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My Apple ID is locked for a week, Apple is a single point of failure

My Apple ID was locked by Apple Oct 3rd. I got an automated email stating that I will receive further instructions for account recovery Oct 10th, a week of pain. There is zero human escalation path, no phone number to call, the iforget link leads to same message as email and no further escalation.Because my Apple ID is frozen, the App Store is completely bricked. I can’t update any apps. Living in Japan, almost all of my critical banking and payment apps (like Rakuten Pay) enforce hard version checks for security. The app refuses to launch, needing update. A locked Apple ID effectively cut off my access to my money and daily transactions.My temporary workaround was digging out an old Android phone to use as a backup for my payment apps (of course these apps are only allowed on one device so now I need to carry two phones). But it made me realize how scary my setup was. If Apple decides to lock account, entire day-to-day life stops, and no one to talk to about it. Going forward I am definitely not storing any important government ID like driver license, payments cards, my number card (similar to SSN in US) on Apple devices.How are you guys decoupling your digital lives from a single OS ecosystem? What does your personal "cold standby" look like to handle this risk?My personal computing infrastructure was all in Apple ecosystem. But this incident made me realize, Apple is SPOF, I need to diversify away from Apple and be also part of non-Apple environment.

Hacker News25d agoToolAI

62FL score
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Nightwatch – a Mac menu-bar app that tells you when tonight is clear

Here in the north of England clear nights are at a premium, and it's never guaranteed that you'll be able to see anything but cloud. That's why I developed Nightwatch, so that I could be notified when one of those precious few nights where I could see into the heavens was available. Finding out whether tonight was worth setting up meant reading forecast charts every evening, usually to learn that it was not. Nightwatch does that reading for me.It sits in the Mac's menu bar and refreshes the forecast every 30 minutes. An hour before sunset it sends one notification, and only if there is an unbroken run of clear sky in astronomical darkness long enough to image in. Otherwise it says nothing: no news means "clouds".I hope this is useful for other hobbyist stargazers too!When I was a child I wanted desperately to be an astronaut or a dinosaur. I'm now old enough to be the latter and was born in a country where the former was never an option. I've had a fascination with the stars since a a friend of my father gifted me a telescope, and have faded memories of sharing time with my father staring at the stars. My father passed away a few years back and the memory of those nights came back, along with the want to share the same experience with my kids and grandchild.The app uses two forecasts: Apple's WeatherKit is the main source and Open-Meteo is a second opinion. When they disagree, the alert says so instead of averaging them.Thin high cloud counts for half. Stacking many short frames works through a veil of cirrus, so treating it as full cloud threw away usable nights.It works on your own horizon: hills come from terrain data, and you set the houses and trees in each direction, so they are accounted for when it says what is up.It works out, a year ahead, when the Moon will pass in front of a planet, a bright star or the Pleiades as seen from your site.You don't need an account. What leaves the Mac is the coordinates of th

Hacker News27d agoToolAI

75FL score
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Graphene – Data analysis toolkit for your coding agent

My friend and I have worked at a number of BI companies and thought: Can’t a coding agent do most of this now?Almost. They just need a context/semantic layer to ensure query correctness and some kind of artifact for publishing findings and visualizations.We built an open source project called Graphene that provides these tools:- Semantic layer that’s more token-efficient than YAML, more deterministic than Markdown (eg, composable, callable metric macros), with a query API that’s well in-distribution (SQL). - MDX-like files for dashboards (Markdown with inlined SQL + HTML components for viz with support for CSS and Javascript) - Connects to popular data warehouses or local DuckDBYour coding agent will build really in-depth reports and can ofc leverage any other skills or context that you've made available to it. We dogfood it inside a monorepo with our website, app source code, planning docs, etc so the agent has access to a ton of context. It’s also nice that the agent can add instrumentation, adjust pipelines and transformations, and add dashboards all in one PR.If you want to try it out, just point your coding agent at https://github.com/graphene-data/graphene/blob/main/docs/set... and ask it to set up Graphene.If you don’t have data to play with, you can clone our example project:1. `git clone <https://github.com/graphene-data/example-flights.git`> 2. `cd example-flights && npm install`Would love any/all feedback!

