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🔥 didilili / ai-agents-from-zero - 🚀 2026 最系统的 AI Agent 速成指南｜智能体实战教程 · 完整学习路径 + 实战项目 + 面试题库 · 对

GitHub热门项目 | 🚀 2026 最系统的 AI Agent 速成指南｜智能体实战教程 · 完整学习路径 + 实战项目 + 面试题库 · 对标大模型应用开发工程师岗位 · 覆盖LangChain / LangGraph / Coze / Dify / MCP / skills / LLM / RAG / 提示词 · 企业级部署与微调 · 从0到企业级落地 + 从学习到上线项目 + 面试准备一体化 | Stars: 3,439 | 43 stars today | 语言: Python

2026-08-05 原文 →
AI 资讯

Is the future of AI local?

Is the Future of Enterprise AI Local? For the past couple of years, the standard approach to AI has been pure brute force: take the biggest, most expensive cloud-hosted frontier model you can get hold of and throw it at every single problem. But this "one-size-fits-all" approach is not going to last forever. We are approaching a transition phase where the capabilities of local models will soon pass the threshold of "good enough," just as the growing costs of frontier models become impossible for businesses to ignore. Here is why I believe the pendulum is about to swing firmly toward local, controlled hardware. 1. The Looming API Cost vs. Value Reality Check Right now, companies are actively encouraging their staff to jump on the AI bandwagon. But this honeymoon phase will eventually hit a wall. In the near future, businesses are going to start heavily scrutinising the cost-versus-reward ratio of their AI deployments. Consider the typical enterprise token burn: If an engineer is burning through £10,000 worth of tokens each month on API calls, are they actually adding £10,000 worth of value? Or have they just established an expensive new habit to climb the internal AI usage leaderboard? When CFOs eventually demand budget cuts, teams relying entirely on cloud APIs are going to be forced to unlearn those expensive habits overnight, or find alternative options. Beyond raw cost, cloud providers will likely continue introducing workflow friction. We've already seen instances where providers shrink token allowances, enforce dynamic rate-limiting based on the time of day, or push traffic to lower-tier models during peak utilisation. Building your core workflows on a third-party API means you will never truly control the throttle. This is not even considering the data ownership issue, up until now the only powerful models were closed weight and there was not much choice in ownership, however now that open weight models are available that can offer similar performance and can

2026-08-05 原文 →
AI 资讯

From Snoring to Science: Fine-Tuning OpenAI Whisper for Sleep Apnea (OSA) Screening

Is your snoring just a nuisance, or is it a health warning? Obstructive Sleep Apnea (OSA) affects nearly 1 billion people worldwide, yet most remain undiagnosed due to the high cost of clinical polysomnography. Today, we are pushing the boundaries of AI Healthcare by repurposing OpenAI Whisper from a speech-to-text powerhouse into a clinical screening tool. In this tutorial, we will explore how to leverage Audio Signal Processing , Hugging Face Transformers , and Librosa to detect breathing patterns. By fine-tuning Whisper on non-speech acoustic events, we can transform a standard smartphone recording into a high-precision OSA screening device. Pro-Tip : If you're looking for more production-ready examples and advanced architectural patterns for AI-driven health monitoring, be sure to check out the deep-dives over at WellAlly Tech Blog . The Architecture: From Raw Audio to Clinical Insight To build an OSA screening algorithm, we don't just need to hear the sounds; we need to understand the rhythm and absence of sound. We use Whisper's robust encoder to capture the spectral features and a custom classification head to identify Apnea-Hypopnea events. graph TD A[Raw Sleep Audio .wav] --> B[Preprocessing: Librosa] B --> C[Noise Reduction & VAD] C --> D[Segmenting: 30s Windows] D --> E[OpenAI Whisper Encoder] E --> F{Event Classification} F -->|Normal| G[Healthy Breathing] F -->|Snore| H[Snore Phase Analysis] F -->|Silence/Choke| I[Apnea Event Detected] I --> J[AHI Index Calculation] J --> K[Final OSA Risk Report] Prerequisites To follow this advanced guide, you'll need: Tech Stack : Python 3.9+, transformers , librosa , torch , and evaluate . Dataset : Ideally, the UCD Snore Database or similar PSG-synchronized audio data. Step 1: Audio Preprocessing with Librosa Before feeding audio into Whisper, we need to clean the signal. Sleep environments are noisy (fans, traffic, etc.). We use librosa to normalize the audio and detect "Voice" (or in our case, Breath) Activity. im

2026-08-05 原文 →
AI 资讯

How Much Does It Cost to Self-Host Open Models on AWS?

Your AI bill tripled last quarter. Your CTO forwarded you an article about companies saving 70% by switching to open models. Now someone is asking you to figure out what that would actually look like. I spent the last few weeks digging into this. The numbers, the hardware, the real trade-offs. Here's what I found, with enough specifics that you can actually make a decision rather than just nodding along to another "open source is the future" think piece. What "Open Models" Actually Means When someone says "open model" they mean an AI model where the weights (the learned parameters that make the model work) are publicly downloadable. You grab the file, run it on your hardware, and you don't pay anyone per request. The big names right now: Meta's Llama 4, DeepSeek V4, Zhipu's GLM-5.2, Moonshot's Kimi K3, Alibaba's Qwen 3.5, and Google's Gemma 4. These aren't toys. Some of them genuinely compete with the frontier models on real benchmarks. Chinese open models now handle over 30% of enterprise traffic on OpenRouter, up from 4.5% in early 2025. That's a massive shift in barely a year. The Architecture: What You Actually Need You want your team to use an open model. Here's the stack, from bottom to top. Hardware (The Expensive Part) A model is a giant file. We're talking anywhere from 4 GB (a small 7B model, quantized) to 1.5 TB (Kimi K3, full weights). That entire file needs to sit in GPU memory to run fast. Why GPU memory specifically? Because generating each word in a response requires billions of multiply-and-add operations. GPUs do thousands of these in parallel. A CPU does them one at a time. The practical difference: a 7B model on a CPU generates 2-5 tokens per second (painfully slow for interactive use). The same model on a GPU generates 30-80 tokens per second (feels instant). For one person on a CPU, it might be tolerable. For a team of 10 all hitting the same endpoint? Unusable. Requests queue up and everyone waits 30-60 seconds for responses. Think of it like

