I would love to use Voz and Ear, but I’d need a version that is competitive with other audio transcription LLMs for platform availability - meaning macOS, Windows and Linux, and supporting GPUs if available.
I love the idea! OKF 0.2 solves for the problem of the AIs generating massive amounts of documentation (far more than humans ever created) and giving it equal-weight over what a human actually approved and committed to. I've been using it to attribute my decisions with specific directions on how to ensure that it takes strong direction from my explicit decisions and clarifies implicit / AI-driven decisioning.
Adding on progressive disclosure to this is brilliant, and I love the idea of a fast, in-memory, single-binary tool. This is a great way to approach the solution to this problem.
One thing I will say though - I would never be able to use this in my enterprise. It would just be too much of an uphill battle to purchase something that is so niche in utility - this tool is not a ton different than just having the md files locally and having it use ripgrep to search over them, and telling CLAUDE to write the OKF files as well as an index when it makes changes, is it? is the index generated dynamically / is anything about the progressive disclosure different than just having the agent manage it while it documents?
If you're going for smaller teams that can buy tools without a ton of approval / procedural overhead, I think that might have some success. another possible solution would be to make the cross-repo search something that you can handle with OSS but you have to self-host, and then pay for support. if you got enough usage and penetration within an enterprise from the teams just using OSS and self-hosting, they might consider buying support after-the-fact.
Running an iOS app on Android is still gonna look like an iOS app. This seems worse than Flutter (reimplement the whole UI) or React Native (abstract on top of native UI.) If we’re ok picking a non-native UI why not just build a Cordova app?
Why don’t you have your actions run on pr or push instead of on commit? Why would you even want that? I’ve never seen actions set up that way. If I push a branch with 100 commits then it’ll only run CI once. It’ll show the rest of the commits in the UI, sure, but that doesn’t mean that it’s the same performance impact. It could very well be 1 db transaction with multiple rows written instead of 100. I think you’re reducing this problem too much without knowing their architecture.
Github runs CI per PR push (obviously), and per main-branch commit (click on commit history in any project with CI, see a build result check mark on each one - that's true if you push a dozen commits too (I've done that)), in nearly all setups I've seen. I'm not sure how much of that is required vs default though.
With enough effort, you can rather obviously run CI per PR commit (it's a programmable system), but I've never seen aUI-integrated way to track the results, aside from browsing custom job names, which is very far from what I'd call "integrated" when compared to PR-level build markers. Similarly, I'm not aware of (but would not be surprised by) any way to disable per-main-branch commit builds, aside from initial pushes.
But I haven't poked around deeply in the settings, and business-account settings are rather different anyway so those might be wildly different / more flexible / more obtuse in exciting ways. Github is a very large and complicated product at this point, darn near anything could exist if you dive through enough UI layers or use old URLs to find soft-deprecated features.
Also, honestly, 100 commits = 1 transaction? That's far more of an over-simplification than anything I've said. It's a massive product with thousands of engineers, there's no chance at all it's just one database.
Jarred is the Bun creator who just got acquihired and converted Bun to Rust. Pretty sure his job now is to burn tokens to make news headlines. Like a developer advocate for LLM usage in general.
What have they iOSed about macOS? The latest versions haven’t lost any features as far as I’m aware. But Windows lost the ability to even pin the task bar to the side of the window in Windows 11… and the ability to stop auto updates.
If anything, iOS is getting more Mac-like, especially the iPad version. It has multi windowing and a window manager now.
Everything. The whole OS has been getting locked down more with each release. More harassment about Apple accounts, harder to run non signed apps, the OS gets less and less configurable. I mean when Launchpad was released, it was clearly and iOS style interface to find your apps. Maybe you aren't old enough to remember when you could literally do anything you wanted with the OS and make it work for you.
The post is still relevant and it's not about not being able to open non-Apple software, but about Apple contacting their servers before anything is started.
Apple is not blocking a lot, but step by step they're building their way into a locked OS, and it's very obvious.
I’ve got a 32 gig m1 MacBook Pro. how would I go about trying Gemma like you mentioned? Would it run at an acceptable speed, and what could I do? Coding?
I figure it might be quite a competent general coding teacher for more, er, consumer programming languages, for want of a better word — python, PHP, JS. Seems to be pretty solid on WP knowledge too.
And I find it curiously interesting when talking about photography. I've been finding it intriguing to ask it about my own photos and make suggestions about other images to research. I just showed it three of my own photos, and asked it to analyse them and recommend photographers I should research. It recommended someone amazing I have never heard of before. But it also recommended a 19th century British photographer who happens to be my lifelong photographic hero — someone whose broad characteristics inform what I do without me slavishly copying them. Bit of a jaw-dropping moment for it to have picked up their influence in subject matter that they would never have approached.
I'm still suggesting it more to people for them to see what the small-model future might look like, because it's so much more capable than one might expect.
On an M1 Max I have been using either Unsloth Studio (which is basically a web app) or LM Studio (nicer app on the Mac). You can use the Google AI Edge Gallery to play with the smaller Gemma models (but at the moment the QAT variants don't seem to be there unless I am missing something).
I think it's likely the 26B QAT model won't fit in your machine — you may be able to fit one of the UD_Q3 or UD_Q2 variants but whether you'll be able to run other things you want at the same time, I don't know.
(The QAT models are "quantization aware training" — AIUI the model weights have been assigned during training to survive four-bit quantization with less loss.)
(I don't think the M1 really gets much benefit from MLX, in case you were wondering, though I could be wrong)
My interest in this model is largely to really get to grips with what small models can actually do, especially with tool calling, because I think it helps comprehend what the value proposition of the cloud models is.
I have been very surprised by the quality and clarity of its answers. It's also helped me understand that much of a typical harness system prompt is likely to be unnecessary now; Gemma 4 seems to be pretty sensible out of the box.
You're absolutely not going to be able to get it to go off and build whole apps from a long prompt; it is not that good, but it does tool calling and thinking, and you should be able to explore pointing a coding harness at it if you turn on LM Studio or Unsloth Studio's API server. You could also use the Llama system tray app (formerly LlamaBarn) or just use llama-server from the llama.cpp distribution.
Probably Pi is going to be a better harness because it can have a minimal system prompt, though I've not tested it with Pi myself.
It seems to know PHP and SQL to a fairly decent depth (and I suspect JS and Python). It also has a unified vision model (it doesn't need a separate mmproj sidecar thingy) that is fairly fast, and it is quite impressive at image analysis.
So you could probably use it to generate image descriptions and tags, summarise text, generate wordpress snippets, that sort of thing.
It can capably answer questions like "Can you characterise this image and suggest further similar images I might like?" — I am currently using this to provoke me to take photos again.
Have a play with the E4B edge model, too — again, much more interesting than I expected.
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