Dev tools · alternatives · 2026
24 alternatives to Codex context bloat? 87% avg reduction on SWE-bench Verified traces
If you had to build a context window manager in 24h, would you stick to the existing model or come up with something better? Here's what I did: 1. Built a proxy that intercepts Codex's calls to OpenAI and rewrites them on the fly. 2. Replayed 3,807 rounds of SWE-bench Verified traces through it: avg prompt 44k → 6k tokens (-87%). 3. Posted it to HN to get the next reduction applied to my confidence interval — starting with the inevitable "How about accuracy?" npx -y pando-proxy · github.com/human-software-us/pando-proxy
Pando-proxy is a context window manager that acts as a proxy intercepting API calls to compress prompts sent to language models. It rewrites requests on the fly to reduce token usage while maintaining functionality… 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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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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- 3M3
2018 · mitmproxy.org · its alternatives →
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[I'm the author] Spall is a web-accessible profiler that I made to help my web-dev friends load gigabyte+ JSON traces without lunch-break-long load times. Recently, Spall got experimental support for auto-tracing with binary traces (along with an in-progress native-port, to give it more memory headroom), which was used to help track down and fix some hard-to-spot lock contention issues in the Odin-language compiler. I demoed it at the Handmade Seattle conference in October, https://guide.handmade-seattle.com/c/2022/spall/, with a head-to-head against Perfetto,…
2023 · gravitymoth.com · its alternatives →
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Tokenwise▲143A smart LLM proxy that shows where you're overpaying
Jun 2026 · tokenwisehq.com · its alternatives →
- 6BA
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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Every MCP tool call dumps raw data into Claude Code's 200K context window. A Playwright snapshot costs 56 KB, 20 GitHub issues cost 59 KB. After 30 minutes, 40% of your context is gone. I built an MCP server that sits between Claude Code and these outputs. It processes them in sandboxes and only returns summaries. 315 KB becomes 5.4 KB. It supports 10 language runtimes, SQLite FTS5 with BM25 ranking for search, and batch execution. Session time before slowdown goes from ~30 min to ~3 hours. MIT licensed, single command install: /plugin marketplace add mksglu/claude-context-mode…
Feb 2026 · github.com · its alternatives →
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Ask Claude Code where your usage went. Token audit, limit diagnosis and usage forensics — built from the session logs already on your machine, nothing leaves it. - kelviq/tare
Aug 2026 · github.com · its alternatives →
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Usage4Claude▲109I kept hitting Claude's 5-hour limit without realizing it, so I made this menu bar app to track it. Shows a color-coded ring (green/orange/red) and countdown to reset. Built it in Swift/SwiftUI, just for myself initially. Right now you have to manually grab your sessionKey from browser DevTools, which is annoying but works. Stores it in macOS Keychain so it's secure. Been actively working on it, already at v1.3.0 with much better UX now. It's open source (MIT) and works with all Claude platforms (web/CLI/desktop/mobile) since they share the quota. Currently…
Oct 2025 · github.com · its alternatives →
- 10AT
I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…
2025 · llmapitest.com · its alternatives →
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Your AI agent is burning 6x more tokens than it needs to just to browse the web. We built OpenBrowser MCP to fix that. Most browser MCPs give the LLM dozens of tools: click, scroll, type, extract, navigate. Each call dumps the entire page accessibility tree into the context window. One Wikipedia page? 124K+ tokens. Every. Single. Call. OpenBrowser works differently. It exposes one tool. Your agent writes Python code, and OpenBrowser executes it in a persistent runtime with full browser access. The agent controls what comes back. No bloated page dumps. No wasted tokens. Just the data your…
Apr 2026 · openbrowser.me · its alternatives →
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Hi HN, I'm George Ciobanu (https://www.linkedin.com/in/georgeciobanunyc). I built pandō ('CAD for code') because I got tired of watching AI agents burn tokens, take forever, and still get it wrong. Here's (one reason) why this happens: AI agents read and edit code as if it's just text. But code is more, much more: it has structure, syntax, relationships, and meaning. Code is Data We built pandō around this insight. It indexes and persists your code's AST in a database, which gives AI agents instant search results, and enforces syntactically correct edits. Here's a full…
Feb 2026 · getpando.ai · its alternatives →
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Hi HN, we're Kiran and Vijay! Over the past two years, we have built a columnar storage engine for observability: logs, metrics, and traces. Today, it's exciting for us to show what we've built on top of that foundation: LLM Agent Observability. Given how non-deterministic agents are, storing all traces without sampling was critical for us. But these traces tend to be in the MBs, sometimes GBs - we needed to store them inexpensively. We also needed the queries and analyses to be fast. To meet both these goals, we store them in S3 in our own parquet-like file format, and query them using AWS…
