Alternatives
Products that do what HyperProphet – The Easiest Way Run FB Prophet at Scale does
We are Anand and Raghav, the creators of HyperProphet (https://hyperprophet.com/), a tool to enable developers to compute thousands of forecasts in minutes using fbprophet without worrying about infrastructure. Scaling forecast computation is a hard problem, often costs developers in productivity. Even established organizations spend a lot of time trying address this, but often the outcomes are not easy to use and manage[1][2]. We spent the last few years building a demand planning product for CPG and retail in which we had to forecast thousands of time-series. Computing such…
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Hey HN! I'm Alex, one of the co-founders at Global Predictions. We are officially launching PortfolioPilot today! Since starting the company 2 years ago, we’ve been working tirelessly to build a personal portfolio management platform that empowers everyday people to feel more confident investing. We focused on aggregating your entire net worth, evaluating across a set of standardized metrics, and offering suggestions based on our commercial-grade Macro Insights & Recommendation Engine. We started by building a product we wanted to use and iterated and refined based on early user feedback. We…
2022
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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
- 7IE
Quick note on how it works and how I've done my batch embedding engine IgniteMS. The whole thing runs as one process using Rust, reading input, tokenizing, packing batches, keeping the queue full. TensorRT handles inference. Python is only as a wrapper. I built it this way because when you use more than couple of GPUs, the GPUs stop being the problem. CPU cannot feed them fast enough. One A100 can go through batches faster than Python can tokenize and feed, so the GPU just sits there idle waiting for work. Most of my time went into optimizing this. At 8 GPUs that was basically the entire…
Jun 2026 · github.com
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Hey HN! I’ve been building Fatebook for the past couple of months. It’s a slack bot to help your team make and track predictions, right where you work. I see forecasting as anti-bullshit technology: - It gives you truthseeking incentives - You communicate your uncertainty as a probability, which is way clearer (70% is better than “probably”) - You can aggregate forecasts to get wisdom of the crowd effects - You can see everyone’s track record, and pay more attention to people who are consistently accurate I’m a fan of prediction markets [0] and forecasting platforms [1]. But predictions on…
2023 · fatebook.io
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2020 · github.com
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We'd like to introduce HN to Spell, which is a tool for easily running ML/DL jobs remotely. As Deep Learning has grown we see engineers and researchers struggle to incorporate running on GPUs into their workflow. So we built Spell to be the easiest way to get code running elsewhere - like the bash '&' operator but for remote machines. Sign up for an account at https://web.spell.run/waitlist, which includes $300 in credits for GPU time. There's a waitlist, but we'll be approving accounts as they come in. Here are some of the features we really wanted and built into Spell:…
2018
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Hi guys, we're Wilhem from Paris and Jean-Daniel from Tokyo, software engineers with a passion for all things cloud (IaaS, PaaS, SaaS). We recently decided to tackle the problem of Capacity Planning with Stacktical, a Scalability Prediction service (https://stacktical.com). For a decade, we've been observing our clients and colleagues trying to nail down their strategy using repeated cycles of defining, collecting and interpreting load testing campaigns. It's funny how most people don't realize how demanding the work of infrastructure managers and their teams really is... While…
2016
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Provide an elevator pitch and target customer, and GPT generates personas to interview, with catchy names like Serverless Sarah. Then simulate user interviews, summarize the outputs and provide follow-ups. It's inspired by the legendary startup book Four Steps to the Epiphany by Steve Blank. I am building a new database (I was one of the creators of CouchDB / PouchDB) and wanted an example app that more fun than just database crud operations. Fireproof is designed to be the easiest way to add dynamic data to any app or page. It uses IPFS multiformat blocks for content addressing (and…
2023 · epiphany.fireproof.storage
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Hi HN, We are launching Denormalized (www.denormalized.io), a serverless real-time data platform built on Kafka and Pinot. We felt a bit burnt out by the sheer developer toil we faced when building application around the real-time data stack and set out to create a platform to allow small teams to be very productive with realtime data without having to glue together an elaborate system to serve real-time as well as time series queries. Here is our motivating post. Would appreciate any and all feedback.
2023 · teamdenormalized.substack.com
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I submitted an earlier version of this a few months ago (as llama2.f90). At that time it had a lot of steps to run and was just a toy, now it's easy to run and is a competitive option for llm inference. See the motivation section for discussion and the `Performance` issue for an ongoing discussion about performance.
