Tony Kam
1.2K posts

Tony Kam retweetledi

Huge news: @CentralHQX has been acquired by @mercury!
24 months ago, we had zero customers and revenue.
Since then:
- Hundreds and hundreds of startups rely on Central
- We've processed nearly $200M in payroll
- 30% of our customers switched from traditional HR/payroll platforms
- We built one of the first AI agents in Slack
Turns out founders want outcomes, not just another dashboard.
When we first met @immad, @MattRHeiman, and @rywiggs and the rest of the Mercury team, it just made sense.
Most of our customers were already using them and they had the same obsession of helping startups and SMBs. And together, we can actually automate the entire back-office now.
Grateful to our customers, investors and our entire team.
Most importantly, thank you to my co-founders @jbwyme and @prankash. Couldn't have done this without y'all.
More to come!

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Big news for @conductor_build!
We've raised a $22m Series A from Spark and Matrix.
We raised this round from @ilyasu at Matrix, who also led our seed round and is joining our board, @nabeel at Spark, @ycombinator, and founders of Notion and Linear. We're grateful to be working with investors we trust and admire.
Here’s how we got here and where we’re going:
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Tony Kam retweetledi

@gvanrossum LLM = CPU (data: tokens not bytes, dynamics: statistical and vague not deterministic and precise)
Agent = operating system kernel
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Tony Kam retweetledi
Tony Kam retweetledi
Tony Kam retweetledi

Tony Kam retweetledi

"LLM in a Flash" for unified memory paging on consumer hardware + Google's TurboQuant!
thestreamingdev()@thestreamingdev
I ran a 35-billion parameter AI agent on a $600 Mac mini. Specs: M4 Mac-Mini 16GB RAM The model doesn't fit in RAM. It pages from the SSD at 30 tokens/second. On NVIDIA, the same paging gives you 1.6 tok/s. Apple Silicon gives you 30. That's 18.6x faster. No cloud. No API keys. $0/month. Here's what it can do 🧵
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maybe the forgetful human mind is a feature not a bug.
infinite memory -> infinite trauma
I forgot how things happened exactly, or who did what, but I remember how I felt.
Intense emotions, processed by the amygdala, help sear events into long-term memory
Andrej Karpathy@karpathy
One common issue with personalization in all LLMs is how distracting memory seems to be for the models. A single question from 2 months ago about some topic can keep coming up as some kind of a deep interest of mine with undue mentions in perpetuity. Some kind of trying too hard.
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Tony Kam retweetledi

Introducing TurboQuant: Our new compression algorithm that reduces LLM key-value cache memory by at least 6x and delivers up to 8x speedup, all with zero accuracy loss, redefining AI efficiency. Read the blog to learn how it achieves these results: goo.gle/4bsq2qI
GIF
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Tony Kam retweetledi
Tony Kam retweetledi















