标签：#opensource

"It runs on my own GPU, so it's basically free." I believed that until I put a meter on it. So I ran a controlled benchmark on one box — an openSUSE machine with a single RTX 3090 — driving three local models through ollama under an identical fixed workload (256-token generations in a loop for ~4 minutes each), while my open-source dashboard priced every run by the real GPU energy it burned : power sampled from nvidia-smi every 10 s, integrated over each run's exact window, multiplied by my actual day/night tariff. One number per model, in euros per million output tokens. Here's the part that made me re-run it. The tiny gemma3:1b came out at €0.118 / 1M tokens — about 5× cheaper than a hosted Flash-class API (~€0.55). But gemma3:27b 's electricity alone was €0.706 / 1M — more expensive per token than just paying the cloud, and that's before a single cent of the GPU's purchase price. "Local" didn't make it cheaper; it made it cost more and I own the depreciation. The mechanism is one line: each token costs watts ÷ throughput , and a big dense model is both slow and thirsty. A newer mid-size architecture ( gemma4:26b ) bought a lot of that back, landing at €0.272 . The full guide is methodology-first and reproducible end to end — minting an ingest key, the stdlib-only client, the exact ollama loop that reads eval_count / eval_duration for real tokens-per-second, reading each run back priced, and the honest caveats (this is marginal GPU energy only — not capex, idle, or cooling — and the absolute numbers round to fractions of a cent; the shape is the finding). Read the full guide on Medium → https://medium.com/@arsen.apostolov/how-much-does-it-actually-cost-to-run-a-local-llm-per-million-tokens-measured-4a90a7f31a48

Has anyone else ended up maintaining the same AI skill in multiple places? I use Claude Code, Codex, Cursor, Gemini CLI, Kimi, and several other AI tools. Over time, I accumulated a huge collection of skills and workflows. The annoying part wasn't creating them. It was keeping them synchronized. A skill would exist in one format for Claude Code, another for Cursor, another for Gemini, and so on. Eventually, I got tired of duplicating everything and built an open-source project called AI Omni Skills. The idea is to keep a single source of truth and generate the formats required by different AI tools. Now I update a skill once and regenerate whatever structure a specific tool expects. I'm curious: How are you managing skills today? Are you duplicating them across tools? What integrations would you want to see? Repo: https://github.com/moatazhamada/ai-omni-skills

Cross-posted from the LLMKube blog . A local 27B coding model, running on hardware in my house, is a coin flip. Some runs it nails the fix in twenty minutes. Some runs it edits the wrong file, writes a test that passes no matter what the code does, and tells you it's done. The bet behind LLMKube's Foreman was never that I would find a local model good enough to trust. It was that I could build a harness I trust more than any single model's output. This weekend tested that bet harder than any benchmark could, because the harness spent the weekend building its own guardrails. Here is the short version of what happened across 0.8.12 and 0.8.13. My local coder built three new gates for itself. One of them shipped with the exact flaw it was written to catch, and the review caught it. Three new contributors sent four clean pull requests while the machines worked. The same model ran on an AMD box and an Apple Silicon Mac, and the Mac quietly won a round nobody expected. And not one byte of any of it touched a cloud API. The thesis, stated plainly Trust the harness, not the model. A coding agent on a local model produces output of wildly variable quality, and no amount of prompt tuning makes a 27B as reliable as a frontier model. So Foreman does not ask the model to be reliable. It wraps the model in a pipeline that is : the coder works in a cloned workspace, a fast in-workspace gate runs gofmt, vet, build, lint, and the unit tests for the packages it touched; a reviewer reads the diff against the issue; and a clean-room Kubernetes Job re-runs the full suite before anything is allowed to call itself a GO. Around all of that sit deterministic rails: scope checks, edit-free-streak detection, repo-map context. The model is a stochastic component inside a system whose job is to make the system's verdict trustworthy even when the component is not. The interesting question is never "is the model good." It is "does the harness catch the model when it is bad." This weekend gave me

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I had used LangChain's RAG chain in production for six months. I could not have told you, off the top of my head, what chunk_overlap did, or why cosine similarity is the right distance metric, or how nomic-embed-text actually turns a sentence into a vector. The high-level library abstracted all of it away. So one weekend I deleted the LangChain dependency and wrote a RAG pipeline from scratch in ~500 lines of plain Python. No framework, no magic. pypdf for text extraction. A 60-line chunker. ChromaDB for the vector store. Ollama for embeddings and the LLM. The whole thing is on GitHub — every module is under 200 lines, every test is deterministic, and you can read the whole thing in one sitting. This is the build log. Not a tutorial — the build log, with the parts that surprised me and the parts I got wrong the first time. Why bother The honest reason: I was using LangChain's RetrievalQA chain and getting answers I didn't trust. Sometimes the model would say "according to the document" when the document didn't say that. Sometimes the citations were wrong. I had no way to know if the chunker was dropping important context, or if the cosine similarity was picking the wrong neighbors, or if the prompt was actually constraining the model. The library was a black box. When you build it yourself, every layer is inspectable. When the answer is wrong, you can add a print statement in pipeline.py line 102 and see exactly which chunks were sent to the LLM. When the chunker cuts a sentence in half, you see it in the test fixtures. When the embedding model gives garbage for some inputs, you can swap in a different model with one constructor parameter. None of that is possible when the whole thing is RetrievalQA.from_chain_type(llm=..., retriever=...) . The other reason: the code I wrote is 500 lines, and it covers the same ground as a 50-line LangChain script. The extra 450 lines are comments, type hints, tests, and explicit error handling. That's the actual complexity. LangCha

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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

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

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