@dunik_7: the difference between a $4 agent run and a $0.40 one comes down to a single idea: the heuristic. lecture 6 of Stanford…
Summary
This Twitter thread highlights Stanford CS221 lecture 6 on heuristics, explaining how A* search improves agent efficiency by using heuristics to guide decision-making. Key takeaways include building heuristics by relaxing problems, the danger of bad heuristics, and the optimality of A* with the right estimate.
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Cached at: 05/23/26, 06:14 PM
the difference between a $4 agent run and a $0.40 one comes down to a single idea: the heuristic.
lecture 6 of Stanford CS221 is Percy Liang on how search gets smart - A* and the heuristics that tell an agent “you’re getting warmer” before it wastes a thousand steps finding out.
80 minutes. three things click after this one:
/ how you build a heuristic from scratch - you relax the problem until it’s easy, then measure from there
/ why a bad heuristic is worse than none - it confidently marches the agent the wrong way
/ why A* with the right estimate is provably optimal, and your “just try harder” prompt is not
same $90K Stanford sequence. lecture 6 of ~20. free.
the next 10x in agents won’t come from a bigger model. it’ll come from a better guess at how far the goal is.
the CEO of Obsidian spent almost 3 hours on camera explaining why your “second brain” doesn’t actually work yet.
the core idea: file over app. your notes outlive the app you take them in.
if they don’t you don’t own them. you’re renting them.
Steph Ango is handing you the deed.
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