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🧵Our unique full-stack approach to AI lets us deliver powerful, cost-efficient products to developers and everyday users. But what exactly does it mean when a technology system is "full-stack,” and why is it so important to our approach? We talked to @rseroter, senior director and chief evangelist at @googlecloud, to break it all down 👇 Where does the phrase “full-stack” come from, and what does it mean when we’re talking about tech? @rseroter: The term "full-stack" originally came out in software development a decade or so ago — usually in regard to applications. Historically, building an app required multiple specialized teams: a front-end developer to build beautiful user interfaces, a back-end developer to handle server-side logic and a dedicated database team. The concept of a "full-stack engineer" emerged to describe a developer who could work across all of these functions independently. Instead of constantly handing off components from one person to another, a full-stack engineer could take an idea from a rough concept all the way to a fully running piece of software.

Today we're releasing a new set of components for building chat interfaces. We've taken the patterns we build every day, rethought the abstractions behind them, and turned them into components you can compose and customize. We're starting with the conversation layer: streaming, scrolling, messages, bubbles, attachments, and markers.

.@Xynovaofficial_ is one of the top rising players in China’s dexterous hand and humanoid robot components space, based in Hangzhou • They develop not only the hands but also all core components (linear actuators, motors, etc) and algorithms fully in-house • They use a hybrid drive system, combining tendon-driven and direct-drive methods • One of the most exciting features is wrist control and finger abduction/adduction, delivering much more natural hand and arm dexterity • They prevent the hand from overheating by placing one of the actuators in the arm (below the palm) • Hands are equipped with tactile sensors and lidars, with 23 DoF (human hands have ~27 DoF) The overall design is deeply inspired by real human hand and arm muscle movements, impressive biomimicry approach @TheHumanoidHub and I interviewed their tech lead so stay tuned for more technical details!

📍A Jiangxi Marvel Ⅲ| Jiangxi Prefabricated Components Begin Their Global Journey #JiangxiRising# #MyJiangxiStory# #components# @zhang_heqing @salahzhang @consulat_de @ChenPingMFA @CGMeifangZhang @CNYouthDaily

Been iterating on @tomosman's loop. This one's winning: /goal produce a verified, code-derived behavioral spec for this web platform, captured in one canonical spreadsheet that carries every feature from spec -> tested -> fixed -> verified. Why: we need a single source of truth that maps every feature to its expected behavior *as the code implements it*, so that gaps and bugs surface and the platform can be driven to a known-good state. The spreadsheet is the source of truth. Work on the current repo. Do Phase 0 and Phase 1 under this goal; when the spec is complete, switch into the /loop below to drive testing and remediation. Keep moving through phases without stopping, except at a real checkpoint (defined below). Phase 0 - Plan (first): Detect the stack, the feature surface (routes, pages, components, API endpoints, background jobs, auth, settings…), and the test infra that already exists (unit/integration/e2e, browser automation, seeds/fixtures, a runnable dev server). Propose (a) how you'll inventory features, (b) the spreadsheet schema, and (c) how you'll test in the loop given what's available. Proceed once the plan holds. Phase 1 - Catalog & spec: Read the code and, for every feature, write a user story + the expected behavior as implemented, citing the file/function. Where the code is ambiguous, or behavior is undefined, log an open question - don't guess. Record every feature as a row in the canonical spreadsheet (create with the xlsx skill). Exit: every discoverable feature has a row. One row, concretely: | Area | User story | Expected behavior (from code) | Status | Defects | Type | Notes / source | |---|---|---|---|---|---|---| | Auth | As a returning user I want to log in with email+password so I can reach my dashboard | `POST /api/login` validates via bcrypt, sets httpOnly session cookie, 302 -> `/dashboard`; bad creds -> 401 + inline error | Spec'd | - | - | `api/auth/login.ts`, `LoginForm.tsx` | Canonical artifact: exactly one .xlsx, updated in place across every phase and loop iteration - never fork into per-phase or per-iteration files. Status flows Spec'd -> Tested-Pass / Tested-Fail -> Fixed -> Verified. The main thread is the single writer. Agentic execution: - Delegate breadth to subagents: fan feature discovery and per-area testing across subagents so the main thread stays focused. - Verify by running, not claiming - report real command/test output; state skips and unknowns plainly. - Checkpoint (pause, ask, end the turn) only for a destructive/irreversible action, a fix needing a genuine product decision, or input only I can give. Otherwise, keep going. - Self-check at each phase/loop boundary via a fresh-context subagent: re-verify the spreadsheet against the code (Phase 1) and against actual results (each loop pass). /loop Quality cycle - once the spec is complete, iterate test -> fix -> re-test until clean. Each iteration, in order: 1. Test: exercise every user story not yet Verified against the running app, preferring the strongest method available (browser/e2e automation > existing suites > documented static check only where execution truly isn't possible). Record actual pass/fail in the same spreadsheet; log every defect with its type (functional/logistical or UX). No app-behavior changes in this step. 2. Fix: think hard about root cause, then fix every functional/logistical and UX defect logged this iteration - cause, not symptom. Scope: only logged defects; no new features, no unrelated refactors. Update each row's status. 3. Re-test: re-run every story touched by a fix using the same method; set Verified, or back to Tested-Fail with notes if the fix didn't hold. Exit when all user stories are Verified and no open functional/UX defects remain. Safety cap: if a story is still failing after 3 full iterations, stop, leave it Tested-Fail with root-cause notes, and report it rather than looping further.