Jul 2026 · oodle.ai · its alternatives →
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I started using Claude Code (claude --dangerously-skip-permissions) and Codex (codex --yolo) and realized I had no reliable way to know what they actually did. The agent's own output tells you a story, but it's the agent's story. logira records exec, file, and network events at the OS level via eBPF, scoped per run. Events are saved locally in JSONL and SQLite. It ships with default detection rules for credential access, persistence changes, suspicious exec patterns, and more. Observe-only – it never blocks. https://github.com/melonattacker/logira
Mar 2026 · github.com · 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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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 · its alternatives →
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This is a small PoC Python project for web server access logs analyzing to classify and dynamically block bad bots, such as L7 (application-level) DDoS bots, web scrappers and so on. We'll be happy to gather initial feedback on usability and features, especialy from people having good or bad experience wit bots. *Requirements* The analyzer relies on 3 Tempesta FW specific features which you still can get with other HTTP servers or accelerators: 1. JA5 client fingerprinting (https://tempesta-tech.com/knowledge-base/Traffic-Filtering-b...). This is a HTTP and TLS layers…
2025 · github.com · its alternatives →
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I built a small Chrome extension for people who write long conversations in Claude and don’t want to unexpectedly hit the 200,000-token limit. Context Compass automatically counts words and estimates token usage as you write. It updates in real time, gives you a visible percentage bar, and helps power users manage sprawling chats without losing work or flow. I built it because I kept getting the “max context” error and realized there was no way to track usage in Claude. No signups, no tracking, just a simple install. Would love any feedback or ideas for improvements. Thanks!
2025 · chromewebstore.google.com · its alternatives →
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Hey HN, I’m Austin, founder of Salestrics (salestrics.com). The Problem: > Most AI agents today are trapped in chat windows. While models are smart enough to run complex multi-step workflows, giving them production access to business context (CRM, support, billing) usually means managing unsafe local API keys or hacking together fragile point-solution scripts. What We Built: We built Salestrics to serve as both an all-in-one revenue workspace (CRM, service desk, billing, docs, email) and a production-grade Model Context Protocol (MCP) execution proxy. How It Works: Unified Context: Instead…
Aug 2026 · salestrics.com · its alternatives →
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I'm an "ideas person" who messes around with AI on a low budget. I got tired of watching my tokens vanish and context windows filling up while agents fumbled around trying to find the right thing. Agents don't flail like they used to with shell tools, but there are still weak/blind spots and back-and-forth episodes — especially when using tools in combination/sequence. So I built "tilth" today. Or rather, AI built it — every line is Opus 4.6. I spent a lot of my precious tokens getting it to "not shit" (at least several of the different vendors' AI overlords assure me it's not…
Feb 2026 · github.com · its alternatives →
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Save 89% of wasted tokens on your AI coding agent
Mar 2026 · rtk-ai.app · its alternatives →
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Raymond here from Butter.dev, an LLM response cache built as a chat-completions proxy. Today we're launching a key feature for the platform: the ability to generalize on dynamic, templated inputs. Caching at the HTTP request level has the obvious problem of generalizability. Nearly no request is identical, due to templated variables (like names) and metadata (like timestamps), so exact-match cache lookups rarely hit. We solve this at Butter by using LLMs to detect dynamic content in requests and derive their inter-relationships, allowing the cache entry to be stored as a template + variables…
Jan 2026 · blog.butter.dev · its alternatives →
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Hold on tight. Contribute to alexpasmantier/grip-grab development by creating an account on GitHub.
2024 · github.com · its alternatives →
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LLM observability is an absolute must-have for anyone running something in prod (or prod-like). While all the observability startups are great, you're essentially sending all your OpenAI usage history - prompts, generations, chats - to a random third party. So this script deploys a basic proxy in your Azure account, catches all incoming OpenAI requests, stores logs in your own resource group, and comes with visualizations premade (charts, timelines, chat history, cost estimation, etc). Thanks for any thoughts and feedback!
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 →