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I Benchmarked Lynkr Against LiteLLM on the Same Backends.

I Benchmarked Lynkr Against LiteLLM on the Same Backends. Lynkr Was Cheaper for Tool-Heavy Workloads Founder disclosure: I built Lynkr, so take this as a technical benchmark write-up, not a neutral industry report. The numbers below come from the same backend providers on both gateways. If you're routing AI coding traffic through a gateway, just switching providers is not enough. The real savings come from reducing the tokens that ever reach the model in the first place. I ran Lynkr and LiteLLM against the same backends — Ollama locally, Moonshot, and Azure OpenAI — across 9 scenarios. On the scenarios that actually look like agentic coding work, Lynkr was cheaper because it does three things before forwarding the request upstream: smart tool selection, TOON compression, and semantic caching. The short version Lynkr was measurably better on the cost-sensitive parts of the workload: Smart tool selection: 53% fewer input tokens, 52% lower cost TOON JSON compression: 87.6% fewer billed tokens on a large tool result, 50% lower cost Semantic cache: 171ms cache-hit response vs 3,282ms on the repeat query path Tier routing: escalated hard prompts to stronger models instead of blindly sending everything to the cheapest route Area Lynkr result Why it mattered Tool selection 53% fewer tokens Removes irrelevant tool schemas TOON compression 87.6% fewer tokens Shrinks large JSON tool outputs Semantic cache 171ms cache hit Avoids repeat model calls Tier routing Escalates hard prompts Doesn’t over-optimize for cheapest path This matters if you're running Claude Code, Codex, Cursor, or similar agent workflows where tools, file reads, grep output, and repeated context dominate your token bill. Setup Same benchmark inputs, same providers, same request shape. Machine: macOS on Apple Silicon Lynkr: v9.3.2 on Node 20 LiteLLM: v1.87.1 on Python 3.12 Backends used: Ollama local, Moonshot, Azure OpenAI Scenarios: 9 total across simple prompts, tools, history, cache, and routing Each scena

2026-06-06 原文 →
AI 资讯

I Built a Free Open-Source EU AI Act / NIST AI RMF / ISO 42001 Crosswalk Tool - Here Is What I Found

Every week I see the same question in AI governance communities: "We already have NIST AI RMF implemented. Does that cover our EU AI Act obligations?" The honest answer is: sometimes yes, sometimes partially, and sometimes not at all. The problem is that nobody had built a clean, free, interactive tool that showed exactly which controls map to which, how strong those mappings actually are, and where the genuine gaps are. So I built one. Live tool: suhanasayyad.github.io GitHub: SuhanaSayyad / eu-ai-act-crosswalk-tool Interactive crosswalk mapping EU AI Act obligations to NIST AI RMF and ISO 42001 controls, with mapping strength indicators, gap analysis, and source links. 30 controls mapped. Free and open source. EU AI Act × NIST AI RMF × ISO 42001 - Interactive Compliance Crosswalk Tool An open-source tool that maps EU AI Act obligations to their equivalents in NIST AI RMF and ISO 42001, with mapping strength indicators, gap analysis, and source document links. Built for compliance teams, AI governance practitioners, and anyone trying to understand how these three frameworks relate to each other. Live demo: https://suhanasayyad.github.io/eu-ai-act-crosswalk-tool Built by: Suhana Sayyad | MSc Cybersecurity, TUS Athlone Why I built this Every organisation dealing with the EU AI Act is being asked the same questions: "We already have NIST AI RMF controls in place. Does that cover our EU AI Act obligations?" "We're pursuing ISO 42001 certification. Does that satisfy the regulation?" The honest answer is: sometimes yes, sometimes partially, and sometimes not at all. The problem is that nobody had built a clean, free, interactive tool that showed exactly which… View on GitHub What the tool does The EU AI Act / NIST AI RMF / ISO 42001 Interactive Crosswalk Tool maps 30 EU AI Act obligations to their nearest equivalents in NIST AI RMF and ISO 42001. For each mapping it shows a strength rating - Strong, Partial, Indirect, or No Equivalent - so compliance teams know which map

2026-06-06 原文 →
AI 资讯

Maybe Coding Agents Don't Need a Bigger Memory. Maybe They Need Continuity.

