gtm nerds: what's the most boring thing that moved your numbers the most? mine was answering replies within the hour
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the hardest part of being a founder wasn't the product. it was giving the same pitch for the hundredth time and still sounding like i meant it
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Ethan Caldwell retweeted
The new Tesla Roadster accelerates faster than a fighter jet taking off from an aircraft carrier
The new Tesla Roadster produces more thrust than an F-16 fighter jet at military power
Roadster Thrust augment:
>80 kN (~18,000 lbf)
F-16 with F100-PW-229:
~79.2 kN (~17,800 lbf) without afterburner
Tesla packed enough cold-gas thrust into a car that exceeds the nonan F-16 engine
With afterburner, the F-16 exceeds at ~130 kN
But the fact this comparison even exists is insane
Tesla just pushed automotive engineering deep into aerospace territory
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Ethan Caldwell retweeted
Same prompt . Claude vs ChatGPT vs Figma Make
Pick your winner 👇
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2) hot take: most early-stage outbound fails at the list, not the copy.
if the person doesn't have the problem this quarter, no subject line saves you.
if the person doesn't have the problem this quarter, no subject line saves you.
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1) things i stopped doing as a founder this year:
— writing cold emails at 1am
— buying lead lists
— pretending i'd follow up
things i started:
— talking to the people who reply
— writing cold emails at 1am
— buying lead lists
— pretending i'd follow up
things i started:
— talking to the people who reply
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Ethan Caldwell retweeted
this is pure f*cking treasure
A Stanford AI research group found how to orchestrate Claude Opus 5.5 and GPT-6.1 Sol together, and it completely breaks the search scaffolding wall
most developers try to combine frontier models by chaining prompts in one context window or ionary loops. you burn tokens on context bloat and freeze models into fixed search rules that out-date themselves within 5 iterations
Stanford's architecture eliminates human-designed search scaffolding and splits the cognitive stack:
> search director: Claude Opus 5.5 plans the search, querying a persistent idea-graph where MAP-Elites and MCTS reduce to single Cypher queries
> candidate proposer: GPT-6.1 Sol explores high-entropy code variations inside isolated execution sandboxes
> zero context bleeding: sessions reset after each iteration to prevent prompt bloat while the graph stores lineage
> evaluator isolation: candidate code never touches the scoring process, stopping reward hacking and test leakage cold
> meta-agent distillation: an offline meta-pass extracts verified lessons and updates search guidance across branches
the benchmark metrics outclass standard discovery baselines:
> 3.2x lower model spend than fixed evolutionary frameworks
> 1st place rank across 7 competitive AtCoder heuristic contests against human competitors
> beats published SOTA on Anthropic kernel builder and 11 mathematical optimization tasks
> 0% reward hacks: candidate-controlled metric tampering eliminated
Opus directs the search graph. Sol explores the code
you stop writing rigid search harnesses. you let frontier models own the discovery loop
A Stanford AI research group found how to orchestrate Claude Opus 5.5 and GPT-6.1 Sol together, and it completely breaks the search scaffolding wall
most developers try to combine frontier models by chaining prompts in one context window or ionary loops. you burn tokens on context bloat and freeze models into fixed search rules that out-date themselves within 5 iterations
Stanford's architecture eliminates human-designed search scaffolding and splits the cognitive stack:
> search director: Claude Opus 5.5 plans the search, querying a persistent idea-graph where MAP-Elites and MCTS reduce to single Cypher queries
> candidate proposer: GPT-6.1 Sol explores high-entropy code variations inside isolated execution sandboxes
> zero context bleeding: sessions reset after each iteration to prevent prompt bloat while the graph stores lineage
> evaluator isolation: candidate code never touches the scoring process, stopping reward hacking and test leakage cold
> meta-agent distillation: an offline meta-pass extracts verified lessons and updates search guidance across branches
the benchmark metrics outclass standard discovery baselines:
> 3.2x lower model spend than fixed evolutionary frameworks
> 1st place rank across 7 competitive AtCoder heuristic contests against human competitors
> beats published SOTA on Anthropic kernel builder and 11 mathematical optimization tasks
> 0% reward hacks: candidate-controlled metric tampering eliminated
Opus directs the search graph. Sol explores the code
you stop writing rigid search harnesses. you let frontier models own the discovery loop
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