Han Qin

Venture Capital Contact Compilation. Explore a detailed list of 1,250+ VC firms, 7,400+ professionals in the VC space (including partners and principals), and 6,400 email contacts. https://payhip.com/b/cs7UQ #startup #startups #vc #venturecapital #investmentbanking #privateequity #managementconsulting #b2b #founders #entrepreneurs

1

1 Comment

Worth reading: Situationsl Awareness by Leopold Aschenbrenner PDF : https://lnkd.in/gkxQ3yMD Web site: https://lnkd.in/gyJUCBhb Interesting perspective, time of course will tell.

2

"AI is powerful, but without the right data foundation, it’s just noise." In the latest Tech Teams Today podcast episode, I had a great conversation with Yongsheng Wu, VP of Engineering at Granica, about why a solid data foundation is crucial for AI success. Here are the top insights from the episode: 1️⃣ Data Foundations Matter: Before diving into AI, ensure your data systems are robust. Solid pipelines & data integrity are critical for meaningful insights. 2️⃣ AI as an Enhancer: AI and ML should optimize existing processes, not solve undefined problems. 3️⃣ Data Quality is King: Curating and labeling data accurately is vital for effective AI models. The better the data, the better the outcomes. 4️⃣ Efficiency & Cost-Effectiveness: Successful AI solutions balance effectiveness with cost efficiency, especially as models scale. Catch the full episode on all major podcast platforms or watch it on YouTube: https://lnkd.in/dbkE__JC #AI #EngineeringLeadership #DataScience #MachineLearning #TechTeamsToday

30

4 Comments

AI memory is fundamental for conversational AI and AI agents. It is critical for building personalized experiences and improving reasoning. Check out Letta’s course to understand how to use AI memory to enhance the customization and quality of your AI app.

15

Check out our Mangusta Capital Vertical AI breakdown below!

20

4 Comments

Semantic Search with Milvus Lite and Voyage AI https://bit.ly/3ZPk3Xt

1

I really like this PMF Framework from Sequoia. https://lnkd.in/gT2nWttx

25

2 Comments

Insightful look into bioplatform strategy, and what it takes to raise a Series A from my partner, Omri Amirav-Drory: https://lnkd.in/dFv7GbF6

19

Sharing a sanitized version of the mu ventures Q2 q'ly LP update. Included musings: 👉 SEOs shrinking impact, and the rise of LLMO (large language model optimization) 👉 examples of the Commerce AI application layer coming to life 👉 a modern fund strategy that accepts companies require less capital, time, and people to trigger the power law 👉 Mu's guide to investing in AI 👉 all 30 companies in the Mu-niverse (and several others we admire) Founders, VCs, LPs - if you like how we think and suspect we can partner, hit us up! https://lnkd.in/dTJ2zjav

52

7 Comments

This month, we announced our $7.5M seed round with more than 30 angels including the co-author of Transformer ("T" part of GPT) and launched Pond’s Decentralized Model Factory and Point System. For those who previously competed on Pond’s platform, we invite you to return and check if your points have been updated: https://cryptopond.xyz/. We welcome you back to push the boundaries of crypto AI together with our battle-tested AI infrastructure. Outstanding models will be integrated with our real-time data and inferencing infrastructure, enabling commercialization. Top performers will unlock a series of exclusive benefits! Let’s dive into more details about Pond’s recent updates: • $7.5M Seed Round: The round was led by Archetype, with participation from Cyber Fund (the Lido founder’s fund, known for its rare direct investments, including Solana pre-seed and Ethereum pre-ICO), Coinbase Ventures, Delphi Digital, and Anagram. More than 30 visionary founders and leaders from NEAR, Exo Labs, Flashbots, ENS, Legion, Plume, Nuffle, OpenGradient, and others contributed as angel investors. • Pond’s Decentralized Model Factory: This crypto-native AI model powerhouse is designed to preserve model ownership for creators while fostering collaboration. The workflow enables users and developers to: 1. Propose ideas 2. Create idea tokens 3. Trade tokens on third-party platforms 4. Reach a predefined threshold 5. Host competitions 6. Transition winning ideas into complete models 7. Utilize Pond’s infrastructure for inferencing, data management, and commercialization. These models can power future applications, including AI agents and beyond. To learn more about Pond’s Decentralized Model Factory and Incentive Mechanism, visit: https://lnkd.in/gMTEJSP3. For a detailed tutorial on Pond’s Factory and how to propose model ideas, check out: https://lnkd.in/gxi9V94Y. Stay tuned for more updates from Pond!

