AI · alternatives · 2026
24 alternatives to Feather – a fresh Tcl reimplementation (WASM, Go)
Hey HN! First time showing something here, but I've been furiously working over the holidays on Feather, a from scratch reimplementation of TCL designed for embedding in modern applications. It's starting out as a faithful reimplementation of TCL without I/O, OOP features, or coroutines. TCL has a special place in my heart because the syntax is so elegant for interactive use, and defining domain specific languages. My motiviation is twofold: faster feedback loops for AI, and moldable software for users. It turns out giving AI agents access to the runtime state of your program makes for…
Feather is a from-scratch reimplementation of Tcl designed for embedding in modern applications, available as WebAssembly and Go versions. It provides Tcl's elegant syntax for interactive use and domain-specific… 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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Hello HN, I don't post on here much, but wanted to get some eyes on a new project I'm just launching. I think we definitely need one more AI code agent.. I'm a long-term C++ dev, and over 30+ years I've created some successful audio dev tools (JUCE, the Tracktion DAW, the Cmajor DSP language). All of these came from me getting annoyed with something I had to use, and deciding to have a go at my own take on whatever it was. So Juggler is my attempt at an AI code agent, after spending too many hours loving what the models could do, but hating the CLI experience, and having some opinions of…
Jul 2026 · github.com · its alternatives →
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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 · its alternatives →
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Context Gateway▲220We built an open-source proxy that sits between coding agents (Claude Code, OpenClaw, etc.) and the LLM, compressing tool outputs before they enter the context window. Demo: https://www.youtube.com/watch?v=-vFZ6MPrwjw#t=9s. Motivation: Agents are terrible at managing context. A single file read or grep can dump thousands of tokens into the window, most of it noise. This isn't just expensive — it actively degrades quality. Long-context benchmarks consistently show steep accuracy drops as context grows (OpenAI's GPT-5.4 eval goes from 97.2% at 32k to 36.6% at 1M…
Mar 2026 · github.com · its alternatives →
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Hey, Jared Palmer (creator of this playground) here. Really excited to ship this. I’ve been building this over the past few weeks to compare LLMs from different providers like OpenAI, Anthropic, Cohere, etc. At Vercel, I manage our Frameworks division (including Next.js, Svelte, and Turbo) and wanted to also dogfood some of the latest features in a slightly larger application. This playground takes a lot of inspiration from https://nat.dev and is built on Tailwind, ui.shadcn.com, and some upcoming Vercel products we’re announcing soon. We’re going to continue adding models to…
2023 · play.vercel.ai · its alternatives →
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Featherless AI▲279Run every 🦙 AI model & more from 🤗 huggingface
2024 · featherless.ai · its alternatives →
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Hey HN, We’ve been building browser agents for a while. In production, we kept converging on the same pattern: deterministic scripts for the happy path, agents only for edge cases. So we built Demonstrate Mode. The idea is simple: You perform your workflow once in a remote browser. Notte records the interactions and generates deterministic automation code. How it works: - Record clicks, inputs, navigations in a cloud browser - Compile them into deterministic code (no LLM at runtime) - Run and deploy on managed browser infrastructure Closest analog is Playwright codegen but: - Infrastructure…
2025 · notte.cc · its alternatives →
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A minimal library for building scalable TCP servers in Erlang - sinasamavati/condor
2018 · github.com · its alternatives →
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I think graph is a wrong abstraction for building AI agents. Just look at how incredibly hard it is to make routing using LangGraph - conditional edges are a mess. I built Laminar Flow to solve a common frustration with traditional workflow engines - the rigid need to predefine all node connections. Instead of static DAGs, Flow uses a dynamic task queue system that lets workflows evolve at runtime. Flow is built on 3 core principles: * Concurrent Execution - Tasks run in parallel automatically * Dynamic Scheduling - Tasks can schedule new tasks at runtime * Smart Dependencies - Tasks can…
2024 · github.com · its alternatives →
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Hi HN, I built webctl because I was frustrated by the gap between curl and full browser automation frameworks like Playwright. I initially built this to solve a personal headache: I wanted an AI agent to handle project management tasks on my company’s intranet. I needed it to persist cookies across sessions (to handle SSO) and then scrape a Kanban board. Existing AI browser tools (like current MCP implementations) often force unsolicited data into the context window—dumping the full accessibility tree, console logs, and network errors whether you asked for them or not. webctl is an attempt…
Jan 2026 · github.com · its alternatives →
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Hey HN, I made Browser-Use, an open-source tool that lets (all Langchain supported) LLMs execute tasks directly in the browser just with function calling. It allows you to build agents that interact with web elements using natural language prompts. We created a layer that simplifies website interaction for LLMs by extracting xPaths and interactive elements like buttons and input fields (and other fancy things). This enables you to design custom web automation and scraping functions without manual inspection through DevTools. Hasn't this been done a lot of times? Good question, as a general…
2024 · github.com · its alternatives →
