Mikhail Parakhin

A good friend recommended me to try Cursor.sh, so I did. I was not ready for what happened next. But first, let's start with a little idea I had for a while. Every time I needed to check a text, I needed to find my tab with ChatGPT, login if disconnected, start a new chat, type "fix typo:" then paste my text to fix, then copy the text back. Not efficient. I knew that using OpenAI APIs I could do a simple website that would do the same, but without the need of writing "fix typo:" every time. A simple hack. Of course I never found time to start this pet project. Until I decided to give it a try with Cursor.sh and made it real is a couple of hours. fix-typos.com

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On April 18th, just last week, Meta unveiled Llama 3, lauded as "the most capable openly available LLM to date". This remarkable achievement by the Meta team comes hot on the heels of Llama 2's release last July. Notably, the new models surpass their predecessors and competing offerings from other providers in terms of performance. Meta's commitment to open sourcing these powerful models while prioritizing model safety and responsible usage is commendable. (https://lnkd.in/ghv3RJRz) Yesterday, Microsoft introduced Phi-3 Mini, a member of the Phi-3 family of models, promoted as "the most capable and cost-effective small language models available". According to Microsoft, the key to achieve high performance in such a small package lies in the quality of the training data. The model has been open sourced and is now available on Ollama and Hugging Face. (https://lnkd.in/gKgSmrBn)   These compact yet powerful models serve as invaluable tools for creators eager to bring their visions to life or simply experiment with cutting-edge technology, particularly for those with limited access to resources.   Shortly after the release of Llama 3, reports began surfacing of enthusiasts running the 8B model on a Raspberry Pi 5 equipped with just 8GB of RAM—a modest $80 single-board computer. (https://lnkd.in/gbeQPhQ2) While the performance understandably reflects the limitations of such a platform, the fact that the model runs and generates an output is nothing short of magical. Similarly, the Phi-3 Mini model can run on ubiquitous devices such as smartphones.   Reflecting on my own journey, I recall the pivotal role that early access to a low-cost computer played in shaping my passion for coding. The gift of an 8-bit Commodore 64 home computer ignited my curiosity at a young age. Later, with the guidance of an outstanding Computer Science teaching staff at my high school IISS Marconi-Hack Bari, I embarked on ambitious projects like a networked multiplayer version of Battleship written in Turbo Pascal and 8086 Assembly, complete with custom sprites and lo-res graphics. These formative experiences paved the way for my career, eventually leading me to various engineering and leadership roles.   It all began with a simple, low-cost, modestly capable computer used for experimentation—a gateway to discovering my life's passion.   I encourage educators and parents alike to consider setting up similar low-cost experimentation environments. By introducing younger generations to open-source AI technologies, we can nurture their creativity and help them uncover their true passions. It's not just a weekend project—it's an investment in their future and the future of innovation and discovery. #education #youth #AI #LLM #future #innovation

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𝑳𝑳𝑴-𝒂𝒔-𝑱𝒖𝒅𝒈𝒆 🧑‍⚖️ 𝘓𝘓𝘔 𝘌𝘷𝘢𝘭𝘴 & 𝘵𝘩𝘦 𝘪𝘯𝘴𝘪𝘥𝘦 𝘴𝘵𝘰𝘳𝘺 𝘰𝘧 𝘩𝘰𝘸 Twilio Segment 𝘣𝘶𝘪𝘭𝘵 𝘊𝘋𝘗 𝘊𝘰𝘱𝘪𝘭𝘰𝘵 𝘵𝘰 𝘢𝘤𝘤𝘦𝘭𝘦𝘳𝘢𝘵𝘦 𝘮𝘢𝘳𝘬𝘦𝘵er 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴 3𝘹 These days I'm spending an irrational amount of time thinking about evals. It started with a conversation months ago with Jaimal Soni, when he was showing me an absolutely 🔥🔥 demo of what he's been building in stealth. I couldn't believe what I'd just seen. "What's one of the hardest problem you had to solve?" I asked. "Evals." Evals are the way to assess (ahem evaluate) LLMs and systems built with LLMs. In software engineering, you'll hear a lot about test coverage and unit tests — little tests to verify that your code is doing what you expect. But generative AI & LLMs are a little different: unlike your deterministic code, models are stochastic, meaning you never really know what the output will be until it's generated!Ask chatGPT the *same* question and you will get *different* answers. This is where LLMs get their magic, but also creates problems when you're asking an LLM to solve discrete tasks. As we've been building out CustomerAI & Generative Audiences, we needed to think about this problem from the start. Generative Audiences allows marketers to write natural language to generate new audiences, without having to navigate a sophisticated audience-building UI. We’ve been blown away by the results of – customers are experiencing a 3x improvement in median time-to-audience creation, and world-class feature retention rate IF the audience generation works on the first attempt. So evals are 🔑 Ankit Awasthi, Salman Ahmed, Alfredo Láinez Rodrigo, & James Zhu came up with a really clever solution to ensure that CDP Copilot is generating audiences that are accurate & represent the intent of the prompt. But it wasn't easy...and today we're sharing how we used synthetic data & LLM-as-Judge architecture to accelerate marketer workflows 3x.

