Alternatives
Products that do what Equis does
A programming language that understands REA accounting
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Hey HN! We’re Arne and Raban, the founders of Emdash (https://github.com/generalaction/emdash). Emdash is an open-source and provider-agnostic desktop app that lets you run multiple coding agents in parallel, each isolated in its own git worktree, either locally or over SSH on a remote machine. We call it an Agentic Development Environment (ADE). You can see a 1 minute demo here: https://youtu.be/X31nK-zlzKo We are building Emdash for ourselves. While working on a cap-table management application (think Stripe Atlas + Pulley), we found our development…
Feb 2026 · github.com
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It can create invoices and receive payments. Not quite production ready, yet. Only need PostgreSQL installed to try. I will add support to choose SQLite when they add native support for geography types.
2024 · github.com
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Numscript is a simple, declarative language that helps you model financial transactions. You can do quite a few things with it, such as modeling: * Payments involving vouchers and a user's prepaid balance * Complex funds destination scenario where the customer gets cash back * Configurable user credit balance spending transactions The main idea is to take the pain out of describing of a system dealing with money movements should behave in traditional languages such as JS/TS/Go/Ruby etc, landing an expressive way to model these movements of value. It is voluntarily broad in…
2024 · playground.numscript.org
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2022 · gist.github.com
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Hey HN, we’re building an open specification that lets agents discover and invoke APIs with natural language, built on the OpenAPI standard. agents.json clearly defines the contract between LLMs and API as a standard that's open, observable, and replicable. Here’s a walkthrough of how it works: https://youtu.be/kby2Wdt2Dtk?si=59xGCDy48Zzwr7ND. There’s 2 parts to this: 1. An agents.json file describes how to link API calls together into outcome-based tools for LLMs. This file sits alongside an OpenAPI file. 2. The agents.json SDK loads agents.json files as tools for an LLM that…
2025 · github.com
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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
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Some technical context on what we ran into building this. MCP tools don't really work for financial data at scale. One tool call for five years of daily prices dumps tens of thousands of tokens into the context window. And data vendors pack dozens of tools into a single MCP server, schemas alone can eat 50k+ tokens before the agent does anything useful. So we auto-generate typed Python modules from the MCP schemas at workspace init and upload them into the sandbox. The agent just imports them like a normal library. Only a one-line summary per server stays in the prompt. We have around 80…
Apr 2026 · github.com
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2023 · github.com
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Small codebases were always a good thing. With coding agents, there's now a huge advantage to having a codebase small enough that an agent can hold the full thing in context. Repo Tokens is a GitHub Action that counts your codebase's size in tokens (using tiktoken) and updates a badge in your README. The badge color reflects what percentage of an LLM's context window the codebase fills: green for under 30%, yellow for 50-70%, red for 70%+. Context window size is configurable and defaults to 200k (size of Claude models). It's a composite action. Installs tiktoken, runs ~60 lines of inline…
Feb 2026 · github.com
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Jan 2026 · github.com
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I've been building a CRM/accounting system for my personal small company. It's still in an early stage and is lacking a few features. But it is finally usable. https://asciinema.org/a/0g9f8c00mrkm018p10r8p8km3 Please share your thoughts
2017
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This is a hobby project of mine that I started a few years ago to learn about programming language implementation. It was created 95% without AI, although a few recent commits include code from Gemini CLI. I started out following Crafting Interpreters, but gradually branched off that until I had almost nothing left in common. Tech stack: Rust, Cranelift (JIT compilation), LALRPOP (parser). Original title: "A small programming language where everything is a value" (edited based on comments)
Jan 2026 · github.com
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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
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I've been working on this small, slightly weird expression-oriented programming language for a little while now and feel ready to share it with others. I use it pretty often now in my day-to-day and work life, as a scratchpad for doing a bit of quick math or picking some pieces of data out of a JSON payload. Would really appreciate any feedback about the syntax, docs, features that are glaringly missing, etc. Before anybody mentions it: I know the performance is pretty lousy when dealing with a lot of data. If you can believe it, the runtime is about 100x faster than it used to be! Long term…
Sep 2025 · blots-lang.org
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Jul 2026 · github.com
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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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