A practical reflection on why coding agents lose the thread between sessions, and why the repository itself is the right place to preserve it. I used to think the problem was memory. That was the obvious diagnosis. Every new coding-agent session started with the same ritual. Open the repository. Read the README. Inspect the project structure. Search for the files that looked important. Reconstruct the task. Guess which commands mattered. Ask again what had already been tried. Then do the actual work. Sometimes. Because a lot of the work was not work. It was orientation. A coding agent can have a large context window and still lose the operational thread. It can have chat history and still fail to know what happened in the last run. It can retrieve semantically similar notes from a vector store and still miss the one fact that mattered: this command already failed. the previous session stopped here. this file looked relevant, but it was a dead end. the validation was not actually run. One day I stopped thinking about the problem as "agent memory". That word was too broad. Too attractive. Too dangerous. Because once you say memory, the temptation is to build a bigger one. A bigger context window. A bigger note store. A bigger vector database. A bigger archive of everything the agent has ever seen, said, touched, generated, or vaguely implied. That sounds powerful, but it is also how you build a very expensive junk drawer. Context is not continuity Context is what the agent has available now. Continuity is what lets the next execution continue from what actually happened before. Those are not the same thing. Long context helps while a session is alive. It gives the model more text to work with. More files. More prior messages. More implementation details. More room. Although it is really useful it does not automatically produce continuity. When the session ends, gets compacted, moves to another tool, switches from one coding agent to another, or simply starts tomorrow

2026-06-06 原文 →
AI 资讯

Three Commands to Make Claude Code Stop Guessing Your Infra

You asked Claude Code to add a query for orders by customer status. It generated a .scan() with a FilterExpression . Your Orders table has 50M rows and three functions already hammering the same partition key. Claude Code had no idea — it read your TypeScript files, not your AWS account. That's the problem. AI coding assistants are literate in your source code. They are blind to your infrastructure. GitHub · npm What Claude Code Actually Sees (and What It Doesn't) When Claude Code reads your codebase, it builds a model of your application: function names, variable patterns, the string "Orders" passed to DynamoDB.DocumentClient . It can follow call chains, infer intent, and generate syntactically correct code. What it cannot do is describe your actual infrastructure: It doesn't know which GSIs exist on your DynamoDB tables It doesn't know how your tables are partitioned or what sort keys you use It doesn't know that listAllOrders() already does a full scan and costs $40/day It doesn't know that 5 functions already write to the same partition key on Sessions So when you ask it to add a new query, it generates something that looks correct. It might use .query() instead of .scan() . But it'll query on an attribute with no index — because it has no way to know which attributes are indexed. It'll write a FilterExpression that reads every item before filtering — which is exactly a scan, just spelled differently. The code compiles. Tests pass. The problem ships. The Three Commands That Close the Gap infrawise gives Claude Code deterministic knowledge of your infrastructure through the Model Context Protocol. Three commands get you there. 1. infrawise init cd your-project infrawise init Runs once per project. Detects your AWS profile and region, asks which databases you use, and writes a single file: infrawise.yaml . That's the only file it creates in your repository — one config, no framework, no SDK changes. 2. infrawise doctor infrawise doctor Before you trust any analysi

2026-06-06 原文 →
AI 资讯

The repo that became its own good-first-issue

I've always loved teaching and helping others achieve things. There is a huge sense of accomplishment leveraging someone else's abilities. Not only telling them "you can!", but rather help them feel "I can". I was working as a developer for some time when I decided it was time to contribute to other projects. But, honestly, finding the right project, the right issue, and having the right timing was much harder than what I expected initially. I decided to create something that could help me out: scrape some orgs, find good-first-issue labels, and aggregate them together in a README file. Contributing to Open Source isn't easy for many reasons: The obvious, the technical: finding your first technically possible contribution is a combination of the right language, the right depth, and knowing which repo to search in. good-first-issues tackled that by scraping the language and the issue title to somewhat give me a little of context to start with. The not so obvious, the human side of it. My first contribution(s) were hard because I felt exposed, my weaknesses were in the wild for everyone to see and point them out to me. At least that was how I felt. And once I found the right issue, and I decided to give that step forward, many times I didn't get an answer back. That kills any motivation left. Probably PR ghosting is the biggest reason why someone quits their willingness to contribute to OSS. At first, good-first-issues was just a place others could come to find issues. But I realised it could be that issue. There were functionalities I wanted to bring and either I didn't have the time, or didn't have on the top of my head how to do them. "I can kill two birds with one stone" I thought. I knew where I wanted the repo to go, and I wanted it to be community-driven. Creating good self-contained, clear and approachable issues is an art in itself: I didn't want to create the obvious "Fix this typo" (which I did initially) or "Add your name as a contributor" issues. But I di

2026-06-05 原文 →
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