Dev tools · alternatives · 2026
24 alternatives to Generate Fine-tunning dataset using deep research in terminal
Just open-sourced a small terminal tool I’ve been working on. The idea came from wondering how useful it’d be if you could just describe the kind of dataset you need, and it would go out, do the deep research, and return something structured and usable. You give it a description, and it pulls relevant info from across the web, suggests a schema based on what it finds, and generates a clean dataset. The schema is editable, and it also adds a short explanation of what the dataset covers. In some cases, it even asks follow-up questions to make the structure more useful. Started off as a quick…
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
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I was experimenting with building a local dataset generator with deep research workflow a while back and that got me thinking. what if the same workflow could run on my own files instead of the internet. being able to query pdfs, docs or notes and get back a structured report sounded useful. so I made a small terminal tool that does exactly that. I point it to local files like pdf, docx, txt or jpg. it extracts the text, splits it into chunks, runs semantic search, builds a structure from my query, and then writes out a markdown report section by section. it feels like having a lightweight…
2025 · github.com · its alternatives →
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Doing research with agents is fun until they blow way past budget, jumble the sources, and don't even give you the best possible answer, just sound confident. And if you want to run some research task on local data - you have no idea where your data ends up after the prompt consumes it. So I built this tool: a deep-research agent with an enforced budget, verified quotes, and a privacy boundary for local data. 1. Never spend more than you budgeted (measured overshoot is 0%). 2. Every claim carries a source 3. Data stays local (give a CSV, it'll analyze it without the data ever leaving your…
Aug 2026 · github.com · its alternatives →
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We recently used DeepSeek V4 Flash as a teacher for finance tasks with GPT-OSS-120B. Distillation works well on this problem. At a constrained 8k token budget, our self-distilled 120B scores 83.61% on FinanceReasoning, above Kimi K3 (81.93%) and Inkling (65.13%). We released the 20B open weights. With V4 as the teacher though, we realized it would be timely to measure if the censorship characteristic of it transferred to the distilled version of the base model. tl;dr it didn't, the teacher answered politically sensitive questions 7 SDs differently than expected, but the distilled model's…
Jul 2026 · ctgt.ai · its alternatives →
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A terminal spreadsheet multitool for discovering and arranging data - saulpw/visidata
2017 · github.com · its alternatives →
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DeepWiki provides up-to-date documentation you can talk to, for every repo in the world. Think Deep Research for GitHub - powered by Devin.
2025 · deepwiki.com · its alternatives →
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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 · its alternatives →
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OpenSheet▲86Hi folks. I've been doing some experiments on how LLMs could get more handy in the day to day of working with files (CSV, Parquet, etc). Earlier last year, I built https://datakit.page and evolved it over and over into an all in-browser experience with help of duckdb-wasm. Got loads of feedbacks and I think it turned into a good shape with being an adhoc local data studio, but I kept hearing two main things/issues: Why can't the AI also change cells in the file we give to it? Why can't we modify this grid ourselves? So besides the whole READ and text-to-SQL flows, what seemed…
Feb 2026 · opensheet.app · its alternatives →
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Hey HN! A few months ago we shared our AI dataset generator as an open source repo, and the response was incredible (https://news.ycombinator.com/item?id=44388093). We got requests from folks who wanted to use it without the hosting overhead, so we created both options: a hosted version (https://www.metabase.com/ai-data-generator for instant use and the source code fully open (https://github.com/metabase/dataset-generator) for anyone who wants to self-host or contribute. Looking forward to seeing how you use it and what you build on top of…
2025 · metabase.com · its alternatives →
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Synthetic data generation is an essential step in training and evaluating LLMs/Agents/RAG pipelines, but tooling around this is still lacking. We're introducing Curator, an open-source library designed to streamline the data curation process. While there are many libraries to prompt LLMs, the semantics of generating synthetic data is different from prompting. For example, we need to process a large number of prompts (sometimes in millions or more) while accepting some failures, utilize several stages of prompting, incorporate human feedback, and filter out bad data using verifiers…
2025 · github.com · its alternatives →
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Hi HN community, I have been working on benchmarking publicly available LLMs these past couple of weeks. More precisely, I am interested on the finetuning piece since a lot of businesses are starting to entertain the idea of self-hosting LLMs trained on their proprietary data rather than relying on third party APIs. To this point, I am tracking the following 4 pillars of evaluation that businesses are typically look into: - Performance - Time to train an LLM - Cost to train an LLM - Inference (throughput / latency / cost per token) For each LLM, my aim is to benchmark them for…
2023 · github.com · its alternatives →
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We developed a new framework that enables flexible control of generated text in language models. By combining several models and/or system prompts in one mathematical formula, it lets you tweak your style and combine model outputs with ease. A handy tool for those working with LLMs, looking for more fine-grained control of stylistic output. More details in our paper: https://arxiv.org/abs/2311.14479. Feedback and potential applications are welcome.
