Alternatives
Products that do what Ateegnas does
A fast and cost efficient tool for Data Annotation
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2019 · ananasanalytics.com
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I built this tool because I wanted a way to just take a bunch of URLs or domains, and query their content in RAG applications. It takes away the pain of crawling, extracting content, chunking, vectorizing, and updating periodically. I'm curious to see if it can be useful to others. I meant to launch this six months ago but life got in the way...
2024 · embedding.io
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Inspectus allows you to create interactive visualizations of attention matrices with just a few lines of Python code. It’s designed to run smoothly in Jupyter notebooks through an easy-to-use Python API. Inspectus provides multiple views to help you understand language model behaviors. If you have any questions, feel free to ask!
2024 · github.com
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Creating data visualizations with AI nowadays often means chat, chat and more chats...and writing long prompts can be annoying while they are also not the most effective way to describe your visualization designs. Data Formulator blends UI interaction with natural language so that you can create visualizations with AI much more effectively! You can: * create rich visualizations beyond initial datasets, where AI helps transforming and visualizing data along the way * iterate your designs and dive deeper using data threads, a new way to manage your conversation with AI. Here is a demo video:…
2024 · github.com
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Hi HN, We're Luke and Phillip, and we're building Spice.ai OSS - a lightweight, portable runtime, built in Rust and powered by Apache DataFusion to locally materialize, accelerate, and query data tables sourced from any database, data warehouse or data lake. Phillip and I first introduced Spice on Show HN in September 2021. Since then, we’ve been schooled and humbled in every way building 100TB+ data and ML systems for the https://spice.ai cloud platform. Along with our customers, we struggled with getting fast, low-latency, high-concurrency SQL query within a budget, accessing and…
2024 · github.com
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I've been exploring the (not so=) amazing potential of AI in coding and have compiled a list of tools. From AI-powered IDEs to code generators, this resource is my contribution to the community. I'm still on the fence about including txt2sql projects, as their functionality seems too basic to me. And I'm personally maintaining this, so your feedback is wellcome.
2025 · aicode.danvoronov.com
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Hello! We just released freeact (https://github.com/gradion-ai/freeact), a lightweight agent library that empowers language models to act as autonomous agents through executable code actions. By enabling agents to express their actions directly in code rather than through constrained formats like JSON, freeact provides a flexible and powerful approach to solving complex, open-ended problems that require dynamic solution paths. * Supports dynamic installation and utilization of Python packages at runtime * Agents learn from feedback and store successful code actions as…
2025 · github.com
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25% faster while cutting token costs by 30%-67%
Jul 2026 · forsale.dynadot.com
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Hi everyone, we are excited to share with you our new release of Data Formulator. Starting from a dataset, you can communicate with AI agents with UI + natural language to explore data and create visualizations to discover new insights. Here's a demo video of the experience: https://github.com/microsoft/data-formulator/releases/tag/0..... This is a build-up from our release a year ago (https://news.ycombinator.com/item?id=41907719). We spent a year exploring how to blend agent mode with interactions to allow you more easily "vibe" with your…
Nov 2025 · data-formulator.ai
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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
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Everything that would be here is in the README. I hope this gets big, it has tons of potential.
2013 · github.com
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2023 · github.com
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Hey HN, We’re two developers (co-founders) with a team of 20 who got tired of spending hours reviewing PRs, so we built Infinitcode.ai, an AI-powered code reviewer that: - *Summarizes PRs in plain English*: No more deciphering 1,000-line diff jungles - *Catches more than bugs*: Security holes, performance pitfalls, code smells, even typos (yes, we’ll flag “vurnerabilities” and vulnerabilities) - *Zero onboarding*: Works instantly—no “let me learn your codebase for weeks” nonsense. Why we’re posting: We’re in alpha and need brutal honesty. Roast our tool, mock our UI, or tell us why AI will…
2025 · infinitcode.ai
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Tl;dr: I trained a classifier to route to the least expensive model and reasoning depth to complete the request. Coupling that with additional automated token efficiency techniques has yielded 3x usage for the same spend. For anyone interested in trying it themselves: https://nerfguard.com Various teammates and I switched over to Codex from Claude Code recently. We still bounce between the tools, but Codex’s speed and steerability coupled with performance gains were hard to ignore. One of the downsides was that the per token pricing kicked in way sooner. This is happening across…
Jun 2026
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Together with a friend, we were developing a golf application. Our codebase grew rapidly and became split between multiple repositories: the iOS app, Android app, backend, front-end, and extra tooling. Both of us also work in larger scale-ups, and we saw the same problem: understanding large distributed codebases becomes progressively harder. Yay for microservices. It takes time to understand and answer questions like: - What calls this function? - What is the impact of changing this interface? - Is this code actually reachable and used? Not a secret that both of us embrace the leverage AI…
Jul 2026 · github.com
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Excited to share a project I’ve been building for months! Would love to receive honest feedback :) My motivation: AI is clearly going to be the interface for data. But earlier attempts (text-to-SQL, etc.) fell short — they treated it like magic. The space has matured: teams now realize that AI + data needs structure, context, and rules. So I built a product to help teams deliver “chat with data” solutions fast with full control and observability (agent tracing, quality scores, etc) — am I wrong? The product allows you to connect any LLM to any data source with centralized context…
Oct 2025 · github.com
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Turn natural-language ML goals into auditable ML pipelines.
28d ago · orchestra-ml.vercel.app
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