Alternatives
Products that do what Kielo does
Decentralized AI inference at up to 50% lower cost.
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Hi everyone! I've been loving building with AI, and over the past few years I've been leaning more and more into Typescript (and bun). My team at inference.net is constantly trying to get more leverage out of AI and find ways to setup our codebase to be able to increase the level of correctness that our AI is able to write code at. This starter repo is a very opinionated way to lay out a repo to lean into AI heavily. It leverages Cloudflare Workers as a deployment target for the API (my goal is to never have to deploy an API on a AWS/Azure/GCP server ever again unless I get to a…
2025 · abeahmed.com
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Hey folks, I’m the creator of WFGY — a semantic reasoning framework for LLMs. After open-sourcing it, I did a full technical and value audit — and realized this engine might be worth $8M–$17M based on AI module licensing norms. If embedded as part of a platform core, the valuation could exceed $30M. Too late to pull it back. So here it is — fully free, open-sourced under MIT. --- ### What does it solve? Current LLMs (even GPT-4+) lack *self-consistent reasoning*. They struggle with: - Fragmented logic across turns - No internal loopback or self-calibration - No modular thought units - Weak…
2025 · github.com
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CAST AI (https://cast.ai) has built a cloud optimization platform that reduces AWS cloud costs 50% to 90%, optimizes DevOps, and automates disaster recovery via multi-cloud with a single cluster. Intelligent optimization engine delivers a cost-efficient, high-performing, and resilient infrastructure for every Kubernetes workload. If it sounds too good to be true - try free. To make it easier CAST AI provides AWS and GCP cloud credentials for free. Visit https://cast.ai Here's how it works: 1.Use CAST AI to deploy your K8s clusters. Next, take a look at the CAST AI…
2021
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2017 · synapse.ai
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Hey HN, I've been building AutoAgents, an AI agent framework in Rust. Today I'm sharing a feature I haven't seen done well elsewhere: composable middleware layers for LLM inference pipelines. The problem Every agent framework lets you swap LLM providers. Almost none of them give you a structured way to enforce safety, caching, or data sanitization in the inference path itself. You end up with guardrails as application-level if-statements, caching bolted on as a separate service, and PII handling as a "we'll add it later" TODO that never ships. This gets worse with local models. Cloud APIs…
Mar 2026 · github.com
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Hi HN, my name is Maria, and I’m a co-founder of Maritime. We started Maritime at MIT to build infrastructure for companies that need to run thousands of isolated AI agents for their customers. Imagine you set up an agent like OpenClaw, or a personal assistant agent with a custom framework, and want to give a separate version of it to every customer/friend. Each customer needs their own agent running in an isolated microVM, with persistent state, secrets, triggers, and sleep/wake behavior. Building such scalable and secure infra will take you months and will cost hundreds of…
18d ago · maritime.sh
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CloudBot gives you a fully configured AI employee with its own cloud computer in one click. Built on OpenClaw. What you get: - Full Ubuntu desktop environment in the cloud - Pre-installed AI agent that sees the screen and controls the computer - 24/7 availability - your AI keeps working while you sleep - Uses your own API keys for AI models - Starting at $69/month The AI can use VS Code, browse the web, run terminal commands, manage files - anything you'd do on a real desktop. I wake up to completed code reviews, finished research reports, and updated documentation. Built this…
Feb 2026 · cloudbot-ai.com
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I have a favour to ask. I’ve been working for a while on Kalavai, a project to make distributed AI easy. There are brilliant tools out there to help AI hobbyists and devs on the software layer (shout out to vLLM and llamacpp amongst many others!) but it’s a jungle out there when it comes to procuring and managing the necessary hardware resources and orchestrating them. This has always led me to compromise on the size of the models I end up using (quantized versions, smaller models) to save cost or to play within the limits of my rig. Today I am happy to share the first public version of our…
2024 · github.com
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I wanted to build an inference provider for proprietary AI models, but I did not have a huge GPU farm. I started experimenting with Serverless AI inference, but found out that coldstarts were huge. I went deep into the research and put together an engine that loads large models from SSD to VRAM up to ten times faster than alternatives. It works with vLLM, and transformers, and more coming soon. With this project you can hot-swap entire large models (32B) on demand. Its great for: Serverless AI Inference Robotics On Prem deployments Local Agents And Its open source. Let me know if anyone…
Nov 2025 · github.com
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We’ve published a set of open-source reference implementations on how to build production-grade Agentic AI applications on AWS. What’s in the repo: • Agentic RAG, memory, and planning workflows with LangGraph & CrewAI • Strands-based flows with observability using OTEL & Arize • Evaluation with LLM-as-judge and cost/performance regressions • Built with Bedrock, S3, Step Functions, and more GitHub: https://github.com/aws-samples/sample-agentic-frameworks-on-... Would love your thoughts — feedback, issues, and stars welcome!
