you would think so! but that's only optimal if the model already has all the information in recent context to make an optimally-informed decision.
in practice, this is a neat context engineering trick, where the different LLM calls in the "courtroom" have different context and can contribute independent bits of reasoning to the overall "case"
Falconer | Lead AI engineer | Bay area, hybrid/onsite | $200-300k + equity
Falconer is building AI-powered knowledge tools for engineers. Knowledge management is a $30b industry, and we intend to build the tools that leverage AI to power the next generation of engineering knowledge. Our vision is to empower every engineer to write as well as the best technical writers, and enable every organization to have internal docs as great as the best external docs are.
We're currently a team of 8 from Stripe and Meta, currently turning our prototype into a generally available product.
Looking for an engineer experienced in evals, inference latency optimization, prompt caching, and LLM agent reliability. Or someone who just loves tinkering with the latest models and newly-published techniques, and wants to work somewhere you try them out in a real product that people use!
- Learn their default layout first, then iterate on your personal preferences. I similarly tried a bunch at first, but realized switching layouts constantly was making it impossible to build up the new muscle memory.
- Spend time practicing away from important coding tasks (e.g. gaming, writing, a side project, a one-off script, etc.). Being able to learn in a forgiving environment, outside of deadlines, gives you the space to allow mistakes and time to correct them
Falconer | Founding team (AI, infrastructure, full-stack, designer) | Bay area, hybrid/onsite | $150-200k
Falconer is building AI-powered knowledge tools for engineers. Knowledge management is a $30b industry, and we intend to build the tools that leverage AI to power the next generation of engineering knowledge. Our vision is to empower every engineer to write as well as the best technical writers, and enable every organization to have internal docs as great as the best external docs are.
We're currently a team of 3 from Stripe and Meta, prototyping the product with design partners, and hiring a few more engineers to round out the founding team. We've raised a $5m seed round and have several customers lined up, we just need to build it!
Falconer | Founding team (AI, infrastructure, full-stack, designer) | Bay area | Hybrid/onsite
Falconer is building AI-powered knowledge tools for engineers. Knowledge management is a $30b industry, and we intend to build the tools that leverage AI to power the next generation of engineering knowledge. Our vision is to empower every engineer to write as well as the best technical writers, and enable every organization to have internal docs as great as the best external docs are.
We're currently a team of 3 from Stripe and Meta, prototyping the product with design partners, and hiring a few more engineers to round out the founding team. We've raised a $5m seed round and have several customers lined up, we just need to build it!
you would think so! but that's only optimal if the model already has all the information in recent context to make an optimally-informed decision.
in practice, this is a neat context engineering trick, where the different LLM calls in the "courtroom" have different context and can contribute independent bits of reasoning to the overall "case"