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GitHub热门项目 | 🪄 Flint is a visualization language that lets AI agents reliably create expressive, good-looking charts from simple, human-editable chart specs. | Stars: 2,372 | 218 stars today | 语言: TypeScript
GitHub热门项目 | 本地优先的跨平台 Claude Code / Agent 桌面工作台：多 Agent、Git Worktree、代码 Diff、技能市场、多模型、Computer Use、任务感知桌面宠物，并支持微信、飞书、钉钉、Telegram、WhatsApp 与 H5 访问。 | Stars: 13,667 | 53 stars today | 语言: TypeScript
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GitHub热门项目 | Self-evolving Context Database for AI Agents. Unify Agent Memory, Knowledge RAG and Skills. | Stars: 27,552 | 180 stars today | 语言: Python
GitHub热门项目 | "OpenSpace: The Skill Management Layer for AI Agents" -- https://open-space.cloud/ | Stars: 7,129 | 91 stars today | 语言: Python
GitHub热门项目 | A curated list of awesome libraries, packages, strategies, books, blogs, tutorials for systematic trading. | Stars: 9,199 | 113 stars today | 语言: Python
600 MiB of headroom My RTX 4070 was running Qwen 35B beautifully after the --cpu-moe trick from a previous run. The tokens/sec were where I wanted them. VRAM sat at 11,714 MiB out of 12,281 — 95% full. That leaves 600 MiB. Not enough for a serious agent. The context window I was giving llama.cpp was -c 4096 . Fine for chat. Not fine when a Claude Code-style agent hands the model 12,000 tokens of tool definitions before it says hello. I wanted -c 32768 . That's an 8× jump. And the memory that grows with context length is the KV cache. Multiply the cache by 8 with 600 MiB free, and llama.cpp dies during warm-up. I know because I tried it first. What actually sits on the GPU After offloading the MoE experts to CPU (the previous chapter's trick), the GPU is holding two things: The attention weights and non-MoE parameters The KV cache — a running record of every token the model has already read The first is fixed. The second grows linearly with context length. Double the context, double the cache. -c 4096 → -c 32768 doesn't just want 8× more tokens processed, it wants 8× more cache resident in VRAM the whole time. There is no room. So the cache itself has to shrink. Two flags llama.cpp takes two flags for KV cache dtype: llama-server -m qwen35.gguf -ngl 99 --cpu-moe -c 32768 \ -ctk q8_0 -ctv q8_0 -ctk is the Key cache, -ctv is the Value cache. Default is f16 (16-bit). q8_0 cuts each in half. Halving both means the KV cache footprint drops by roughly 50%. That freed-up VRAM is exactly what I need to make the context 8× bigger without touching the model weights. The measurement Same prompt, same seed, two runs — one at f16 KV, one at q8_0 KV: KV dtype Max -c I could allocate Tokens/sec (decode) Perplexity delta f16 (default) 4096 ~34.6 baseline q8_0 32768 ~34.1 negligible in my tests The speed loss is inside noise. The context is 8× longer. The quality drop I could not tell apart from run-to-run variance. Community measurements agree: symmetric q8_0 KV lands somewhere unde
Granola debutes an Apple Watch app for in-person notetaking
We run a React app and an Angular admin panel at work. Same company, same brand, and on paper the same design. On screen it was a different story. The React button had a 6px radius; the Angular one had 4px. The focus rings were two slightly different blues. Nobody noticed until somebody did. And every time design changed a token, someone got to hand-port it into two codebases. Twice the work, and it still drifted. So I went looking for something that treated both frameworks as equals. The React kits don't speak Angular. The Angular ones don't share a look with anything on the React side. Nothing let me define the design once and have it show up, the same, in both. So I built bpdm/ui . One rule: the look lives in tokens I gave myself one hard rule: nothing about how a component looks is allowed to live inside the React or Angular code. Colour, spacing, radius, the easing on transitions, all of it sits in @bpdm/tokens as plain CSS variables, and both framework packages just read from there. The component owns structure, behaviour, and the accessibility plumbing. The look comes from the tokens. @import "tailwindcss" ; @import "@bpdm/tokens/tokens.css" ; Change one token and both frameworks move together. There's no "now go sync the Angular theme" step, because there's only one theme to sync. Four ship in the box (two light, two dark). Override the variables and you've re-skinned all of it. The same component, twice React: import { Button , Badge } from " @bpdm/ui " ; export function Example () { return ( < Button variant = "primary" > Get started < Badge appearance = "soft" > New </ Badge > </ Button > ); } Angular: import { Component } from " @angular/core " ; import { BpdmButton } from " @bpdm/ng " ; @ Component ({ selector : " app-root " , imports : [ BpdmButton ], template : `<button bpdmButton>Get started</button>` , }) export class App {} Same padding, same radius, same focus ring. The accessibility isn't literally shared code: Radix does that work on the React s
