There have been a few posts from people who have used these XREAL or other AR glasses with an Android and foldable keyboard.
Also, IIRC there was reporting that Apple considered doing the compute for Vision Pro on a device at the hip. Instead of what they ended up with which was just a battery.
I could see this coming back around again where a future iPhone is all of the brains or a lot of the brains for AR glasses.
Hey, I enjoyed reading through your blog entry. What do you see as the natural conclusion of this work so far? How far do you think you would be interested in taking it, provided enough tokens and time?
Of this work? We're taking it all the way to shipping a distro with full hardware support on all released Macs and later to upstream Linux. We've taken significant care to respect Apple's copyright and publicly document all our processes. We don't think this will take too long, our development velocity has been pretty good, see: the GPU driver.
Hello, I'm Rob and I've been working on Contextify since October of last year. Besides creating Back to the Future time machines for the docs page [1], I've been working on adding a Windows client since mid-July.
Contextify indexes your entire local Claude Code and Codex sessions into a local SQLite/FTS index as they happen. This lets you aim your agents at your real-time and complete historical AI-coding (or other project type) history.
It works purely locally but has an option for hosted or self-hosted cloud sync to read it on another machine, along with a hosted option if you want turn-key.
HN and some existing users requested a Windows client. In some cases it is because their self-hosted setups include Mac, Linux and Windows boxes that are all generating CLI AI transcripts (sometimes headlessly)
This product started as a macOS app; 1.8.0 brings it to Windows (x64 and ARM64) and extends the Linux CLI. It is free for personal use and has a source-available self-hosted option. I have commercial licenses available, and a few very much appreciated customers so far.
On Windows it's a background agent plus a CLI, with no history-browsing UI yet.
Lots of interesting development was needed to pull this off (including a shared Swift core!) in a way where it could be updated as easily as prior to the release. I'll share some of that in a comment below.
If you use Windows and would be open to giving this a go, I'd appreciate any feedback here or via rob@contextify.sh.
I basically live in rio all day long. It’s fast. Looks great. Only annoyance is I have to do
TERM=xterm-256color ssh … cuz they don’t know what Rio is
But otherwise it’s awesome
Ghostty I like too. Fast hackable easy to configure I just use it every now and then probably because launching it via spotlight is longer than typing Rio
Interesting to see all of these tab / orchestration features.
I have a bunch of skill tooling that build out iterm2 windows that cover worktrees specified in a project-config.yml. It even includes tab color.
More recently I added a skill to recover all of these windows in their prior state including resuming the conversation the agent was running. This is to recover from the need to restart from an OS update or something bad happened.
I use skills all day, every day. One of my greatest annoyances right now is having to switch between `/` and `$` in moving between CC and codex. At one point I attempted to hack the CC TUI to accept $ but failed, I may yet return to that.
All of my skills are custom to my workflows except Contextify (more on that at the end) This extends to how I distribute them across multiple development machines.
They live in my `cli-ai-setup` repo alongside agent settings, git worktree tooling, iTerm workspace restoration (important, machines have to restart and crash sometimes), code review scripts, and machine setup guides. I use git to carry changes between machines and a setup script to symlink the skills into a shared directory that Claude Code and Codex both use.
I have a `skills-and-settings` skill specifically for deciding where new skills belong and how to make them available (project, global, application). I also have a custom `skill-create` skill that turns sessios into new skills or updates existing ones.
Importantly, I also have entire custom applications I have not yet made open source that my cli-ai-stack relies on. I do expect to distribute these so they live in their own repo and are symlinked or installed in as appropriate.
For maintenance, I've largely handled this manually and organically. When a skill is not performing, I'll use the context of the situation as the ~1 shot or pull in more examples for the ai:
This skill seems to not be performing as expected on [something].
This has happened a couple of times now use /total-recall to find similar recent situations for example [something I remember]"
Recommend updates to the skill and upon approval commit and push them...etc.
My other machines watch this repo and the symlink structure means that the updates are carried into live cli-ai sessions almost immediately.
This past week I was exploring the automatic skill improvement behavior described in the Anthropic blog guest post with their partner org. I'd previously build a "dreaming" skill that works okay and think there may be some value yet to plumb there.
