armanj

reminds me of the last year hype around SesameAILabs/csm, people losing their mind over how realistic it was, only for them to disappear and never ship. sorry can't get hyped over some mp3s.

This part of a recent talk points out exactly this problem, and (likely) why human expertise will remain important in the age of LLMs doing everything: https://youtu.be/X7vf7koPxKg?t=2806

also ios 27. this is the second time Apple Wallet has frozen when I tried to pay at the cashier. It's really embarrassing.

i found it a bit later than others and didn't really explored it deep; but yeah still couldn't find what i was looking for out of the box

I've had a fairly thorough exploration of how I can give my agent a whiteboard so we can work on architectures together. To my surprise, current solutions (including Excalidraw) were not good enough and didn't deliver what I wanted. I ended up finding Mermaid to be the most agent-friendly medium and coded an Obsidian plugin for it. It works okay; we can work on the same doc while I bring my own AI agent, and we can brainstorm together. https://community.obsidian.md/plugins/mermaid-relay

it's a freezer lgtm

"meta" and "personal". one of you is lying.

systemd is all you need

You're absolutely right

any particular reason not using terra or sol, given the generous limit quotas and frequent codex resets?

might as well rethink about their pricing on gpt-realtime. for a quick test, spoke to that model for ~5 minutes, ended up spending near $1.

the moment debate becomes philosophical it's over

Bonsai 8B and 1.7B were on Qwen3.5 the benchmark is from a few months ago. However I'll add Qwen3.6 to the benchmark too.

Bonsai vs Qwen (quick) Benchmark: https://github.com/ArmanJR/PrismML-Bonsai-vs-Qwen3.5-Benchma...

can someone tldr me why choose apple container (and its ui) over docker (and orbstack)

one main marketing leverage of 23andMe, AncestryDNA, etc are fulfilling the curiosity of people who want to know which part of the world their genes are from. I guess that dataset should be preparatory.

funny how we may have to wait even longer for llms to pick up this update in their pre-training

useless fact: there is no mention to "gpt" in this article. the ai is referred to as "An internal OpenAI model".

I'm in Toronto and I can confirm.

i assumed you're connected to internet and using codex/claude code

for vibe coding stuff, especially when you're outside touching grass, I believe MacBook Neo is perfect. it fills the gap between the phone remote control (which is too painful for chatting with ai cli) and, well, not having any dev device.

seems like `karpathy/autoresearch` on steroids

> buy

good luck

How reliable is this uptime? and why it's sooo different from gh's official status numbers?

i've been a zed user for almost 6 months. i've encountered maaany bugs which i reported, or that were already reported. they're still there. meanwhile, every single update shipped a feature or bugfix for "ai agents".

not sure how 1.0 ships with that massive pile of bugs. but ai agents are the first-class citizen in this editor, and developer experience is not a priority.

funny thing is i uninstalled zed right before the 1.0 release. kinda relieved i didn't miss anything.

I have a few lightweight apps using deepseek api, and funny how the initial credit I topped up for using r1 is still left. Nothing makes the user happier than getting more for less. cc: anthropics with its fancy token-wasting claude code "features"

hn is this true

I did a quick benchmark & compared it with Qwen3.5: https://github.com/ArmanJR/PrismML-Bonsai-vs-Qwen3.5-Benchma...

in my results, accuracy-wise Ternary-Bonsai-8B is on par with Qwen3.5-4B. But in accuracy-per-byte, bonsai is the clear winner:

=> Ternary-Bonsai-1.7B achieved 65.1% from 462 MiB, beating Qwen3.5-0.8B by 12 points while being ~5% smaller on disk. => Ternary-Bonsai-4B is the accuracy-per-byte winner above 1 GiB. 83.0% from only 1.1 GiB, within 2 points of Qwen3.5-4B at 40% of the weight size.

they show strong promise on edge devices and where disk space is limited. I think this lab is worth watching.

while it seems even with 4.7 we will never see the quality of early 4.6 days, some dude is posting 'agi arrived!!!' on instagram and linkedIn.