This year I've built more MCP servers than I meant to, across three pretty different shapes: vellum : a self-hosted server over a folder of markdown, so my agent has a memory that's just files I own. (Open source, MIT.) Metrifyr : a marketing-data server that puts GA4, Search Console, AdSense and Tag Manager behind one connection. A read-only gateway over a company's internal user-data stack, federating half a dozen internal services behind one audited MCP surface. Personal, product, enterprise. Totally different data, totally different stakes. And yet the things that made each one good , versus a flashy demo that falls over the moment a real agent uses it in anger, were the same every time. Here are the seven that keep recurring. 1. Every tool you expose is a tax on the agent's context This is the one nobody warns you about. Every tool definition (name, description, JSON schema) gets loaded into the model's context on every single request , whether or not it's ever called. Twenty tools of boilerplate and the agent is reading pages of plumbing before it touches your data. So the design pressure runs the opposite way from a normal API: fewer, broader tools beat many narrow ones. vellum has 15 core tools and I fought to keep it there. If a tool doesn't earn its slot in the context window on most requests, it shouldn't be a tool. 2. Tools are for acting. Resources are for reading. The corollary to #1. Most servers expose everything (including "read this thing") as a tool. But MCP has a better primitive for reading: resources . In vellum, every note is a resource at a stable URI ( vellum://note/projects/x.md ). The agent attaches a document by reference instead of spending a tool round-trip to fetch it, and it can subscribe to that URI and get told the moment the note changes. Reading through a resource costs no tool definition. Reading through a tool costs one on every request. Use the right primitive. 3. Fail closed on auth, fail open on limits A token with no grants
After a lovely and productive conversation with your client, with still ringing ears, you check the coding agent's last log messages on a ticket that adds a discount to a product. The message was: "Done, I added the 10% discount and all tests pass. Stopping. " Well ... you know it's just not true, so you dig further and quickly realize that the discount functionality was never actually added and the tests it reported passing had never been run. The agent reached the end of the loop, looked at its own work, and called it finished. That call is the thing that shipped. This has a name. A paper published this June, From Confident Closing to Silent Failure , calls it false success: the agent asserts the task is complete while the actual state of the system says otherwise. It is common, and it holds up across capable models. On AppWorld, a benchmark for long-horizon coding agents, 75.8% of the runs that actually failed still ended with the agent claiming it was done. The researchers then put five different LLM judges on those completion claims, varying the prompts each time, and every one of them landed barely above a coin flip, because the thing each judge was reading was the closing sentence, and the closing sentence reads as confident whether the work happened or not. What told a real done apart from a false one turned out to be cheap and mechanical: a look at the actual state of the system. A lightweight deterministic state check caught four to eight times more false successes than the best of the judges. The paper has a name for the mechanism underneath, a hallucination of verification: the model narrates having checked something it never checked, and that narration is indistinguishable, sentence for sentence, from a report of a check that really ran. That gap, between what the agent said and what the system did, is what this piece is about. A loop runs five arms: generate, check, steer, retry, stop. The series opener named them; four pieces since took the check that
Apple's new program gives you lease options for iPhone, iPad, Apple Watch and Mac.