For skill creation, I have a skill that reads the official skill docs for both Claude Code and Codex. This way the skills are built to handle both platforms particularities. I automatically pull those docs into local Markdown daily, so it has a regularly refreshed reference for what each tool supports.
As mentioned above, I have built Contextify (https://contextify.sh) which provides a sql database of all of my Claude Code an Codex session transcripts across all of my development machines. The skill for this (/total-recall) is the most important skill I have and I use it constantly.
I also use "do not overengineer and do not gold plate." I also sometimes tell it to "avoid bike shedding." These are high signal words to the model(s) and they work!
I also have found what I believe is a bad training bias in the design of release related CI workflows toward proof of release artifact provenance.
Both major frontier models love provenance programming in CI, so much that they will spin endlessly trying to solve basic CI functionality at the same time as ensuring SHA's match up across lengthy (often already complex) cross-system pipelines.
I had thought some of my durable context was causing this, and sought to strip anything that might be triggering this behavior.
But then I come upon some more work in release workflows comes up, and boom its back! I couldn't believe it, I called the AI out on it and it agreed it had been told specifically not to do this but was doing it anyway. It did kindly stop and remove the commit(s).
Somewhere, something was oversampled in training because the AI will try their damndest to build this stuff. The worst of it is that it can often involve lengthy, sometimes resource-heavy CI runs so the validation of this unnecessary stuff can have very long feedback loops.
And, sometimes you actually need the provenance. In this case, I've had success forcing the AI to split the work up into functional capability completely devoid of artifact ~chain of custody and get that right before attempting any kind of provenance work.
Bit of a rabbit hole on this, but the above cost me a lot of burned tokens so hopefully helps someone...or some AI.
I’m interested! I struggled to get goos detail on my Back to the Future Delorean 3D animation on my docs hero: https://contextify.sh/docs/
I posted this previously and people wanted more info so here is how I built it, with a deeper dive into some film scene recreation I worked on focusing on a scene from Apocalypto:
It is a 3D-ish model being animated by a procedural motion system, rather than a pre-rendered/model animation.
The car started from a low-poly DeLorean glTF. I had the model rebuild the geometry into inline Canvas code rather than just dropping a model into three.js.
The flight itself is procedural: position/rotation, hover, pitch, wobble, flames, camera, etc. are all driven by code and can be scrubbed/debugged.
That link has a link to an interactive page where you can adjust the car’s path and when it reaches 88mph if you want to see.
It is not mobile friendly atm, though.
I would like to apply textures to the people and scenery in the gauntlet scene. Just haven’t had the time or tokens, as getting motion and pathing and camera angle right seemed the mvp.
On the Fable 5.2 eval summary, Opus 5 only beats Fable on SWE-bench multilingual and multimodal.
I primarily use the models via interactive sessions enhanced with custom tools and skill. For that Opus 5's benchmark superiority has not materialized into greater productivity and frankly has been quite a let down.
The outputs are too often unreadable even after adding recommended prompts. There is an ongoing problem with the heron_brook system prompt affecting orchestration. [1]
I've used Opus 4.8 since the second week Opus 5 was released.
Over this time, Fable 5 has been reliably fantastic. Both in planning and direct execution on complex changes across code and infra.
I'm a bit surprised that there doesn't (seem) to be a section discussing ~performance across different modalities. This system card and blog post too-often default to an API-based use case when the gander primarily experience Anthropic's models via interactive sessions.
I understand waiting to comment until Opus 5.1 is available and handles these problems, though I am hopeful that Anthropic will confront the elephant in the room on Opus 5's failure to delivery great interactive sessions and the widespread negative feedback on the release.
It would show the org is paying attention, taking steps to balance model evals between interactive and API use. Also, some empathy for customers that wasted time trying to make opus 5 work for them.
Also, IIRC there was reporting that Apple considered doing the compute for Vision Pro on a device at the hip. Instead of what they ended up with which was just a battery.
I could see this coming back around again where a future iPhone is all of the brains or a lot of the brains for AR glasses.
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