标签：#AI

post 8: You run a container. It starts successfully. The application works. So… is it production-ready? Not necessarily. The real test of a production container isn't what happens when everything works. It's what happens when something goes wrong. What happens when the application consumes all available memory? What happens when the process crashes? What happens when the application is running, but isn't actually healthy? Where do the logs go? How do you know something is wrong before users tell you? And when the container fails, how do you find the actual cause? Running Docker in production isn't just about starting containers. It's about making them reliable, observable, manageable, and recoverable. 1. Production Starts With Boundaries A container that works perfectly on a developer's laptop can behave very differently under production load. Development often prioritizes: Speed Convenience Easy debugging Frequent changes Production prioritizes: Reliability Predictability Security Observability Recovery One of the first production questions is: What happens if this container consumes more resources than expected? That's where resource limits come in. 2. Resource Limits – Don't Let One Container Consume Everything Without appropriate resource limits, a container can consume more host resources than intended. For example: docker run \ --memory = 512m \ --cpus = 1.0 \ nginx This limits the container to: 512 MB memory 1 CPU Why does this matter? Imagine one application suddenly starts consuming several gigabytes of memory. Without appropriate limits, it could affect other workloads running on the same host. Resource limits create boundaries between workloads. But remember: A resource limit doesn't fix a memory leak. It only limits how much damage that container can cause to the host. So now we have another question: What if the container is running, but the application inside it is broken? 3. Health Checks – Running Doesn't Mean Healthy One of the most important produc

The New York Times has a long article ( alt link ) on surveillance systems aimed at babies. They are increasingly using AI. Nanit and its rivals want to own 24/7 health tracking for the sub-four-foot set. And their already astonishing levels of baby data collection are just the beginning. Nanit recently raised $50 million from investors to expand its use of A.I. and use its camera to track speech and language development, motor skills and more, while extending its presence in children’s bedrooms into early adolescence.

The glasses, called Legato Frames, integrate the company’s patented hearing-assistance technology into the arms of eyewear frames.

The focus is on agentic capability and predictable enterprise deployment.

Bill Gates has been reflecting a lot on AI lately, and the process has triggered a stark awakening. Once a staunch AI optimist, the Microsoft cofounder is now deeply pessimistic about what AI means for our collective future. Having been conspicuously quiet on AI issues recently, Gates is back with a nearly 6,000-word essay seeking […]

Pet owners, rescue agencies, and wildlife groups are calling for new safeguards as AI makes it harder to tell whether animals, from polar bears to house cats, are real or fake.

Runable says 60%–70% of its 1 trillion-plus token usage in the last 90 days came from paying customers.

Vibecoding: How to Manage an AI Coder and Not Drown in Spaghetti Code Forget fairy tales about AI doing everything for you at the touch of a button. Without strict control, vibecoding quickly turns into a mess of broken, unmaintainable code. Modern vibecoding isn't blind generation — it is strict architectural supervision . To build real products, you must change your approach to context and redefine your role in the process. Forget Persona Prompting: Context is the Only King of Modern Prompting Fables like "Act as a Senior Developer" were left back in 2023. Modern LLMs don't need roleplay — they need the cleanest, deepest context possible . Why "Persona Prompting" is Outdated AI doesn't start coding better just because you called it a senior dev. It needs concrete technical boundaries. Skip the foreplay and provide the AI with technical specifications: Stack and versions: Not just "React", but React 18, Next.js 14 (App Router), Tailwind CSS . Architectural constraints: Show folder structure, naming conventions, and API response formats. Rule files ( .cursorrules / .clauderules ): Load strict rules into the project that the AI must follow at all times (e.g., "Never use any in TypeScript, write functional components only" ). Humans as Strict Regulators, Not Blind Consumers of Code The biggest danger of vibecoding is shipping AI-generated garbage straight to production without looking. If you blindly consume whatever the AI spits out, your project is doomed. 1. Total Quality Control and Code Review You act as the Technical Regulator and Censor . You never take the AI's word for it. Read every diff : Check exactly what the AI changes. Don't let it rewrite working modules from scratch just to add one button. Don't know how to code? Use basic logic: adding a single button shouldn't make 30 lines of code disappear from main.py or app.js . Force it to justify decisions: If the AI suggests a library, ask: "Why this one over a native solution? Will it impact performance?" 2.