40

1 Comment

💼 Dive into the world of venture capital with our curated list of **270 VC firms in #SanFrancisco**! From firm details to portfolio links, we’ve got you covered. Explore the possibilities: https://lnkd.in/dqizhN92 #startup #startups #venturecapital #funding #vc #managementconsulting

3

Ever wondered how fine-tuning impacts the safety of your LLMs and applications? Find out below... I have been having a lot of conversations with folks who are building with LLMs, and one of the most common topics that comes up is the impact of fine-tuning on the safety and security of an LLM. Sometimes it comes up as a question and sometimes as a myth that fine-tuning makes my model "more secure" because it will only answer based on the fine-tuning data set. We at SydeLabs will soon be publishing a more detailed report around this, but here is a very quick experiment we did with one of the most popular LLMs. We fine-tuned it on a fairly small dataset (~1000 rows of data). The data involved simple questions and answers with no toxic or unsafe content. Here are the results of the responses we got for a question before and after fine-tuning the model.

64

Thrilled to finally reveal the launch of FlexAI, led by the incredible Brijesh Tripathi and Dali Kilani, which just raised a $30M seed round to make AI compute infrastructure more efficient and accessible to everyone. Our investment thesis: 1. Strong macro-tailwinds - Compute for AI is a mature market with sustainable demand growth from new models over the next years - But the compute demand is already exceeding data center capacities and supply of GPUs, creating a supply constraint for Nvidia’s top units - AI model development is attracting large amounts of funding, much of which will be dedicated to computation and costs/performance will matter more and more. 2. The need for a European AI infrastructure - Compute power is high on the geopolitical agenda. Who will control AI will control the world. - European AI model companies rely today on US-based hardware and US-based cloud providers to build "European" AI models - If Data sovereignty and security matter, then there is an urgent need for a European AI compute platform 3. An exceptionally skilled team to build this European AI compute platform - Brijesh has built customized systems and hardware for AI use cases at NVIDIA and Apple before, and designed + scaled compute systems for autonomous driving at Tesla and Zoox, as well as with the Intel super-compute platform - Dali has a demonstrated track record of executing and delivering both hardware and software projects, and leading engineering teams (this is the second time we back him, not a coincidence!) - both have known each other for a long time and worked together at NVIDIA. have built GPUs themselves and were there when Apple and Tesla decided their launch their own chips for the exact same reasons (cost and performance trade-off + reduce dependency on third party suppliers). - They have already demonstrated the strength of their network by signing key partnerships even before product launch and have assembled Tier 1 advisors around FlexAI. Thrilled to be partnering with Alpha Intelligence Capital, Heartcore Capital , Elaia , Frst and Motier Ventures to build the AI compute infrastructure the world needs, made in Paris (with love). https://lnkd.in/e3aXXZSB cc Partech

220

4 Comments

GPTZero has skyrocketed to profitability within 18 months, raking in millions. With 500% ARR growth in six months and user growth from 1 million to 4 million in a year. While many AI-detection companies struggle with accuracy, GPTZero excels, leveraging vast data and advanced LLM models using the latest open-source tools. Initially celebrated for helping teachers spot AI-generated student work, GPTZero's clientele now includes government agencies, grant writers, hiring managers, and AI training data labelers. One founder noted, "We have a big data advantage with millions of examples of human versus AI-generated text," proving the quality of data fed into AI models is crucial. https://lnkd.in/gREnQ2ps #ai #sustainabledevelopmenttechnology #emergingtechnology

5

Batch processing for LLM inference activity: "One sign of that is the rise of batch processing. Today, popular consumer AI products like ChatGPT and Perplexity provide users with near instantaneous responses, otherwise known as real-time inference. However, businesses don't always need immediate responses and are often willing to wait hours, days, or even weeks for responses, as long as they don't have to shell out as much money. Founders of cloud and inference providers have told me there's growing pressure from business customers for this kind of flexibility, otherwise known as batch processing." -- Nathaniel Whittemore on "The AI Daily Brief" (with much deserved shout-out to his inspo from Stephanie Palazzolo at The Information) I fall into this bucket as we have a lot of backfilling to do as we're building out the underlying data in our service, and some of that could just as well be done in the wee hours of the night. The new stuff flowing in I want processed ASAFP, but these backfills—I'd mos def take more cost-efficient delayed batch processing. There's also use cases like needing (near) real-time inference for the first inference pass for timeliness / contemporaneousness value, but then a second pass slow-time inference for wider classification exercises over an ever-growing data corpus. I could see us mixing Sonnet & Haiku models in both cases. Just a heads up to Anthropic Mike Krieger as you contemplate this, along with Amazon Web Services (AWS) Jeff Barr Mark Roy Swami Sivasubramanian on the Bedrock side of things...and I don't mean the "batch" mode that doesn't seem to have any real value other than helping AWS manage its own rate limit challenges. (DeepCast is using Bedrock.)

11

2 Comments

In this week's episode of the Mobile Dev Memo podcast, I speak with notable X personality Trung Phan about the evolving social media landscape, and how to tame the mayhem and pandemonium of X. https://lnkd.in/dVHMzbui

1