Alternatives
Products that do what llm-lean-log does
A CSV-based logging format optimized for LLM token usage.
- 1LC
Outlines is a Python library that focuses on text generation with large language models. Brandon and I are not LLM experts and started the project a few months ago because we wanted to understand better how the generation process works. Our original background is probabilistic, relational and symbolic programming. Recently we came up with a fast way to generate text that matches a regex (https://blog.normalcomputing.ai/posts/2023-07-27-regex-guide...). The basic idea is simple: regular expressions have an equivalent Deterministic-Finite Automaton (DFA) representation. We…
2023 · github.com
- 2AN
When building workflows that rely on LLMs, we commonly use structured output for programmatic use cases like converting an invoice into rows or meeting transcripts into tickets or even complex PDFs into database entries. The model may return the schema you want, but with hallucinated values like `invoice_date` being off by 2 months or the transcript array ordered wrongly. The JSON is valid, but the values are not. Structured output today is a big part of using LLMs, especially when building deterministic workflows. Current structured output benchmarks (e.g., JSONSchemaBench) only validate…
Apr 2026 · interfaze.ai
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- 4KO
We've open-sourced Klarity - a tool for analyzing uncertainty and decision-making in LLM token generation. It provides structured insights into how models choose tokens and where they show uncertainty. What Klarity does: - Real-time analysis of model uncertainty during generation - Dual analysis combining log probabilities and semantic understanding - Structured JSON output with actionable insights - Fully self-hostable with customizable analysis models The tool works by analyzing each step of text generation and returns a structured JSON: - uncertainty_points: array of {step, entropy,…
2025 · github.com
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- 7RL
We've been building data pipelines that scrape websites and extract structured data for a while now. If you've done this, you know the drill: you write CSS selectors, the site changes its layout, everything breaks at 2am, and you spend your morning rewriting parsers. LLMs seemed like the obvious fix — just throw the HTML at GPT and ask for JSON. Except in practice, it's more painful than that: - Raw HTML is full of nav bars, footers, and tracking junk that eats your token budget. A typical product page is 80% noise. - LLMs return malformed JSON more often than you'd expect, especially with…
Mar 2026 · github.com
- 8DE
Hey! I wanted to share a tool I've been working on. It's still very early and a work in progress, but I've found it incredibly helpful when working with Claude and OpenAI's models. What it does: I created a Python script that dumps your entire Git repository into a single file. This makes it much easier to use with Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. Key Features: - Respects .gitignore patterns - Generates a tree-like directory structure - Includes file contents for all non-excluded files - Customizable file type filtering Why I find it useful for…
2024
- 9LB
Hey HN, Spark event logs run into 100s of MBs and offer a wealth of insight into your workloads but making sense of them has always been quite a bit prohibitive. We’ve recently built a lightweight tool that automatically parses Spark event logs and surfaces targeted insights to help you optimize your data jobs. Whether you’re chasing down a bottleneck or balancing performance vs. cost, the profiler got you covered with real-time configuration recommendations, data skew analysis, and more. Curious how it works in action? Check out this quick Loom video for a walk-through:…
2025 · datasre.ai
- 10BP
2024 · instill.tech
- 11LT
2013 · r.32k.io
- 12DB
I've been doing some data cleaning for my fine tuning projects using LLMs, and decided to just build a package for it as a side project. Check it out here: https://github.com/databonsai/databonsai Some features: - categorization (labelling), transformation and decomposition (text into structured format) - validates llm outputs - batch mode batches up the inputs/outputs so you don't send the prompt (schema, fewshot examples) for every row of data, saving a significant amount of tokens There are some similarities to the Instructor repo, but this is simpler and made for…
2024 · github.com
- 13LA
G'day, HN! I'm one of the maintainers of `llm`. I've been working alongside a trusty group of contributors to bring this project to life, and we're now at a point where we're ready to share it with the world. Large language models (LLMs) are taking the computing world by storm due to their emergent abilities that allow them to perform a wide variety of tasks, including translation, summarization, code generation, and even some degree of reasoning. However, the ecosystem around LLMs is still in its infancy, and it can be difficult to get started with these models. `llm` is a one-stop shop for…
2023 · github.com
- 14