The people who know your AI agent best may be the people least able to see all of its flaws. Not because they are bad engineers. Because they built it. Years ago, when I was taking art classes, my teacher told me something I've never forgotten: “Sara, you can't judge your own art.” I remember thinking, of course I can. 😂 Then she explained. After spending hours looking at the same piece, your eyes get filled with it. You stop seeing what is actually there. You see what you expect to see. I've used that lesson everywhere since. And I think AI agents have the same problem. You designed the requirements. You designed the system. You know why every decision was made. Then you design the evaluation and ask: “Does my agent actually work?” That's where the blind spot can appear. Your evaluation may end up testing the system according to the same assumptions that created it. The evaluator can inherit the system's assumptions Consider a simple requirement: “The agent should answer customer questions accurately.” Seems reasonable. So the team creates an evaluation set with questions that have clear intent and well-defined answers. The agent performs beautifully. 94%. Green dashboard. 🎉 But an external evaluator might ask a different question: What happens when the customer's request has two plausible interpretations? Now you have a different test: “Can I change my billing address?” Does the agent answer immediately? Does it ask which account or address the customer means? Does it make an assumption? The original evaluation may have been technically correct. It just never tested the ambiguity. That is the blind spot. Internal evaluation is still essential This isn't an argument that internal teams shouldn't evaluate their own systems. They absolutely should. The people who built the system understand its requirements, architecture, constraints, tools, and intended behavior better than anyone. That knowledge is extremely valuable when designing evaluations. But it can also crea

It’s a glorious day in Kirkland, Washington, an affluent Seattle suburb on the eastern shore of Lake Washington. The temperature is in the mid-80s, and the sky is incapable of being any more blue. The view from the Gates Ventures conference room overlooks the Carillon Point Marina, where a flotilla of expensive boats bob in…

Diagrid Catalyst 2.0 applies Dapr-based recovery, signed workflow history and execution attestation across several agent frameworks. Architects should compare it with framework-native durability and established workflow engines, while evaluating benchmark evidence and operational trade-offs. By Mark Silvester

If you're building or evaluating an AI agent that can shop and check out on its own, you've probably run into the new "agentic commerce" protocols: ACP (OpenAI + Stripe + Meta), AP2 (Google), and UCP. They define how an agent talks to a merchant to create a checkout session, apply a payment token, and get an order back. Stripe's own test mode covers the payment half fine — test cards, test API keys. But there's no hosted "fake merchant" you can point your agent at to verify the protocol half: does your agent correctly create a session, handle a 422 idempotency conflict, parse the order response, retry politely? You either mock it yourself from the spec, or risk finding out against a real merchant. So I built acp-sandbox — a small hosted mock merchant implementing the ACP checkout API, live at https://acp-sandbox.flo-voice1.com . What it does It implements the real checkout_sessions lifecycle from ACP's 2026-04-17 spec : create, retrieve, update, complete, cancel. Responses match the actual CheckoutSession / Order / Error schemas for the fields it supports — I pulled the OpenAPI spec directly rather than guessing field names. # get a test key, no signup curl -X POST https://acp-sandbox.flo-voice1.com/keys \ -H "Content-Type: application/json" -d '{"email":"you@example.com"}' # create a session against the demo catalog curl -X POST https://acp-sandbox.flo-voice1.com/checkout_sessions \ -H "Authorization: Bearer acps_test_..." \ -H "Content-Type: application/json" \ -d '{"line_items":[{"id":"item_demo_headphones","quantity":1}],"currency":"usd"}' Every request/response is logged per API key ( GET /logs ), so you can see exactly what your agent sent when something doesn't work. What it deliberately doesn't do (yet) No real payment processing — complete always succeeds once you send any payment_data . No OAuth delegate_authentication flow. No fulfillment options (shipping/pickup) — every session goes straight to ready_for_payment . Fixed demo catalog (4 items), not a rea

