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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 →
AI 资讯

I built an invoice generator with no backend — the whole app is one HTML file

Every invoicing tool I tried wanted an account, a subscription, and a copy of my client list on its servers — then charged me monthly to put my own logo on my own invoice. So I built the opposite. Billfold is a complete invoice generator that runs entirely in your browser. No account, no backend, no build step. The whole app is a single index.html file you could email to yourself. It's MIT-licensed and the source is right here: github.com/quantum-hacker0/billfold . Here are the three parts that were actually fun to build. 1. "No server" isn't a privacy policy — it's the architecture The usual pitch is "we take your privacy seriously." That's a promise you have to trust. I wanted it to be a fact you can verify : Open DevTools → Network, create an invoice, and count the requests. It's zero. There's nothing to upload because there's nowhere to upload it. Data lives in localStorage . The app is HTML/CSS/JS inlined into one file — no framework, no bundler, no node_modules . Download it once and it works offline forever. 2. Sharing an invoice without a database — put it in the URL hash This was the interesting constraint. How do you send someone a view-only invoice when you have no server to store it on? The trick: encode the whole document into the URL hash fragment . The fragment (everything after # ) is the one part of a URL that browsers never send to the server — it stays client-side. function shareLink ( state ) { const json = JSON . stringify ( state ); const encoded = btoa ( unescape ( encodeURIComponent ( json ))) . replace ( / \+ /g , ' - ' ). replace ( / \/ /g , ' _ ' ). replace ( /=+$/ , '' ); // base64url return location . origin + location . pathname + ' #v= ' + encoded ; } The recipient's browser reads the fragment, decodes it, and renders the invoice locally. The data rides inside the link and never touches a host — not even mine. PDF export, by the way, is just window.print() with a print stylesheet. 3. Invoices as URLs — with an npm package Because the a

2026-08-04 原文 →
AI 资讯

Agent-Reach absorbed Bilibili's 412s — your agent kept working

Bilibili's 412 Incident, Explained: How v1.5.0 Absorbed It In June 2026, Bilibili quietly began rejecting yt-dlp with HTTP 412 errors. Agents wired to scrape it broke — except the ones sitting behind Agent-Reach, which rerouted the channel before most developers noticed. Agent-Reach is a local, MIT-licensed capability layer that gives shell-capable coding agents live internet access by selecting and routing to upstream CLIs rather than proxying data itself . When Bilibili started 412-blocking yt-dlp in June 2026, v1.5.0 rerouted the Bilibili channel to bili-cli with zero user action, while YouTube kept using yt-dlp untouched . The fix landed centrally: the maintainer reordered backends, so no individual builder had to patch a private integration. Quick Answer: When Bilibili began returning HTTP 412 to yt-dlp in June 2026, Agent-Reach v1.5.0 automatically rerouted its Bilibili channel to bili-cli — agents kept working with no user action. The release passed 32 end-to-end tests across 13 channels and grew its suite from 107 to 162 tests. The framing shift matters: v1.5.0 describes itself as a capability layer, not a tool collection. Each platform gets an ordered primary-plus-fallback backend list; after setup, your agent calls those CLIs directly and Agent-Reach never sits in the data path . The June 11, 2026 release passed 32 end-to-end tests across 13 channels and grew its test suite from 107 to 162 tests . Platform Primary backend Fallback Web pages Jina Reader — YouTube yt-dlp — GitHub gh CLI — RSS feedparser — Bilibili bili-cli OpenCLI (subtitles) Twitter/X twitter-cli OpenCLI Reddit OpenCLI rdt-cli XiaoHongShu OpenCLI xhs-cli LinkedIn linkedin-mcp Jina Reader Global search Exa via mcporter — "capability layer: multi-backend routing + real doctor + OpenCLI" — Agent-Reach v1.5.0 release framing (source: Agent-Reach CLAUDE.md ). The behavior is easy to model. The following minimal snippet — which was executed and returns exit 0 — illustrates the "absorb and keep wo

2026-08-04 原文 →
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