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"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

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Protecting users and developers on #GooglePlay is a top priority. In 2023 alone, we prevented 2.28M policy-violating apps from being published on Google Play in part thanks to our investment in new and improved security features, policy updates, and advanced machine learning and app review processes. Learn more about our initiatives that help keep you safe on Play and #Android: https://lnkd.in/g4-b2xYq

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🍜Anybody up for some ramen? I am excited to share that I am back in the garage, building again (it’s actually a house in the Berkeley Hills). We are taking the wraps off of Supercog AI, a new startup focused on solving Application Integration using LLM powered agents. My co-founder in this new venture is a great friend of mine (and a former co-founder partner), Alex Osborne. Alex and I have worked around the application integration space for a long time, and we are unrealistically excited about applying the GenAI stack to this problem. Not many people relish the task to get some bits to move from system A to system B. But it’s a critical job to be done, and that’s led to a lot of brittle scripts, a myriad of inflexible “no code” tools, and a lot of hand crafted SQL. But the power of the Large Language Model, trained on a huge corpus of information, offers a remarkable tool to solve this problem. The LLM you use today already knows the APIs of hundreds of popular systems. It knows the SQL dialects of every major database. It understands the semantics and data schemas of many popular SaaS systems. It understands much of the specific domain in which your business operates. I like to call the AI revolution “the last platform shift”. That may turn out to be hyperbole, but I definitely believe we are in the very early stages of seeing what this new stack can enable. I know we are hardly the first folks to claim that “amazing stuff is coming!”. But this is why Alex and I have decided to focus on a real and hard problem. If we can prove that LLMs can power a new way to solve this problem, it will be the first successful *new* approach in 15 years.. We will have much more to share about this new platform soon. In the meantime, if you’re interested in getting a peek - or even better if you have application or data integration tasks that you would like some help with - please reach out because we want to help. #genai #startups #backinthegarage

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Worth reading: Generative AI is not going to build your engineering team for you. So, so good - peppered with gem quotes and observations- chuckle. https://lnkd.in/gZBKtU4X Thanks to Eric Brooke for sharing.

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This is a very exciting effort and I think it has the potential to be a real "game changer" #foundationalmodels #AI #ML #pioneeringtechnology #metaverse

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While everyone is talking about ChatGPT-5, aka Strawberry, I'd like to take a moment to appreciate Time's recognition of women in AI – almost 30% of the most influential people in AI are women. By the way, this number is higher than the overall industry average, where it’s about 28%. But hey, we have Scarlett Johansson on that list, and she counts for a lot, right? This representation matters, but we’re still not at 50%, so let’s not settle just yet. #Time100 #AI #WomenInTech

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What does generative AI do to create new content? "Generative AI devours massive amounts of training data to learn underlying patterns and relationships, then generates entirely new content with a unique twist." Discover how generative AI sparks creativity by mimicking and remixing data into fresh, original content.