2024 was the year AI was everywhere. The ads, the praise, the feeling that something had already been decided without me. I am in my fifties. I had to decide whether to watch or take part. I decided to take part. I started with VS Code, on a paid plan. I did not have to think about what to build. Something had been sitting in my head for years: an automated trading system. I have lived fifty years, and while raising children the money got tighter, not looser. Financial freedom was moving away from me, not toward me. So I wanted to make money with automated trading. I think the idea first arrived in my mid-forties. That is why the decision to take part came so quickly. Whatever I said out loud would simply get built. That was the hope I walked in on. I do not really know how to code. But the AI would handle that part, so I trusted it. Where the illusion first cracked A year inside VS Code taught me that two names mattered: GPT and Claude. I used them in turn. I used them one at a time. Two problems. First, even on a paid plan the usage ran out fast. Faster than I expected. The road ahead was long and I was sitting still, waiting for a quota to reset. The second one was worse. The explanations were excellent. The results were not. That is where the illusion cracked for the first time. I still could not let go, so I paid for more. Adding Cursor bought me some headroom. And a different problem showed up immediately. Switch the model and it wants to start over Change the model, and it wants to rewrite everything from the beginning. Handed code written by a different AI, it would rather replace the whole thing than edit it. That is when I understood that switching AI mid-project is a bad idea. Everyone talks about pricing. Almost nobody talks about this one. And this is the one that actually held me back. I spent a lot of time fighting the tool. In the end I paid for Claude's hundred-dollar plan, and from then on I worked with Claude. What I gained: the job nobody wanted

The schema catalog for an AI assistant is the artefact that answers the question "what does this database look like right now". Whether the database is Postgres, MySQL, SQL Server or Redshift, the shape of the problem is the same: the catalog carries table names, column names, types, keys, and enough relationships to let the assistant write a query that resolves. It lives somewhere between the database and the assistant, has to stay in sync with a database that changes underneath it, and is almost always built the same weekend the team decides they want an AI assistant reading their data. It runs fine for the first three tables. The problems start around the fourth week, and none of them look like the same problem twice. The distinction worth naming early is between the connection layer (how the assistant reaches the database) and the knowledge layer (what the assistant knows about the database's shape). The connection layer receives most of the attention, because credentials, network isolation and query cost are visible failure modes and easy to argue about. The knowledge layer is where most of the actual quality of the assistant lives, and it decays quietly. The AI database context page covers why this second layer matters at all when the first one exists. Why not just point the assistant at the database Connecting the AI directly to production is the shortest path and the one most teams reject after five minutes of thinking about it. The assistant would get read access on tables it should not see, its queries can be arbitrarily expensive, its credentials would live somewhere they should not, and the audit trail becomes hard to reason about. What most teams end up building is a layer in between: a representation of the database that the assistant can read cheaply and safely without ever touching production. That layer is what this article is about. It is not the connection. It is the catalog. The five recipes teams build Ask fifteen senior developers how to build

TL;DR An agentic RAG pipeline treats retrieval as a tool the AI agent can call, evaluate, and call again rather than as a fixed step. The pipeline can search an internal knowledge base first, then use real-time web search when the available evidence is missing, weak, or outdated. Internal documents and web results should be converted into a shared evidence format before the model generates an answer. A reliable system must preserve URLs, publication dates, document identifiers, and the claims supported by each source. Retrieval quality, web-search precision, citation correctness, latency, cost, and stopping behaviour should all be evaluated. A basic RAG pipeline works well until the answer is not in the knowledge base. Imagine an enterprise copilot that can answer questions about internal product documentation. It performs semantic search against a vector database, retrieves several relevant passages, and passes them to a language model. For questions covered by the indexed documents, the system may work remarkably well. Then a user asks about a release announced yesterday, a recently changed regulation, or how the company’s product compares with a new competitor. The vector database cannot retrieve information it has never indexed. A conventional pipeline may return no answer, but it may also produce a confident response from incomplete or outdated context. Adding a Web Search API helps solve the freshness problem, but it introduces another decision: when should the system trust its internal knowledge, and when should it search the open web? An agentic RAG pipeline places that decision inside the retrieval workflow. What Makes a RAG Pipeline Agentic? A traditional RAG pipeline usually follows a fixed path: transform the question into a search query, retrieve the most similar passages, add those passages to the prompt, and generate an answer. An agentic RAG pipeline allows the model to make decisions between those stages. Retrieval becomes a tool rather than a manda

Ringg has raised $10 million from Peak XV as a part of its Series A extension.

