was reading ted’s site & just remembered devcenter. devcenter was consequential to a lot of techies circa 2017 & devcenter square was one of the best places to be on the internet. learnt a lot, met acekyd, fari & got many awesome gigs. s/o to akinfals & other champs on the team
it’s insaneee that this is elizabeth holmes and not some hollywood actress
diff architectures for diff jobs
Loops vs. Graphs, clearly explained!
loops are great, and they have a ceiling you can watch happen:
a loop goes around. it produces, checks, corrects, and goes around again.
after six passes you have one job, done very well.
after six hundred passes you still have one job, done very well.
Graph engineering fixes this by moving the decision up a layer: not how well one job gets done, but which jobs exist to be done at all.
you need both, and here is the sentence that resolves the whole confusion:
the loop lives inside a node. the graph lives between them.
↳ inside one unit: produce, check, correct, repeat until green
↳ between units: split, fan out, merge, gate, send back
Prompts → Context → Harness → Loops → Graphs
the loop does not go away when you build a graph.
it moves inside, and now there are three of them running at once on three things you would never have thought to run.
the trick is being selective about what becomes a node.
only spend a model where judgment lives.
merging, ranking, deduping and schema checks are edges, and edges are code. free, instant, and they cannot be argued out of a verdict.
one thing to know before you scale it.
a loop that cannot fail is not a loop, it is a repeat with a bill attached.
and the check people write is almost always the wrong kind.
↳ the test suite exits 0 is a check. the diff touches only the files in the plan is a check
↳ the output looks good, the model says it is confident, no errors were raised, none of those are checks
that last one catches careful people.
absence of an error is not evidence of correctness, and a loop built on it will confidently repeat a mistake until the budget runs out, with a clean log the whole way.
and the one that eats whole nights: when a unit fails, return that unit, not the batch.
send back four slices because one failed and you have just rewritten three correct ones. do it twice in a run and the run never converges.
below i have quoted my full guide on graph engineering. it covers the three topologies, the verifier patterns, and where the gate should actually open.
save this and read it below ↓
david retweeted
“CLAUDE I’VE EXPLAINED THIS BUG TO YOU 10 TIMES AND YOU STILL KEEP BREAKING IT, THINK LIKE A SOFTWARE ENGINEER WITH 15 YEARS OF EXPERIENCE AND FIX IT PROPERLY MAKE NO MISTAKES”
david retweeted
when software had a soul
there was a moment around 2005 when using a Mac felt like touching something alive.
the dock bounced. the genie effect swooped. exposé scattered your windows like cards on a table. none of it was strictly necessary. all of it felt like someone cared – not about metrics, but about the feeling of using a machine.
software back then had texture. it had a philosophy. you could feel the person behind it. someone made a decision to make that icon beautiful, to animate that transition just so, to write that error message with a little warmth. apps had personalities. some were weird. some were over-designed in ways that would make a modern PM flinch. but they were alive.
the web was the same. personal sites were genuinely personal. blogs felt like letters. forums had regulars. you knew who made what. the internet had neighborhoods, and each one felt different.
nothing was optimized for scale. things were made by people who loved what they were making.
somewhere along the way, we traded all of that for growth.
A/B tests flattened the edges. design systems standardized the personality out. everything got faster, smoother, more consistent – and somehow less interesting. the quirks were removed because they didn't test well. the warmth got cut because it wasn't measurable. we optimized our way into a world of things that work perfectly and feel like nothing.
now every app looks the same. every interface follows the same patterns. every product speaks in the same calm, frictionless voice, siloed in their own little islands. the humanity got rounded off.
and then came AI agents. and the speed got inhuman.
now you can generate an entire product in an afternoon. ship a feature before lunch. spin up ten variations before anyone's had their coffee. the gap from idea to code is basically zero.
which sounds incredible. and it is. but there's a catch.
when making things are too easy, the slop comes for free too. mediocre things don't look obviously bad – they look fine. they work. they ship. they pass review. and now there are infinite of them. the internet is filling up with software that functions but means nothing. interfaces that are correct but feel dead. products made by agents, reviewed by no one, shipped into the void.
this is the thing that keeps me up at night. not that AI will replace people who care. but that it will drown them out.
here's what I still believe: the best things are made by people who couldn't help themselves. someone who lost sleep over an icon. who rewrote the same line of copy twelve times. who added an animation nobody asked for because it made the thing feel right. that obsession – that's not inefficiency. that's the whole point.
AI doesn't make that irrelevant. it actually makes it rarer and more valuable. taste is not a markdown skill. caring is not a parameter. the weird, specific, "soul" thing you put into something – that can't be programmed into existence.
the path forward isn't to make more slop faster. it's to finally give people with real vision the tools to make the thing they always imagined but couldn't build alone. the designer who had the idea but couldn't code. the kid who saw something nobody else saw. the person who cared too much about something most people wouldn't notice.
if we get this right, we don't get a faster factory. we get a renaissance. more strange, personal, opinionated software made by teams of people who care and mean it.
that's still possible. but only if the people who care get the space and tools to actually express themselves – and don't just hand the wheel to the agent and walk away.
david retweeted
The @ilyasut episode
0:00:00 – Explaining model jaggedness
0:09:39 - Emotions and value functions
0:18:49 – What are we scaling?
0:25:13 – Why humans generalize better than models
0:35:45 – Straight-shotting superintelligence
0:46:47 – SSI’s model will learn from deployment
0:55:07 – Alignment
1:18:13 – “We are squarely an age of research company”
1:29:23 – Self-play and multi-agent
1:32:42 – Research taste
Look up Dwarkesh Podcast on YouTube, Apple Podcasts, or Spotify. Enjoy!
david retweeted
Our open models are here.
Both of them.
openai.com/open-models
david retweeted
Quitting programming as a career right now because of LLMs would be like quitting carpentry as a career thanks to the invention of the table saw.
