Autonomous Cloud for Serverless and AI Infra. Leaping Beyond K8s

Hacker News

For the last 3 years, I have been working on a way to solve inefficiencies in software infrastructure. Introducing Taubyte. What is Taubyte? Taubyte is an open-source, autonomous cloud platform focusing on serverless and AI infrastructure. Designed to be edge-native and GitOps-centric, Taubyte aims to democratize cloud computing and reduce ops overhead. Why Choose Taubyte? The Future is Autonomous! - Ready-to-Go Platform: Unlike Kubernetes, which often requires additional products, Taubyte delivers a fully-functional cloud network out-of-the-box. - Low to No Ops: Taubyte's autonomous nature eliminates the need for constant operational maintenance. Taubyte's self-governing system literally runs itself! - Horizontal Scaling: Built-in support for horizontal scaling, no additional configurations needed. - Serverless Focus: While Kubernetes can support serverless via add-ons, Taubyte is natively designed for serverless computing. Repos and Tools: - Tau (github.com/taubyte/tau): Deploy on servers to form a Cloud Network—a connected ensemble of Taubyte nodes identified by an FQDN. - Dreamland (github.com/taubyte/dreamland): Use on your PC/laptop for E2E testing and local cloud simulation. - Tau-CLI (github.com/taubyte/tau-cli): GitOps-centric CLI for managing projects across different Cloud Networks. - VM-Orbit (github.com/taubyte/vm-orbit): Extend WebAssembly VM capabilities for serverless function calls. Dive Into Our Showcases & Resources: - 8bit.ws: The cloud, but cooler. An AI playground built on Taubyte featuring serverless LLM via llama-cpp and NVIDIA, supercharged by VM-Orbit. Check Out the Plugin Source Code. - Sandbox: Experiment on sandbox.taubyte.com via tau-cli or console.taubyte.com. - Documentation: Comprehensive guides available at tau.how. Get Involved: - Test It: Use Dreamland for local tests or sandbox.taubyte.com for a live experience. - Join the Revolution: PRs, issues, and your genius are all welcome.

Visit product View on Hacker NewsSep 13, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, code, open · Missing: mac, agents, macos

83%83% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: llama, ide, io · Missing: https docs, excited, just released

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, calls · Missing: plus, intuitive, reviews

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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