mcp-use v2 rebuilt from scratch for stateless 2026-07-28 MCP spec

CA

Capgo

Мгновенные обновления для Capacitor-приложений. Выпускайте исправления за минуты, а не недели. Отправляйте OTA-обновления пользователям без задержек App Store.

OP

OpenAlternative

OpenAlternative — каталог open-source альтернатив проприетарному софту. На сайте собраны проекты из разных категорий с информацией о возможностях, стеке технологий и метриках GitHub. Платформа монетизируется через платные размещения и партнёрские ссылки.

PR

Provenmetal

Hey HN, we’re Will & Johnny from ProvenMetal (https://provenmetal.com). You send us design files or specs and we give you assembled boards domestically in days. The US produced 30% of PCBs globally in 2000, now they produce 4%. Chinese manufacturers have completely dominated this space at 55% of global production. Now, the need for a domestic PCB supply chain is higher than ever before, yet the infrastructure has been dissolving over the last 2 decades. What is left is mostly small family run manufacturers (CMs) that have been operating in largely the same, labor intensive way since the early 2000’s. When you place an order through a CM it typically takes several days to receive a quote and complete design for manufacture review, and then you have to source all of the components (the hardest part) and bare boards yourself, then wait anywhere from a few days to several weeks for the assembly and testing of those boards. We started off assembling circuit boards out of a garage with prosumer grade equipment (NeoDen YY1, Glenbrook X-ray, solder paste stencils, and manual rework stations). We believed that by owning the manufacturing process, we could turn all the front of house automations inwards. But here’s the thing… manufacturing circuit boards with prosumer grade equipment out of a garage takes a huge amount of time. Suddenly we were spending 90% of our time assembling circuit boards instead of growing the business. We were completely capacity constrained, with fancy software automations that are not the binding constraint at low volumes. Then we got our heads out of the weeds, took a step back, and realized that we were trying to solve the wrong bottlenecks. These manufacturers are good at manufacturing and terrible at the front of house (quoting, DFM review, and part procurement). When you look at the full process, you realize that assembly is not the bottleneck. So we stopped trying to solve the problem that we can’t solve at this stage. We measured the bottlenecks and nailed down the ones that we are best positioned to solve right now. When a customer wants a domestically manufactured circuit board, it is not a straight forward process. We are making that process easy through front of house automation. A customer gives us their design files, and we automatically procure components, and co-ordinate bare board fabs and assembly houses to get quotes, design review, and manufacturing completed in a very tight loop. How we solve part procurement (you can’t assemble boards with no parts!): When a user places an order with us, our system automatically sources their bill of materials across US and overseas distributors. However, when we work with customers during the design process, our plug-ins interact with KiCAD and Altium, sending the BOM to our ordering platform, which allows us to automatically procure components before layout is finalized. KiCAD plugin: (https://github.com/proven-metal/provenmetal-kicad) Altium plugin: (https://github.com/proven-metal/provenmetal-altium) This enables us to order long lead time parts in advance, suggest alternatives if parts are out of stock, and solve the biggest bottleneck in the process. We store parts in our hq in SF, and then kit the boards and route them through our network. We also do long term storage of parts for long lead time items. How we solve endless emails: Every manufacturer wants the same information in a different shape. There’s usually a few days of back and forth emailing to achieve clarity. We’re building a profile per manufacturer and sending the order to fit their requirements. This may sound trivial but it removes a multi-day round trip on most orders, which on a quick-turn build is a meaningful share of the time. How to solve design review: Each manufacturer has individual capability sheets, and so we have a heuristic harness that Fable 5 uses to check the board for DFM issues. We’re building a streamlined process for end-to-end pcb manufacturing, but is that enough to solve the supply chain? Not a chance. Capacity is the problem, and no amount of smart software will solve it. We’re extracting real slack from the system today, that slack is finite, and at some volume the only remaining move is adding physical capacity. We think that’s where this goes. We charge a simple margin on the order value depending on order complexity with fully transparent quote breakdowns. We took our first paying order in less than a week and we’ve done roughly $70k across 11 orders in 6 weeks. We are very interested in your opinion. We’re working in a problem space that has problems everywhere, we’re constantly pulled in wild directions regarding which problems we solve and how we go about solving them. What do you know that we can learn from?