More agents do not automatically produce better engineering. They usually add total tokens, duplicated context, handoff delay, and integration risk. Their defensible advantages are narrower: reduced elapsed time for independent work, isolated investigation, or specialist evidence that one agent might omit. The useful question is therefore not “Can this task use subagents?” It is: Does this task contain independent, bounded work whose value exceeds the coordination cost? Codex How To now includes a dependency-free benchmark for testing that question instead of answering it from intuition. Disclosure: I maintain Codex How To , the independent open-source project containing the benchmark, evaluator, and measurements used here. The minimum decision rule Use one agent when the change is small, the interface is unsettled, or several steps must edit the same central files. Consider bounded orchestration only when all of these are true: The task has at least two genuine ownership surfaces. Each writer can own exclusive paths. The interface between those paths is frozen before implementation. The controller retains integration, system checks, and final review. Every worker returns concise evidence rather than a narrative transcript. One external acceptance bar can evaluate every execution method. flowchart TD A["One task contract"] --> B{"Independent write surfaces?"} B -- "No" --> C["One agent or sequential work"] B -- "Yes" --> D{"Frozen interface and exclusive paths?"} D -- "No" --> C D -- "Yes" --> E["Bounded workers"] E --> F["Controller integrates and evaluates"] F --> G{"Coverage or elapsed-time value exceeds coordination cost?"} G -- "Unproven" --> H["Keep measuring"] G -- "Repeated evidence" --> I["Adopt for this task class"] Job titles are not ownership boundaries. “Backend agent,” “test agent,” and “review agent” may still collide on the same files or execute dependent stages. A useful boundary is concrete: one writer owns incident/** , another owns web/** , and n

디지털 자산 시장은 지금 변곡점에 서 있다. 블록체인 기술이 본래 파괴적이고 반체제적인 힘에서 벗어나 전 세계 금융 시스템의 점점 더 통합된 구성 요소로 진화하면서, 심오한 변화를 목격하고 있기 때문이다. 이러한 패러다임 전환은 흥미로운 이중성을 보여준다. 한편으로는 전통 금융 기관(TradFi)이 기존 시스템을 강화하기 위해 블록체인을 적극적으로 수용하고 있고, 다른 한편으로는 암호화폐 기반 기업들이 주류 금융과의 간극을 메우기 위해 규제적 정당성을 끊임없이 추구하고 있다. 최근의 이러한 움직임들은 분산원장기술(DLT)이 새로운 하이브리드 금융 아키텍처의 토대가 되는 미래를 예고하며, 이 복잡한 춤사위를 더욱 부각한다. 이러한 흐름의 중요한 한 걸음은 미국 주() 은행 협회들이 2027년 출범을 목표로 전국적인 블록체인 네트워크인 "뱅크체인 얼라이언스(BankChain Alliance)"를 발표한 일이다. 39개 주 협회의 지원을 받는 이 이니셔티브는 스테이블코인, 결제, 토큰화된 예금을 은행 시스템의 규제 범위 내에서 육성하는 것을 목표로 한다. 동시에, 암호화폐 인프라 기업인 제로해시(Zerohash)가 초반의 난관에도 불구하고 미국 통화감독청(OCC)의 신탁은행 인가를 확보하려는 끊임없는 노력은 암호화폐 산업이 주류의 수용과 규제 통합을 향해 나아가려는 의지를 잘 보여준다. 이러한 사건들은 고립된 현상이 아니다. 블록체인의 혁신적인 잠재력이 기존 금융 구조에 의해 형성되고 흡수되는 한편, 암호화폐 벤처들은 확립된 법률 및 규제 준수 프레임워크 내에서 운영하려 하는 중요한 단계를 나타낸다. 광범위한 기술적 야망의 맥락에서, 일론 머스크의 스페이스엑스(SpaceX)가 루이지애나에 1,000억 달러 규모의 우주공항을 건설할 계획이라는 소식은 블록체인과 직접적인 관련은 없지만, 미래 인프라를 재정의할 최첨단 기술에 막대한 자본과 전략적 투입이 이루어지고 있음을 보여준다. 이는 디지털 자산 인프라에 대한 금융 부문의 대규모 구축과도 유사하다. 이 글은 이러한 금융 블록체인 발전의 함의를 깊이 탐구하고, 기술적 기반, 실제 선례, 그리고 내재된 한계를 분석할 것이다. 블록체인 기술의 탄생은 특히 2009년 비트코인(Bitcoin)과 함께, 2008년 금융 위기 동안 전통 은행 시스템의 실패와 중앙집중화에 대한 인식에 대한 직접적인 대응이었다. 탈중앙화, 투명성, 중개자 제거라는 핵심 원칙은 가치 이전과 기록 보관에 대한 대안적인 비전을 제시했고, 이는 처음에는 전통 금융의 회의적인 시선을 받았다. 그러나 기반이 되는 DLT가 성숙해지면서, 금융 기관들은 운영 효율성을 높이고, 결제 시간을 단축하며, 비용을 절감하고, 데이터 무결성을 개선할 수 있는 심오한 잠재력을 인식하기 시작했다. 이처럼 전면적인 거부에서 전략적 채택으로의 점진적인 변화는 지난 10년간의 특징적인 흐름이었다. 전통 금융이 블록체인에 매력을 느끼는 이유는 현재 번거롭고 비용이 많이 드는 프로세스를 간소화할 수 있는 능력 때문이다. 블록체인에서 실제 자산을 나타내는 토큰화된 자산은 즉각적인 결제, 분할 소유권, 그리고 유동성 증가를 약속한다. 특히 규제 대상 기관이 보유한 법정화폐 준비금으로 뒷받침되는 스테이블코인은 암호화폐의 프로그래밍 가능성과 효율성을 전통 화폐와 관련된 안정성 및 신뢰와 결합한 디지털 교환 매체를 제공한다. 이러한 융합은 미국 내에서 복잡하고 진화하는 규제 환경 속에서 진행되고 있다. OCC, SEC, 그리고 주 은행 부서와 같은 다양한 연방 및 주 기관들은 디지털 자산과 DLT 응용 프로그램을 어떻게 분류하고 감독할지에 대해 고심하고 있다. 기술적 야망의 엄청난 규모는 금융 분야에만 국한되지 않는다. 스페이스엑스가 2027년 건설을 시작하고 2029년 첫 비행을 목표로 루이지애나에 1,000억 달러 규모의 우주공항을 건설할 계획을 발표한 것은 다양한 분야에서 최첨단 인프라에 막대한 투자가 이루어지고 있음을 증명한다. 이 프로젝트는 블록체인과는 별개이지만, 궤도 데이터 센터든 차세대 금융 레일이든 미래 기술 패러다임을 지원하기 위한