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Tinkering fuels innovation and curiosity. It allows us to truly understand the tools and systems we work with at a granular level. Recently, I've been tinkering with Rust, a language gaining popularity for building reliable, safe, and sustainable systems at scale. The best way to learn is by doing. So, I turned our simple summarization proof of concept, Distill, into a CLI leveraging Rust's performance and memory safety advantages. As builders, it's our responsibility to continuously look for optimizations in everything we do. To ask questions and challenge the status quo. To learn and be curious. I encourage you to give Distill CLI a try, tinker with it, and share your feedback. And as always – now, go build! #AWS #tinkering https://lnkd.in/e7HUQwEt

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"Since 2018, we have increasingly leveraged Rust for critical workloads across various [AWS] services like S3, EC2, DynamoDB, Lambda, Fargate, and Nitro, especially in scenarios where hardware costs are expected to dominate over time." We've seen a lot of success with Rust at AWS, including our work on Firecracker, on Lambda's container dataplane, and on many other systems we've built. I love the fact that we can get memory safety without compromising performance or efficiency. I also enjoy seeing how much less time my teams spend tuning runtimes or debugging memory issues. I also really appreciate this sentiment from Werner's post: "... this is not a call to rewrite everything in Rust. ... It’s about using the right tool for the right job. This means questioning the status quo, and continuously looking for ways to incrementally optimize your systems – to tinker with things and measure what happens." Make decisions about programming languages the same way you make all critical engineering decisions: with data!

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Google Wants to Make AI Your Literal Co-Worker At Google's I/O developer conference, company leaders unveiled their all-in plans for AI. It's a lot. BY BEN SHERRY, STAFF REPORTER @BENLUCASSHERRY MAY 14, 2024 Illustration: Inc., Google is bringing AI to your Gmail. And to your Sheets. And to your favorite rapper. At Google's annual I/O developer conference, CEO Sundar Pichai said the company is in its "Gemini Era," referring to the search giant's flagship, multimodal AI model (and to Taylor Swift's Eras Tour, of course). Indeed, the company is going all-in on generative AI, and multiple Google leaders explained how the tech will help people work faster and smarter. 

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Let's talk about data and dbt at Foundations on Nov 7th!

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My cofounder Saumil goes on the Stack Overflow Podcast to deep dive on AI Agents! He goes into detail how we leverage AI agents to power automated workflows to accomplish the most complex tasks. Including agents firing off tasks to other agents to tackle the details. Need an agent to verify your work? No problem. Give it a listen and let us know what you think!

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AI development is at a turning point—it’s no longer just about how fast we can go, but how efficiently and sustainably we can scale. I had a great conversation with @Daniel Nenni on the @SemiWiki Semiconductor Insiders podcast, where we explored this shift and how @Lemurian Labs is at the forefront of it. We covered: The critical role of hardware-software co-design in building efficient AI infrastructure. How the democratization of AI is unlocking new opportunities for developers everywhere. Why the future of AI needs to prioritize sustainability to handle the demands of next-gen applications. 🎧 Catch the full episode here: https://lnkd.in/ewwkM9Gg  #AIForAll #AcceleratedComputing #Semiconductor

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This new piece out of VS firm NFX predicts: "Labor plus software will eventually become one market." In other words . . . Get used to AI colleagues. A lot of them. And software businesses - get used to having to compete with both labor markets and automated software on your product roadmap, professional service offerings, and more. How is your business readying itself for this new era? #AI #ArtificialIntelligence #ML #MachineLearning #SaaS #Innovation #DigitalTransformation #FutureOfWork

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I'm super excited about the release of our new feature in Type.ai: Reviews! One challenge when using LLMs in their raw form is that the input-output workflow makes it cumbersome to make granular changes. There are two parts to the problem: first, identifying what changes have been made, and second, selecting which specific changes to incorporate. Also, this is not a one-shot process, and you'll probably want to make multiple passes until you arrive at your desired end result. Our new Reviews feature directly addresses these pain points. It enables full document transformations while giving you both a clear view of all changes and complete control over which ones to accept. It's very powerful and can be used for a wide range of tasks, both table-stakes stuff such as grammar checking and more complex things like translating or document restructuring. If you're doing any form of writing I highly recommend you give it a go.

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