Hacker News29d agoToolAI

75FL score
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Made an open-source Lego AI generator

Hi there :-) New on HN, first time posting.Past year, around December, I started experimenting with making ChatGPT and Claude generate source code in LDraw language.This LDraw is literally an "assembly" language, a low-level programming language that describes how to assemble LEGO pieces together into models, one placement instruction at a time.When executed by specific tools, like e.g. LDView, LeoCAD, Studio... these instructions become LEGO CAD models, that can be interacted with, modified, etc.Or, in other words: one LDraw source file in .mpd or .ldr format is equivalent to one LEGO CAD model.So, the idea I had was: if I manage for maybe ChatGPT or Claude to generate high-quality LDraw source files... then, they would actually be generating high-quality LEGO CAD models, right?Then, after months of iterations and trying one thing after the other... it worked!!!Long story short: using GPT-6 Astra and Opus 5.5, I've managed to create a python toolset, instructions, and docs for agents in general. Now, these can be used by them to generate LDraw models.I've packed it all as a dockerized web app for others to try and experiment, with several providers (and agents) to choose from: OpenAI, Claude and OpenRouter.Here's a bunch of exmaples: https://anteloc.github.io/index-samples.html.If you are curious about the internals of an .mpd model, the "what was the agent picturing on its mind", open the .mpd file that got your attention on a text editor, and read the first line under the ones starting with "0 FILE".I'd really appreciate feedback and comments, let's see where this goes =)

Hacker News28d agoToolAI

65FL score
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Yantra – an LALR(1) parser generator for C++

Yantra is a C++ parser generator: lexer, parser, and AST walker all generated from one tool. It builds the whole AST first, then walks it.Most LALR parser generators (Yacc, Bison, Lemon) run your semantic actions during parsing, as each rule reduces, bottom-up.That means at the time a rule's action runs, you don't yet know what its parent looks like. This pushes a lot of grammars toward hand-built AST classes and a separate walking pass whenever you need to look ahead into siblings or defer a decision until more context is available.On the other hand, Yantra always builds the whole AST first, then walks it top-down in a separate pass, calling your semantic actions as it goes. A parent rule's action can run before its children are visited.A single grammar can define more than one walker. For example, one that emits C++, another that emits Java, from the same parse. The AST and the walker classes are both generated for you.A small example (full version, with compile commands, in the README): start := expr; expr := expr(a) PLUS expr(b) %{ std::cout << "Adding" << std::endl; %} expr := NUMBER(N) %{ std::cout << "Number: " << N.text << std::endl; %} NUMBER := "\d+"; PLUS := "\+"; WS := "\s+"!; Running this on "1 + 2 + 3" prints: Adding Number: 1 Adding Number: 2 Number: 3 The outer "Adding", the root of the tree, prints first, before either of its children. That's only possible because the whole tree exists before any action runs.Some other things about it: integrated lexer with mode support (for things like nested comments), an optional amalgamated single-file output mode with a generated main(), C++23, MIT licensed.It's young (0.5.1, pre-1.0) and single-maintainer, so treat it as early. I'd rather know what breaks than have it look more finished than it is.Known gaps are listed at https:/&#x2

Hacker News1mo agoToolAI

65FL score
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Open-source model routing for coding agents at Astra-level performance

A few months ago we started building a model router for coding agents because we thought we could outperform any single model with an ensemble approach. Recently we’ve achieved that milestone and I want to talk about how we did it.First of all, a quick explanation: the Weave Router (https://github.com/weave-os/router) plugs into any coding agent (e.g. Claude Code or Codex) and intelligently switches between LLMs. So, for example, Astra handles tricky debugging or complex system design tasks, and Deepseek v4 Flash handles simple frontend updates.What we’re announcing today is our new routing model, which we’re calling Weave Router 2.0. We benchmarked 2.0 against GPT-6 Astra on Terminal Bench 4.0 and SWE Atlas. On both benchmarks, the router had equivalent pass rates. On Terminal Bench, the router hit 52% of Astra’s cost, and completed tasks 2.2x faster. On SWE Atlas, the router cost 54% as much as Astra and ran 2.5x faster. (Full results on our website at https://weaveos.com/router!)It turns out training a model to route effectively - taking into consideration model capabilities, costs, cache awareness, and more - is a really hard problem! I want to talk about three ways we were able to improve so much over the last few months: 1) a new architecture, 2) larger training data set size, and 3) smarter cache-eviction impact calculation.1) a new architecture. Our initial approach used an RL model without many priors. While RL is still an important part of the story, the cost of fully exploring the space of routing decisions is very high, so we’ve taken some shortcuts that have significantly improved performance.Consider how large the search space for the routing problem is. Take a typical coding agent session, with ~100 agent turns (i.e. 100 LLM API calls). Technically there are 100 chances to select a model. If we assume a roster of ~10 models (of course there are lots more but we can remove any that are Pareto dominated), then there a

Hacker News1mo agoToolAI

78FL score
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