Posted by david927 11 hours ago
Ask HN: What are you working on? (August 2026)
It's an automation game and factory builder with "falling sand" physics. Think Noita meets Factorio.
I also remember playing that Powder Toy game on my phone in grade school, and I have a ton of nostalgia for these kinds of physics.
Will there be significant hidden mechanics/riddles/lore built into the world in a Noita-like way, or is the physics engine the limit of that comparison?
How does such a deal work? Do they contact you? Or are there places where you can promote your game to get it on Game Pass?
It's my publisher (Hooded Horse) handling it so not sure about the exact process. I don't think it's easy to get a day 1 deal though so I'm very grateful for it.
Good luck on your game & launch!
It started getting some search impressions and clicks, so I decided to launch the next 9 of the top 10 ideas that ChatGPT came up with for this type of site - won't post all of them here, but https://the-dwell.com/ (data on ship and truck dwell times/port congestion) is doing the best thus far from a traffic perspective.
First I had it send me a weekly analysis of the GA4/GSC data to see what could be improved about the site, but I've since automated that - each week it reviews traffic and search queries, then updates articles or creates new ones to meet demand. So now in theory I have a full engine to launch these sites and have them self-improve over time. Kinda neat to see all of that being done autonomously.
The thing that these sites really need are some backlinks, so I'm thinking through how to get those. I had ChatGPT generate some lists of places that might find real value in linking to these sorts of things, but I'm unwilling to let an AI agent start emailing people. I'm going to try sending some emails to some of the places that seem like a really good fit, see what the response is, and if it's good try to figure out how to do that on a larger scale without AI email spam.
But you're right that it'd make sense to have an aggregate view on the level above (e.g. https://the-dwell.com/anchored). I shall go command Fable to figure out all the places where this would be appropriate and add it.
https://store.steampowered.com/app/4564800/Dictatorship_Simu...
We will soon add more import conveniences, like asking your llm to generate importer for your special pdf formats. See https://finbodhi.com/changelog for details.
We also write about related topics:
We wrote about comparing investment options: https://finbodhi.com/docs/blog/compare-charts
Benchmarking your returns: https://finbodhi.com/docs/blog/benchmark-scenarios
Understanding double entry account: https://finbodhi.com/docs/understanding-double-entry
https://feedbun.com - a browser extension that decodes food labels and recipes on any website for healthy eating, with science-backed research summaries and recommendations.
https://rizz.farm - a lead gen tool for Reddit that focuses on helping instead of selling, to build long-lasting organic traffic.
https://persumi.com - a blogging platform that turns articles into audio, and to showcase your different interests or "personas".
Took a long break earlier this year to recharge, but now I'm back at it again, mostly working on Feedbun, about to launch it as an early alpha. :)
Parallelly, I'm working on an attention-based memory retrieval system that achieved SOTA on LongMemEval, LoCoMo, and code retrieval benchmarks. https://github.com/AttemorySystem/attemory/
Maybe later I'll build a live-streaming AI agent that plays Slay the Spire while conversing with viewers and remembering everything in chat!
What do you mean by agent in this context? Does it mean LLMs?
What search problem are you solving for deck building and is MCTS for combat the same as that in Go AIs?
I categorize the game's actions into three types: combat, deck-building, and other interactions.
For combat, MCTS is used. Yes, it's similar to the approach used in Go AI.
For deck-building, a sparse graph search is used: the goal is to rapidly identify winning deck templates within the graph structure.
Only the remaining aspects are delegated to the LLM to make reasoned decisions.
I really enjoy working on the app though. The backend is Elixir, with a Phoenix LiveView dashboard. I've got a cluster of 3 nodes running, talking to each other. The probes that check the monitoring targets are tiny go binaries, spread across hosting providers across the world. I've also got synthetic probes now, running in little playwright sandboxes.
A lot of the challenges have been around how to distribute work across the probes, with different strategies for the basic TCP/HTTP probes vs the synthetic workers, and how to effectively store large amounts of events (Clickhouse).
You send it your events via email, online, in-app, or text message even, and it puts it into your calendar for you, letting you know if there’s conflicts and keeping track of who is assigned to what, not just who’s attending or not. My goal is to have it proactively notify you when there’s potential issues like rain, traffic, or something else.
You can connect it to ChatGPT, Claude, or other agent through MCP so you don’t even have to use the built-in AI. And there’s an API, SDK and CLI for those who want to tinker and build custom displays or interactions.
There’s an always-free version to get started and try it out. You can find it at https://mavolife.com
I've been a bit obsessed with chess lately and have read/heard things like "X is the top engine move, but no one under master plays that line". To me this begs the questions "which line are players below master playing" and "which line are players at/above/below my rating playing". This question motivated me to build a tool to analyze the move distribution (win/draw/loss rate) from a given position across rating buckets (400, 500, 600... 3000). Luckily, https://database.lichess.org/ provides an open/free database of games that have just the data required to build such a tool.
The Rated Analysis tool consists of an 1) an offline job to compute/store the positional move distribution bucketed by rating, and 2) the https://rated-analysis.pickle-dev.com/ webpage to serve the data along with some nice to haves like loading user games from platforms like chess.com/lichess.org.
The bet is that current models are good enough that anyone can one-shot a fun browser game. What they can't generate is the multiplayer part (server, WebSockets, state sync, hosting), so that's the only part we've built: a browser SDK plus a hosted relay. Deploy a game, get one link, and everyone who opens it is in the same room.
Quickest way to see it: open any game on https://antics.gg/games in two tabs.