Show HN: TRiP – a complete transformer engine in C built from scratch just by me https://ift.tt/hoJ4UjI

Show HN: TRiP – a complete transformer engine in C built from scratch just by me https://ift.tt/sJtQFvD April 30, 2026 at 11:48PM

Show HN: Phase Router – capacity-aware routing for MoE https://ift.tt/lF2nfvX

Show HN: Phase Router – capacity-aware routing for MoE https://ift.tt/5fpDFEi April 30, 2026 at 11:37PM

Show HN: A programming language where the only token is the word "vibe" https://ift.tt/iQ5j9Ia

Show HN: A programming language where the only token is the word "vibe" Fuzzy opcode windows. You don't need an exact number of vibes, just roughly right. https://wevibe.fyi April 30, 2026 at 11:14PM

Show HN: FusionCore: ROS 2 sensor fusion that outperforms robot_localization https://ift.tt/o29iO5j

Show HN: FusionCore: ROS 2 sensor fusion that outperforms robot_localization I built sensor fusion for a mobile robot and reached for robot_localization like everyone does. After spending too long fighting navsat_transform, UTM zone boundaries, and YAML covariance tuning, I wrote my own. FusionCore is a 22 state UKF that fuses IMU, wheel encoders, and GPS in ECEF directly (no coordinate projection, no extra node). It estimates IMU bias, adapts its noise covariance automatically from the innovation sequence, and gates outliers with a chi squared test on every sensor. I benchmarked it against robot_localization EKF on 6 sequences from the NCLT public dataset (University of Michigan, real robot, real GPS, RTK ground truth). It wins 5 of 6. On the 6th sequence (fall, degraded GPS over a long period) it loses badly. RL UKF diverged to NaN on all six. Configs, methodology, and full reproduce instructions are in the benchmarks/ folder. https://ift.tt/fLhu3qE April 28, 2026 at 08:46PM

Show HN: Generative UI Library for React https://ift.tt/Kd6wCQA

Show HN: Generative UI Library for React https://ift.tt/9bhJ4WV April 30, 2026 at 02:28AM

Show HN: Send your first Peppol e-invoice in 5 minutes (EU mandate live) https://ift.tt/7QHGuOS

Show HN: Send your first Peppol e-invoice in 5 minutes (EU mandate live) https://getpeppr.dev/ April 30, 2026 at 12:36AM

Show HN: A new benchmark for testing LLMs for deterministic outputs https://ift.tt/R8lLrVa

Show HN: 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. https://ift.tt/azlci6e April 29, 2026 at 11:01PM

Show HN: Open Bias – proxy that enforces agent behavior at runtime https://ift.tt/SUf65jN

Show HN: Open Bias – proxy that enforces agent behavior at runtime https://ift.tt/UZCHAo7 April 29, 2026 at 01:32AM

Show HN: I built a dating SIM that prepares you for your date https://ift.tt/lYZwagB

Show HN: I built a dating SIM that prepares you for your date https://ift.tt/UNViIbW April 29, 2026 at 12:16AM

Show HN: Ragnerock, an AI data analysis tool https://ift.tt/jWTU4aX

Show HN: Ragnerock, an AI data analysis tool Hi HN, I’m Matt Mahowald, and together with my cofounder John, we’re launching the public beta of Ragnerock today. As a data scientist, you spend the majority of your time wrangling data. Even though you might have a set of techniques and tricks you like to use, how exactly you treat a particular source of data tends to be fairly bespoke, so you end up writing custom logic each time. Ragnerock was born from the observation that modern LLMs can be used to automate a lot of the grunt work involved in this process, while still allowing for fully customizable pipelines. What’s more, by leveraging techniques like constrained decoding, it’s possible to provide a unified query interface regardless of the data source - bridging raw data sources like text and images with your existing structured data living in your databases. Ragnerock has four main components: - A workflow designer that lets you build LLM-driven data processing and analysis pipelines - A job orchestration layer that runs those workflows - A query interface which lets you inspect the results of those workflows with plain SQL - A notebook system which is 100% API-compatible with Jupyter and runs on your existing kernels, so you can easily pull data into your existing environments and analyses Ragnerock also supports bring-your-own AI (OpenAI, Anthropic, and Google APIs), databases, and blob storage, so you can join with your existing datasets and have all outputs flow to your data lake. We’re particularly excited about our web crawling feature, which allows you to scrape websites and trigger workflows on updates: for example, you might point Ragnerock at your favorite blog and run a workflow to assess posts for topics and sentiment. You can try it out at https://ift.tt/gFxXoM8 ; no credit card needed and the first 20 hours of compute are free. It’s an early-stage product so we’re especially interested in feedback. Happy to answer any questions - John and I will be around in the comments today. https://ift.tt/gFxXoM8 April 28, 2026 at 11:33PM

