Product Catalog

IB

I built a new word game, Wordtrak

Hi HN! Looking for feedback on this 1v1 and daily word dueling game I've built over the last few months. Play here: https://wordtrak.com/ Or on iOS here: https://apps.apple.com/us/app/wordtrak/id6760442363 (Android version soon!)

BR

Brainio

Show HN: Brainio – Markdown notepad that turns notes into visual mind maps

BH

Bhatti

Bhatti spins up Linux VMs on any box with KVM — Pi 5, Hetzner AX, cloud VM with nested virt. - Each VM has its own kernel, filesystem, and IP - Idle VMs pause their CPUs and snapshot themselves to disk; the next request wakes them in 3.7ms warm or 360ms cold (p50, Hetzner AX102) - Publish any port → public URL with auto-wake on first hit - Pull any OCI/Docker image as a rootfs, or save a running sandbox as one - Multi-tenant from day one — per-user bridges, encrypted secrets, rate limits - Single Go binary, Apache 2.0 The decisions page is the most fun read on the site: vsock state after restore, why all snapshots are Full, the systemctl shim, the ARP retransmit trick. curl -fsSL bhatti.sh/install | sudo bash (sudo because the daemon needs /dev/kvm and sets up the Firecracker jailer + a bridge; the CLI-only install — pipe to plain `bash` — needs no root) Site: https://bhatti.sh Repo: https://github.com/sahil-shubham/bhatti Decisions & learnings: https://bhatti.sh/docs/under-the-hood/decisions/

GH

GhostBox

Show HN: GhostBox – disposable little machines from the Global Free Tier.

MR

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!).

AN

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.

SY

SyncVibe

Show HN: SyncVibe – Code with friends in the terminal, each with your own AI

FA

Figma alternative where AI works with vector primitives, not code

Show HN: Figma alternative where AI works with vector primitives, not code

TP

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.

LC

LLMs consume 5.4x less mobile energy than ad-supported web search

The standard AI energy debate compares server-side LLM inference to a server-side Google query. I think this misses most of what actually happens on a mobile device during a real search session. I built a parametric model of the full end-to-end mobile search session: 4G/5G radio energy, SoC rendering cost for a 2.5MB page, programmatic advertising RTB auctions running in the background, and network transmission costs for both sides. Then compared it to an equivalent LLM session. Main finding across 10,000 Monte Carlo draws: on mobile, a standard LLM session uses on average 5.4x less energy than a classic ad-supported web search session. Programmatic advertising alone accounts for up to 41% of device battery drain per session. Caveats I tried to be explicit about: - Advantage disappears on fixed Wi-Fi/fiber - Reverses for reasoning models - Parametric model, not empirical device measurement. Greenspector has offered to run terminal measurements for v2 - Jevons paradox applies SSRN working paper, not peer-reviewed. Methodology and Monte Carlo distributions fully documented in the paper. Happy to defend the assumptions. DOI: 10.2139/ssrn.6287918

AT

Atomic

Show HN: Atomic – Local-first, AI-augmented personal knowledge base

IB

I built a toy that plays grandma's stories when my daughter hugs it

This was a project I built for my daughter's first birthday present. For context, I'm a surgical resident in the UK by background and am currently taking a year out of training to study a masters in computer science. My daughter just turned one. There are two things she really loves: the first is particular soft toy that she just can't live without, and the other is a good story book. Her grandparents live hours away and I didn't want her to forget what they sound like between visits. I wanted her to hear them whenever she missed them. My parents brought my brother and I up with incredible stories and books from all sorts of cultures, many of the stories being passed down from their parents before them. I didn't want my daughter to miss out on that. Finally, I was sick of missing storytime with her when I had to leave for night shifts. I wanted her to hear my voice before she slept every night. For all these reasons, I decided to build Storyfriend. It's her favourite soft toy with a custom made speaker-module inside. I combined my surgical skills with the skills I was learning as a CS student. Along the way I dipped my toes into the world of 3D printing, CAD and electronics design. When she hugs the toy, it plays stories read by her grandparents. She can take the toy with her anywhere and hear the stories anytime she wants - it works offline and has internal storage. It meets my wife's strict no-screen rule (which is getting harder to stick to as the days go by). I've recorded some of the stories that we would read together, so that on nights when I'm working she still has me there to read her a bedtime story. The bit I'm most pleased with: grandparents don't need an app. They just call a phone number. The audio routes through my server and pushes to the toy over WiFi. My own 86-year old grandmother in a rural village in another country can do it by just making a regular call via her landline, as she has done for many years - no help needed, no apps required, no smartphones involved. Hardware is a BLE/wifi module with a MAX98357 chip and custome battery management system, all soldered together, placed in a 3D printed enclosure and placed into a compartment that I stitched into her cuddly toy. Firmware pulls new messages when connected to WiFi and stores them on an SD card. So far I've sold a few hand-made units to parents and grandparents who resonated with the project. Site: https://storyfriend.co.uk Would love feedback on the technical approach, the product itself, or anything else. Happy to answer questions about the build