2023 · github.com · its alternatives →
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Simon(sfarshid) and I spend a lot of time on GitHub. As data nerds we put together a quick tool to explore your repository’s data. How it works: - Data Loading: We use dlt to pull data (issues, PRs, commits, stars) from GitHub - Semantic Layer: Relta wraps the underlying dataset into a semantic layer so the LLM doesn’t hallucinate. - Text-to-SQL: A text-to-SQL agent transforms your plain-English question into a query using the semantic layer - Generative Charts: assistant-ui dynamically generates a chart based on the SQL query - Refinements: If the semantic layer can’t handle your question,…
2024 · github.com · its alternatives →
- 15GT
2013 · databasetestdata.com · its alternatives →
- 16NA
Hey HN - our team wants to open source a project called NASTY (NASTY Abstract Syntax Tree thingY) that we built for ourselves. NASTY was built to maintain testable/composable data pipelines. Our team was ripping our hair out trying to maintain dbt/SQL scripts across different data warehouses (Redshift, BigQuery, Postgres, Snowflake) on top of ever shifting data foundations maintained by our customer's internal data teams. NASTY is the result of our learnings from field experience. We wanted to write abstractions so that we could reuse code. We wanted to bundle those abstractions…
2024 · getnasty.dev · its alternatives →
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2019 · tailgraph.live · its alternatives →
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I want to share a new dataset of 331 reward-hackable environments. These are real environments used in Terminal Bench and adjacent benchmarks. I first got interested in this because, as a reviewer of Terminal Bench, I noticed a lot of our tasks were hackable. I also noticed that many contributors to the benchmark do so because it provides credibility when selling environments to labs. Hence, TBench tasks are, in my opinion, held to a higher quality standard than those being used today for RL. No one is spending hours manually reviewing the $1B in tasks being purchased by major labs. As far…
Apr 2026 · github.com · its alternatives →
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DeepGraph.co is for sale on Spaceship. Secure checkout and quick transfer. See all purchase options. No hidden fees.
2025 · deepgraph.co · its alternatives →
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Read expert insights on synthetic data, data privacy, and modern software testing. Explore articles from Tonic.ai’s AI engineering team.
2022 · tonic.ai · its alternatives →
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A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance in Text-to-SQL - eosphoros-ai/DB-GPT-Hub
2023 · github.com · its alternatives →
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Last week I benchmarked my open-source Deep Research tool against ChatGPT, Perplexity, and Gemini. I caught OpenAI fabricating 4-5 citations that don't exist. Gemini reduced real hazard ratios by 30-40%. So I built ASK Mode: every answer gets automatically verified against a second round of sources. Each claim marked [OK], [??], or [NO]. - ~400 verified answers for $1 - 2-3 minutes per query - No RLHF nannying - it answers what you ask - Full verification report with every response The gap between chat (unverified, stale training data) and deep research (20+ minutes) needed filling.…
Feb 2026 · github.com · its alternatives →
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Hello all! Very happy to share this toolkit that allows you to fine-tune your choice of open-source LLMs on your data! The toolkit also allows you to run ablation studies across LLMs, prompt designs, training configurations, and can ingest different data files -- all through just ONE YAML file! After fine-tuning, you can also run a bunch of tests to ensure that the fine-tuned LLM behaves as expected, enabling faster time-to-production! Why this toolkit? Why now? While closed-source LLMs have become popular for chat-based applications, enterprises are considering a shift to self-hosted SLMs…
2024 · github.com · its alternatives →
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