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AI 资讯

I built a compiler for how AI agents should write to you

I kept correcting AI agents in the same ways: "too long," "answer first," "use a diagram," "assume I know the jargon." Each correction improved the current exchange, but the preference was not represented as durable system state. I built /calibrate-comms to make that state explicit. It is an open-source skill inside an Obsidian vault, used by both Claude Code and Codex. The model: nine operational dials The skill does not try to discover a personality type. It calibrates nine choices that directly change how an answer is rendered: Dial Practical question Density Tight sections or full reasoning? Sequence Answer first or chronological build-up? Modality Prose or diagrams for relational content? Abstraction Concrete example or principle first? Tradeoff One recommendation or several options? Detail Main path or edge cases too? Jargon Define terms or assume expertise? Tone Casual, neutral, or formal? Context-giving Should the agent extract missing context or split an overloaded brief? Prior → calibration → directives The workflow has three stages: L1 PRIOR → L2 SAMPLE REACTION → COMPILE → CLAUDE.md hypothesis empirical override shared by both agents Quick mode asks one bespoke forced-choice proxy per axis. Those questions are deliberately labelled as proxies, not validated psychometrics. Deep mode must fetch the exact items from supported open-access instruments before use; if the source cannot be obtained, the skill stays in Quick mode and makes no validation claim. The prior is then challenged through pairwise samples. For sequence, the contrast looks like this: Build-up first: We traced the latency spike to N+1 queries, then found lazy loading in a loop—so the fix is eager loading. Answer first: Fix: eager-load the association. Why: lazy loading in a loop caused N+1 queries and the latency spike. The user's pick is revealed preference. If it contradicts the prior, the sample wins. The compiler is the useful part The final profile is not a score report. A deterministi

2026-06-27 原文 →
AI 资讯

Day 6: my language now compiles to WebAssembly — and I emit the bytes by hand

I'm building LOOM — a small open-source language that is a machine-checked trust layer for AI-written code. I don't write it by hand anymore: an organism I built grows it, day and night, on my own machine. This is Day 6, and the whole day went to one thing — WebAssembly . Why this was a real test LOOM already runs three ways: an interpreter, and backends that compile checked code to Python and JavaScript. The thesis is "trust survives translation" — effects and provenance, proven once, hold the same on every target. WebAssembly is the strongest test of that: a low-level stack machine with linear memory, nothing like Python or JS. And there was a constraint. This machine's clang has no wasm target, and I install nothing paid or heavy. So I don't compile to wasm through a toolchain — I emit the wasm bytes myself (LEB128, the type / function / memory / global / export / code sections, the i32 stack machine) and run them through node's built-in WebAssembly . Zero dependencies. From fib to a value runtime, in a day Every step was prototyped and proven (wasm output == interpreter output) before it touched the kernel: The integer core — arithmetic, comparison, if , first-order calls and recursion. fib(10) becomes 61 bytes of real WebAssembly and returns 55, identically on the interpreter, Python, Node and wasm. A value runtime — let and integer lists in a real linear-memory heap (a bump pointer + a $cons cell allocator; head / tail are i32.load , empty is i32.eqz ). A list sums and folds by recursion, inside wasm. Sum types — (variant Tag e) becomes a tagged cell [tag-id | payload] ; match loads the tag, compares, binds the payload, branches. You can watch it: the live playground has a Compile → WAT button and WASM · fib / list-sum / match examples. Type a program, see it become real assembly, in your browser. Honest scope: ints, let , integer lists and sum types compile to wasm today. Records, closures and effects are the next frontiers (closures are the hard one — a func