Cómo montar un motor de contenidos que no te arruine (julio 2026) Si sigues pagando 300 euros al mes por herramientas "todo en uno" de marketing, estás tirando el dinero. A mediados de 2026, la tecnología para automatizar ha bajado tanto de precio que los costes de infraestructura de contenidos son casi ridículos. La clave no es la herramienta cara, es conectar piezas pequeñas con APIs baratas. Aquí tienes cómo tengo montado mi flujo de trabajo ahora mismo. La pila tecnológica (el stack) Para automatizar sin gastar, olvida las plataformas de marketing tipo HubSpot o plataformas cerradas. Mi setup actual es este: Cerebro: Claude 3.5 Sonnet (vía API). Es mejor razonando que GPT-4o para tono editorial. Orquestador: n8n (corriendo en una VPS de 5 euros al mes en Hetzner). Base de datos: Notion (vía API para gestionar el calendario). Distribución: Ghost para el blog y la API de LinkedIn/X para el alcance. Coste total: Menos de 15 euros al mes. Paso 1: El disparador (el calendario en Notion) No uses un Excel. Usa una base de datos de Notion con cuatro columnas: Estado , Título , Prompt_Contexto y Fecha_Publicación . Cuando cambias el estado de "Borrador" a "Listo para generar", el webhook de n8n se dispara. Aquí es donde empieza el ahorro. No envías toda la base de datos, envías solo el registro nuevo. Paso 2: El prompt como código, no como texto La mayoría de la gente comete el error de pedirle a la IA: "escribe un post sobre X". Sale basura genérica. En 2026, si no das contexto, el contenido no posiciona ni recibe interacción. En tu nodo de n8n, construye el prompt de forma dinámica. Así es como envío la estructura a la API: { "model" : "claude-3-5-sonnet-20260620" , "system" : "Eres un redactor técnico senior especializado en SaaS B2B. Tu estilo es directo, sin paja, sin adjetivos innecesarios. Evitas los clichés de marketing de 2024. Tu objetivo es educar, no vender." , "messages" : [ { "role" : "user" , "content" : "Escribe un artículo corto basado en este punto clav
As a site reliability engineer in a global company, I'm running a modern (well, relatively modern, to be honest and modest) cloud-native stack: HashiCorp Vault as the secret manager, workloads on Kubernetes clusters in AWS (EKS), and development workflows automated through Jenkins (legacy) and GitLab CI. This setup is, quite likely, familiar to you — it's the normal playbook in the cloud-native era. In theory, we have the right tools for both security and efficiency: After all, we have a state-of-the-art secret manager integrated with everything. But in reality, it's far from the truth. See if you resonate with the following scenarios: Scenario A: A new colleague just joined the team. Manager: "Your initial password to log in to your corporate account came to me via email, but since you can't log in to your mail account just yet, here, take a picture of my screen." (In some companies, taking a picture of a computer monitor would get you fired, I'm not kidding.) Scenario B: A developer needs a temp password to access a database. Dev: "Where is the newly created temporary password? Need it for debugging." Ops: "In the Vault." Dev: "I can't access Vault." Ops: "No, you can't. It's not safe to open UI access to Vault. Corporate policy." Dev: "Then how can I get the password?" Ops: "Well... Technically, the password isn't in the Vault. There is a Jenkins pipeline that calls the Vault API to generate a temp password, then stores it in Jenkins secrets. You need to request access to the corresponding Jenkins pipeline, trigger it, then get the secrets from Jenkins." Dev: "Why on earth do we store secrets in Jenkins when we have Vault, which we aren't allowed to use?" Ops: "Corporate policy, just told you." Scenario C: A new ops team member needs to update a certificate for a service running in production for the first time. Ops: "Where is the old cert?" Mentor: "In K8s as a secret." Ops: "Where is the cluster?" Mentor: "In AWS." Ops: "How do I access that?" Mentor: "You need
I read a post this week where someone connected three MCP servers to one agent and watched it casually request the same access it'd need to hit production. The comment thread was full of "yeah, that's the whole problem with MCP" takes, and I almost scrolled past it — I don't run three servers, I run one. Then I actually opened server.py to check, and realized my one server has the exact same shape of problem, just folded into a single file instead of spread across three. server.py is a FastMCP server with 8 tools split across two unrelated jobs: GitHub profile/repo reads, and DEV.to article reads and writes. Both credentials get loaded the same way, at import time, into the same process environment: def load_env ( path = " .env " ): try : with open ( path ) as f : for line in f : line = line . strip () if line and not line . startswith ( " # " ) and " = " in line : k , v = line . split ( " = " , 1 ) os . environ . setdefault ( k , v ) except FileNotFoundError : pass load_env () and two helper functions read them back out: def _gh ( path , method = " GET " , data = None ): req = urllib . request . Request ( f " https://api.github.com { path } " , method = method ) req . add_header ( " Authorization " , f " token { os . environ [ ' GITHUB_TOKEN ' ] } " ) ... def _dev ( path , method = " GET " , data = None ): req = urllib . request . Request ( f " https://dev.to/api { path } " , method = method ) req . add_header ( " api-key " , os . environ [ " DEV_TO_API " ]) ... Nothing here is a bug in the sense of "wrong output for some input." Every tool does exactly what it says: get_github_profile reads GitHub, create_article writes to DEV.to. The problem is one level up, in what the process boundary actually protects. I'd been thinking of GITHUB_TOKEN and DEV_TO_API as belonging to different tools , scoped by which function reads them. They don't. They belong to the process . Every one of those 8 tools runs with both credentials sitting in its environment, whether the tool ne
The tech world has been building up towards the shift to a fully agentic development life cycle for a few years now. AI is changing how software gets built. Across the Puppet ecosystem, we're seeing a shift toward more agentic engineering workflows. AI helps generate code, shape documentation, and accelerate how Puppet modules evolve. This brings real benefits in speed and consistency, but it also raises important questions from the community: How are AI-generated changes validated? How do you ensure consistency across modules? What does this mean for contributors and maintainers? These are exactly the kinds of questions we should be asking! This article will outline how Perforce and the Puppet team are approaching the use of AI in our open source modules and repositories. How We Build Trust in AI-Assisted Contributions At Perforce, AI is a core part of our process and our teams operate within a defined, governed framework for development. We don’t rely on trust in the tool itself. We rely on the processes around it. Whether a change is written by a person, generated with AI, or some mix of both, they are held to the same standards before it’s accepted and released. In practice, that means: Human review is always the gate: Every change is reviewed by maintainers. AI can assist, but it doesn’t replace accountability. AI works within established patterns: AI-generated code isn’t created in isolation. It’s guided by the same module structures, conventions, and expectations that already exist across the ecosystem. Validation is continuous and enforced: AI doesn’t change our standards. It reinforces them. AI-generated changes go through the same checks as any other contribution: Test suites Integration validation Functional verification AI output is a starting point, not a final artifact: Generated code is iterated on, refined, and aligned before acceptance. We treat AI as an accelerator, not an authority. The community plays an important role Open source means visibilit
Logitech's new gaming boss, Robin Piispanen, tells me he likes the idea of gaming handhelds. "It's such a charming value proposition," he says, as we sip iced vanilla lattes at my local cafe. But he's not building one right now. He's not sure when or if Logitech should try again, after its underwhelming experiment with […]
Apple has officially introduced "Apple Upgrade," a new leasing program that aims to make it easier to get your hands on the latest iPhone, Mac, iPad, and Apple Watch models. The service is launching today in the US, and works like a car lease - allowing users to keep a device at the end of […]
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I work on PostSlate, a video editing tool, and this comes out of our own work. We run ML models on-device, face detection and embedding among other things, which means we can't assume anything about the user's GPU. NVIDIA discrete, AMD, Intel integrated, Apple Silicon, all of it. That rules out CUDA immediately, we needed one backend that runs everywhere. We landed on ncnn's Vulkan backend. Numbers on a 4070, fp16: ArcFace R50 (face embedding): 30 ms on ONNX CPU → 3 ms on ncnn Vulkan SCRFD (face detection): 25 ms → 2.5 ms Model size: ArcFace 174 MB (ONNX fp32) → 87 MB (ncnn fp16 weight storage) Of course the real speedup comes from offloading compute to the GPU, but this wouldn't be possible without the power of Vulkan. The speed wasn't even the deciding factor, it's that Vulkan drivers already exist on every machine we ship to. This means that we don't have to force the user to download a specific runtime and no vendor-specific installs. Full writeup with the rest of the numbers: https://getpostslate.com/blog/faster-local-inference submitted by /u/ppchaos [link] [留言]