标签：#ens

The trajectory of AI agents over the past two years has been remarkably clear: from single-purpose tools to personal assistants. Everyone runs their own agent, feeds it tasks, gets results back. It works well for individual productivity. Then comes the question every team eventually asks: can these agents work together? The answer is yes, but the problems you encounter along the way are rarely the ones you expected. They aren't about model capabilities or prompt engineering. They're about communication, context, and coordination — the same class of problems that distributed systems engineers have been solving for decades, now showing up in a new form. Here are three challenges that caught us off guard when we started building agent collaboration into Octo , an open-source workplace platform where AI agents and humans share the same communication space. Challenge 1: Context Visibility Boundaries When you use an agent personally, context management is straightforward. You decide what information the agent sees; its output comes back to you. The boundary is clean — it's just your workspace. In a team setting, that boundary dissolves. One of the first issues we ran into was surprisingly simple. We had an agent summarizing discussions across several channels. During testing it started pulling roadmap discussions from a product channel into an engineering planning thread. Nothing sensitive leaked externally, but it immediately exposed how unclear our context boundaries were. Traditional software handles this through API gateways, data permissions, and microservice boundaries. But agent context isn't just structured data — it includes conversation history, reasoning chains, and intermediate states. An agent's thought process during a task is valuable context, but it might also contain information that shouldn't cross team boundaries. What you need is fine-grained context visibility control. Not "everything open" or "everything closed," but dynamic rules that determine whic

Mach's approach differs sharply from some of its peers.

TL;DR: I built a .NET library that renders Helm charts and drives Kubernetes releases without shelling out to the helm CLI. 129/129 templates across ingress-nginx, cert-manager, external-dns, podinfo, and metrics-server now render successfully. The main entry point is HelmSharp.Action, with lower-level packages available for chart loading, rendering, Kubernetes operations, and release storage. MIT licensed, looking for feedback and early adopters. Why I Built This At work, our .NET services deploy to Kubernetes through Helm. Every Docker image had to bundle the helm binary — another dependency to manage, another layer in the image, another surface for CVEs. I wanted to cut that out entirely and do Helm-style rendering directly in-process. The .NET ecosystem doesn't really have this. There are YAML libraries. There are Kubernetes client libraries. There are template engines. But nothing ties them together the way helm template does — values merging, named templates, include , range , toYaml , the whole Sprig function set, all wired into a single render pipeline. So I started building one. (This is also my first real open source project — I'd spent years consuming OSS without contributing back, and HelmSharp is what came out of deciding to change that.) What HelmSharp Does HelmSharp is a multi-package .NET SDK (net8.0 / net9.0 / net10.0) that covers: Package What it does HelmSharp.Action High-level Helm client — TemplateAsync , UpgradeInstallAsync , RollbackAsync HelmSharp.Chart Chart loading from directories and .tgz , values merging, --set / --set-json style overrides HelmSharp.Engine Helm-style template rendering — 100+ Sprig/Helm functions HelmSharp.Kube Kubernetes apply, delete, and wait (no kubectl needed) HelmSharp.Release Release history stored in Kubernetes Secrets (Helm-compatible) HelmSharp.Repo Chart repository index, pull, and search Plus Registry , Storage , PostRenderer extension points Here's the lower-level rendering API — no result objects, no stdout

We’ve all been there: waking up feeling like a zombie despite getting eight hours of sleep. While wearables give us data, they often fail to give us foresight . What if you could predict your stress levels 24 hours in advance? 🚀 In this tutorial, we are going to tackle HRV prediction (Heart Rate Variability) using a state-of-the-art Temporal Convolutional Network (TCN) . By leveraging the Oura Ring API and deep learning, we’ll transform non-stationary biometric time series into actionable insights. Whether you're into time series forecasting or building the next big health-tech app, mastering Temporal Convolutional Networks (TCN) is a game-changer for handling long-term dependencies without the vanishing gradient headaches of traditional RNNs. For those looking for more production-ready examples and advanced biometric signal processing patterns, I highly recommend checking out the deep-dives at WellAlly Blog , which served as a major inspiration for this architecture. The Architecture: Why TCN? Traditional LSTMs are great, but they process data sequentially, making them slow and prone to memory loss over long sequences. TCNs, however, use Dilated Causal Convolutions , allowing the model to look back exponentially further into the past with fewer layers. Data Flow Overview graph TD A[Oura Cloud API] -->|Raw JSON| B(Pandas Preprocessing) B -->|Cleaned HRV/Activity| C{Feature Engineering} C -->|Sliding Windows| D[TCN Model Training] D -->|Dilated Convolutions| E[Stress Trend Prediction] E -->|24h Forecast| F[Dashboard/Alerts] style D fill:#f9f,stroke:#333,stroke-width:2px Prerequisites To follow along, you'll need: Tech Stack : Python, TensorFlow/Keras, Pandas, Scikit-learn. Data : An Oura Cloud Personal Access Token (or use the mock data generator provided). Difficulty : Advanced (Buckle up! 🏎️). Step 1: Fetching Biometric Data First, we need to pull our "Readiness" and "Sleep" data. Oura provides high-resolution HRV samples (usually 5-minute intervals during sleep).