I ran across this video a few days ago and couldn’t stop watching it. It’s about something ordinary & boring, a plastic gas lighter. But it changes how one thinks about manufacturing. That lighter in so many of our homes, holds pressurised gas. It has over 30 microscopic parts, has to pass international safety codes, & travel 10,000 miles by sea, & the total cost of doing all that, materials, labour, freight, every middleman along the way, comes to fifteen U.S cents. So how does anyone make money on this? Turns out almost the entire world’s supply comes from one place: a county called Shaodong, in China’s Hunan province. It wasn’t always there. But today, Shaodong has 114 lighter-related companies packed into the place & between them they source more than 200 different components from each other, all within a 20-kilometre radius. They supply something like seventy percent of the world’s disposable lighters. And the industry alone employs over 80,000 people locally. Nobody there is winning on cheap labour anymore. They’re winning by shaving a thousandth of a cent off the thickness of a plastic wall, or redesigning a base so a few thousand more units fit into the same shipping container. It took my thoughts back to an old professor of mine, Michael Porter. His 1980 book, Competitive Strategy, is still the 1st book most MBAs read, the one that gave the world the Five Forces and basically invented modern strategic thinking. But there’s a quieter piece of his work, on industrial clusters, that never got nearly the same attention, and it is the one that explains exactly what is happening in Shaodong. His argument was that nations and regions rarely win because of cheap inputs. They win when rival firms and specialist suppliers crowd into the same small geography for long enough that they keep pushing each other past what any one of them could manage alone. He found it in the Swiss watchmaking towns of the Jura, in the German printing press industry and in Italy’s ceramic tile and footwear districts (interestingly, it’s the SAME blueprint which built Morbi, in Gujarat, into the world’s second-largest ceramic cluster, now outproducing Italy by volume. I have posted before, about Morbi) None of these started out as giants. The neighbourhood made them giants. Which is exactly why it’s so relevant to India’s climb up the global manufacturing table I’ve also attached a slide with this post that I saw recently and which shows us breaking into the top 5 manufacturing globally. (A quick reference check told me that we may not have overtaken Korea yet, but the trajectory’s clear) That climb has happened on the back of scale: bigger plants, bigger parks, more FDI. I should declare an interest here, because the Mahindra Group set up 2 of India’s first integrated, plug-and-play business cities, in Chennai in 2002 & Jaipur in 2006. Both have been extremely successful. Chennai’s business zone alone today employs 45,000 people.. But I admit that we need to think differently. A park brings in investors and hands them a ready plot, power, water & roads A cluster is a completely different animal: hundreds of small, specialised suppliers, each obsessed with doing a tiny thing better than anyone else, feeding off each other’s presence for years until no outsider can compete with the whole. I think that’s the work ahead of us now. Not just more factories, and not just more parks. Policymakers & developers like us need to start consciously pulling as many of the inputs and resources a sector needs, the toolmakers, the component suppliers, the testing labs, the logistics specialists, into the same neighbourhood. Shaodong and Morbi both got there by accident, one town stumbling onto a way to shave a thousandth of a cent off a lighter wall, the other discovering it had the clay and, later, the gas pipeline for tiles. We don’t have the luxury of waiting for accidents anymore. We need to do it on purpose

