have any questions, anything goes! [1] https://github.com/jbarrow/commonforms [2] https://github.com/SimplePDF/simplepdf-embed/tree/main/copil... [3] https://copilot.simplepdf.com/?share=a7d00ad073c75a75d493228...
My retired dad and I made a daily, somewhat difficult, quiz
My dad makes the questions, I made the site. I think the genre and the level of difficulty is suited for HN. Hope you enjoy. (I promise no AI-generated questions, they are all hand made!).
A new benchmark for testing LLMs for deterministic outputs
When building workflows that rely on LLMs, we commonly use structured output for programmatic use cases like converting an invoice into rows or meeting transcripts into tickets or even complex PDFs into database entries. The model may return the schema you want, but with hallucinated values like `invoice_date` being off by 2 months or the transcript array ordered wrongly. The JSON is valid, but the values are not. Structured output today is a big part of using LLMs, especially when building deterministic workflows. Current structured output benchmarks (e.g., JSONSchemaBench) only validate the pass rate for JSON schema and types, and not the actual values within the produced JSON. So we designed the Structured Output Benchmark (SOB) that fixes this by measuring both the JSON schema pass rate, types, and the value accuracy across all three modalities, text, image, and audio. For our test set, every record is paired with a JSON Schema and a ground-truth answer that was verified against the source context manually by a human and an LLM cross-check, so a missing or hallucinated value will be considered to be wrong. Open source is doing pretty well with GLM 4.7 coming in number 2 right after GPT 5.4. We noticed the rankings shift across modalities: GLM-4.7 leads text, Gemma-4-31B leads images, Gemini-2.5-Flash leads audio. For example, GPT-5.4 ranks 3rd on text but 9th on images. Model size is not a predictor, either: Qwen3.5-35B and GLM-4.7 beat GPT-5 and Claude-Sonnet-4.6 on Value Accuracy. Phi-4 (14B) beats GPT-5 and GPT-5-mini on text. Structured hallucinations are the hardest bug. Such values are type-correct, schema-valid, and plausible, so they slip through most guardrails. For example, in one audio record, the ground truth is "target_market_age": "15 to 35 years", and a model returns "25 to 35". This is invisible without field-level checks. Our goal is to be the best general model for deterministic tasks, and a key aspect of determinism is a controllable and consistent output structure. The first step to making structured output better is to measure it and hold ourselves against the best.
Rip.so
Show HN: Rip.so – a graveyard for dead internet things
SyncVibe
Show HN: SyncVibe – Code with friends in the terminal, each with your own AI
Figma alternative where AI works with vector primitives, not code
Show HN: Figma alternative where AI works with vector primitives, not code
Live Sun and Moon Dashboard with NASA Footage
Show HN: Live Sun and Moon Dashboard with NASA Footage
zot
Why I Built Another coding agent harness?: https://dev.to/patriceckhart/zot-why-i-built-another-coding-... Github Repo: https://github.com/patriceckhart/zot
Plate
Show HN: Plate – The fastest way to run projects without becoming a PM tool
Utilyze
The standard GPU utilization metric reported by nvidia-smi, nvtop, Weights & Biases, Amazon CloudWatch, Google Cloud Monitoring, and Azure Monitor is highly misleading. It reports the fraction of time that any kernel is running on the GPU, which means a GPU can report 100% utilization even if only a small portion of its compute capacity is actually being used. In practice, we've seen workloads with ~1–10% real compute throughput while dashboards show 100%. This becomes a problem when teams rely on that metric for capacity planning or optimization decisions, it can make underutilized systems look saturated. We're releasing an open-source (Apache 2.0) tool, Utilyze, to measure GPU utilization differently. It samples hardware performance counters and reports compute and memory throughput relative to the hardware's theoretical limits. It also estimates an attainable utilization ceiling for a given workload. GitHub link: https://github.com/systalyze/utilyze We'd love to hear your thoughts!
Time Pin
Hi! Any history nerds here? I made Time Pin, a little game inspired by Geo Guessr but history-themed. You can play it here(it works on both desktop and mobile). Any feedback is appreciated: https://www.crazygames.com/game/time-pin Now some details: The goal is to guess the time and place that a character is from. You base your guess on some environmental photos, and on questions that you can ask the character(you have 12 questions but can only ask 5 so you have to choose carefully). The closer you are the more points you get. At the end, a portrait picture of the character is revealed, as well as educational resources to learn more about their culture and era(articles, videos, podcasts etc). The game only has 5 levels currently, but I hope to have over 100 someday. It’s tough to create levels because it requires some research, plus generating photos with AI(AI is necessary otherwise we’d only have photos starting from the 19th century when the camera was invented). My goal for the game was to create a challenge, and also maybe spark some curiosity for history.