PO

Popcorn

Show HN: Popcorn – An open-source browser cloud that runs inside TEEs

HY

Hyperprobe

Hi HN, this is Shailendra and Karan here. We are building a fast and safe way for coding agents to debug issues live in production. When prod breaks, it lets Cursor, Claude, and others drop virtual breakpoints or probes safely in your running code, and extract the exact variable values that logs don’t have. All this saves time and effort for engineers who’d otherwise dig through logs and traces or redeploy with console.logs or print statements until they find the root cause. Here is the link to the video that explains this: https://www.youtube.com/watch?v=ivV7I--ta5c Agents write most of our code now. This shrinks the useful context engineers need to debug AI written code, a problem not helped by the limited telemetry added in the same code by AI. So when something breaks in prod, the first instinct for an engineer is to open logs or throw them to your agents. But if the line you are looking for is not there, agents will start guessing the root cause on non-existent data, forcing you to add a log, and redeploy. This analysis-inference loop of agents with existing data does not come cheap, burning a lot of tokens. And the add log, redeploy cycle is so slow and painful that it makes engineers hate on-call. Our approach lets agents capture telemetry on-demand at the exact moment and point of failure, killing the log-redeploy cycle and getting the most accurate RCA while burning fewer tokens. The obvious problem is making it work on a running service. You can't pause a live service the way you'd pause a debugger on your laptop. Getting the value out of a running process safely, without pausing a thread or slowing the host is the challenge.We are making this happen. Before this I ran engineering at a 100 member team. Then Karan and I spent three years on HyperTest which was a testing tool. At HyperTest, we turned production traffic into integration tests using OpenTelemetry. That was production instrumentation too. The hard parts of pulling real runtime state out of a running service without breaking it, were the hard parts we learnt to put together. We learnt some other lessons the hard way too. HyperTest tried to prevent bugs with better tests, and adoption was a fight every time. Calls kept getting cancelled because teams were firefighting production. Testing was hygiene. Broken prod was hair on fire. This made us see where priorities lie. This seeded the idea of building a truly autonomous on-call agent i.e. one that takes an alert, probes, diagnoses and fixes it in a few minutes. But this is how it works as of now: You talk to your coding agent the way you already do. Tell it what's wrong: "checkout returns 200 but some users are seeing their order fail, find out why." It locates the line in your local code, connects to us over MCP, and drops a probe on that line in the running service. The probe is read-only and sits dormant until real traffic hits. When hit, it captures the local variables at every frame of the call stack at that exact moment. It hands them to the agent, which diagnoses with real data. There are two pieces. An SDK that runs inside your service, and an MCP server your coding agent talks to. The SDK is what makes setting probes (virtual breakpoints, log or metric) possible without a redeploy. In Node and Python it hooks in-process. In Java it attaches as a JVM agent, instrumenting at the bytecode level. Either way the service keeps running and serving traffic. Nothing pauses. When your agent wants to look at a line, it calls the MCP server, which tells the SDK to place a probe there. When a request hits the line, the SDK captures what the probe asked for, sanitizes it in-process, and streams it back to the agent via the MCP. This can run inside prod, so a probe can read any value sitting in that variable. We ensure redaction happens in-process, inside your own container's memory. This is before anything goes on the wire. Keys like password, token, authorization, ssn and credit card are redacted by default and you add your own. Also the probes read but never write, and if you want no captured state to ever leave your network you can self host the server, broker, and even the database in your infra. On overhead: when idle, the SDK adds negligible memory and effectively nothing to throughput and response time. Probes only cost anything while actively capturing. Also captures are bounded. A separate monitor watches in real time and pulls every active probe if overhead ever spikes. Every log-and-trace tool hands the agent data that already exists and asks it to reason backward to what probably happened. We think it is more useful to give agents eyes and ears into the running code, so they capture what they need when they need it, right at the point of failure. This seems like the simplest and fastest way to debug prod incidents. We’d love the community to try this in any environment to debug any known or unknown issue by just chatting with your coding agent. And let us know what more features you need to make this a truly autonomous on-call agent Supported platforms: NodeJs, Java, Python.