2026-06-27 原文 →
AI 资讯

TMX: The open standard AI agent memory has been waiting for

TMX: The open standard AI agent memory has been waiting for The problem no one talks about: your agent's memories are prisoners. If you build an AI agent today using Mem0, your memories are locked in Mem0. Switch to Zep? You lose everything. Move to a new framework? Start from zero. This is exactly the problem email had in 1970. Every system had its own format. You couldn't send an email from one system to another. Then SMTP was invented. And email became universal. Today I'm publishing TMX v0.1 — the SMTP of AI agent memory. What is TMX? TMX (Truvem Memory eXchange) is an open, model-agnostic JSON format for storing, exporting, and importing AI agent memories across any platform, framework, or provider. It looks like this: { "tmx_version" : "0.1" , "exported_at" : "2026-06-26T20:00:00Z" , "source" : "truvem" , "agent_id" : "my-agent" , "memories" : [ { "id" : "550e8400-e29b-41d4-a716-446655440000" , "content" : "User prefers dark mode and concise responses" , "created_at" : "2026-06-01T08:30:00Z" , "updated_at" : "2026-06-01T08:30:00Z" , "expires_at" : null , "tags" : [ "preference" , "ui" ], "source_model" : "gpt-4o" , "metadata" : {} } ] } That's it. Plain JSON. Human-readable. Portable. Why this matters Right now, the AI agent ecosystem is exploding. Every week there's a new memory provider, a new framework, a new cloud service. But every one of them uses a proprietary format. This means: Developers are locked to their first choice forever Agent memories can't travel between clouds Switching providers = losing everything your agent learned This is the biggest hidden tax in the agentic AI stack. TMX fixes it with a single open spec that anyone can implement — for free, with no approval needed. The 5 core principles 1. Open — No license required. Implement TMX in any product, commercial or otherwise. 2. Model-agnostic — Works with GPT-4, Claude, Gemini, Mistral, Llama, or any future model. 3. Framework-agnostic — LangChain, CrewAI, Mastra, AutoGen — doesn't matter

2026-06-27 原文 →
AI 资讯

Back to Simplicity: Why We Built CALM, a Lock-Free Single-Thread Messaging Library for .NET

Hello everyone! For many years, I have been developing equipment control software and long-term support products using .NET/C#. Based on the experiences gained through working with various development teams, I would like to talk about why I created CALM (Cooperative Async Lock-free Messaging) , an open-source library for .NET. The Magic of UI Thread + Event-Driven + async/await My journey with .NET/C# began around the days of .NET Framework 4.0. At the time, code-behind in Windows Forms was incredibly intuitive. It allowed me to join the development team and become productive in a very short period. Back then, executing time-consuming operations on a separate thread and returning the results to the UI thread via callbacks was painful and error-prone. However, the introduction of async/await in .NET Framework 4.5 completely shifted the paradigm. The SynchronizationContext magically handled thread marshaling behind the scenes, and our code became amazingly simple. "Running asynchronous operations safely on a single thread (the UI thread) via an event-driven approach" — this seamless developer experience was my starting point. Divide and Conquer, Dependency Management, and CQS As our product evolved, the codebase grew, and the development team expanded beyond a certain size. Suddenly, the software became exponentially complex. Following industry best practices, we introduced MVP and MVVM patterns to separate the UI from the business logic. We also refactored our domain models based on Domain-Driven Design (DDD) and Clean Architecture principles, alignment with the team's domain knowledge. While this helped organize the logic within individual models, it introduced a new nightmare: complex dependencies between models. We struggled heavily with initialization, especially with models that had circular or mutual dependencies. To break this web of tight coupling, we introduced a mechanism that combined the Observer pattern (inspired by Android's EventBus) with the philosoph

2026-06-27 原文 →
AI 资讯

OpenAI unveils GPT-5.6 amid US AI regulatory drama

Less than 24 hours after news broke that OpenAI would stagger its next model release at the request of the Trump administration, that model, GPT-5.6, is here. On Friday, the company unveiled the limited preview of its new GPT 5.6 model suite: Sol, the flagship; Terra, a medium-tier model for "high-volume work"; and Luna, a […]

2026-06-27 原文 →
AI 资讯

Vercel Introduces Eve, an Open-Source Framework for Building AI Agents

Vercel has released Eve, an open-source framework for building, deploying, and operating AI agents in production. The framework uses a filesystem-based project structure to organize agent instructions, tools, skills, subagents, communication channels, and scheduled tasks, enabling developers to define agent behavior while reducing the amount of supporting infrastructure they need to implement. By Daniel Dominguez

2026-06-27 原文 →
AI 资讯

Anthropic’s Mythos mess is only getting worse

It's been two weeks since Anthropic took its Mythos-class models offline after a Friday evening ultimatum from the Trump administration. The company sprang into action immediately, sending a barrage of executives to Washington, DC. But updates have been suspiciously lacking, with no resolution in sight. Anthropic declined to comment multiple times this week about the […]

2026-06-26 原文 →
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