Open-source real time data framework for LLM applications
Hey HN, I am the founder of Tensorlake. Prototyping LLM applications have become a lot easier, building decision making LLM applications that work on constantly updating data is still very challenging in production settings. The systems engineering problems that we have seen people face are - 1. Reliably process ingested content in real time if the application is sensitive to freshness of information. 2. Being able to bring in any kind of model, and run different parts of the pipeline on GPUs and CPUs. 3. Fault Tolerance to ingestion spike, compute infrastructure failure. 4. Scaling compute,…
Open-source real time data framework for LLM applications launched on May 23, 2024 with 92 votes, #526 of 4,539 launches that month and more than 90% of that year's launches. The site was still online in September 2026, 2 years on.
In plain words
Tensorlake offers Indexify, an open-source framework designed for LLM applications that operate on constantly updating data. It addresses production challenges including real-time content processing, flexible model deployment across GPUs and CPUs, fault tolerance during traffic spikes and infrastructure failures, and scalable compute resources. The framework targets developers building decision-making LLM systems that require reliable performance in dynamic data environments.
written from the facts on this page · September 2026
From the sources
Sandboxes for AI agents. Securely run untrusted, LLM-generated code in isolated Firecracker microVM sandboxes that scale from one to thousands — with snapshot, suspend/resume, and durable orchestration.
Isolated sandboxes, hosted Git, and a versioned filesystem for deploying agent harnesses, running RL rollouts and evals, and building CI that scales. Run persistent sandboxes that give an agent its own computer, or spin up ephemeral ones to isolate each tool call. Give the agent its own filesystem, shell, packages, and processes instead of sharing the app server runtime. Sandboxes sleep on inactivity and wake instantly when invoked — sessions can run for hours or days. Compile code, run databases, process large files. Bring any Linux stack. Execute LLM-generated code inside secure microVMs, isolated from the harness. Mount a durable volume and it shows up as an ordinary POSIX directory.…from getindexify.ai
In the maker’s words, at launch
Hey HN, I am the founder of Tensorlake. Prototyping LLM applications have become a lot easier, building decision making LLM applications that work on constantly updating data is still very challenging in production settings. The systems engineering problems that we have seen people face are - 1. Reliably process…
Does a similar job
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TensorZero2025 · github.com · ▲307Hi HN! We're Gabriel & Viraj, and we're excited to open source TensorZero. To be a little cheeky, TensorZero is an open-source platform that helps LLM applications graduate from API wrappers into defensible AI products. 1. Integrate our model gateway 2. Send metrics or feedback 3. Unlock compounding improvements in quality, cost, and latency It enables a data & learning flywheel for LLMs by unifying: • Inference: one API for all LLMs, with <1ms P99 overhead • Observability: inference & feedback → your database • Optimization: better prompts, models, inference strategies • Experimentation:…
- WMWe made glhf.chat – run almost any open-source LLM, including 405B2024 · glhf.chat · ▲161
Try it out! https://glhf.chat/ Hey HN! We’ve been working for the past few months on a website to let you easily run (almost) any open-source LLM on autoscaling GPU clusters. It’s free for now while we figure out how to price it, but we expect to be cheaper than most GPU offerings since we can run the models multi-tenant. Unlike Together AI, Fireworks, etc, we’ll run any model that the open-source vLLM project supports: we don’t have a hardcoded list. If you want a specific model or finetune, you don’t have to ask us for it: you can just paste the Hugging Face link in and…

- UDUnify – Dynamic LLM Benchmarks and SSO for Multi-Vendor Deployment2024 · ▲91
Hey HN! I’m the founder of Unify, and we’ve just released our Model Hub, which provides a collection of LLM endpoints with live runtime benchmarks all plotted across time: https://unify.ai/hub A key finding is that static tabular runtime benchmarks for LLMs simply do not work. It’s necessary to take a time-series perspective, and plot the variations through time. We currently have 21 models provided by: Anyscale, Perplexity AI, Replicate, Together AI, OctoAI, Mistral AI and OpenAI, with more on the roadmap. We test across different regions (Asia, US, Europe), with varied…
I built a tensor library from scratch in C++/CUDA2025 · github.com · ▲119Hi HN, Over the past few months, I've been building `dsc`, a tensor library from scratch in C++/CUDA. My main focus has been on getting the basics right, prioritizing a clean API, simplicity, and clear observability for running small LLMs locally. The key features are: - C++ core with CUDA support written from scratch. - A familiar, PyTorch-like Python API. - Runs real models: it's complete enough to load a model like Qwen from HuggingFace and run inference on both CUDA and CPU with a single line change[1]. - Simple, built-in observability for both Python and C++. Next on the roadmap is…
OpenLLMetry – OpenTelemetry-based observability for LLMs2023 · github.com · ▲154Hey HN, Nir, Gal and Tomer here. We’re open-sourcing a set of extensions we’ve built on top of OpenTelemetry that provide visibility into LLM applications - whether it be prompts, vector DBs and more. Here’s the repo: https://github.com/traceloop/openllmetry. There’s already a decent number of tools for LLM observability, some open-source and some not. But what we found was missing for all of them is that they were closed-protocol by design, vendor-locking you to use their observability platform or their proprietary framework for running your LLMs. It’s still early in the…
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