Show HN: 49Agents – Infinite canvas IDE for AI agents https://ift.tt/sWCyP60

Show HN: 49Agents – Infinite canvas IDE for AI agents https://ift.tt/No5yS1n April 28, 2026 at 07:36AM

Show HN: Vibe-coding video games with Claude (Day 14: Tetris) https://ift.tt/7hUQtGO

Show HN: Vibe-coding video games with Claude (Day 14: Tetris) I used to run a flash games website (SWF files) years ago. I've made a few games of my own. I'm also an avid gamer and love to play games of all kinds. I'm also a software engineer, and a few days ago I decided I wanted to run a games website again. So I bought the domain gamevibe.us and with the help of Claude I've been vibe-coding one video game every day since. Happy to answer questions, take feedback, etc https://ift.tt/KUuJpWX April 27, 2026 at 11:03PM

Show HN: WaveletLM – wavelet-based, attention-free model with O(n log n) scaling https://ift.tt/ga6ZAI9

Show HN: WaveletLM – wavelet-based, attention-free model with O(n log n) scaling WaveletLM is a wavelet-based, attention-free architecture that replaces self-attention with learned lifting wavelet decomposition, a Fast Walsh-Hadamard Transform, per-scale gated spectral mixing with SwiGLU activation, an inverse FWHT, and wavelet reconstruction. Combined with expanded MLPs and sparse product-key memory, this yields a model with O(n log n) scaling in sequence length. With 23.8 PPL on WikiText-103, WaveletLM beats both GPT-2 Medium, which was trained on 80× more data, and Transformer-XL Standard, which uses recurrence to extend its effective context. It is undertrained and underregularized due to budget constraints, so there is much room for development and improvement. I invite anyone who is curious to examine the model, test it out, and extend its capabilities further. All code and weights are fully open source, and a PG-19 run will be completed in 2-3 days. Generations can be done in 4-5 GB VRAM at 28.8 tokens/second, and the model is trainable in 16.25 hours with 20 GB of VRAM, both on a 5090. README for comparison tables, instructions, logs, and future plans: https://ift.tt/ItRvPb2 Weights: https://ift.tt/YpVdFUb Generations: https://ift.tt/GQwICRV... The following samples were chosen for coherence, not factual accuracy. Factuality will require scaling and downstream techniques such as RAG and instruction tuning. > The history of the city is reflected in its architecture, which includes the historic Old Town and New Castle County Courthouse Square Historic District. The building was designed by John H. Stevens, who also designed the Albany-Fulton Celebration in 1906 and built a steel-hulled shipyard on the lake shore. > The album was released on August 25, 2007 by Sony Music Entertainment and features several songs from the record including "Never Say Die", "The Show", "Don't Cry for Me Argentina" and a cover of "I Can Only Imagine (But You Are Not Alone)". > The species was first described by Swedish zoologist Carl Linnaeus in 1758 as Agaricus adustus. The genus name is derived from the Latin words perma "to tie", and pous ("like") means "with a large head". In 1821, French mycologists Jean-Baptiste de Lacaille placed it in section Cricetae of the order Carnivora. He later renamed it Spongiforma punctata after the Greek kribensis. https://ift.tt/ItRvPb2 April 27, 2026 at 12:48AM