Synaptic

#AI coding tools are white hot right now! 81% of developers now using AI #coding tools to supercharge their workflows. 🚀 Here’s how the landscape has evolved in the last year! 👇 1️⃣ AI Coding Agents are becoming increasingly valuable and effective Across most AI application, “Assistants” i.e. suggesting tools, have been more successful than “Agents” i.e. automated execution. Coding is seeing a strong uptake in agentic tools. Factory, Tusk (YC W24), Pythagora (YC W24) are pushing toward fully autonomous systems that could manage entire coding processes. As these tools evolve, the shift from AI copilots to full-fledged AI “engineers” will be interesting to watch out for. 2️⃣ AI applications for downstream use cases in software dev are rapidly gaining traction Use of AI is expanding beyond code writing to use cases across the software development lifecycle, such as code efficiency (Million), automated bug resolution (CodeRabbit). Specific solutions are also targeting quality-of-life improvement, e.g. frontend dev (Xamun.AI), web crawling (Indexical), SQL generation (Vanna AI). 3️⃣ It's a fast-maturing sector within #GenAI, and winners are emerging AI coding tools have demonstrated clear value – large funding rounds, acquisitions like Fig and extensive developer adoption is proof. Startups like Zed Industries, Continue, Vanna AI are seeing remarkable developer traction on GitHub. Cursor, Fig , Redocly, Warp continue to be hugely popular and have attracted funding. New startups like Augment Code, Factory are making their mark – Factory, in particular, is seeing significant web traction this year with 300%+ QoQ growth in traffic. 4️⃣ Expect intensifying competition in the next few years Widespread developer adoption, low switching costs, decreasing costs and multiple foundational model options will ensure numerous opportunities for emerging startups as well as indie developers coming up with interesting solutions. Read more on the sector at https://bit.ly/4gp3xDi

#Web3 is redefining the future of the internet! Web3 is transforming the internet as we know it, ushering in a #decentralized era where users have more control over their data, privacy, and online interactions. In 2024, the Web3 ecosystem is evolving faster than ever, with #blockchain technologies, decentralized applications (#dApps), and token-based economies reshaping industries and the way things work. We’ve curated an exclusive report featuring 75+ high-growth Web3 startups, from angel to Series B, across applications, DeFi, dev. tools and protocols. 👀 Sneak peek at the categories: Access & Identity: Phantom, Xverse B2B Applications: Zealy, Mojito B2C Applications: Pudgy Penguins , Magic Eden Decentralized Finance: Artemis, PYOR Infrastructure/ dev tools: Nillion, Kima Network Layer/ protocol: Zircuit, Story Protocol Download the list now—easily accessible Google sheet with startup details & founders. https://bit.ly/3XPwEsw

The Consumer Health sector is booming! A $1.8 trillion global market, driven by an aging population, viral wellness trends, and the increasing demand for better health management. Over 1,200 niche startups have emerged in this space in just the past two years! We’ve curated an exclusive report featuring 100+ high-growth Consumer Health startups, from bootstrapped innovators to Series B, making waves in mental health, personalized wellness, and wellness tech. 👀 Sneak peek at the categories: ▪ Chronic Disease Management: Miga, Carda Health ▪ Health Stores & E-Pharmacies: Farmako: Medicines in 30 minutes, HyugaLife ▪ Men’s Health & Wellness: MANUAL, Bold Care ▪ Mental Health & Wellbeing: Allminds, Daybreak Health ▪ Nutrition & Lifestyle: Nourish, Flex AI ▪ Telemedicine & Virtual Care: ZoomDoc Health, DrOnline Healthcare ▪ Wellness & Wearable Technology: Pvolve, OxeFit ▪ Women’s Health & Reproductive Care: Oova, Midi Health Download the list now—easily accessible Google sheet with startup details & founders. https://bit.ly/47hATA1

Open Source AI is evolving at lightning speed, with a wave of new startups emerging and reshaping the industry. The landscape has significantly changed since our Jan’24 coverage! 1️⃣ Open-source AI models have closed the gap with their closed-source counterparts. OS models like Mistral AI and Meta Llama have established a strong foothold in the AI community and match/outperform top proprietary models like GPT, Claude in various benchmarks. Other OS models, such as Alibaba Cloud's Qwen & 01.AI's Yi, are also catching up. 2️⃣ OS AI innovation has expanded downstream, from Models and Dev tools to training and monitoring 150+ players are innovating across different use cases in OS AI – expanding beyond just foundational models and devtools to model training, fine-tuning and monitoring. 3️⃣ Fundraising in OS AI is impressive in volume as well as size, even by Gen AI standards Open Source AI startups are standing out with large deal sizes, highlighting their growing influence and potential in the industry. NVIDIA’s $300M acquisition of Deci AI (Acquired by NVIDIA), Scale AI’s $1B Series F, Mistral AI’s $640M Series B are proof. Supabase is a likely contender for a sizeable deal in the near future. Check out our detailed research on the Open Source ecosystem here: https://bit.ly/4g02qtK