A polished UI can make a product feel completely different, but getting there usually takes more than...

Honestly, when I saw the headline "Someone in Korea used AI to cut the prep time for a dose of Korean herbal medicine from 300 minutes to 5," the first thing that caught my eye wasn't "whoa, robots can make herbal medicine now." It was how they did it—because they happened to get right the one thing most people get wrong when they think about applying AI. What Onerve Did Let's start with the facts. There's a Korean startup called Onerve (오너브), backed by the Korea Institute of Oriental Medicine, working on automating the manufacturing of Korean herbal medicine (한약). Their system is called HAP. It connects AI with electronic medical records (EMR) to automate the entire flow—from prescription input, to manufacturing, cleaning, packaging, and inventory management. The key is the raw material: they use standardized, freeze-dried herbs in a "cartridge" format—turning herbs that used to require on-site boiling and heavy manual labor into uniform, standardized modules. The result: prep time for a single dose of Korean herbal medicine dropped from around 300 minutes to around 5. They won a CES Innovation Award and closed a Series A round of roughly 6.2 billion won. And they're not alone—another Korean company, Camelotech (with its Cameleon system), is doing almost the same thing and also showed up at CES. So "Korean herbal medicine automation" is turning from a one-off experiment into an actual category. What I'm Actually Paying Attention To Isn't the Speed—It's Which Layer They Automated If all you take away from this is "300 minutes became 5," you're missing the most important part. When people see AI moving into an industry with a thousand-plus years of tradition behind it, the gut reaction is usually panic: "Are even Korean medicine doctors about to get replaced by AI?" But if you look closely at what Onerve actually automated—it's the manufacturing , not the diagnosis and prescribing . Deciding which medicine a person should take, how to adjust the dosage, how to read t