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Last year we launched a project called Psychic that did moderately well on hacker news, but was a commercial failure. We were able to find customers, but none with compelling and overlapping use cases. Everyone who was interested was too early to be a real customer. This was our launch: https://news.ycombinator.com/item?id=36032081 We recently decided to revive and rebrand the project after seeing a sudden spike in interest from people who wanted to connect LLMs to data - but specifically through browsers. It's also a problem we've experienced firsthand, having built scraping…
2024 · github.com · its alternatives →
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Finyuus▲81A code-first language for durable, governed AI workflows
Aug 2026 · github.com · its alternatives →
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Hey, Wanted to share what my friend and I built — Feather. It provides investors with all imaginable financial data, without breaking the bank. Effectively 90 percent of the Bloomberg Terminal, at 5 percent of the price. We just opened sign ups for early access — all you need to sign up is your email address. We’ll open access to the software in order of sign ups, and we’d love to have you onboard. Check it out! https://try-feather.com
2022 · its alternatives →
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We’re Robin, Louis, and Thomas. Pipelex is a DSL and a Python runtime for repeatable AI workflows. Think Dockerfile/SQL for multi-step LLM pipelines: you declare steps and interfaces; any model/provider can fill them. Why this instead of yet another workflow builder? - Declarative, not glue code: you state what to do; the runtime figures out how. - Agent-first: each step carries natural-language context (purpose, inputs/outputs with meaning) so LLMs can follow, audit, and optimize. Our MCP server enables agents to run pipelines but also to build new pipelines on demand. - Open…
Oct 2025 · github.com · its alternatives →
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Hi HN, We’re the team at Feather Labs, and we built Feather (https://askfeather.ai), an AI tax assistant designed to assist how professionals handle modern Tax research. General LLMs are a liability for tax work because they lack a hierarchical understanding of the law. They often conflate IRC Title 26 with non-authoritative blog posts or outdated Treasury Regulations. We built Feather to move past "plausible" prose toward audit-defensible reasoning. The Technical Challenge: Standard RAG often chokes on the tax code for a few specific reasons: 1. Hierarchical Fragmentation: Simple…
Feb 2026 · askfeather.ai · its alternatives →
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Hi HN! I'm Erik. We built Butter, an LLM proxy that makes agent systems deterministic by caching and replaying responses, so automations behave consistently across runs. - It’s a chat completions compatible endpoint, making it easy to drop into existing agents with a custom base_url - The cache is template-aware, meaning lookups can treat dynamic content (names, addresses, etc.) as variables You can see it in action in this demo where it memorizes tic-tac-toe games: https://www.youtube.com/watch?v=PWbyeZwPjuY Why we built this: before Butter, we were Pig.dev (YC W25), where we…
Oct 2025 · butter.dev · its alternatives →
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Hi HN, Erik here. Today we launch Butter, an OpenAI-compatible API proxy that caches LLM generations and serves them deterministically on revisit. Since April, we’ve been working on this concept of “muscle memory,” or deterministic replay, for agent systems performing automations. You may recall our first post in May, launching a python package called Muscle Mem: https://news.ycombinator.com/item?id=43988381 Since then, the product has evolved entirely, now taking the form of an LLM Proxy. For a deep dive into this process, check out:…
2025 · docs.butter.dev · its alternatives →
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Hi HN! I want to share our latest project at NEXA AI. We developed AI agent foundation models designed to transform how developers create AI agent powered apps. One major challenge we've observed with current human-computer interactions is that many simple, one-step tasks become unnecessarily complex, multi-step workflows due to limitations of current GUIs. AI agents can solve this, but existing AI agent models are slow and costly. To tackle these issues, we built lightweight AI agent models based on our Octopus V2, small language models for function calling (You can learn more about our…
2024 · nexa4ai.com · its alternatives →
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My implementation of Thorsten Bell's book (Writing an Interpreter in Go) - actuallyachraf/monkey-giggle
2020 · github.com · its alternatives →
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Hi HN, I built MOL, a domain-specific language for AI pipelines. The main idea: the pipe operator |> automatically generates execution traces — showing timing, types, and data at each step. No logging, no print debugging. Example: let index be doc |> chunk(512) |> embed("model-v1") |> store("kb") This auto-prints a trace table with each step's execution time and output type. Elixir and F# have |> but neither auto-traces. Other features: - 12 built-in domain types (Document, Chunk, Embedding, VectorStore, Thought, Memory, Node) - Guard assertions: `guard answer.confidence > 0.5 : "Too low"` -…
Feb 2026 · github.com · its alternatives →
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We are the developers of Phoenix, which we released in April of this year with a goal of bringing LLM observability to the notebook. In the time since, the growth of LLM frameworks and complex agent workflows led us to add support for LLM spans and traces and introduce a simple Eval harness for testing the data from those spans. The latest Traces & Spans release of Phoenix offers: -Out of the box tracing for LlamaIndex and LangChain -Fully local execution, no data sent anywhere, outside of your own LLM calls -Ability to get a common dataframe format across frameworks back to a notebook for…
2023 · github.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →