parth

founder @renaibuild

[Eren Chen @ErenChenAI]

From Elegance to Sudden Death

[MiniMax (official) @MiniMax_AI]

M3 would never 🙂‍↔️

As a matter of fact, the weights are now open, too.

https://t.co/g4Ybfa2kWH

[xin @bryanwangxin]

苹果的发布会视频中，但凡有说到 Siri 的地方，都会切掉音频中 3k、4k、5k、6kHz 频率部分，以防观看视频时附近的苹果设备激活 Siri。

[Shrey @askshrey]

@arpit_bhayani Slash - but for every company in the world.

https://t.co/ZpUo889j5x

[Brian Armstrong @brian_armstrong]

Good take

My guess is
- demand for intelligence is near infinite
- but 80% of workloads will be running on 99% cheaper models within 12-18 months
- 20% of workloads will still run on latest gen models where IQ maxing is important (scientific breakthroughs, higher level ochestrator agents?)
- rough analogy might be what % of macbooks or gaming PCs sold have the maxed out specs for CPU/GPU, prices are falling much faster than Moore's law here though
- this leads me to think the limiting factor will be energy and compute, not better models

At Coinbase we're working hard on routing prompts to cheaper models where appropriate, and in some cases have been able to keep costs roughly flat, while token usage continues to grow exponentially.

[parth @KashikarParth]

so what the fuck should you be bluffing about?

[parth @KashikarParth]

coffee and bipartite graphs

[parth @KashikarParth]

meanwhile minimax m3

[Negligible Capital @negligible_cap]

*UBER SETS $1,500 MONTHLY CAP ON SOME AI CODING TOOLS FOR STAFF

$UBER officially reeling in the Claude budget after blowing their AI budget earlier this year.

Undoubtedly more companies to follow

[parth @KashikarParth]

@kupsas101 @sflorimm Cool

[dax @thdxr]

think back to projects you've worked on in the past

it's hard not to imagine they'd have been completed way faster now that we have ai

but everything still feels as slow and as difficult as ever

[Omar Rizwan @rsnous]

your phone rings and you pick it up and it's the result of an async function that you called many years ago

[Rhys @RhysSullivan]

software engineering is solved btw

[parth @KashikarParth]

@anwesh_bh @Dishaaa122 will deal with it when i have to @anwesh_bh

[Shrey @askshrey]

https://t.co/lXxRSo5pdm

[parth @KashikarParth]

@anwesh_bh @Dishaaa122 word

[Arpit Bhayani @arpit_bhayani]

The more I build agentic systems at Razorpay, the more I understand that - at its core, it is an agentic loop with tool calls, integrations, and retrieval. The hard part is...

actually making it run reliably, at scale, under real production load. And this is what makes system design even more important.

Your AI system is still expected to scale. It will still need microservices, message queues, consistency guarantees, load balancing, work distribution, state management, rate limiting, throttling, fallbacks, service-to-service communication, QoS, etc.

It is great that you are looking into AI and are interested. You should be. Everyone should be. But it is important not to skip system design and cs fundamentals. I know it seems overwhelming, but it is what it is.

First principles are not going anywhere, and that is super essential for actually building applied AI systems and running them reliably at scale. If you are a backend engineer and are kind of skipping these things, pause and reflect once.

It is always good to be great at system design, not because it will help you crack interviews (it will), but because it will make you meaningfully better at your job. Seeing it firsthand.

Remember, you will not be shipping prototypes to production. The difference between prototype and production code is 15 components and 1000 commits.

[parth @KashikarParth]

@anwesh_bh dudes be inventing math, like bro just use a calculator