Kevin Hu

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Kevin Hu

Kevin Hu

@_newhaiku

🏘️

Palo Alto, CA Katılım Haziran 2019
250 Takip Edilen204 Takipçiler
Kevin Hu
Kevin Hu@_newhaiku·
probably nothing
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Kevin Hu
Kevin Hu@_newhaiku·
three years ago we started building tools for agents that didn't exist yet they exist now
John Kim@john_sungjin

Today, we're launching Village to let investors orchestrate teams of agents to scale their judgment. Three years ago, we bet that both humans and agents would need new tools to fulfill the promise of LLMs to transform research. Village is that tool, the first IDE for research built from the ground up so your agents can answer bigger questions, learn from your feedback, and build the AI research infrastructure for your firm. We're starting with public equity research, enabled by our EDGAR and earnings call transcript datasets, and we'd love to hear what use-cases you'd like to see next. The feeling of manipulating huge amounts of information and collaborating with hundreds of Assistants is really special, and I'm excited for you to experience it. Our beta sign up link is below!

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Alex Reichenbach
Alex Reichenbach@A_Reichenbach_·
We raised $4.1M for this moment: introducing Structify Flows. It's the first “vibe data engineering” platform that lets you do enterprise-grade data work in minutes: With Structify, just describe what you want in plain English. No code. No hiring. No backlogs.
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John Kim
John Kim@john_sungjin·
making our @conviction embed submission public to share some long-overdue writing on: - why vertical AI products haven’t hit widespread adoption among investors - our differentiated product vision - why we’ll win link below. lmk if you love it or hate it
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Kevin Hu
Kevin Hu@_newhaiku·
@andersonbcdefg one of the worst AI SDKs in the history of AI SDKs, maybe ever
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Ben (no treats)
Ben (no treats)@andersonbcdefg·
@kvvnhu gemini WOAT. i truly believe the models are not bad. the software layer they've wrapped them in is one of the most horrific things i have ever experienced
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Kevin Hu
Kevin Hu@_newhaiku·
gemini refuses to talk about openai and chatgpt–and even questions about anthropic get cut off mid-response
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Runpod
Runpod@runpod·
congrats to @ryancruzb to be the first one to solve the 4090 scavenger hunt! competition is still on, we'll pick from everyone that solves it by Friday 5 pm PST the faster you solve it, the higher your chances of winning happy hunting!
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Jesse Dodge
Jesse Dodge@JesseDodge·
Today we released a new version of OLMo 7B, which has significantly improved performance on MMLU. We also discuss a lot of how we got the improvements, big shoutout to the team! Check out that performance-efficiency tradeoff 🤩 this new model is on the Pareto frontier!
Ai2@allen_ai

Announcing our latest addition to the OLMo family, OLMo 1.7!🎉Our team's efforts to improve data quality, training procedures and model architecture have led to a leap in performance. See how OLMo 1.7 stacks up against its peers and peek into the technical details on the blog: blog.allenai.org/olmo-1-7-7b-a-…

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Arvind Narayanan
Arvind Narayanan@random_walker·
This is the craziest example of the ongoing AI frenzy I've ever seen. As far as I can tell this means that a single developer can make queries that burn $1,400 worth of compute for free *per day* (e.g. "reverse the following 1M token string") — 50 * (7+21) = 1,400. HT @simonw
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Nate Sanders
Nate Sanders@nlsanders·
@abacaj Wait, how’d you get this rainbow nvtop?!? Ha
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anton
anton@abacaj·
H100s are really something else, these things can crunch
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Kevin Hu
Kevin Hu@_newhaiku·
@abacaj .from_pretrained(..., device_map=device) instead of calling .to(device) afterwards should speed things up
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anton
anton@abacaj·
Why is HF model loading so slow? Using torch.load with mmap on mistral is 5x faster than HF
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Kevin Hu
Kevin Hu@_newhaiku·
cancel culture has gone too far
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Kevin Hu
Kevin Hu@_newhaiku·
@jerryjliu0 @disiok Great guide on fine-tuning! The evals could use some work tho: - BGE works best with instructions, but these aren't prepended. - Half the texts exceed BGE's 512 token limit and are silently truncated. After adding both, vanilla bge-small-en's hit rate is 0.87, on par with Ada.
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Jerry Liu
Jerry Liu@jerryjliu0·
One major way to improve your RAG system is to fine-tune your embedding model ⚙️ We’ve created a full repo/guide (@disiok) on fine-tuning embeddings over any unstructured text (no labels needed) 🌟 5-10% improvement 📈 in evals + runs on your MacBook! github.com/run-llama/fine…
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Aman Sanger
Aman Sanger@amanrsanger·
gpt-3.5-turbo is criminally underrated at coding When using it with Azure's completion endpoint instead of OpenAI's chat endpoint, you can get a jump in HumanEval performance from <50% to 74%! This blows claude v1.3 out of the water, which sits just below 60% perf. [1]
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Kevin Hu
Kevin Hu@_newhaiku·
"—Drastically increases the risk of flameout when the private markets correct." You got that from a Bill Gurley interview, right? Do you have any thoughts of your own or were you just gonna plagiarize his whole argument about startup overvaluation and the coming correction?
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Kevin Hu
Kevin Hu@_newhaiku·
—Well as a matter of fact I won't, because raising huge rounds at sky-high valuations drastically increases the risk of flameout—
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Kevin Hu
Kevin Hu@_newhaiku·
Of course that's your contention. You just launched your first company after reading The Lean Startup—you're convinced that constant iteration and pivoting based on customer feedback is the only way to build an empire.
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Keşfet

@A_Reichenbach_ @conviction @andersonbcdefg @runpod @ryancruzb @JesseDodge @random_walker @simonw