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Products that do what Batch LLM does
Process CSV files with AI saving 50% on API requests
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I spent a few hours last weekend testing whether AI can replace code by executing directly. Built a contact manager where every HTTP request goes to an LLM with three tools: database (SQLite), webResponse (HTML/JSON/JS), and updateMemory (feedback). No routes, no controllers, no business logic. The AI designs schemas on first request, generates UIs from paths alone, and evolves based on natural language feedback. It works—forms submit, data persists, APIs return JSON—but it's catastrophically slow (30-60s per request), absurdly expensive ($0.05/request), and has zero UI…
Nov 2025 · github.com
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Browser LLM demo working on JavaScript and WebGPU. WebGPU is already supported in Chrome, Safari, Firefox, iOS (v26) and Android. Demo, similar to ChatGPT https://andreinwald.github.io/browser-llm/ Code https://github.com/andreinwald/browser-llm - No need to use your OPENAI_API_KEY - its local model that runs on your device - No network requests to any API - No need to install any program - No need to download files on your device (model is cached in browser) - Site will ask before downloading large files (llm model) to browser cache - Hosted on Github…
2025 · andreinwald.github.io
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Nowadays, a common AI tech stack has hundreds of different prompts running across different LLMs. Three key problems: - Choices, picking from 100s of LLMs the best LLM for that 1 prompt is gonna be challenging, you're probably not picking the most optimized LLM for a prompt you wrote. - Scaling/Upgrading, similar to choices but you want to keep consistency of your output even when models depreciate or configurations change. - Prompt management is scary, if something works, you'll never want to touch it but you should be able to without fear of everything breaking. So we launched Prompt…
2024 · jigsawstack.com
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My puny version of ChatGPT. This was based on the excellent LLM lecture series by Andrej Karpathy: https://www.youtube.com/watch?v=kCc8FmEb1nY The main points of differentiation are that my version is token-based (tiktoken) with code to load up multiple text files as a trining set. Plus, it has a minimal server which is a drop-in replacement for the OpenAI REST API. So you can train the default tiny 15M parameter model, and use that in your projects instead of ChatGPT. I trained it on 20Mb of Project Gutenberg encyclopaedias, then fine-tuned it on 120 dad jokes, to get a Q: A:…
2023 · github.com
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So, it feels like this should exist. But I couldn't find it. So I tried to build it. Agentflow lets you run complex LLM workflows from a simple JSON file. This can be as little as a list of tasks. Tasks can include variables, so you can reuse workflows for different outputs by providing different variable values. They can also include custom functions, so you can go beyond text generation to do anything you want to write a function for. Someone might say: "Why not just use ChatGPT?" Among other reasons, I'd say that you can't template a workflow with ChatGPT, trigger it with different…
2023 · github.com
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Henry, Matt and James here – we’re building an open source toolkit that makes it easy to integrate an LLM-powered copilot that talks to your API into software products. It works by calling API endpoints which you choose to expose to it. This lets the chatbot complete tasks within your software in response to natural language queries. It’s also open source, so you don’t have to send user data to another 3rd party. We support Llama 2, but we haven’t fine-tuned Llama 2 yet (coming soon) so highest accuracy is seen with GPT-4 or fine-tuned GPT-3.5 (much faster). We started working together 2…
2023 · github.com
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Hello everyone, This past Saturday, I decided to make purchase on Cursor after hearing how good it is with Composer function that allow to generate code on fly, especially on Claude Sonnet (not an affiliate of the products, just curious!). To make the purchase worthwhile, I challenged myself to build a Google Sheets extension in just 24 hours that allows users to connect with any Language Model (LLM)—whether it’s ChatGPT, Claude, Groq, or Gemini. The Idea The idea was simple: a formula like =Claude(prompt, model) that lets users integrate AI anywhere in their sheets. The most impressive…
2024 · aisheeter.com
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During my winter holiday I played with ChatGPT and built an integration with the OpenAI Completion APIs and Excel. (And yes technically not ChatGPT but as close that we can get with the APIs that are available.) I found this incredible useful for my work on Filestar. I wrote some instructions here on how to try this yourself: https://www.linkedin.com/pulse/use-chatgpt-excel-get-superpo... Please let me know if you figure out any useful prompts.
2023 · linkedin.com
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We are excited to show Promptly (https://trypromptly.com), a prompt management platform for LLM apps that makes it easy to experiment, share and manage prompts in production. With Promptly, users can: - Try out different prompts and model parameters for various providers - Quickly share prompt snippets together with parameters and generated output. Think of it as CodePen or JSFiddle for prompts - Create high level endpoints on top of provider APIs (Open AI, DreamStudio etc) with templated and versioned prompts - Use built-in caching for endpoints that will help save on Open AI…
2023 · trypromptly.com
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We made ParallelGPT - a tool that lets you bulk process chatGPT on a spreadsheet UI instead of one-by-one conversation style. Hopefully this saves you time and hassle - especially if you want to build an operational process around it and want to collaborate your team. You can give it a quick spin on this live demo playground: https://demo.rowy.io/table/parallelGpt#sideDrawer=%22table-i... I am the co-founder of Rowy(https://www.rowy.io), an open-source low-code platform, and we built ParallelGPT as a fun showcase of what you can do with low-code. There are tons…
2023 · parallelgpt.ai
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Hello HN! We're building a caching solution for LLMs (ChatGPT, Claude). By combining cutting-edge approaches, such as edge computing, prompt compression, vectorization, and others - it can reduce your AI bills by up to 10x and significantly lower response times. Key Features: - cost efficiency: our system stores frequent queries, reducing the number of upstream (paid) API calls - fast responses: with various nodes globally, we reduce latency by serving data from the nearest location - scalability: designed to handle increasing loads and data sizes without degrading performance. The cache…
2024 · edgematic.dev
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Hi HN, I built Prompt Builder (https://www.promptbuilder.space/) because I was frustrated managing complex AI prompts in plain text boxes. The core idea: instead of one giant textarea, you compose prompts from draggable, reorderable blocks. Each block can have its own XML/custom tags, visibility toggles (so you can A/B test sections), and duplicates. Key technical details: - Live compiled preview: the right pane shows the exact string being sent, updating as you drag/toggle blocks - Dynamic variables: define {{var_name}} once, use across blocks, change in one…
12d ago · promptbuilder.space
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