gurkanwal singh brar

Up for banter around ML, Engineering and @Arsenal.

[Asymmetric Adventures @asymadventures]

If Tesla charges $1 per mile and it costs roughly 50 cents per mile, that would result in a gross margin of 50% and likely operating margin of around 35%. Hmm.. what does that sound like?

So, if Robotaxis are cheaper and a better experience than owning a car, what’s it worth?

Let’s do some quick maths.

[gurkanwal singh brar @brargk]

@y0b1byte @ShankarAnoushka has been a constant coding companion for a decade now

[Punjabi Vocabulary @punjabivocabul1]

ਦੁਹਾਈ ਨੀ ਦੁਹਾਈ
ਨੈਣਾਂ ਨਾਲ ਹੋਣ ਲੱਗੀ ਨੈਣਾਂ ਦੀ ਲੜਾਈ

ਸੰਦਲੀ ਦੁਪੱਟੇ ਉਤੇ ਬੂਟੀਆਂ ਨੇ ਕਾਲੀਆਂ
ਕਿਥੇ ਕਿਥੇ ਖਹਿੰਦੀਆਂ ਨੇ ਕੰਨਾਂ ਦੀਆਂ ਵਾਲੀਆਂ
ਜ਼ੁਲਫ਼ਾਂ ਦੇ ਕੁੰਡਲੇ 'ਚ ਮੌਤ ਲਟਕਾਈ
ਦੁਹਾਈ ਨੀ ਦੁਹਾਈ

-- ਨੰਦ ਲਾਲ ਨੂਰਪੁਰੀ

[gurkanwal singh brar @brargk]

@SullyOmarr vertically integrated agents ftw

[gurkanwal singh brar @brargk]

@seema_amble @GoodPoint123 Do you expect agents to fill this gap or copilot like assistants?

[gurkanwal singh brar @brargk]

@thatguybg time to see results of your experiments goes up the lower you go into the stack. When you can’t rely on experiments you have to rely on conviction, and nothing strengthens that more than religion

[gurkanwal singh brar @brargk]

@nealkhosla Totally. Exactly the same argument for Devin / coding copilots and software engineering. A good engineer solves for edge cases, copilots solve for boiler plate which can accelerate but not replace.

[gurkanwal singh brar @brargk]

@GergelyOrosz definitely one of the few systems where closed loops with formally constructed critics of llm outputs can be built.

[gurkanwal singh brar @brargk]

@jpdeol do you not like your gas fresh? i see nothing wrong here

[gurkanwal singh brar @brargk]

@sh_reya I remember seeing some chatter in the crazy days of last year around LLMs carrying a world model. What’s your read on that? Is some level of physics is a required part of a world model?

[gurkanwal singh brar @brargk]

@officialKrishD @morgancheatham yep makes sense, seems like note taking will be a feature into existing products much like google meets, zoom adding fireflies like note taking into themselves

[gurkanwal singh brar @brargk]

@anshublog Wallet holders make stuff happen :)

[gurkanwal singh brar @brargk]

@medjournalist @morgancheatham Do any of them have traction?

[gurkanwal singh brar @brargk]

@adamsilverman closer to 99 i’d say. But from what i can tell, agent is a nice gtm strategy - as they onboard customers they will find the exact vertical use case the agent is needed for and build guardrails to serve those. Effectively ending up as a ML Api company, which is not a bad end goal

[gurkanwal singh brar @brargk]

@sudobunni because some teams start treating it as the one true god, as against another tool around which the processes should change as the team grows

[Eshan Chordia @eshanchordia]

Yogesh and I are excited to announce we have raised a $2.8M pre-seed round! The last 8 months have been an amazing adventure, but there’s so much more to come. We are expanding the team, building AI training infra that’s never been built before, and launching our protocol 👇

[gurkanwal singh brar @brargk]

@max_paperclips @rao2z @sh_reya I agree, but in cases where a formal method isn’t available and you need human verifiers , you need to gradually eat up their verification hours with a model.

[gurkanwal singh brar @brargk]

@rao2z @sh_reya The key is identifying when an input - output pair is novel, or out of distribution for the verification dataset . I haven’t hit this yet, but iam hoping conventional confidence based approaches can help here.
@rao2z do you have any pointers on this front?

[gurkanwal singh brar @brargk]

@rao2z @sh_reya Absolutely, a slow but compounding approach is introducing humans in the loop to criticize / verify and gradually building out conventional models to replicate them. You still need humans in the loop even as verification dataset grows because novel scenarios arise.