Naman

building the AI operating system for your company. airo health YC S16. dapper labs.

[Naman @namank]

https://t.co/SIJJwyEU16

[Naman @namank]

@garrytan argh was looking forward to gbrain gstack improvements from fable

[Naman @namank]

@mynameisyahia 👏 tis what i did too, 100 people dms best growth strategy ever

[Naman @namank]

@arag_agrawal @bradflora I have no doubt agents will outnumbers humans some day. Just don't see it happening in the next two years

[Naman @namank]

time to rethink everything

[Taelin @VictorTaelin]

this is my personal singularity moment

this post may sound like a paid ad. I only wish. I'm concerned, more so than happy. the world is changing, and, among the scenarios where AI goes terribly wrong, inequality is the most realistic, yet, the one Anthropic seems to be the least concerned about. I'm glad OpenAI is taking the opposite stance: *personal AGI for everyone*. I think this is a commendable position in the times we live. but who am I in the queue of the bread?

anyway, Fable is here, so I'll just report my first-hour experience

first of all, all my pet prompts are solved.
→ λ-calculus puzzles
→ bug questions
→ one-shot apps
all are trivial to it.

I don't have anything harder other than my
ongoing work

so, in the last several days, I've been toying with HVM5, a new interaction net evaluator with a faster loop.

after writing the first version, I left 32 GPT-5 agents working for ~20 hours each. this resulted in up to 2x speedups, but the file size increased by 2-fold and quality decreased significantly.

I then simplified the whole thing into an even simpler core, and left Opus 4.8 and GPT 5.5 optimizing it for 8 hours. Opus got a legit 6% - 34% speedup in most benches. GPT got better results, but, sadly, an unusable file.

I then asked Fable to optimize it.

2 hours later, it landed a 1770% speedup in one case, 100%+ in other 4, and 22% in average. yes, in 2 hours it outperformed me, opus 4.8 and a swarm of gpt 5.5 agents, by one order of magnitude.

that could not possibly be legit. "it must be hardcoding the benchmarks" (GPT trauma). so I read its explanation and what it did was, indeed, the most high impact optimization one could try first. seems like HVM5 was wasting a lot of time garbage-collecting unused branches of pattern-match nodes. I had optimized that for static mats, but not for dynamic mats. skill issue. Fable figured how to do it for these, resulting in a massive speedup in some benches

but wait, is that *correct*? I'm not sure yet, it is credible, but this is the kind of thing that is very easy to get wrong on interaction nets. the problem is, when I was ready to start auditing Fable's solution so I could tell whether it was buggy or legit, it interrupted me to tell me it had found a massive bug on the code *I* had written.

... wait, what?

so... for garbage collection purposes, I stored a bit on lambda term pointers that meant "the variable bound by this lambda has been freed, so, its lambda must free whatever argument it is applied to". that's fine. yet, on duplicator nodes, I also used the same bit to mean "one of the duplicated variables was freed, so, treat this dup as a passthrough no-op". so, if a lambda entered a duplicator, it would mistake the lambda's collection bit for its own, resulting in corrupted interaction!

that's a mouthful, why I'm writing this?

just so you can appreciate the sheer absurdity of what just happened. I didn't ask it to find bugs. I asked it for an optimization. and even if I did ask it to find bugs, this bug is so astonishingly subtle and specific, identifying it takes mastering the domain to an extent that it beyond even me. I'd easily need hours or days to fix it, *if* I ever came across it. chances are it would just go unnoticed. and Fable found it and fixed it like it was nothing, while it was busy adding a 17x speedup to a file that neither I, nor Opus 4.8, nor a fleet of GPT 5.5 managed to barely make 2x faster.

oh and there is also another tab where it is also ripping through Bend's codebase and finishing everything I had to do

I don't know what to say anymore

this isn't about Anthropic or OpenAI, this is about our collective future as a species. the world is changing, and we need to be aware of it, and discuss how to handle this change.

receipt below . . .

[Naman @namank]

@saranormous @Suhail Markets for foundational work is huge, with near infinite ICPs

[sarah guo @saranormous]

cursor, lovable, cognition numbers all a big narrative violation. wasn’t everything in the path of agi labs (especially the #1 fight, coding agents) supposed to die, not accelerate

[Naman @namank]

@maxkolysh narrowing down on your ICP let's you get specific in messaging, calls, and product features. huge win for both sellers and buyers.

hard to get to that point though!

[Naman @namank]

@bcherny Curious how is this different than a team of agents spawning more agents

[Naman @namank]

@hosseeb @brandonkz Could be weight as well

[Naman @namank]

Ideally the loop will arrive at the appropriate state machine for a given problem.

[David @dzhng]

don't use loops, design state machines

[Naman @namank]

@paulg Everyone needs a system

[Paul Graham @paulg]

"What if the model companies do this?" is the new "What if Google does this?" I.e. the meaningless question investors ask that shows either that they're stupid or that they dislike you and are looking for ways to find fault.

[Naman @namank]

@NarangShyamal Best guess on why they rejected?

[Naman @namank]

Isn't this just while (tests_failed > 0){ llm.lfg() }

[Peter Steinberger 🦞 @steipete]

Here’s your monthly reminder that you shouldn’t be prompting coding agents anymore.

You should be designing loops that prompt your agents.

[Naman @namank]

@dakshgup breathing loop is the most efficient way to breathe. how do you not get that already.

[Naman @namank]

@hnshah @trevin @NousResearch interesting, based on this i would give simple tasks to open claw and complex + long running tasks to hermes. which is really the difference in the model each can access

[Naman @namank]

@hnshah @trevin @NousResearch Curious why run both

OV and Hermes are essentially orchestrators right? Are there cases in which one is better than the other

[Naman @namank]

@pumpernikhil that’s harsh