Beyond file trees: why AI coding assistants need smarter context
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Re: Beyond file trees: why AI coding assistants need smarter context
#2When debugging code, experienced developers don't read every file—they follow execution paths and understand system architecture. But today's AI coding tools try to read all files and get bogged down in unnecessary details.
With context windows limited to 200K tokens, cramming in random files isn't just inefficient, it's impossible for large codebases. If you’re debugging a failing test, you only need to understand the relevant files in the call chain. It's not about more context, it's about relevant context. That's what Nuanced provides through static analysis and machine learning.
Re: Beyond file trees: why AI coding assistants need smarter context
#3When debugging code, experienced developers don't read every file—they follow execution paths and understand system architecture. But today's AI coding tools try to read all files and get bogged down in unnecessary details. With context windows limited to 200K tokens, cramming in random files isn't just inefficient, it's impossible for large codebases. If you’re debugging a failing test, you only need to understand t…
Maybe show us an example of this graph in the blog post?
Re: Beyond file trees: why AI coding assistants need smarter context
#4Function calls are definitely one part of adding context, but lots of others that codegen tools are probably missing. Have you guys considered post-mortems, pagerduty outputs, slack threads focused on specific issues, etc.?
Re: Beyond file trees: why AI coding assistants need smarter context
#5Function calls are definitely one part of adding context, but lots of others that codegen tools are probably missing. Have you guys considered post-mortems, pagerduty outputs, slack threads focused on specific issues, etc.?
Yeah! We actually did an experiment where we provided AI tools with memory profiler outputs, Sentry exception reports, and telemetry from Datadog.
Re: Beyond file trees: why AI coding assistants need smarter context
#6Function calls are definitely one part of adding context, but lots of others that codegen tools are probably missing. Have you guys considered post-mortems, pagerduty outputs, slack threads focused on specific issues, etc.?
Yeah! We actually did an experiment where we provided AI tools with memory profiler outputs, Sentry exception reports, and telemetry from Datadog.
Will share results soon.