The past couple months we may be witnessing what the Applied AI layer will look like at scale. Despite some of the initial critique that this would just be a thin layer on the LLM, it’s turning out that actually driving agentic workflows in an enterprise is far more complex. And anywhere there’s complexity you generally gain a moat and value over time. Here are a few of the components that appear to make up the playbook based on the examples we’re collectively seeing in coding, legal, healthcare, customer support, financial services and other fields: * Build the features that bridge the gap between the intelligence and the workflow. Some workflows can be automated by simply going to a general purpose interface, but others need tuned interfaces and features tied to the work they’re augmenting or automating. They need features that are specific to capturing the kind of data that’s needed as context for the agent. And they need a variety of bespoke tools for the agent to use, and unique interfaces for the human-in-the-loop UX. Going far deeper than just presenting the output tokens is clearly critical, and the more depth there is here definitionally the more sustaining value. * Act as the model router balancing frontier intelligence with cheaper models. A natural advantage that any model neutral platform has is that it can naturally (in a business model-aligned way) leverage whatever level of intelligence is necessary for the workflows they’re automating to get done. There are plenty of scenarios where you need GPT-5.5 or Fable level capability, and also lots of workloads where a more efficient closed or open weights do the trick. Only the companies that have deep evals on specific tasks across all models, and the ability business model wise to leverage them, are in a great position. * Drive the actual implementation and change management via FDE or equivalent. A big reason the applied layer works at scale is that most enterprises need some degree of help and support with change management in implementing agents for their workflows. Data has to be cleaned up and moved to modern systems, processes have to be re-engineered and documented, workflows have to be evaled, SLAs have to get achieved, and so on. All of this is going to be unique for every type of process that gets implemented, which means the companies that have expertise in a given domain and come with all the relevant best practices will be in a strong position. * Implement domain specific GTM that creates expertise in that field. Beyond FDEs the companies that can build sales and GTM motions aligned to their domains also have a natural advantage. Most IT and line of business leaders have too many things to do in any given day; so if you’re not on their agenda, likely someone else is. Depending on the industry, there are entirely different sets of language you use, ways of working through security and compliance, regulatory controls you have to support, industry events that companies convene at, different system integrator and consulting partners you need to work with, and so on. The more generalized this gets the less you can speak the customers language, which is where the applied layer has a leg up. A final note. There remains a view that a lot of this is all mitigated by model intelligence alone, and the bitter lesson solves all of this in the limit. That’s possibly true, but enterprises need help changing *today*. And many aspects of how to bring intelligence to real world work don’t only depend on the axis of the pure capability of the model, so most of what you’re doing now to win ends up being important no matter how good the models get.

China's Five-hundred-meter Aperture Spherical Radio Telescope (FAST) completed the installation of six domestically developed steel cables on Monday in Pingtang, southwest China's Guizhou Province, fully replacing the core components of its cable-driven suspension system. The six cables act as the "muscles" of the telescope, supporting its 30-tonne cabin — known as its "eye" — and enabling ultra-precise, real-time positioning and tracking at a height of 140 meters across a range of 206 meters.

Five layers. Seventeen minutes. One structure built to last for decades. This is Stage 4: Lamination. Heat, vacuum pressure, and precision engineering transform glass, EVA, and solar cells into a fully sealed solar module ready for the next phase of production. This is where individual components become energy infrastructure.