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Clay Christensen's Milkshake Marketing (2011)

https://www.library.hbs.edu/working-knowledge/clay-christensens-milkshake-marketing
2•vismit2000•2m ago•0 comments

Show HN: WeaveMind – AI Workflows with human-in-the-loop

https://weavemind.ai
3•quentin101010•8m ago•0 comments

Show HN: Seedream 5.0: free AI image generator that claims strong text rendering

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A contributor trust management system based on explicit vouches

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Show HN: Analyzing 9 years of HN side projects that reached $500/month

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The Floating Dock for Developers

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Arcan Explained – A browser for different webs

https://arcan-fe.com/2026/01/26/arcan-explained-a-browser-for-different-webs/
2•walterbell•14m ago•0 comments

We are not scared of AI, we are scared of irrelevance

https://adlrocha.substack.com/p/adlrocha-we-are-not-scared-of-ai
1•adlrocha•15m ago•0 comments

Quartz Crystals

https://www.pa3fwm.nl/technotes/tn13a.html
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Show HN: I built a free dictionary API to avoid API keys

https://github.com/suvankar-mitra/free-dictionary-rest-api
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Show HN: Kybera – Agentic Smart Wallet with AI Osint and Reputation Tracking

https://kybera.xyz
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Show HN: brew changelog – find upstream changelogs for Homebrew packages

https://github.com/pavel-voronin/homebrew-changelog
1•kolpaque•25m ago•0 comments

Any chess position with 8 pieces on board and one pair of pawns has been solved

https://mastodon.online/@lichess/116029914921844500
2•baruchel•27m ago•1 comments

LLMs as Language Compilers: Lessons from Fortran for the Future of Coding

https://cyber-omelette.com/posts/the-abstraction-rises.html
2•birdculture•29m ago•0 comments

Projecting high-dimensional tensor/matrix/vect GPT–>ML

https://github.com/tambetvali/LaegnaAIHDvisualization
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Our Stolen Light

https://ayushgundawar.me/posts/html/our_stolen_light.html
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Matchlock: Linux-based sandboxing for AI agents

https://github.com/jingkaihe/matchlock
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Show HN: A2A Protocol – Infrastructure for an Agent-to-Agent Economy

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Drinking More Water Can Boost Your Energy

https://www.verywellhealth.com/can-drinking-water-boost-energy-11891522
1•wjb3•44m ago•0 comments

Proving Laderman's 3x3 Matrix Multiplication Is Locally Optimal via SMT Solvers

https://zenodo.org/records/18514533
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Fire may have altered human DNA

https://www.popsci.com/science/fire-alter-human-dna/
4•wjb3•47m ago•2 comments

"Compiled" Specs

https://deepclause.substack.com/p/compiled-specs
1•schmuhblaster•52m ago•0 comments

The Next Big Language (2007) by Steve Yegge

https://steve-yegge.blogspot.com/2007/02/next-big-language.html?2026
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Open-Weight Models Are Getting Serious: GLM 4.7 vs. MiniMax M2.1

https://blog.kilo.ai/p/open-weight-models-are-getting-serious
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Using AI for Code Reviews: What Works, What Doesn't, and Why

https://entelligence.ai/blogs/entelligence-ai-in-cli
3•Arindam1729•1h ago•0 comments

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https://www.solnix-lang.org/
3•maheshbhatiya•1h ago•0 comments

DoNotNotify is now Open Source

https://donotnotify.com/opensource.html
5•awaaz•1h ago•2 comments

The British Empire's Brothels

https://www.historytoday.com/archive/feature/british-empires-brothels
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What rare disease AI teaches us about longitudinal health

https://myaether.live/blog/what-rare-disease-ai-teaches-us-about-longitudinal-health
2•takmak007•1h ago•0 comments
Open in hackernews

Show HN: SwiftAI – open-source library to easily build LLM features on iOS/macOS

https://github.com/mi12labs/SwiftAI
74•mi12-root•5mo ago
We built SwiftAI, an open-source Swift library that lets you use Apple’s on-device LLMs when available (Apple opened access in June), and fall back to a cloud model when they aren’t available — all without duplicating code.

SwiftAI gives you: - A single, model-agnostic API - An agent/tool loop - Strongly-typed structured outputs - Optional chat state

Backstory: We started experimenting with Apple’s local models because they’re free (no API calls), private, and work offline. The problem: not all devices support them (older iPhones, Apple Intelligence disabled, low battery, etc.). That meant writing two codepaths — one for local, one for cloud — and scattering branching logic across the app. SwiftAI centralizes that decision. Your feature code stays the same whether you’re on-device or cloud.

Example

  import SwiftAI
   
  let llm: any LLM = SystemLLM.ifAvailable ?? OpenaiLLM(model: "gpt-5-mini", apiKey: "<key>")

  let response = try await llm.reply(to: "Write a haiku about Hacker News")
  print(response.content)
It's open source — we'd love for you to try it, break it, and help shape the roadmap. Join our discord / slack or email us at root@mit12.dev.

Links

- GitHub (source, docs): https://github.com/mi12labs/SwiftAI

- System Design: https://github.com/mi12labs/SwiftAI/blob/main/Docs/Proposals...

- Swift Package Index (compat/builds): https://swiftpackageindex.com/mi12labs/SwiftAI

- Discord https://discord.com/invite/ckfVGE5r and slack https://mi12swiftai.slack.com/join/shared_invite/zt-3c3lr6da...

