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.
Figma alternative where AI works with vector primitives, not code
Show HN: Figma alternative where AI works with vector primitives, not code
SyncVibe
Show HN: SyncVibe – Code with friends in the terminal, each with your own AI
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.
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!).
GhostBox
Show HN: GhostBox – disposable little machines from the Global Free Tier.
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/
Brainio
Show HN: Brainio – Markdown notepad that turns notes into visual mind maps
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!)
Furwall
Furwall is a tiny macOS menu bar app. While you're at the keyboard or mouse, the FaceTime camera looks for a human face or upper body. When it doesn't find one, the keyboard stops accepting input. Cat walks across your laptop, nothing happens to your code. Some notes: Apple's Vision framework runs locally. Video is processed in memory and never uploaded. On a block, Furwall saves one local JPEG to ~/.furwall/catpures/. A second Vision pass throws out anything that isn't a cat, so the daily count in the menu only reflects confirmed cats. There is now a folder on my disk that is slowly filling up with photos of Pepper and Beets walking across my keyboard. The camera turns on only while you're at the computer (typing, mouse motion, app switch, screen wake) and powers down 30 seconds after the last activity. The green camera dot tracks that. The keystroke drop uses a CGEventTap at .defaultTap. Furwall ships unsandboxed because of this. A .listenOnly tap with Input Monitoring is enough to see keys, but dropping them needs .defaultTap, which needs Accessibility, which the App Sandbox blocks. Watching keystrokes is sandbox-compatible; stopping them is not. Mouse events are observed (to wake the camera) but never intercepted or dropped, so the menu bar always works. Three escape hatches: click the icon and quit, mash Escape five times in 1.5 seconds for a 5-minute pause, or revoke Accessibility in System Settings (macOS invalidates the tap). If Vision stalls for any reason the keyboard fails open after 10 seconds, which is better than soft-bricking the machine. Furwall never uploads camera frames or keystrokes. Its own network traffic is Sparkle update checks plus the donate sheet's anonymous totals/click counter. One short charity slug per click, no user identifier. The donate item in the menu opens the donate page of a vetted animal-welfare charity for your system Region. Ten orgs across nine regions: Alley Cat Allies and PetSmart Charities in the US, Cats Protection in the UK, Cat Protection Society NSW in Australia, Toronto Cat Rescue in Canada, NSPCA in Ireland, SPCA in New Zealand, Deutscher Tierschutzbund in Germany, La SPA and Fondation 30 Millions d'Amis in France, Japan SPCA in Japan. Each org is registered or recognized under its local charity or nonprofit regime, and the list gets re-vetted every release. No money flows through the app. macOS 15+, signed and notarized, MIT. https://olliewagner.com/furwall
BattleClaws
Show HN: BattleClaws – A battle arena where AI agents fight autonomously
Free tool to see how much AI bots are costing your site
Show HN: Free tool to see how much AI bots are costing your site