2026-08-05 原文 →
AI 资讯

One Rails request, one event: production context for coding agents

Wide Events is a Rails gem that puts the production context a coding agent needs onto one OpenTelemetry root span per request or job. In one production search request, the root event showed 30.0 seconds total duration, 446 ms of Postgres time, and 29.4 seconds of outbound HTTP time. That was enough to focus the investigation on an external dependency. The trace then identified a POST that took 28.9 seconds. The trace contained 82 spans and 20,261 bytes of attribute JSON. The root event contained 40 attributes and 1,420 bytes. This is not a token benchmark, but it shows why the root event is a more compact starting point for an agent. Agents can read the code, but not the running system A coding agent starts with an unusual advantage: it can search every model, controller, job, migration, and test in a few seconds. It also starts with a serious blind spot. The repository cannot tell it: which account experienced the problem which build was running which feature-flag variant was active how many queries the request issued whether a semantic-search leg degraded how much an LLM call cost whether the same symptom appears in one tenant or every tenant Those answers often exist somewhere, but “somewhere” might mean a trace waterfall, application logs, a feature-flag service, product analytics, and a database console. Pulling all of that into a context window is expensive and usually requires several joins that were never designed in advance. A wide event changes the starting point. The app accumulates the context it learns while processing one unit of work, then attaches the completed flat map to the OpenTelemetry root span. The span is marked main=true , so every request or job can be queried as one row. request or job -> Rails and domain context accumulate -> child spans contribute dependency counts and timings -> one flat map is flushed onto the root span -> ClickHouse stores one queryable row The trace still exists. Wide Events gives it an application-shaped index. If y

2026-08-05 原文 →
AI 资讯

I Built a Server Agent Because Uptime Checks Tell You What Failed, Not Why

A status page has a blind spot. It can tell you that your API is returning 502s. It can tell you that a TCP port stopped accepting connections. It can tell you when the incident started. It usually cannot tell you why . Was the application host out of memory? Was disk I/O saturated? Did load climb for 40 minutes before users noticed? Was the server completely healthy and the real problem somewhere else? Those answers often live in a separate monitoring product, disconnected from the incident timeline and disconnected from the status page. That is why I built Servers for StatusPage.me. It is a small, customer-installed host metrics agent and dashboard. You install it on a machine you operate, and it reports CPU, memory, swap, load, disk, and network metrics back to your account. The important part is not “now there are more graphs.” The important part is seeing an outage and the host evidence around it on the same timeline. External checks answer one question. Host metrics answer another. Regular uptime monitoring is still the right tool for the outside-in view: Can users reach the website? Is the API returning the expected response? Does DNS resolve correctly? Is the database port open? Did a scheduled job run? But those checks do not run inside your infrastructure. A healthy HTTP response does not prove that a background worker is about to run out of memory. A timeout does not prove that the app server is overloaded. And an incident can start with a slow disk or growing swap usage long before an endpoint is fully unavailable. The distinction is simple: External monitoring tells you what users can see. Host metrics help explain what the machine was doing when they saw it. You need both. What Servers includes Each registered host gets a dedicated dashboard page with: CPU user, system, and I/O wait utilization Memory use Swap use Load averages Disk use and read/write throughput Network inbound and outbound throughput A human-readable OS description for account owners

2026-08-05 原文 →
AI 资讯

How EvalPort's Grader System Works: 11 Types for LLM Evaluation

How EvalPort's Grader System Works When designing EvalPort, the grader system was the hardest part to get right. Every eval framework has its own way of scoring LLM outputs — DeepEval uses metric classes, Promptfoo uses assertion objects, Inspect AI uses solver functions. We needed a system expressive enough to cover 90%+ of real-world eval needs, but simple enough that any framework could implement it. The result: 11 grader types that carry their own semantics. A grader isn't just a name — it specifies its parameters, its model, its threshold. An eval suite is self-describing. The 11 Grader Types exact_match — Compare output to expected output, optionally ignoring case. contains — Check if the output contains a substring. regex — Match against a regular expression. semantic_similarity — Embed output and expected output, compare cosine similarity against a threshold. llm_judge — Use an LLM to evaluate the output against a prompt template. The most powerful grader. json_schema — Validate that the output is valid JSON matching a JSON Schema. json_path — Extract a value from JSON output using a JSONPath expression, then compare it. code — Run a function to evaluate the output. human — Defer to human review. model_graded — Compare the output to a reference answer using a model. custom — Escape hatch for graders not covered by built-in types. How Graders Connect to Test Cases A test case references graders by ID. Multiple graders can evaluate the same test case. The ResultSet records each grader's score separately. Why This Design Works Self-describing: An eval suite carries everything a framework needs to execute it. Framework-agnostic: Any framework can implement any subset of grader types. Extensible: The custom type lets frameworks bring their own graders. Comparable: Results from different frameworks use the same grader IDs. Try It pip install evalport-sdk npm install evalport-sdk Spec: https://github.com/adhabnr-ux/evalport/blob/main/spec/SPEC.md Repo: https://gith

2026-08-05 原文 →
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