Comments

deanputney•5mo ago
Awesome, this is a good idea! Having a nice wrapper to make LLM calls easier is very helpful too :)

Nice to see someone digging in on the system models. That's on my list to play with, but I haven't seen much new info on them or how they perform yet.

mi12-root•5mo ago
We’ve begun internally evaluating the model and will share our findings more in details later. So far, we’ve found that it performs well on tasks such as summarization, writing, and data extraction, and shows particular strength in areas like history and marketing. However, it struggles with STEM topics (e.g., math and physics), often fails to follow long or complex instructions, and sometimes avoids answering certain queries. If you want us to evaluate a certain use case or vertical, please share it with us!
keyle•5mo ago
Needs more example on custom.
mi12-root•5mo ago
Thanks for the feedback! When you say “custom,” do you mean additional integrations with LLM providers, or more documentation on how to build your own custom integration? If you mean the former, we’re currently focused on stabilizing the API and reaching feature parity with FoundationModels (e.g., adding streaming). After that, we plan to add more integrations, such as Claude, Gemini, and on-device LLMs from Hugging Face.
jdmg94•5mo ago
There is no examples or documentation on `CustomLLM` the README file has examples on `SystemLLM` and `OpenaiLLM` but there's no way for us to know if we need to bring in guff files, ollama, hugginface, etc.
achrafmam2•5mo ago
I’ve added documentation on creating new LLM integrations in SwiftAI [https://github.com/mi12labs/SwiftAI/blob/main/Docs/Guides/cu...]. We also have a proof-of-concept using MLX-optimized models from Hugging Face [https://huggingface.co/mlx-community], which we’ll release once it’s ready.
achrafmam2•4mo ago
We added an experimental support for MLX see README [https://github.com/mi12labs/SwiftAI/?tab=readme-ov-file#loca...]. We also added an chat demo where you can interact with 20+ models [https://github.com/mi12labs/SwiftAI/tree/main/Examples/ChatA...]
jc4p•5mo ago
I do a lot of AI work and right now the story for doing LLMs on iOS is very painful (but doing Whisper or etc is pretty nice) so this is existing and the API looks Swift native and great, I can't wait to use it!

Question/feature request: Is it possible to bring my own CoreML models over and use them? I honestly end up bundling llama.cpp and doing gguf right now because I can't figure out the setup for using CoreML models, would love for all of that to be abstracted away for me :)

mi12-root•5mo ago
That’s a good suggestion, and it indeed sounds like something we’d want to support. Could you help us better understand your use case? For example, where do you usually get the models (e.g., Hugging Face)? Do you fine-tune them? Do you mostly care about LLMs (since you only mentioned llama.cpp)?
jc4p•5mo ago
Thank you! I’ve been fine tuning tiny Llama and Gemma models using transformers then exporting from the safetensors that spits out — My main use case is LLMs but I’ve also tried getting YOLO finetuned and other PyTorch models running and ran into similar problems, just seemed very confusing to figure out how to properly use the phone for this.
mi12-root•5mo ago
Thanks for sharing the details—that makes a lot of sense. Fine-tuning and exporting models on-device can be tedious nowadays. We’re planning to look into supporting popular on-device LLM models more directly, so deployment feels much easier. We'll let you know here or reach out to you once we have something
rafram•5mo ago
You may get a letter from Apple’s lawyers because of the name - Swift and SwiftUI are trademarks, and this seems like something they’d want to keep for themselves.
andsoitis•5mo ago
> Swift and SwiftUI are trademarks

This is called SwiftAI, though.

reactordev•5mo ago
If they can prove enough similarities or overlap with their brand, they’ll find the way. And since it targets macOS/iOS specifically, there you go.
steve1977•5mo ago
Als considering that Apple probably has Apple Intelligence trademarked
rafram•5mo ago
Right, and if it were called IBM AI, they’d get a letter from IBM instead. You can’t just tack something onto the end of a trademarked brand name.
farseer•5mo ago
IBM and Apple are company names, which means the intent to mislead can play a role during legal proceedings. Compared to banning the use of "Swift" in any phrase or sentence by any future company for the foreseeable future.
leobg•5mo ago
Or from Taylor’s.
farseer•5mo ago
What if I make a SwiftDelivery app? Would that also get me sued?
fragmede•5mo ago
Probably not. I am not a lawyer though. The Apple Corps vs Apple Computer lawsuit may make for some interesting introductory reading about various aspects of intellectual property law for you, if you are so inclined.

https://en.wikipedia.org/wiki/Apple_Corps_v_Apple_Computer

lawgimenez•5mo ago
Another vibe coded.
mi12-root•5mo ago
While we did use AI coding tools, we put significant thought into the design of SwiftAI and would greatly value feedback on it (see system design doc above). All AI-generated code was carefully reviewed and often, rewritten (no yolo)
hbcondo714•5mo ago
Mind me asking what your thoughts are on the overall quality of Apple's on-device LLMs? I've found that LanguageModelSession always returns very lengthy responses:

https://developer.apple.com/forums/thread/789182?answerId=85...

mi12-root•5mo ago
I tested the system LLM with a long article using two prompts: one asking for a summary in at most 20 words, and another asking for a one-sentence summary. In both cases, the model followed the instructions correctly. Regarding your second point in the link above: maximumResponseTokens: 500 corresponds to roughly 1,500–2,000 characters in English. For the AFM tokenizer, a token typically represents 3–4 characters. Could it be why you are getting large outputs? If you share your prompt(s), we’d be happy to take a closer look. You can reach us on Slack, Discord, or privately at root@mi12.dev