Brian Ji

266 posts

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Brian Ji

Brian Ji

@brianjji

technology, markets & media

Katılım Nisan 2019
1.6K Takip Edilen15K Takipçiler
Brian Ji
Brian Ji@brianjji·
@mrexits insanely america-coded last name
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Brian Ji
Brian Ji@brianjji·
@david_perell “He who jumps into the void owes no explanation to those who stand and watch.” — Jean-Luc Godard
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David Perell
David Perell@david_perell·
What’s the best sentence or paragraph you’ve ever come across?
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Brian Ji
Brian Ji@brianjji·
@maxdesalle I suspect that the models won’t be the product, and that they will be commoditized over time.
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Maxime Desalle
Maxime Desalle@maxdesalle·
@BrianJJi Exactly! It's all about incentives. OpenAI needs funds to pay for compute, while Meta already has it and wants to prevent a new AI leader from emerging.
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Brian Ji
Brian Ji@brianjji·
Pretty ironic that Zuck and Meta are doing what OpenAI had originally been funded to do.
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Eric Jorgenson 📚 ☀️
Eric Jorgenson 📚 ☀️@EricJorgenson·
Most people instinctively fight to protect their job and source of income -- even if they hate it, and it's killing them. It requires imagination, faith, courage, effort, and support to take the leap between jobs, careers, or industries.
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Jacob Rintamaki
Jacob Rintamaki@jacobrintamaki·
Here's a non-dilutive grant that I couldn't find @1517fund grant sheet below: Unitary Fund - 4k grant, specifically for quantum/quantum-adjacent projects (unitary.fund) btw if you know of more non-dilutive grants please comment them here! twitter.com/DStrachman/sta…
Danielle Strachman 💗 🐈 💃 🪴 🎸 🎨 🐕@DStrachman

It's been 30 Under 30 with money for years -- finding undiscovered and overlooked talent is a different game. Looking for other early stage belief capital -- lots of great orgs here! #gid=0" target="_blank" rel="nofollow noopener">docs.google.com/spreadsheets/d…

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Kyle Harrison
Kyle Harrison@kwharrison13·
The best businesses are never born at the height of their own hype cycles: • Stripe (2009): Everyone was focused on social media • Coinbase (2012) Everyone was focused on VR • OpenAI (2015) Everyone was focused on IoT • Anduril (2017): Everyone was focused on crypto
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Brian Ji
Brian Ji@brianjji·
@ADoricko haps, and to many more. bright things ahead brotha.
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Augustus Doricko
Augustus Doricko@ADoricko·
I’m 24 today. Never been more excited about the future or more grateful for the past&present A few things I’m thinking about for the next year 1/5
Augustus Doricko tweet mediaAugustus Doricko tweet media
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keshav
keshav@keshavchan·
aaaaa i finally came up with a good name and a cover for the podcast, time to interview rare pokémons (link to the show and past episodes in bio)
keshav tweet media
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Christian Garrett
Christian Garrett@CGarrett_15·
The new @TegusHQ update is wild. Fully integrated BamSEC and Catalyst. So sick.
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Brian Ji
Brian Ji@brianjji·
@nabeelqu + making tweets with external links much less likely to get any sort of reach.
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Nabeel S. Qureshi
Nabeel S. Qureshi@nabeelqu·
I think it was short-sighted for Twitter to make its algorithm so brutally hits-based; tweets are either mega-viral or else barely get impressions. Nowadays I see interesting people posting more on [forbidden site] or even LinkedIn, where the new user on-ramp is much smoother.
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Appa Bollera
Appa Bollera@aiyappa__·
DMs open if you're thinking about the vertically integrated or hybrid approach Working with a few startups running this playbook now - it's going to be the future template for tech
Luke Sophinos@lukesophinos

I'm seeing 4 sets of vSaaS companies today... Classic: Selling SaaS into one industry Digital Franchises: SaaS + Business In A Box + Ops Support Vertically Integrated: M&A led + SaaS + Ops Takeover Hybrid: SaaS + M&A + Selling their SaaS to other companies Let's go deeper...

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Brian Ji
Brian Ji@brianjji·
@kokoxsu In general, I think there are lessons from ML we can take and re-apply to human performance engineering. Particularly focused on the data ingestion end of things: e.g. finding novel / efficient new training data (for whatever maximization function one chooses).
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koko 𝑥𝑠
koko 𝑥𝑠@kokoxsu·
Intuition to reason is like neural networks to if-then statements - your biological NN and artificial NN are both function approximators. If you train your BNN on enough high-quality data, you’ll be able to model very complex reasonings w/o explicitly knowing the logic chain - just like how NLP can do sentiment analysis without needing to hard code it with if-then statements
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Keşfet

@mrexits @nearcyan @david_perell @maxdesalle @EricJorgenson @mr_robot1234567 @0FJAKE @jacobrintamaki