This is a library I've been working on with versions for C++, java & C# where you make custom formats and Log based on them, for example you could make an ERROR Like this: [ERROR] [date, time-stamp, time-zone] (file:thread-ID:line) but you would write it like this: // define the master format master_style = "%C[%N]%c%S%G[%D %T %Z]%c %M %G(%F:%t:%L)"; // tell the logger what colour to use for a given name logger.add_format("ERROR", master_style, Colour::RED); so now the [ERROR] part will be red, the time area will be grey, and the location grey, but you could really make…
Jun 2026 · github.com
- 15LA
Hey HN! We're excited to introduce Logwise, our new AI-powered log analysis tool. (built by two devs who hate logs) Product page: https://logwise.framer.website/ Logwise makes debugging and incident response faster for developers. It uses natural language processing to automatically parse log data, surface insights, and detect anomalies. We built Logwise to eliminate the manual sifting of log analysis. Key features: - Search logs in plain English - no complex queries needed - Auto-generated alerts highlight potential issues - Contextual debugging advice speeds incident…
2023 · logwise.framer.website
- 16AT
Creator here. I built lsq to solve a simple but annoying workflow problem: having to leave the terminal just to make quick notes in Logseq. Technical details: - Written in Go using Bubble Tea for the TUI - Reads Logseq's config.edn for format preferences - Supports both external editor ($EDITOR) and TUI modes - Handles both Markdown and Org formats Core design decisions: 1. Zero-config default installation (uses standard ~/Logseq path) 2. Single command to open today's journal (just 'lsq') 3. TUI mode for Logseq-specific features (TODO/priority cycling) The project started as a…
2024 · github.com
- 17LL
llmdog – a lightweight TUI for prepping files for LLMs (recursive selection, .gitignore support, clipboard integration). https://github.com/doganarif/llmdog
2025 · github.com
- 18LT
Current AI-assisted CLI tools are often part of larger systems and work better on Linux. I built llm-term to address these. It's a Rust-based tool that compiles into a single binary file. You only need to download the binary, add it to your PATH, and configure your OpenAI key to get started. While llm-term offers an option for gpt-4o, it works great with gpt-4o-mini. So it's not costly. I appreciate any feedback or suggestions.
2024 · github.com
- 19TS
Hi HN! We’re Ethan and Danny, the authors of Tangent (https://github.com/telophasehq/tangent), a Rust-based log pipeline where all normalization, enrichment, and detection logic runs as WASM plugins. We kept seeing the same problems in the OCSF (https://ocsf.io) community: 1) Schemas change constantly. Large companies have whole teams dedicated to keeping vendor→OCSF mappings up to date. 2) There’s no shared library of mappings, so everyone recreates the same work. 3) Writing mappers is tedious, repetitive work. 4) Most pipelines use proprietary DSLs that are…
Nov 2025 · github.com
- 20TS
Hi everyone, I just released an open source load testing tool for LLMs: https://github.com/twerkmeister/tokenflood === What is it and what problems does it solve? === Tokenflood is a load testing tool for instruction-tuned LLMs hat can simulate arbitrary LLM loads in terms of prompt, prefix, and output lengths and requests per second. Instead of first collecting prompt data for different load types, you can configure the desired parameters for your load test and you are good to go. It also let's you assess the latency effects of potential prompt parameter changes before…
Nov 2025 · github.com
- 21BA
Hey HN, solo dev here. After years of frustration with how LLMs handle complex documents, especially PDFs with tables, I decided to build a solution myself. My approach uses a Markdown conversion step to preserve the table structure, which seems to work surprisingly well for chunking. This little parser is the first public piece of a much larger, privacy-focused AI platform I'm building. I'm pretty much running on fumes financially, so any feedback, critique, or support is massively appreciated. Happy to answer any questions about the approach!
Nov 2025 · github.com
- 22AL
Hey HN! After struggling with complex prompt engineering and unreliable parsing, we built L1M, a simple API that lets you extract structured data from unstructured text and images. curl -X POST https://api.l1m.io/structured \ -H "Content-Type: application/json" \ -H "X-Provider-Url: demo" \ -H "X-Provider-Key: demo" \ -H "X-Provider-Model: demo" \ -d '{ "input": "A particularly severe crisis in 1907 led Congress to enact the Federal Reserve Act in 1913", "schema": { "type": "object", "properties": { "items": { "type": "array", "items": { "type": "object", "properties": {…
2025 · l1m.io
- 23AI
Hi I am Jan, CTO @ Pathway. A use case we have been working on with LLMs is to let people know when an answer to their query changes due to revisions of source documents. Obviously, we want to avoid periodically re-computing all queries for the LLM. Why I think it’s cool? - We don’t spin in a loop to repeat with the LLM. - Alerts are LLM-deduplicated - no spamming users with typo fixes - And the best - our framework, Pathway takes care of handling the updates, the example looks nearly like a regular, static RAG chatbot. More context + GIF of how it works for Google Drive document alerts:…
2023 · github.com
- 24LH
2024 · github.com
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