It had already been like this long before widespread LLM adoption: quality hiring was only really possible through manual candidate scouting on LinkedIn, at conferences, through word of mouth, and so on.
Sending a CV had already become mostly useless 3–4 years ago because of the huge amount of noise: candidates applying from all over the world, often even spoofing their actual location, FAANGs firing, flooding the market with (in theory) great candidates with great resumes.
The solution is the same one that has been successfully used forever: a trial period.
Luckily, a video interview with a senior developer is still enough to spot a good candidate.
Go through real code: add a bug to a branch of your codebase, have the candidate share their screen on TeamViewer, and let them debug and fix the issue. Ask questions live to understand how they reason about the system, how they would test whether the change works, and so on.
This will filter out 99% of candidates. But it is still possible to get lucky, which is why the trial period matters.
I’ve never had major issues and have always hired very strong engineers. I only had to terminate someone after the trial period twice.
I do the same, and faced the issue that claude/codex loose context when doing subtasks (and subagent don't have plan mode).
So I've built Agentbox to be able to launch from claude/codex multiple VMs with claude/codex (can also mix).
The parent agent watch for prompts and questions, enforce /review, /simplify, that the sub agents file a PR and wait for bugbot comments etc.
This way the parent agent running in a /goal don't loose context, enforce a good workflow, manage the backlog and parallelize/merge back the work on the main repo
you are right, I've experimented with cp -a on macOS as well for https://github.com/madarco/agentbox and in the end found it's actually faster to use worktrees inside docker containers while mounting your .git repo inside them.
Then after the node_modules (or apt packages) are installed, take a docker commit snapshot.
Now I have truly isolated parallel workspaces in <10s.
Also the system was easy to adapt to cloud environments as well so now I have Hetzner, Vercel, Daytona as well (using their native snapshotting systems for fast boot after the initial setup)
can't agree more, I now run my agents in parallel with "agentbox claude", "agentbox opencode" and it teleport my project and settings to an hetzner VPS
I think it's because the library calls directly the LLM AND saves any debug/trace info. While other tools let you use the standard (eg OpenAI) sdk and uses an https proxy to intercept the requests
On the contrary. Unless some AI armageddon happens, humans will be nurtured by AIs like in the movie Wall-E. Totally dependent by it, but a true utopia.
oh god, of all the amazing problems to solve with this wonderful technology, you surely did pick the most useless. What's this obsession with interviews on HN?
Hired a bunch of people in my career and 1 call was enough with a success ration of 98%
Nice demo but its lacks one feature to make it practical: splitting the input documents in chunks.
Without it the embeddings will be too broad, and when retrieved the docs will consume a lot of input tokens, making the request slower/expensive
Sure, because this implementation won't work in practice. He's embedding the whole documents at once, without splitting, summarization, Q&A extraction etc.
copy on that! I often use the "print by object" feature, and it's super useful being able to print each object in a different color with no color changing
Sending a CV had already become mostly useless 3–4 years ago because of the huge amount of noise: candidates applying from all over the world, often even spoofing their actual location, FAANGs firing, flooding the market with (in theory) great candidates with great resumes.
The solution is the same one that has been successfully used forever: a trial period.
Luckily, a video interview with a senior developer is still enough to spot a good candidate.
Go through real code: add a bug to a branch of your codebase, have the candidate share their screen on TeamViewer, and let them debug and fix the issue. Ask questions live to understand how they reason about the system, how they would test whether the change works, and so on.
This will filter out 99% of candidates. But it is still possible to get lucky, which is why the trial period matters.
I’ve never had major issues and have always hired very strong engineers. I only had to terminate someone after the trial period twice.