У большинства self-hosted VPN-панелей одна и та же боль: Docker-стек, внешняя БД, реверс-прокси и куча конфигов, которые надо связать между собой, прежде чем хоть что-то заработает. Мне хотелось наоборот — что-то, что можно закинуть на свежий VPS и поднять меньше чем за минуту. Так появилась РосПанель : self-hosted панель для администрирования личного VPN-сервера на Xray-core , который поставляется одним статическим бинарником . React-фронтенд вшит через go:embed , база — встроенный SQLite, отдельного веб-сервера нет. Поставил, открыл, добавил юзера. Главная идея: один бинарник, ничего лишнего Цель, которая определила всё остальное, — радикальная простота. В отличие от Marzban и 3x-ui, у РосПанели нет Docker-обвязки, нет внешней БД и нет отдельного веб-сервера, который надо настраивать. Всё живёт в одном исполняемом файле: Веб-интерфейс собирается в web/dist и вшивается в Go-бинарник на этапе сборки. Состояние хранится в SQLite (чистый Go-драйвер modernc , то есть без CGO ). Конфиг Xray всегда генерируется из базы и применяется супервизором — SQLite это единственный источник правды, а не JSON, который правят руками. В итоге деплой — это просто положить бинарник и systemd-юнит. Никакой оркестрации, ничего не надо держать в синхроне. Что она на самом деле делает РосПанель — это панель управления (control plane), а не VPN-клиент. Она настраивает и обслуживает ваш собственный сервер: генерирует конфиг Xray, выдаёт ссылки на подписки и показывает статистику. Протоколы из коробки — один конфиг Xray, один набор учёток: VLESS-Vision (TCP/443 + uTLS-fingerprint) Trojan-WS (через fallback на 443) Hysteria2 (UDP с port-hopping) VLESS-gRPC-REALITY (отдельный порт, маскировка под чужой TLS) Маскировка — панель спрятана за секретным путём. Любой другой путь отдаёт сайт-заглушку (11 готовых шаблонов), так что сервер неотличим от обычного хостинга. Без знания /<secret>/ форму логина не найти. TLS, который просто работает — ACME через Let's Encrypt или ZeroSSL, авто-продление и self

I built a Chrome extension called Margin — a news reader that lives in the browser's side panel and shows one bite-sized story at a time, instead of an infinite-scroll feed. This is a build log: the decisions, the constraints that pushed back, and a couple of things I had to solve in slightly unusual ways. Why the side panel Chrome shipped chrome.sidePanel in MV3 a while back and most uses I saw were utility tools — note-taking, translation helpers. Nobody was using it for content consumption. News felt like a good fit: a side panel that stays open next to whatever you're working on, where you tap through headlines in a couple of minutes without leaving the page. The reading model is intentionally narrow: one card, one headline, one short summary, tap to read the full article at the source . No infinite scroll, no algorithmic feed. If you've used InShorts, the shape will be familiar. The stack Preact + Vite + @crxjs/vite-plugin . Preact because the side panel is a small UI surface and I didn't want React's weight for what's essentially a card stack and a settings screen. @crxjs/vite-plugin handles the MV3-specific build wiring (manifest generation, service worker loader, HMR for the extension context) that would otherwise be a lot of manual plumbing. The constraint that shaped onboarding chrome.sidePanel.open() requires a user gesture . You cannot call it from a background service worker on install — Chrome will throw. That one constraint shaped the whole first-run experience. My first instinct was "just auto-open the panel on install so people see it immediately." Doesn't work. The fix ended up being two-pronged: On chrome.runtime.onInstalled with reason === 'install' , open a real browser tab with a short walkthrough (find the icon → pin it → open the panel). The button on that page calls sidePanel.open() — valid, because the click is the gesture. The first time the panel itself is opened, show an in-panel welcome screen before onboarding, nudging the user to pin