#SpaceTech startups are shooting for the stars 🚀 ! SpaceTech has seen $22B in funding across 1k+ deals in the last 3 years. We’ve put together the #SpaceTech Startup List – 50 high-potential startups, from pre-seed to Series C, driving cutting-edge innovation in this space! Sneak peak: Some of our favourite startups from the list 👇🏼 Earth Observation: GalaxEye, Open Cosmos Satellite Communication: Northwood, Oxford Space Systems Satellite Launch: PLD Space, Rocket Factory Augsburg - RFA, AgniKul Cosmos Space Exploration/Surveillance: AstroForge, HEO Space Operations: Reflex Aerospace, Impulse Space, ReOrbit Download the list here: https://bit.ly/3MbA1Dd

Open source is revolutionizing AI, outpacing tech giants in speed, adaptability, and efficiency. There has been a ~148% surge in developer contributions on GitHub and over $8B in investments over the last 2 years, underscoring the amazing OS ecosystem in AI. We’ve put together #OpenSource AI Startup List – 75+ top startups, from bootstrapped to Series B, driving the open-source AI development! Sneak peek: Some of our favorite startups from the list 👇🏼 1️⃣ Dev Tools/Infra: unstructured.io | LastMile AI | Qdrant 2️⃣ Monitoring & Observability: :probabl. | Protect AI | Patronus AI 3️⃣ Model Deployment & Inference: Lepton AI | Baseten | Replicate 4️⃣ Model Training & Fine-tuning: Gretel | Robovision | Predibase 5️⃣ Foundational Models: Segmind | Mozilla.ai | LightOn Download the list here: https://bit.ly/3WD1DWO Check out our detailed research on the Open Source ecosystem here: https://lnkd.in/gXMt-ZqR

We love #Marketmaps! They provide a structured way to track and understand a sector. We've been monitoring Generative AI closely (who isn't!), and maintain a searchable collection of market maps internally. We're making this resource public for everyone to access and collaborate. Introducing the #GenAI Market Map Library – a live collection of over 100+ awesome market maps from leading VCs like Sequoia, a16z, BVP. Find the best Gen AI market maps in one place; search & filter by verticals, use cases, and geography. Streamline your Gen AI research! https://bit.ly/45WGpaB

Looking to #fundraise this year? Find the right events to network with #LPs with LP-GP Conferences Calendar - a curated list of Top 100 LP-GP events to attend in 2024-25. Access the global calendar with dates, locations, website, agenda, expected audience, and more – all in one place.👇 Click here to access the full list: https://bit.ly/3RH1Iag

We surveyed 125 Private Funds on how they manage #portfoliodata. Here are some key insights 🔽 1️⃣ Portfolio tracking is not working for private investors Only 14% of private investors are satisfied with their current portfolio tracking process – driven by a lack of automation and standardization. 2️⃣ Manual effort and lack of automation is pervasive 53% of respondents spend more than 5 hours analyzing portfolio company updates (per portfolio company); another 29% spend between 3-5 hrs per update. 3️⃣ Portfolio tracking is a significant cost for funds 65% of funds spend more than $500K per year on portfolio tracking, including personnel and tools; most investors expect 10-30% increase in the next 2 years. 4️⃣ Portfolio data use cases go beyond LP reporting & Valuations Benchmarking and Market Intelligence are important use cases, with over 35% of respondents choosing them in their top 3 use cases. 5️⃣ LLM adoption has been low for Portfolio Intelligence use cases Standardization and Accuracy are the top 2 KPCs, chosen by 64% and 57% of respondents respectively, for portfolio tracking solutions. AI struggles with both aspects. Get the full report: https://lnkd.in/gRt2rtg3

We recently hosted an interactive webinar with Sagar Chandna, Sr. Partner & CTO at RunwayFBU, on "Becoming More Data-Driven in 2024." Some interesting insights from our conversation 👇 1️⃣ What does “data-driven” mean Data-driven = reducing human time and judgment across a fund’s activity stack. As startups increase - you want to access and find the best ones without bias systematically. “You cannot have that approach with a manually driven process” 2️⃣ Build, to understand buying ”Always build first, but only to gain the knowledge of what to buy.” Build something basic to understand your needs better and make informed decisions while buying. Buying should involve shipping off activities you don’t want to do internally. 3️⃣ Prioritize usability over quality, when it comes to data "As long as you fix the data usability, ingestion, and also its life cycle through your workflows...quality will improve.”  Drive use cases through data and the need to extract more out of those use cases will improve the quality of data over time. 4️⃣ Capture ALL internal data, to be future-ready Capture all your internal data, even trivial details like clicks on email signatures.  "I don't use all of that, not today. But eventually, I hope to use a lot of that information." This is a great step to being future-ready when it comes to tech & AI. 5️⃣ AI is good for summarization, but is a long way from “VC copilot” ”Can you use it? Yes, you can. But how far away is AI from really, taking over parts of an investor’s job? It's a long, long time away”. AI adds value in summarizing and processing data at scale and internal data is crucial to contextualizing. Watch the full conversation at https://lnkd.in/gwjxnV6t Rohit Razdan | Christian D'Alessandro