Behrad Khodayar

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Behrad Khodayar

Behrad Khodayar

@behradkhodayar

SWE | 2 Successful Exits && ∞ Failures

~ Katılım Eylül 2021
170 Takip Edilen126 Takipçiler
Fab
Fab@fabrice_mayrand·
This is insane Cheaper Inference is selling API access to most major models for 30% off. Use OpenAI, Anthropic, and Google models through a drop-in OpenAI-compatible API. No contract, no migration required.
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Filip Hric
Filip Hric@filip_hric·
OMG WHAT IS HAPPENING?!
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Filip Hric
Filip Hric@filip_hric·
what an INSANE goal 🏴󠁧󠁢󠁥󠁮󠁧󠁿🇦🇷
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Behrad Khodayar
Behrad Khodayar@behradkhodayar·
@paw_lean The point is Argentina is running full "thinking mode": lots of waiting, back w best outcome. hard to beat.
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Pauline P. Narvas
Pauline P. Narvas@paw_lean·
This was the first time I really got into football, so I’m so gutted 😭 But it’s also… Argentina. Goat team!
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Pauline P. Narvas
Pauline P. Narvas@paw_lean·
🏴󠁧󠁢󠁥󠁮󠁧󠁿🏴󠁧󠁢󠁥󠁮󠁧󠁿🏴󠁧󠁢󠁥󠁮󠁧󠁿 Been listening to Wonderwall all day today
Pauline P. Narvas tweet media
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Behrad Khodayar
Behrad Khodayar@behradkhodayar·
Argentina playing in "thinking mode": Lots of waiting, but back w/ best results.
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Behrad Khodayar
Behrad Khodayar@behradkhodayar·
Claude Code is fine, but configuring a fully working environment to extract best out of it takes time, I mean providing the required rules, skills, sub-agent configurations, etc... to an extent that I had recently created a boilerplate starter template for my day to day stack of Python,TS,PSQL,Docker (available on my GH). Codex as my frens say requires much less configurations. Looking forward to giving it a ride.
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Behrad Khodayar
Behrad Khodayar@behradkhodayar·
@antirez Lol, same here, but I managed to solve all my life's problems w/ Fable.
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antirez
antirez@antirez·
I was burning all my Fable tokens like Cartman in Casa Bonita since 7th of July was the last day and then...
antirez tweet media
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Behrad Khodayar
Behrad Khodayar@behradkhodayar·
@zephyr_z9 Meta fading out. "This year" is a decade & not even targeting Mythos.
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Zephyr
Zephyr@zephyr_z9·
No Mythos from Meta
Jukan@jukan05

@alexeheath's latest reporting on Meta AI: “He expects to surpass Opus-level performance this year with the big frontier model Meta is training, codenamed Watermelon.” $META

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Sam Altman
Sam Altman@sama·
GPT-5.6 sol launches thursday! happy building
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Behrad Khodayar
Behrad Khodayar@behradkhodayar·
@NickSzabo4 remember ur concerns? Ppl r literally "thanking" Meta for this now! The problem is users value convenience waaaaay more than privacy.
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Behrad Khodayar retweetledi
Lilian Weng
Lilian Weng@lilianweng·
A super long overdue (3+ years?) post on scaling laws. Compute is expensive. Scaling laws are a way to help us reason about the optimal compute allocation between data and model size before committing to a large run. The post covers what scaling laws predict, how compute-optimal allocation works, why Kaplan et al. and Chinchilla disagree, and how data limits + fitting details make extrapolation tricky. lilianweng.github.io/posts/2026-06-…
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Behrad Khodayar
Behrad Khodayar@behradkhodayar·
After building a few fullstack (Python/FastAPI + React + PostgreSQL) apps, I've refactored (& continue to make it better) a full-stack Claude Code template, which improves it's performance (basically to a teammate, not an afterthought). Go: 1- clone as template 2- put ur project context in CLAUDE.md 3- & u're good to go. start shipping: 👇
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Behrad Khodayar retweetledi
vik
vik@vikhyatk·
👀👀
vik tweet media
QME
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Behrad Khodayar
Behrad Khodayar@behradkhodayar·
@mardehaym Seems they need a smarter model to stop 'em from doing this. everybody's lost their mind smh
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Mark Ajzenstadt
Mark Ajzenstadt@mardehaym·
POV: your team's 24 hour bill after using Fable 5. Stop using the best models. They are uneconomical.
Mark Ajzenstadt tweet media
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Ivan Fioravanti ᯅ
Ivan Fioravanti ᯅ@ivanfioravanti·
This morning towards rifugio Bertone.
Ivan Fioravanti ᯅ tweet mediaIvan Fioravanti ᯅ tweet media
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Behrad Khodayar
Behrad Khodayar@behradkhodayar·
@scaling01 & generate a new one so the new generation of models solve them
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Lisan al Gaib
Lisan al Gaib@scaling01·
I now expect every new generation of models to solve a previously unsolved problem
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