I've been growing my open source portfolio one contribution at a time, and this week I landed on something genuinely interesting in livekit/agents (11k+ stars, the framework behind a ton of real-time voice AI agents). The bug If you're building a voice agent on a realtime model (OpenAI Realtime, xAI, Gemini Live), the model streams your transcription back in chunks. A single utterance can fire many user_input_transcribed events before it's final — token by token for OpenAI/xAI, or as one big interim blob for Gemini. If you want to react exactly once per utterance (say, show a "user is typing" indicator on your frontend via RPC), you need a stable key to correlate all those interim events together. That key already existed internally — InputTranscriptionCompleted carries an item_id . But when the framework re-emitted it upward as the public UserInputTranscribedEvent , the item_id was silently dropped — leaving consumers with no reliable way to dedupe across providers. The fix Small once you see it: add the field, forward it. class UserInputTranscribedEvent ( BaseModel ): transcript : str is_final : bool item_id : str | None = None # new ... def _on_input_audio_transcription_completed ( self , ev : llm . InputTranscriptionCompleted ) -> None : self . _session . _user_input_transcribed ( UserInputTranscribedEvent ( transcript = ev . transcript , is_final = ev . is_final , item_id = ev . item_id ) ) Two files, about 10 lines of real change. The actual work was tracing the event from the realtime model layer, through AgentActivity , up to AgentSession , to find exactly where the field got swallowed. The takeaway I didn't need to understand all of livekit-agents to land this — just one event's lifecycle, end to end. Small, well-scoped issues are the most achievable way into a big codebase, especially when someone's already mapped the territory in the issue itself. PR is up, CI green, waiting on review: github.com/livekit/agents/pull/6172

GitHub热门项目 | OpenAI-compatible proxy that stacks the free tiers of 16 LLM providers (~1.7B tokens/month) behind one /v1 endpoint — plus any custom OpenAI-compatible endpoint. Smart routing, automatic failover, encrypted keys. Personal experimentation only. | Stars: 11,267 | 226 stars today | 语言: TypeScript

We all know the grind of working with data, even with AI tools: every experiment starts with re-explaining everything, every iteration needs you to prompt, wait, review, correct, and repeat. And the moment you close the session, everything learned is gone. It makes us the bottleneck, and this hinders human-AI collaboration... So I built 𝐎𝐩𝐞𝐧𝐃𝐚𝐭𝐚𝐒𝐜𝐢, an autonomous agent purpose-built for DS/ML, and tested it on Kaggle. I enrolled in a recent competition, ran the agent with no hints, no guidance, while ironing my shirts. In one shot, it landed AUC 0.95, a top-30% finish out of 3K+ teams and 36K+ submissions using hashtag#Anthropic's Claude Sonnet 4.6. (More on this in README) The top-1 outperformed this agent by merely 0.004, but at the cost of massive manual effort even while using popular AI tools. The needed a dozen model families, deep learning, 400-feature notebooks, AutoML sweeps across many libraries, and 186 models ensembled carefully. Essentially a few weeks worth of effort and time!! OpenDataSci abstracts away all the complexity and has so much to offer for DS/ML automation: → Owns the entire development lifecycle from EDA to final evaluation → Plans, codes, and executes autonomously in a secure local sandbox → Self-reviews and corrects before anything reaches you → Remembers your data across sessions, gets smarter each run → Runs parallel experiments and ensembles → Has advanced context management for token efficiency and quality → Ships with predefined skills for DS/ML, so it knows how to do things right → Bring your own knowledge: out-of-the-box support for custom skills → Works with any major LLM provider (hashtag#Anthropic, hashtag#OpenAI, hashtag#Bedrock, hashtag#VertexAI, hashtag#Ollama, hashtag#vLLM, and any OpenAI-compatible server). This and so much more!! You set the goal. It does the work. No data science knowledge required. 🔗 https://github.com/f4roukb/open-data-sci 📦 pip install open-data-sci Spin it up on your data and see what it achieves!

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