2023 · github.com
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Democratisation of local AI is key. I've been working on pushing the limits of commercial hardware, squeezing any extra bit possible. My Scientific Agentic AI hareness helped me to reallocate every single bit of it. I rewrote the Kernel, I went down the CUDA rabbit hole until I have been able to explain any bit and any ms of computational power involved in the process pushing the Qwen 30B-A3B from 8 tok7s to 19 tok/s with llama.cpp up to 22.2 tok/s with my project and 109 tok/s on not novel content and speeding up the prefill by 5-9X
Jul 2026 · github.com
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Hi HN, We’re a small team working on 13Radar.com, which we launched about two weeks ago after 4 months of development. I’m the founder, and together with the team we’re building a platform that tracks hedge fund portfolios in real-time based on SEC Form 13F filings. AI has been a major helper in our workflow. For a single webpage, we often consult multiple AI systems in parallel, generating different versions and comparing them side by side before deciding on the final design or implementation. More than 60% of the research, design, and coding involved AI assistance. For UI design we used…
Nov 2025 · 13radar.com
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Predictobot lets you build a predictive model without any programming. You upload a spreadsheet of data, specify the column you want to predict, and it automatically builds a predictive model for a regression or classification problem based on the other columns. You download a new spreadsheet, with the model right in the Excel formulas. That lets you make predictions going forward. Other services like this are really aiming at helping programmers to build models. I wanted to make something that a regular non-programmer could use, to get insight into their data. It is open for registration…
2014
- 18WB
Here is a production-first Keras-inspired LM framework, built with the advice of François Chollet (ex-Google, creator of Keras and ARC-AGI), our technical advisor. This system have already been deployed in production with our clients (which is why we have already every LLMOps practice implemented). It is also compatible with Jupyter and Marimo to integrate seamlessly in you Data Scientists workflows. You can try the code examples online on HF space and you can find more information in the documentation and FAQ. If you have any feedback for us don't hesitate to join our discord! More releases…
2025 · github.com
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Hi HN! I've been hacking on this side project for the last month or two with the goal of making it dead simple to use cloud GPUs. I ran into this problem personally during the phd, and built my own tooling around it. I always thought it'd be fun to try to turn that tooling into a more general product... and bitbop.io is the result! All you have to do is run `ssh bitbop.io`, and you get your own personal dev GPU workstation in the cloud. Looking forward to hearing your thoughts!
2024 · twitter.com
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Hey HN, After GPT-3 created waves in the tech industry, a lot of AI tools were emerging and with that, some AI website builders But the results seemed way too generic to us. It felt like the developers were rushing to catch the wave instead of building a proper tool We took our time, did months of RnD and finally came up with something better than what others in the market are doing. It’s got better design output. While it’s still in beta, I wanted to show HN what we did. Will appreciate the feedback when you guys try it out. Here is the link to signup for the beta:…
2024 · dorik.com
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We built RapidFire AI, an open-source Python tool to speed up LLM fine-tuning and post-training with a powerful level of control not found in most tools: Stop, resume, clone-modify and warm-start configs on the fly—so you can branch experiments while they’re running instead of starting from scratch or running one after another. - Works within your OSS stack: PyTorch, HuggingFace TRL/PEFT), MLflow. - Hyperparallel search: launch as many configs as you want together, even on a single GPU - Dynamic real-time control: stop laggards, resume them later to revisit, branch promising configs in…
Sep 2025 · github.com
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Hey hackers, the world needs more AI researchers with good taste, and hardcore software folks have some of the best. Many software friends mentioned they learn better from implementations than from papers, but existing open-source examples rarely go beyond basic nanoGPT-level demos. To help bridge that gap, I spent the last two months full-time reimplementing and open-sourcing a self-contained implementation of every major modern deep learning technique from scratch. The result is beyond-nanoGPT, containing 20k+ lines of handcrafted, minimal, and extensively annotated PyTorch code. I'd love…
2025 · github.com
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Hi HN, We just launched BloomPilot — a minimal AI-powered overlay designed for Bloomberg Terminal users. It's built for financial professionals who want faster GPT-enhanced insights, a lightweight terminal interface, and modern tooling on top of the Bloomberg infrastructure they already use. Key Features: GPT-4o analysis integrated into Bloomberg-style command line Built-in fallbacks (Alpha Vantage, Polygon, Finnhub) if Bloomberg API is unavailable One-time payment of 299 USDC via Phantom wallet (Solana) Terminal-style UI with keyboard-first design and command history Real-time data…
2025 · prestigious-albatross-928.convex.app
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Hi everyone, Anuj here, Dataherald CEO. There are a bunch of EXTERNAL data sources in the world that people use a lot -- think census data, Bureau of Labor Statistics (unemployment, inflation), but even proprietary data sources. I was tired of getting these into useable data pipelines every time I need need to use them. Dataherald simply creates data pipelines to hundreds of data sources and then lets you create auto-updating data viz -- all in under 60 seconds. The goal is to enable non-tech users to more easily use data, which has been a huge problem for me throughout my career. We…
2022 · medium.com
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