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I Built RAG From Scratch in Python to Understand It. Here's What I Learned.

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

2026-06-22 原文 →
AI 资讯

Midjourney V7 for Product Photos in 2026: What I Actually Use It For

My Testing Setup I used Midjourney V7 (midjourney.com, Standard plan at $30/mo for this project — volume was too high for Basic) over five weeks across six product photo projects: lifestyle context images, background replacement concepts, packaging mockups, and mood-board style reference images for briefing photographers. Some outputs went live in ads. Others were used internally. A few were scrapped entirely. Two specific examples: I generated 12 lifestyle context images showing a skincare product in a bathroom setting — no actual product in the image, just the environment and mood — and used them as ad backgrounds with the real product composited in afterward. Results were strong. I also tried to generate images of the actual product itself from reference photos. That failed in ways I will explain. Pricing: Standard plan at $30/mo. For product photo work at volume, Basic at $10/mo runs out fast. Budget for Standard if this is a regular workflow. 1. Lifestyle Context and Environment Images This is where Midjourney V7 earns its place in a product photo workflow. Generating the environment — a kitchen countertop, a gym bag, a coffee shop table — without needing to stage or shoot it is genuinely useful. I needed eight lifestyle backgrounds for a supplement brand's ad campaign. Real location shoots for eight setups would have cost $3,000 and taken two weeks. I generated the environments in Midjourney, exported them, and composited the real product in using Photoshop. Total cost: $30 for the month's Midjourney subscription and four hours of compositing work. The images ran in paid Meta ads for six weeks. CTR was in line with our studio-shot creative. Nobody asked if the backgrounds were AI-generated. The key: generate the environment only. Do not try to put your specific product into the Midjourney image. Composite it in post. That division of labor is where the workflow holds up. 2. Packaging Mockups for Concepts That Do Not Exist Yet Before you manufacture a product o

2026-06-22 原文 →
AI 资讯

When AI Agents Start Working Together: Three Challenges No One Talks About

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

2026-06-22 原文 →
AI 资讯

HelmSharp: render Helm charts from .NET without shelling out to helm

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

2026-06-22 原文 →
开发者

Лёгкая панель для управления личным VPN-сервером на Xray

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

2026-06-22 原文 →
AI 资讯

I opened my first PR to LiveKit's agents repo — here's the bug I found

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

2026-06-22 原文 →
AI 资讯

𝗪𝗵𝗮𝘁 𝗶𝗳 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐲 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝐭𝐚𝐬𝐤𝐬 𝘄𝗮𝘀 𝐟𝐢𝐧𝐚𝐥𝐥𝐲 𝘄𝗶𝘁𝗵𝗶𝗻 𝗿𝗲𝗮𝗰𝗵?!

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!

2026-06-21 原文 →
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