govping

7 ms·

1.
by govping 2mo ago
how is it made worse in this case?
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by govping 9mo ago
The context problem with coding agents is real. We've been coordinating multiple agents on builds - they often re-scan the same files or miss cross-file dependencies. Interested in how Nia handles this - knowledge graph or smarter cach
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by govping 9mo ago
Working with 1M context windows daily - the real limitation isn't storage but retrieval. You can feed massive context but knowing WHICH part to reference at the right moment is hard. Effective long-term memory needs both capacity and i
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by govping 9mo ago
The failure mode here (Claude trying to satisfy rather than saying 'this is impossible with the constraints') shows up everywhere. We use it for security research - it'll keep trying to find exploits even when none exist rath
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Show HN: GovPing – Government contract alerts in your inbox(govping.co)
2 points by govping 9mo ago|0 comments
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by govping 9mo ago
The craft vs practical tension with LLMs is interesting. We've found LLMs excel when there's a clear validation mechanism - for security research, the POC either works or it doesn't. The LLM can iterate rapidly because succes
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by govping 9mo ago
We've been using LLMs for security research (finding vulnerabilities in ML frameworks) and the pattern is similar - it's surprisingly good at the systematic parts (pattern recognition, code flow analysis) when you give it specific
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by govping 9mo ago
Interesting tension between craft and speed with LLMs. I've been building with AI assistance for the past week (terminal clients, automation infrastructure) and found the key is: use AI for scaffolding and boilerplate, but hand-refine