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Stop using your agent logs just for debugging. Use them to train your own model. Today we're launching Agnost AI (YC S26)'s first model: agnost-*******-0.1 Trained for our first customer on their existing production traces & it beat the frontier: +22.9% task success −90.2% latency −94.5% cost If you... show more

shubham

3,853 subscribers

696,344 次观看 • 1 个月前 •via X (Twitter)

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47 条评论

shubham 的头像

shubham1 个月前

We're live at

dhruvieiei 的头像

dhruvieiei1 个月前

we’ve been cookinggg

shubham 的头像

shubham1 个月前

cooking is an understatement

Chen Avnery 的头像

Chen Avnery1 个月前

most of my agent logs are the agent being confidently wrong and me quietly fixing it somewhere the log never sees.

shubham 的头像

shubham1 个月前

get agnosted

Saksham 的头像

Saksham1 个月前

sick, we are daily users!

shubham 的头像

shubham1 个月前

cardboard getting better 10% every day

hari_haran 的头像

hari_haran1 个月前

lessgooo bois!!

shubham 的头像

shubham1 个月前

lfg indeed

William Lindholm🎂 的头像

William Lindholm🎂1 个月前

Congrats on launch! The speed you’re moving st is insane!

Arlan 的头像

Arlan1 个月前

cool

Alexander Ren 的头像

Alexander Ren1 个月前

Super cool! Also love the launch video

shubham 的头像

shubham1 个月前

thanks alex

Aneesh Panda 的头像

Aneesh Panda1 个月前

this is crazy, congratulations on the launch folks!

nikhil · sys/quests 的头像

nikhil · sys/quests1 个月前

helll yeahhh!!

shubham 的头像

shubham1 个月前

hell yeah indeed

Ziyan Karmali 的头像

Ziyan Karmali1 个月前

@elonmusk you need this for @bot trust me

Frank Lee 的头像

Frank Lee1 个月前

love agnost

Pratyush Rai 的头像

Pratyush Rai1 个月前

Congrats on the launch Shubham. Wishing you the best. Looking forward to trying Agnost AI hopefully soon.

Harsh Savergaonkar 的头像

Harsh Savergaonkar1 个月前

insane!! lessgoooo 🤯

Ajit Sadalagi 的头像

Ajit Sadalagi1 个月前

@Scobleizer I wish there was a way to train the model on the fly. Every time I acquire new data or knowledge, the model should be trained within a few minutes.

Agrit Tiwari 的头像

Agrit Tiwari1 个月前

🔥

Bek 的头像

Bek1 个月前

Congrats, best team

annaaa 的头像

annaaa1 个月前

lessgoooo 🚀

shubham 的头像

shubham1 个月前

lfg indeed

Hasan (3dblur) 的头像

Hasan (3dblur)1 个月前

bangers

Danylo Borodchuk 的头像

Danylo Borodchuk1 个月前

Fire

Chetan 的头像

Chetan1 个月前

Lfggg!!!

shivansh 的头像

shivansh1 个月前

no wayyyy 🤯🤯 this is sooo goodd

shubham 的头像

shubham1 个月前

thanks joshi

sam 的头像

sam1 个月前

Okei

Hai Ta 的头像

Hai Ta1 个月前

This is very cool congrats on the launch!!

shubham 的头像

shubham1 个月前

thanks hai ta!

harsh 的头像

harsh1 个月前

Fire!!🔥

Vir 的头像

Vir1 个月前

the team is cooking hard

aman 的头像

aman1 个月前

So cool! Congrats on the launch guys 🚀

shubham 的头像

shubham1 个月前

thanks aman

fj_nm | AI Systems & Automation 的头像

fj_nm | AI Systems & Automation1 个月前

Production traces are already the best training set most teams ignore. Logs stop being waste and become the moat. Curious how you filter noisy traces before fine-tune — that step decides if the model learns your process or your bugs.

Parvez Shaikh 的头像

Parvez Shaikh1 个月前

Congrats on the launch , those latency and cost numbers are impressive. Curious to see how this scales across more use cases.

Parth 的头像

Parth1 个月前

congrats on the launch!

shubham 的头像

shubham1 个月前

thanks parth

Ayush Pandey 的头像

Ayush Pandey1 个月前

Lesss gooo Congratulations guys!!!!

Nick Khami 的头像

Nick Khami1 个月前

lfg

shubham 的头像

shubham1 个月前

lfg indeeed

Anthony K 的头像

Anthony K1 个月前

Agent logs are usually buried in observability and never turned into a signal. Training on production traces makes sense. How do you avoid feedback loops when the improved agent changes the trace distribution?

Piyushh 的头像

Piyushh1 个月前

🚀🚀🚀

Bhoomika 的头像

Bhoomika1 个月前

lesgooo, congrats guys

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