A tool to properly observe your LLM's context window
TL;DR: You can’t fix what you can’t see. I built a tool that pulls apart LLM context into meaningful components that you can see, measure, and then...
A tool to properly observe your LLM's context window launched on October 30, 2025 with 8 votes, #1,955 of 8,178 launches that month and more than 75% of that year's launches.
What it does
TL;DR: You can’t fix what you can’t see. I built a tool that pulls apart LLM context into meaningful components that you can see, measure, and then meaningfully engineer. Context engineering is one of the most important aspects of AI Engineering. And yet, we don’t have the tools to really see the context, or pull it apart into tangible components we can analyse for relevance. That kind of observability of the context seems to be missing. If we want to fight the “Garbage In, Garbage Out” phenomenon, we should be able to observe the garbage in the context to prune it out. Existing observability tools seem to be primarily focused on system metrics like latency, cost, error rates, or high-level…from blog.nilenso.com
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Twigg2025 · twigg.ai · ▲157Hi HN, we’re Jamie and Matti, co-founders of Twigg. During our master’s we continually found the same pain points cropping up when using LLMs. The linear nature of typical LLMs interfaces - like ChatGPT and Claude - made it really easy to get lost without any easy way to visualise or navigate your project. Worst of all, none of them are well suited for long term projects. We found ourselves spending days using the same chat, only for it to eventually break. Transferring context from one chat to another is also cumbersome. We decided to build something more intuitive to the ways humans think.…
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Hi HN, We've just published a lot of original, visual, and intuitive explanations of concepts to introduce people to large language models. It's available for free with no sign-up needed and it includes text articles, some video explanations, and code examples/notebooks as well. And we're available to answer your questions in a dedicated Discord channel. You can find it here: https://llm.university/ Having written https://jalammar.github.io/illustrated-transformer/, I've been thinking about these topics and how best to communicate them for half a…
ThoughtDAG – An editable context graph for LLM conversationsAug 2026 · chenxiachan.github.io · ▲136ThoughtDAG indexes local agent conversations across tools, finds the turns relevant to your work, and turns them into editable context graphs.
LLM Attention VisualizationSep 2026 · ishamf.dev · ▲176I often need to convert times between time zones, so I built this tool to convert between them quickly and efficiently. 1. Add the cities you want to convert between (this is saved in the browser). 2. Drag on the map until the time on the source city is the time you want. 3. Look at the time at the other city. It's even more helpful to use on a phone since it doesn't require typing to convert between time zones or look up the time in another city (assuming you already added the cities you care about). If you need to convert a time far in the future, you can input the date and time in the…
Context GatewayMar 2026 · github.com · ▲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…
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Source: Show HN post ↗
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