Just shipped individual pages for every programming language tracked in the HN Hiring Index.
Each language gets its own page, mention history, salary ranges, common stacks, seniority breakdown, visa sponsorship rate, and how it compares to peers.
→ adamtal.me/hacker-news-hirin…
In the next version of Kai:
Kai–Git bridge makes transitions painless: Kai users keep capturing semantic snapshots and assertions; the bridge writes tidy git commits with Kai evidence in commit trailers.[1/2]
Kai capture just went from 9.2s to 2.1s on a 64K file repo.
Replaced JSON zero heap allocations on read. The same approach git uses for its index, adapted for Go.
v0.9.58: github.com/kaicontext/kai/re…
Shipped 10 releases for Kai this week (v0.9.14→0.9.23).
Now:
• capture/push = change-aware (not O(all files))
• CI skips unaffected work
• tracks AI vs human code
You AI can actually understand your codebase.
github.com/kaicontext/kai/di…
Next version of Kai:
Created a pull-through cache on Artifact Registry, one command, zero Dockerfile changes.
First pull hits Docker Hub, everything after is cached.
In this version of Kai:
We open-sourced Kai Server!
Also shipped a CI system that deploys itself — all in one week.
Kai CI builds and deploys Kai.
Hosted:
kaicontext.comgithub.com/kaicontext/kai-se…
This release gives you Kai grep.
Now I use Kai grep instead of Claude code grep
Yesterday, I had Claude refactor. A standard text search made it look safe.
But Kai grep flagged a hard dependency in another module.
I would have shipped a production bug.
github.com/kailayerhq/kai
Super excited because in a few seconds Kai will be deployed by Kai.
Once this is done, Kai Server will be open sourced, and you can do this too on your own hardware or cloud.
Your agents will be so happy.
A few pilot teams told me the same thing after using Kai:
“This gives me peace of mind shipping AI-generated code.”
AI can generate changes faster than humans can review.
Kai gives both the developer and the AI agent a shared understanding of the repo.
github.com/kailayerhq/kai
Claude Code: 100k tokens, 20 min for my refactor.
With Kai: 20k tokens, 2 min.
Semantic infrastructure for AI agents. Call graphs, dependencies, impact analysis — structured, not grepped.
claude mcp add kai -- npx -y kai-mcp
Open source: github.com/kailayerhq/kai
Hours of coding made 10-minute CI feel free. Feedback loop? Not your problem.
Agent does it in 5 minutes. Now CI is the feedback loop.
Nothing changed in your pipeline. Everything changed in your baseline.
The number one CI strategy most teams rely on is:
“Just run everything.”
It works — as long as compute is cheap and teams are small.
But what happens when PR volume doubles?
What happens when agents generate 10x more changes?
my family is moving and I have my Riven discs from my childhood.
They are in perfect condition but I kept them in this book.
I want them to have a special home.
Would you want them?
Cc @hannahgamiel in case