2025 · github.com
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We've built InferX, a specialized runtime environment that fundamentally changes how LLMs are served. The core problem we solve is the latency bottleneck in AI inference, especially with large models. Current systems waste resources or suffer from painfully slow cold starts. InferX's AI-native architecture, with its "snapshot" technology, enables: * *Sub-2s cold starts:* Spin up models instantly. * *High density:* Serve more LLMs on the same GPUs. * *Optimal efficiency:* Maximize GPU utilization. This isn't just another API; it's a new execution layer designed from the ground up for the…
2025 · github.com
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gm gm, We’re excited to show our project, Hive Network, a new frontier for decentralized AI agents that operate both on-chain and off-chain. Our mission is to make AI more powerful and transparent, and we’re inviting you to join us in this revolution. What is Hive Network AI? -- Hive Network AI is a platform where developers can create, deploy, and manage AI agents that function autonomously across blockchain and traditional networks. Our system addresses significant issues in the AI space, such as the lack of transparency, difficulty in monetizing models, and insufficient research funding.…
2024 · hivenetwork.ai
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Hi HN! I’m Thunder. Longtime lurker and first time poster. I’m excited to present Moneta (https://moneta.studio/) with my co-founder Rob. *Moneta is a conversation-as-code platform for building multiplayer AI-native applications in which the AI can reactively update the application based on interactions with users or other AI via CRDTs.* The key idea is that rather than using a conversation to generate an application, in Moneta the conversation *is* the application. We call this idea 'conversation as the engine of application state' (CATEOAS). This means that instead of saying…
2025
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Hi HN! I’m so excited to show my another open-source project here. It is a PoC project. Distributed Inference is a project to demonstrate an approach to designing cross-language and distributed pipeline in deep learning/machine learning domain, using WebRTC and Redis Streams. This project consists of multiple services, which are written in Go, Python, and TypeScript, running on Docker. It allows setting up multiple inference services in multiple host machines, in a distributed manner. It does RPC-like calls and service discovery via my other open-source projects, go-inventa and…
2023 · github.com
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Read the full blogpost at https://rach.codes/blog/Introducing-Bhumi (click on reader to see the technical breakdown!) AI inference should be fast, but in practice it’s painfully slow. Inference bottlenecks slow down LLM-powered chatbots and AI workflows everywhere. I built Bhumi to fix that. Bhumi is a Python library designed for developers, yet its performance-critical core is implemented in Rust (via PyO3) for near-native speed. This hybrid approach delivers up to 2.5x faster response times across providers like OpenAI, Anthropic, and Gemini—without changing the…
2025 · bhumi.trilok.ai
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May 2026 · grunden.ai
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Kimi K3 has 2.78 trillion parameters and ships as 1.42 TB of weights. It clearly does not fit in the memory of a laptop. But K3 is a Mixture-of-Experts model. For each token, only a small fraction of its 896 experts per layer is activated. That changes the problem: the entire model does not need to be resident in RAM, as long as the weights required by each token can be reached quickly enough. We built WASTE — the Weight-Aware Streaming Tensor Engine — to explore that idea. WASTE keeps the dense, repeatedly used part of the model resident in memory, stores the routed experts in an…
Jul 2026
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